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JMP Pro Neural Network Tuning Add-In - (2023-US-30MP-1408)
Monday, October 16, 2023
JMP Pro’s Neural Network platform provides a powerful and flexible tool for generating predictive models for many types of data. Users optimize the models by adjusting various parameters and then compare individual models within the platform. This process is carried out manually, however, which takes a significant amount of user interaction and may not necessarily uncover an optimal model.   This presentation demonstrates the use of a JMP Pro add-in designed to screen neural network tuning parameters. The add-in creates a fast, flexible space-filling design based on user input, then runs a neural network model for each set of parameters. A graphical report helps users identify optimal models, so they can then run the selected model(s) or continue tuning the parameters to build their understanding of the system. Finally, the add-in allows users to save the results from the tuning optimization and return to it later, which is helpful when dealing with complex neural network models that might take considerable computing time.   Various JMP customers and Community members have inquired about a neural network tuning capability in JMP; this feedback was used to guide the development of this add-in.   The NN Tuning add-in is available for download in the add-in section of the JMP Community. Any updates will be posted there. If you find the add-in helpful, please let me know how you are using it and if you have any suggestions for future versions!     All  right. Hello  everyone. My  name  is  Scott  Allen. I'm  a  Systems  Engineer  at  JMP. I'm  going  to  be  presenting a  neural  network  tuning  add- in that  I've  developed  really to  help develop  neural  network  models in  an  automated  way  to  build  many  models, find  out  which  parameters  are  most effective  in  building  the  best  model. What  I'd  like  to  do  is go through just a little bit of background on  motivation  for  building  this  add- in. I'm  going  to  do  maybe  a  walkthrough, the  description, what's  going  on  in  the  background when  you  run  the  add- in and  then  show  a  use  case  for  it. A  sample  workflow. Then just really wrap  up   with  some  lessons  learned. This  was  my  first  big  scripting  project, so  certainly  learned  a  lot  along  the  way. I'd like  to  share  that   and  then  as  well  as  what's  next. First,  just  I  want  to  take 3, 4 minutes here just  on  the  background  and  motivation. A lot  of  other  machine  learning platforms in JMP  have  some  sort of parameter  tuning  function, whether  it's  a  tuning  table  that  you create  outside  the  platform, like  in  bootstrap  forest  or  boosted  trees, or  an  integrated  tuning  design like in  support  vector  machines and  the  new  XGBoost  platform. But  there's  not  one  for  neural  networks. That  led  to  some  customers   that I work with asking about it. "Is  this  something  that's  available? Are  there  scripts  out  there   that  we  can  use? Is  there  an  add- in?" There  are  a  few  things  out  there, but  maybe  not  everything  in  one  place. Then  on  a  personal  level,  I've  never undertaken  a  large  scripting  project. I've  done  some  small  JSL  scripts to  automate  workflows, or  to  clean  up  data, but  I  had  never  built  an  application. This was, I  really  wanted  a  project   to  help me learn. I  used  this  as  an  excuse  to  learn  JSL. The  neural  network  platform  in JMP is really  powerful, and  so  I'll  just  launch  it  really  quick. I'm  sure  if  you've  seen  it... I  will  mention  I  am  using JMP PRO version  17.1 . That's  where  all  the  testing  has  happened for  this  add- in. I  have  not  gone  backwards  many  versions. I  would  suggest if  you  are  going  to  use this you  need  to  have JMP  PRO  and  at  least  version 17.1. The  JMP  neural  network platform is  really  powerful and  I  just  want  to  take  a  few  minutes to  go  through  what  this  looks  like. I  think  everybody  has... If  you've  used  neural  networks, you've  gone  through  this workflow and  it's  a  really  nice  flexible platform  for  developing  these  models. You  can  start  off  with  a baseline model and  maybe  you're  not  satisfied with  the  performance of  your  model  in  this  case. You  can  just  go  back  to  the  model launch and  you  can  adjust  the  parameters  here. You  can  go  and  generate  another  model. It's  easy to  start changing  these  parameters and  finding  models  that  might  be the  most  appropriate  for  your  system. But  there  are  a  lot  of  options  in  here. You've  got  different  number  of  nodes, you've  got  one  or  two  layers, there's  boosting  options  as  well  as a  lot  of  different  fitting  options. You  don't  always  know... It's  not  always  clear  which  of  these parameters you need to change in  order  to  get  the  best  model for  the  data  table  that  you're  working  in and  the  predictors  that  you're  using. It's  not  always  clear if  adjusting  a  parameter is  going  to  lead  to  a  better  model. You  just   adjust  up a nd  down, maybe  take  big  steps to  figure  out  what's  going  on. It does  require  a  lot  of   manual  adjustments  by  the  user in  order to  generate  these  models. They  are  pretty  fast, so  it's  not  time- consuming  to  create  these but  it d oes  require  a  lot  of  clicks. My  goal  here  and  some  of  my  inspiration was  really  to  provide  a  single  platform for  tuning  and  evaluating those  neural  network  models. I  was  also  inspired there  were  some  tuning  scripts  out  there. Nick  Shelton  had  a  script that  would  help  you  build that table and  run  it  and  evaluate  those  models. Mark  Bailey  had  developed an  add- in  for  this  as  well, and  there  were s ome  community  posts  that were  trying  to f igure  out  how  to  do this. Those  were  all  some  starting  points for  this  add- in. Then  I  really  like the  XGBoost  add- in  interface. It's  really  clean, it's  got  some  graphical  outputs and  it's  really  easy  to  navigate and  lets  you  auto tune  those  networks. The  add- in  that  I'm  developing   isn't  auto tuning, it's  really  more  of  a  brute  force  method. Just  build  lots  of  models and  then  have  a  graphical  interface  there to  help y ou  find  the  best  one for  your  system. Let's  go  on  and  move  on t o  just   what  the  add- in  does  and  how  to  use  it. To  launch  the  add- in,  just  like  most  add- ins, you  go  to  the  add- in  menu, and  it's  called  Neural  Network  Tuning. That  takes  you  to  the  launch  dialog. This  gives  you  a  column role  interface that's very  similar to  many  other  platforms. We  can  just  select  all o f  our  factors  here and  our  response. Currently,  it  allows  a  single  Y  response, and  it  can  be  either  continuous or  categorical. The  add- in  currently  only  has the  two  validation  methods, so  you  can  specify  a  validation  column with  both  validation  and testing. Settings  if  you  have  that,  if  you  don't, it's  just  going  to  do  a  random  holdback, the  default  for t he  neural  network. Then  you  can  specify  and  toggle or  toggle  the  informative  missing  as  well. But  we'll  just  go  with  this  for  now. We  click  OK,  and  that  takes  you   to  the  tuning  launch. This  tuning  dialog,  first,   gives  you  a  little  bit  of  information about  what's  in  the  data  table and  what  you  specified  as  column  roles. Right  now  we've  got  the  data  table here, the  model  validation  method, as  well  as  whether  or  not   we  have  informative  missing  on  or  off. Then  we've  got  the  DOE  option. What  this  add- in  will  do,  is  look  at  all the  parameter  ranges  that  you've  set and  create  a  fast,  flexible,   space- filling  design. Really  the  only  design  specification   you  need is  to  set  the  number  of  trials   in  this  design. In  this  case  we've  got  by default it's  going  to  be  set  to  20, and  there are  some  rules  of  thumb. Maybe  you  want  to  specify at  least  10  treatments  in  this  design for  every  factor  that  you're  looking  at, but  it's  completely  up  to  the  user, and  so  we'll  come  back  and  adjust that  if  we  need  to. You  can  also  replicate  this  design with  a  different  random  seed. In  some  cases  if  you  want  to  see how  robust  you  might  be to  certain  parameters,  you  can  run the  same  neural  network  model  20  times, but  all  with  different  random  seeds to  see  how  robust  the  model  is  to  that. Then  you  can  toggle  whether  or  not  you want  to  see  the  DOE  dialog  that  shows  up. Once  you've  gone  through the DOE  options  here, you  can  come  down to  the  neural  network  tuning. I  tried  to  stay  pretty  true to  the  original  platform in  the  neural  network  by  using similar  language  and  options  here. This  is  where  you  go, a nd  instead  of  specifying  a  single  value for  one  of  these  parameters, we're  going  to  specify  a  range. Also,  I'm  limiting  a  little  bit  the  available  options for  the  type  of  neural  network  that you're  going  to  create. In  this  case  you  can  generate   a  single  hidden  layer, you  can  generate  two  hidden  layers, or  one  hidden  layer  with  boosting. Currently,  the  neural  network  platform  doesn't  allow a  second  layer  with  boosting. If  you  try  to  do  that in  the  neural  network  platform, it  will  give  you  a  warning  saying  that   it's  going  to  ignore  that  second  layer. In  this  add- in,  I  just  make  it  so  that  you  zero  out  all  those  second  layers when  you  activate  boosting. In  this  case  we're  going  to e xplore this  data  table  a  bit and maybe  we're  going  to  set  some wide  range  of  tuning  parameters. I'm  going  to  go  from  0-9  on  each  of  those activation function  number  of  nodes. Over  on  the  fitting  options,  you  don't  have  to  necessarily know what  might  be  best  for  your  system, you  can  just  select  them to  be  part  f  the  DOE,   or  in  the  case of these  transform  covariates  and  robust  fit  options. You  can  toggle  them  to  always  be  off, always  be  on,  or  include in the DOE. We'll  include  both  of  those  in  the  DOE for  this  example. Penalty  methods. You  can  test  any or  all  the  different  methods. If  you  don't  specify  any  penalty  method, it  will  run the  default  penalty  method  squared. But  we're  going  to  check  all  of  those. You  can  also  specify the  number  of  tours  per  model. In  retrospect,  I  probably  didn't  need to  add  this but because  you  could  do  something  similar by  just  replicating  all  of  the  designs and  seeing  the  results from  each  of  those  models. But  it's  here  in  case  you  want to  adjust  the  number  of  tours. You  can  also  specify  a  random  seed. In  this  case,  I've  got, 3,4,5,6. Six different  factors  in  this  design, and so  I'm  just  going  to  maybe   turn t his  up  to  60. I'm  going  to  create  60  models. Before  I  click  Run,   I'm g oing  to  talk  a  little  bit  about what's  going  on  when  you  click  Run. The  first  thing  that  happens when y ou  click  Run, is  it's  going  to  create  a  factor  table with  only  the  selected  parameters. We select it. In  this  case  it'll  create  a  factor  table   with  single- layer  nodes, all  of  the  different  fitting  options. It's  not  going  to  put  the  second l ayer   or  any  of  the  boosting  parameters into  the  factor  table. Then I'm  going  to  create  a  response table where  the  training,  validation, and  testing  R- squared  values are specified a nd  then  it's  going to  combine  those  into  a  single  table. This  is  what  the  sample DOE  table   will  look  like. It's  just  going  to  have  the  responses as  the  R- squared  values and  then  all  the  different  parameters. Then  these  just  get  passed  into  the neural  network  platforms  row- by- row, and  the  results  show  up  in  the  data  table. Just  like  we're  saying,  each  of  those  runs in  the  design  is  going  to  go  into  the neural  network  model  platform  sequentially  and  then  we  have  a  little  dialog  box that's  going to  indicate  the  overall  process. In  the  case  of  really  long- running   neural  networks, it's  good  to  just  see  how  quickly   the  whole  design  is  going  to  go. I  would  recommend  before  specifying  a  large  number  of  trials  in  this  design that  you  run  one  or  two  ahead  of  time  just  to  see how  long  they're  going  to  take. If  you  have  fairly  small  data  set,  then   they  go  pretty  quickly,  but  if  you  have a  complex  data  set,  they  can  take   sometimes  quite  a  while  to  run. In  this  case  I'm  just  using  a  sample data  set  from  the  sample  data  set  library. This  tablet  production. It's  not  the  best  data  set  to  run for  neural  networks  just  because there  aren't  a  whole  lot  of  observations, but  I'm  using  it  just b ecause the  neural  networks  run  really  quick. In  this  case,  if  we're  satisfied   with  all  of  the  specifications that  we  have  in  our  model,   we  can  just  go  ahead  and  click  Run. What  this  has  done  is  it's  created that  design  table and  now  it's  passing  through   each  of  those  trials into t he  neural  network  platform and  giving  an  output. If  we  look  at  the  home  window, all  this  has  done  is  it's  taken that  tuning  result  table  and  I've  hidden  it and  I  just  bring i t  out   into  the  same  window  here. Let's  just  go  on  to  the  next  one. We  can  work  through  that  data  table  here,  so  it's  interactive. What  you  first  see  up  here  is  graph  builder  report. T his  shows  you   all  of  your  different  parameters, so  the  different  activation  functions as  well  as  the  number  of  nodes is  the  scale, and  how  the  validation  R- squared  changes with  respect  to  each  of  those individual  parameters. In  the  case  of  categorical  parameters, you g et  a  box  plot  showing  the  difference in  the  values  this  way. We  can  see  in  this  case maybe  for  robust  fit, it's  got  a  wider  range,  but  on  average  it's g oing  to  be  better. The  median  value  is  better. Is  higher  R- squared. We  can  also  look  down  here at  the  data table, it's  going  to  sort  the  data  table by  the  validation  R- squared and  it  just  shades  those  a  deeper  purple depending  on  the  value. Let's  see. We can use... One  reason  I  just  kept   this  data  table  as  it  is, is  we  can  explore  it  just  like  we  explore any  data  table  in  JMP, so  you  can  look  at  column  headers or  you  can  launch  graph  builder or  do  various  analyses  on  this  data  table as  it  is  right  here. There  are a  few  additional  data  columns or  columns  that  are  added to  this  data  table. One,  it  records  the  random  seed. If  you  want  to  reproduce  this, you  know  what  random  seed  was used in  generating  the  n eural  network  models,  shows  you  the  informative  missing, shows  you  the  elapsed  time   for  each  of  the  models. If  you  are  building  large  models or  you  might  want  to  minimize  the r untime, then  you  can  get  an  estimate o f  how  long it  might  take  for  you  to  run additional  models. nDOE  just  records  what  DOE  was  run. This  was  the  first,  so  it  gets  a  1. As  you'll  see  we  can  run  a  second  and  third,  and  you  can  then  use  those as  some  graphing tools. Also  tells  you  the  type  of  neural  network that  was  run. Whether  it  was  single- layered,   double-  layer, or  a  single- layer  with  boosting. There's  a  couple  of  other columns  that  are  added. This  factor  column  just  lets  you   keep track  of  the  factors that  were  used  in  the  design in  case  you  want  to  compare  models that  have  different  factors  that  were  used  instead  of  just  parameters, and then  also  a  row  column. The  next  steps  so  we can  continue  building  out. Maybe  in  this  case   we're  exploring  the  parameters. Now  we've  got  a  baseline of  a  single  neural  network and  maybe  we  want  to  see  how  this compares  to  a  two- layer  neural  network. We  can  go  back  to  the  tuning  launch which  was  just  minimized. You  can  see  that  all  of  this  report, the  graph  builder  report as  well  as  the  table  are  just a ppended to  the  tuning  launch. We  can  go  back  up  to t his  tuning  launch   and  maybe  I  want  to  look  at two  hidden  layers. We'll  go  to  two, and  now  I'll  put  in  a  range  of those  results  or  those  parameters and  keep  everything  else  the  same. You  can  see  it's  going  to  preload  the  random  seed  that  I  generated in  the  first  DOE  just  to  make  sure everything's  using  the  same  random  seed. We'll  keep  it  at  60,  even  though  we  might  want  to  increase  it by  a  little  bit  since  we're a dding   some  more  factors, but  keep  it  simple,  we'll  just go  with  60. One other  thing  just  to  mention  is, in  the  case  it's  probably  pretty low  likelihood t hat  all  of  these   would  be  set  to  zero  in  a  design, but  if  they  are   it'll  just  go  to  the  default  with  JMP, which  is  a  single  10H  layer. You  don't  have  to  worry  about  having  a  neural  network  with  zero  layers or  zero  nodes. If  we're  satisfied  with  this,  we're  just  going  to  click  Run. While  this  is  running,  I'm  just  going   to  bring  up  the  home  window. You  can  see  what  it's  doing. We've  got   our  original  tuning  result  table  here, and  it's  built   a  second t uning  result  table, and when  it's  done, i t  just  appends  that to  our  original  table. Now  I  can  see  in  orange, is  all  of  those  second- layer  models. We  can  see  in  this  case, maybe  having  two  layers was  not  very  beneficial  in  this  case. Looks  like  in  pretty  much  all  cases, those  neural  networks  are  not  as  good. Then  maybe  just  to  round  this out,  we're  going  to  go and buld some  neural  networks  with  boosting. Boosting  is  a  really  nice  way  to  increase the  prediction  power  of  our  model. We're  going  to  specify  some boosting  levels  from  1-10,  as  well  as the  learning  rate  from  0.1  to  1. Once  again  our  random  seed  is  the same, and  these  are  going  to  take a  little  bit  longer,  so  I'm  just  going to  decrease the  number  of  runs just  in  the  interest  of  time. We  can  click  Run  here,  we  get  our  dialog  box  that  tells  us our  overall  progress,   and  we  can  see  these  in  the  cases where  we've  probably  got  lots  of  boosting, they  might  be  taking  a  little  bit  of  time. In  this  case  boosting  neural  networks, or  neural  networks  that  had  boosting are  now  this  purple,  and  we  can  see   in  pretty  much  all  the  cases purple  is  higher,  especially  when  it  looks  like we  can  see  by  these  box  plots  as  well. Then  we  can  also  see  in t his  case   over  here  our  boosting. Maybe  we  haven't  found   the  maximum  number  of  boosts, it  looks  like  it  continues  going  up, so  we  might  want  to  add  some  additional boosting  layers  or  maybe our  learning  rate  is  best at  this  middle  to  high  value instead  of  at  the  lowest learning  rate  values. The  next  thing  we  might w ant  to  do   once  we  have  this  initial  view of  our  system,  is  to  run  a  few  of  these neural  networks  and  compare  them. We  can  do  that  a  few  ways. We  can  look  in  our  table  here a nd  run or  and  select  some  neural  networks based  on  the  graph,  and  you  can  see they're  highlighting  down  below in  our  data  table  as  well. Maybe  what  we  want  to  do  is  run  the  top  four  or  five. I  want  to  run  the  top  five. I don't  have  to  go  back  into   the  neural  network  platform  to  do  this, I  can  just  run  it  by  clicking this  button,  run  selected  models. Now  it's  going  to  run  those   back  into  the  neural  network  platform and  give  us  a  little  output  that  shows  us the  row  in  our  data  table  that  was  run. This  is  just  the  standard neural  network  output  now. We  pass  through  the  same  random  seed, so  if  we  take  a  look  at R-squared values, we're  going  to  get  the  same  neural  network  that  was  developed because  we're  using  that, passing  through  that  same  seed. Then  we  can  come  through and  we  can  take  a  look  at more... Either  the  more  summary  statistic  values or  the  actual  versus  predicted, or  get  the  profilers. Whatever  we  might  want  to  do to  compare  these  neural  networks. One  other  thing  we  might  want to  do  at  this  stage is  create  some  additional  visualizations. We  can  go  through and  we  can  show  this  tuning  table. If  we  don't  want  to  work  on i t  here,   we  can  just  unhide  it, and  now  we've  got  a  data  table  that  we  can  use, and  we  can  go  into  graph  builder and  maybe  we  want  to take  a  look  at  validation  versus  training  R- squared just  to  see  how  that's  looking,  or  we  can  go  through. Because  we've  got  the  number  of  DOE, we can  track  how  our  neural  networks have  improved  with  our  tuning. I  can  go  into  graph  builder   and  I  can  use  that  nDOE, and  maybe  we  want  to  look  at  training  and validation R-squared, we  can  turn  on  box  plots  and  we  can  see how  our  tuning  has  progressed  over  time with  each  subsequent  design  that  we  ran. Other  option  is  if  you  want  to  do   some  more  analytical  type  analysis  here, we  can  go  into  screening and  we  can  run  predictor  screening. We  can  take  all  of  those  parameters,  load  them  in  as  predictors, and  look at  our  training   and  validation  R- squared. Click  OK,  and  now  we  can  see  which  factors  might  be  most  influential in the  training  or  validation. Not  surprisingly,  those  learning  rates   and  boosting a re  good  at  this  validation. There's  no  end  to  what  analysis you  might  want  to  do, on  here  it's  just   what  your  goal  of  the  neural  network  was and  where  you  want  to  go  with  it. But  you  can  do  that  all  in t his  data  table  which  now  has  160  neural  network  models. Maybe  these  neural  network models  take  a  little  bit  of  time, so another  thing  you  can  do is  we  can  just  save  this. I  want  to  go  file,  and I  can  save  this. I'm  just g oing  to  save  it  to  my  desktop as T uning  Results. I  can  come  back  to  this  later. Now  that  I've  got  it  saved, I'm  going  to  close  this. One  thing  that  you'll  get  a  warning, when  you  close  it. You  should  just  hide  this  data  table. If  you  close  it,  it's  going  to  take  it out  of  the  neural  network  platform and  you  won't  be  able   to  continue  your  work, but  you  can  hide  it. When  you  get  back  to  the  add- in, when  you  close  this  window, it's  just  going  to  remind  you . You can  close  it  without  saving   or  you  can  cancel  to  go  back  and  save  it. We're  just  going  to  click  OK. When  I  save  this  tuning  table  for  later, so  what  I  can  do  is, is  now  I  can  relaunch  that  add- in and  I  can  go  into  the  tuning  add- in, click  OK. But  instead  of  resetting  any  parameters and  clicking  Run,  I  can  go  to  Load. What  I  can  do  is  I  can  load a  previously  saved  tuning  table, and  what  this  does  is  let  me  in  the  case  of  neural  networks that  take  a  long  time, you  don't  have  to  start  over  from  scratch. You  can  just  keep  building  on  the  table that  you  wanted,  or  maybe  you  do some  additional  analyses  offline   and  want  to  come  back  in and  build  some  more  models, so  you  can  go  through and  when  you  specify all  the  different  parameters  and  number of  tuning  runs  you  want  to  have, it's  just  going  to  continue  appending  it   to  this  table. With  that  let's  minimize  this. That's  quick  run  through   of a ll  the  different  options in  this  tuning  table. It's  really  a  brute  force  way. You  can  see  there's  no  auto tune. Right  now  it's  just  running all  those  different  parameter  combinations  giving  you  an  idea of where  your  optimal  model  might  be . How  you  might  work  this  into  your  workflow so  you  can  explore  those  parameters, this  is  essentially  what  we  did  today. We  looked  at  single- layer  models, we  looked  at  models  with  two  layers. Single- layer  with  boosting. Another  thing  you  might  do   is  go  back  and  say, "W ell,  where  are  my  optimal  models?" They're  here. I  can  go  back  to  the  tuning  launch and  maybe  I  want  to  recreate  those  models and  go  more  towards  this  range. It  looks  like I'm in  the  middle  range  here,  is  optimal, so  instead  of  going  from  0-9, maybe  I  go  from  5-7 . Linear  models  also  might  be  best in  the  middle, and  maybe  the  gaussian,   there are  some  good  ones  on  the  left, so  we'll  go  from  1-8  or something  like  that,  seven  or  eight. Then  we  can  look  at  which... In  this  case,  there  wasn't  an  advantage to  having  it  on  or  off,  so  we  could  just  have  those  off, or  off  or  on. But  it  looks  like  maybe  in  this  case the  weight  decay  was  the  best  method, so  we  could  just  include  that  one and  then  go  through and  build  some  more  models with  a  narrow  range  of  those  parameters. We  can  also  check  parameter  sensitivity to  a  random  seed. In  this  case  we  can  pass  through  either constant  values o r  a  narrow  range. You  can  pass  through   a  constant  set  of  parameters just  by  setting  each parameter  equal  to  each  other, and  it'll  pass  that  through   the  neural  network  as  five  every  time, or  you  can  just  specify  the  min  value,  and  that  will  pass  it  through as  a  constant  value. If  we  wanted  to  we  could  look  at maybe this  was  a  neural  network  that  was  one  that  we  identified  as  optimal, but  I  want  to  see  how  robust it  is  to  the  random  seed. What  we  can  do  is   we  can  just  say  just  run  it  once, but  let's  replicate  it with  20  random  seeds. That  might  actually  take a  little  bit  of  time, so  let's  go  down  to  10. Then  we  can... Actually,  I  don't  want  to  do that  on  this  one, because  I've  got  all  those  others,  but  we'll  just  run  this. You  might  want to  do  this  on  a  clean  data  table where  you're  not  adding  it  to  the  design, but  we  can  use b ecause  we  have the  number  of  DOEs  we  could  find where  that  random  seed  was. We  can  see  here,   all  the  different  random  seeds and  I  can  go  into  graph  builder   and  I  could  just  put  in the  training  and  validation  R- squareds, and  then  I  can  use  local  data  filter  now on  my  random  seed  to  show  everything   except  the  one  where  we  had  all  those. Now  I  can  see  how  robust my  model  is  to  that  random  seed. That's  the  same  design now  run  10  times, and  so  on  average  we  can  see what  the  median  value  is or  the  range  of  expected  results. Then  finally,  w e  can  load  those  saved  data  tables. Those  are  really  beneficial   for  neural  networks that might have really  long  computing  times,   so  you  can  save  it  off and  come  back  to  it  later. Those  are  some  workflows   that  might b e  helpful  to  you  as  you  build your  neural  network  models. I  just  want  to  wrap  up with  a  few  lessons  learned. This  was  the  first  scripting  project   that  I  went  through that  developed an  application, and  so  I  think  this  is  pretty  obvious to  anybody  that's  developed  any JSL or  built  any  applications,  the  JMP  community  is  really an  invaluable  resource. I  would  say  95%  of  all  my  questions, I  would  just  go  to  the  community and  someone else  had  a  similar  issue. Really  recommend  if  you  are  interested in  building  add- ins, building  applications,  the  community is  really  the  first  place  to  go. Really,  your  imagination  and  your  targets are  the  limiting  factor in  developing  these  applications. A ligned  with  that is because  there are  so  many  possibilities, there  was  a  need  to  keep  this  focused. In  this  case,  I  just  have... I  think  in  one  iteration I  had  many  different  outputs,   different  graphs, but  they  weren't  really the  heart  of  the  application. Because  there's  so  many  possibilities, I really  tried  to  keep  this  focused on  just  giving  some  graphical  output   to  navigate  the  different  models and  run  them  and  identify   which  might  be  most  appropriate. What's  next? I  really  want  feedback. It's  selfish,  but  it's  what  I  really  want. I  would  love  to  hear  how  people   can  use  this,  how  it's  benefiting  them. You  can  find  this  in  the  community, in  the  add- in  section  of  the  community. Please  feel  free  to  make  a  post  here, tell  me  how  you're  using  it. I  do  have  a  few  known  issues, and  as  I  gather  more, I'm  sure  it's  not  100%  bug- proof,   but  this  will  be  the  place  to  go to  get  the  current  version  of  the  add-in  as  well  as  understand  any  known  issues. But  I'd  love  to  hear  how  you're  using  it, and  the  community   is  a  great  place  to  do  that. I  would  also  like  to  hear ideas  on  additional  reports. I  kept  this  pretty  simple,  but  if  you  find  that  there's  a  report that  you're  generating every  time  you  run  this, I'd  like  to  know  what  that  is a nd  maybe we  can  build  that  into  the  add- in. I'm  also  going  to  continue  debugging. There  are  a  few  known  issues  that  I'm working  through  right  now, and  then  maybe  a  larger  goal  is  to  try to  do  that  auto tune. Instead  of  doing  this  brute  force  method, maybe  start  out  with a small  number o f  parameters, figure  out  which  ones are a ffecting  the  response  the  most, and  then  having  a  directed  tuning. With  that,  I'd  like  to  thank you for  listening  to  this  presentation. This  is  a  really  fun  project  and  I hope  you  find  it  useful, and  would  love  to  hear  about  it. Thanks.
Event has ended
Restrictions on Public Resources for Abortions: Impacts on Fertility Rates Based on Maternal Age - (2023-US-PO-1404)
Monday, October 16, 2023
This research examines impacts to United States fertility rates as a function of state legislative restrictions on the use of public resources (e.g., Medicaid funds) for abortions. Data from LawAtlas and the Centers for Disease Control's WONDER databases were used to model 2021 fertility rates based on maternal age group and abortion legislation. A two-way ANOVA of rank transformed fertility rates was used to identify impacts of legislative restriction across six maternal age groups. Latent class analysis was used to identify patterns in state abortion restrictions composition and their relationship with fertility rates based on age.    It was revealed that the impact of abortion restrictions targeting public resources on fertility rates varied based on maternal age; for example, women ages 15-29 had lower fertility rates when there were no restrictions. Additionally, legislative restrictions on multiple categories of public resources were associated with higher state fertility rates. The poster includes visualization of summary statistics and findings with maps and charts. The poster demonstrates a method for addressing unbalanced data using transformation, as well as the use of latent class analysis with binary categorical variables.     Welcome  to  this  poster  session. My  name  is  Renita  Washburn, and  my  colleague  and  I, Dr.  Mary  Jean Amon. For  this  project, we  examine  the  relationship between  legislative  restrictions  on the  use  of  public  resources  for  abortions and  their  impact  on  fertility  rates  based on  maternal  age  in  the  United  States. We  believe  that by  examining  these  regulations, we  could  offer  insight  into  potential impacts  of  future  legislative  changes, thus  aiding  and  understanding  the  dynamics and  potential  consequences  of  such policy  shifts  on  fertility  rates. I'll  start  by  quickly  discussing our  data  sources, and  then  I'll  demonstrate  how  we use  JMP  to  perform  a  two-way  ANOVA and  a  late-in  class  analysis to  investigate  these  relationships. There  were  two  data  sources used  for  the  study. The  first  was  legislation  that  was obtained  from  a  law,  ATLAS  policy surveillance  program  dataset. The  secondly,  we  got  the  2021  fertility rates  from  CDC's  WNDYR  database. The  data  sets  were  combined  together based  on  the  mother's state  of  residence  in  the  year, with  a  one  year  lag  in  the  birth  status so  that  the  legislation  was  in  effect when  the  pregnancy  began. Our  first  objective  was  to  identify impacts  of  restrictions on  public  resources  on  fertility  rates. We  broke  the  legislation  down into  three  buckets, whether  the  state  had  no  restrictions, whether  they  had  restrictions but  excluded  Medicaid, and  then  whether  they  were restrictions,  including  Medicaid. A  two-way  analysis of  variance  was  performed to  determine  whether  there  was a  statistically  significant  difference between  the  mean  fertility  rates  based  on these  three  buckets  in  the  maternal  age. We  first  started  by  visualizing  these three  categories  with  the  map  that  we made  with  JMP's  Graph  Builder. From  there,  we  observed  that  there  was an  uneven  number  of  states  in  each of  the  restriction  categories. We  used  a  common  practice  of  running the  analysis  with  the  rate  transform  data to  avoid  any  challenges from  the  imbalance  data. I'll  demonstrate  first  how  we  add  this ranking  to  the  data  set, and  then  we'll  go  through the  two-way  ANOVA. First,  you  start  with  Analyze, Distribution. We're  going  to  put  in  the  variable  that  we want  to  do  the  rank  transformation  on, which  is  fertility  rates and  to  Y  and  hit  OK. We  go  down  to  our  lovely  red triangle  then  to  Save  and  Ranks. That's  going  to  save  the  ranking of  from  lowest  to  highest  of  the  fertility rates  onto  our  main  data  set. We're  just  checking  that  it's  there. Next,  with  the  results of  the  rank  fertility  rates, we're  going  to  use that  to  do  our  two-way  ANOVA. Back  to  Analyze  Fit  Model, we're  going  to  add  in not  the  original  fertility  rates, but  that  ranked  fertility rate  that  we  just  created  to  our  Y. We're  going  to  add  in  our  two  independent variables  in  full  factorial, so  that  we  can  do  that  two-way for  the  maternal  age  and  that  restriction category  will  hit  Run. Then  it  gives  us  the  same  outputs that  we  would  obviously  expect, but  it's using  those  rank  transformations. We  can  go  down  and  look  at  the  effect summary  to  see  that  there  are statistically  significant interactions  going  on  there. The  two-way  ANOVA  identify  that  there  were statistically  significant  differences in  those  fertility  rates  based  on  maternal age  and  the  presence  of  restrictions. Specifically,  we  observed  that  women  ages 15  to  29  had  lower  fertility  rates when  there  were  no  restrictions present  in  their  state. The  next  objective  was  to  identify patterns  in  the  state  abortion restriction  composition. We  use  a  late-in-class  analysis  because  we had  these  binary, these  yes  or  no  indicators  for  six different  categories  of  interest. They  were  related  to  government  funds, government  facilities, and  other  various  programs  like  state insurance  programs  for  state  employees. We'll  demo  how  we  use  JMP's  clustering to  group  those  states  together based  on  these  six  different  categories, and  then  how  we  use  Graph  Builder  to  help us  display  the  results and  interpret  them  a  little  easier. To  run  the  late-in  class  analysis, we're  going  to  analyze  clustering, late-in  class  analysis. We're  going  to  put  in  our  six binary  indicators  into  our  Y. Then  we  could  adjust  the  number of  clusters  and  it  would  run  more  than just  three,  but  we're  just  going  to  use three  for  our  purposes  to  keep  it  simple. After  running  it,  we  have  this  high-level characterization  about each  one  of  the  clusters. But  let's  create  an  additional visualization  that'll  start  helping  us interpret  how  the  states  fell  out in  each  one  of  the  three  clusters. To  do  that,  we're  going to  go  to  Graph  Builder. We're  going  to  take  our  state  of  residence and  put  it  down  on  map  shape  that  lets  JMP know  that  we're  wanting  to  make  a  map. Then  we're  going  to  put  our  most  likely cluster,  which  is  added  to  the  data  set after  we  run  that  LCA,  and  we're  going to  use  that  to  actually  color  it. This  gives  us  a  really  quick  look at  how  each  of  our  states that  actually  had  restrictions, which  of  the  three clusters  they  fell  out  into. Then  you're  able to  use  Graph  Builder  again to  create  other  visualizations  like we  did  that  allow  you  to  compare  each of  the  states'  composition  of  their restrictions  and  their  fertility  rates using  the  three  clusters that  we  created  with  the  LCA. When  we  did  that, what  we  observed  is  that  when  there  were restrictions  on  multiple  types  of  public resources,  it  was  often  associated  with higher  fertility  rates  for  those  states. I  just  want  to  thank  you for  viewing  the  poster  session. The  goal  was  really  to  demonstrate  how  we used  JMP  to  examine  the  relationships between  abortion  restrictions  that  were targeting  public  resources  and  fertility rates  across  multiple  maternal  age  groups. By  performing  the  two-way  ANOVA on  that  rank  transform  data, we  observed  that  women  ages  15  to  29 had  lower  fertility  rates  when there  were  no  restrictions. Through  the  use  of  late-in-class  analysis and  visually  analyzing  the  results, we  observed  that  restrictions  on  multiple categories  of  public  restrictions  were associated  with  higher  fertility  rates. Thank  you  again.
Event has ended
Why Aren't You Using App Builder Already? - (2023-US-30MP-1401)
Monday, October 16, 2023
App Builder was introduced in JMP 10. In the beginning, it had its bumps and warts and was, arguably, a bit of a challenge to use. As a long-time JSL scripter, I, too, was reticent to use App Builder and preferred to code everything from scratch, even dialog boxes. But I got tired of the work needed to make small visual tweaks to my dialogs and custom report windows, so I gave App Builder another look -- and have not looked back. If it's worth an interface, I'm using App Builder.   In this session I talk about some of the basics of creating applications with one or more interactive elements. I discuss key elements that make App Builder great, along with some lesser-known features. Topics include building multiple module applications, scoping, managing windows, working with the App Builder interface, how JSL is different with App Builder, and working with other JMP elements such as Data Tables, Add-In Builder and Workflows.     In  this  session,  we're  going to  talk  about  Application  Builder.   If  you've  ever  had to  build  an  application  in  JMP that  required  visual  elements, say,  a   Report window  or  a dialog  box, Application  Builder is  the  tool  that  you  want  to  use. It  will  allow  you to  create  that  application more  quickly  and  more  easily. Additionally,  it  will  let  you  create an  application  that's  more  compact, that's  easier  to  maintain, and  more  robust. I'm  going  to  assume that  you're  watching  this because  you're  interested in  Application  Builder, but  you  also  have a  little  bit  of  JSL  background. You  may or  may  not  have  used  Application  Builder, but  you  know  a  little  bit about  scripting   in JMP. Let's  get  started. I've  got  two  examples that  I'd  like  to  use  to  illustrate some  of  the  visual and  programming  characteristics that  might  not  be  obvious or  that  might  not  be  documented for  the  Application  Builder. I  am  going  to  start with  a  very  simple  example that  I've  actually  attached to  a  menu  here. It's  just  a  dialog  box. It's  going  to  allow  me to  navigate  my  directory  structure either  from  a  tree  that  I  already  have  set or  I  can  pick  a  different  directory. Once  I'm  there, I  can  look  to  see  whether  or  not files  of  specific  types  are  available, select  that  file,  and  then  open  it  up. Now,  one  of  the  important  aspects of  this  particular  example is  that  it  doesn't  involve  a  data  table. We're  going  to  see  in  the  second  example how  to  deal  with  data  tables, when  I  want  to  incorporate data  tables  into  my  application. It's  a  little  bit  trickier, so I'm  saving  that  example   for second. Let  me  put  this  off  to  the  side so  we  have  something  to  reference. Okay,  so  let's  go  ahead and  get  started  with  Application  Builder. Under  Files,  New,  Application. I  will  almost  always  start with  this  blank  application. Reason  I  do  that  is  I  can  always  get to  any  of  those  templates. I  could  build  any  of  those  samples starting  with  this  blank  application. Blank  application  is  just  going  to  give  me more  flexibility in  terms  of  where  I  want  to  go. Now,  if  you're  unfamiliar with  Application  Builder, if  you've  never  used  it  before, the  layout  is  relatively  straightforward. On  the  far  left, we've  got  our  source  panel, and  in  that  source  panel are  all  of  the  items  that  I'm  going  to  use to  display  information, to  organize  elements,  and  to  do  things, things  like  buttons  and  checkboxes. In  the  middle,  I've  got  my  palette where  I  actually  build  the  visuals of  my  application. On  the  right, I  have  an  Objects  window  that  shows  me the  tree  structure of  the  item  that  I  built. I've  got  my  Properties  window where  I  can  select  an  item and  change  its  properties. That's  one  of  the  things  that  makes Application  Builder  faster  to  use, is  that  I  can  go  in  there, I  could  select  an  item,  select  an  object, and  change  the  properties  interactively, not  having  to  worry about  what  the  name  of  the  message  is or  writing  up  the  code to  make  those  changes. Now,  what  I  find is  that  I  often  use  the  same  properties when  it  comes  to  specific  elements. Things  that   typically  have some sort of  text  element, I  like  text  of  a  certain  size and  a  certain  style. Certain  container  boxes, I  like  to  have  borders  around  them. Rather  than  drag  and  drop  items into  this  palette  and  make  changes, what  I  often  do is  I  will  start  with  a  template. Here  I've  created  a  template that  will  allow  me  just  to  copy  and  paste from  the  template  into  my  palette. For  example,  for  my  list  box, I've  changed  the  font  on  that  list  box, changed  the  font  size. I'm  just  going  to  Control  C  to  copy  that. I'm  going  to  paste  it into  my  new  application. The  great  thing about  working  from  a  template is  that  it  works for  composite  items  as  well. In  this  case,  I've  got  multiple  items. I  have  two  button  boxes that  are  stuck  next  to  one  another using   a horizontal  list  box, and  then  I've  got  a  horizontal  center  box. I  can  actually  copy  those, Control  C,  copy  those  en masse, and  paste  those  into  my  application. Now, a  couple  of  things  about  item  selection. If  you  work  quite  a  bit  with  PowerPoint, one  of  the  things that  you're  going  to  find  out is  that  to  select an  item  in   Application  Builder is  a  little  bit  different. Whereas  in  PowerPoint, you  need  to  select  the  entire  item, with  Application  Builder, all  you  need  to  do  is  select  part  of  that. You'll  notice  that  by  selecting part  of  that  horizontal  center  box, the  entire  box  is  selected. That  makes  items  much  easier  to  select in  the  sense  that  all  I  need to  do  is  grab  part  of  the  item to  select  the  entire  item  in  entirety. Now,  I  have  a  second  option that's  available  to  me  as  well. That  is,  if  I  find  an  item  hard  to  select, I  might  have,  let's  say, a  hierarchy  of  container  boxes and  it's  hard  to  get  to  the  right  box, I  might  have  tiny  objects that  are  behind  other  objects and  it's  just  hard  to  grab  onto. I  can  always  make  my  selection from  my  Objects  panel  as  well. Here,  I'll  select  my  Objects  panel and  you'll  notice that  it  selects  the  item  in  my  palette. The  only  drawback  to  this is  that  I  can  only  select  one  item from  the  Objects  panel. But  again,  it  makes  it  very  handy if  that  item  is  very  hard  to  grab  onto. Let  me  move  on to  my  next  couple  of  items I  want  to  talk  about. To  do  that,  what  I'd  like  to  do is  move  on  to  a  partially  built… Here,  I've  partially  built  my  application. Here,  I've  got  all  the  components. I  just  need  to  group  them. That  brings  me to  another  piece  of  functionality with  an  application  builder that  you  might  not  be  aware  of. Let's  take  these  three  items  here. What  I  want  to  do  is  I  want to  group  them  together  horizontally. You  might  think,  "Well,  to  do  that, I'll  take  a  horizontal  list  box, I  will  drop  it  into  my  pallette, and  then  drop  those  items and  position  them  in  the  palette." When  I  am  putting multiple  items  into  a  container, I  find  it  much  easier to  select  those  items, right-click,  and  say  Add  Container. By  doing  so, I  can  apply  the  container  to  the  items rather  than  the  other  way  around. One  of  the  other  advantages of  having  this  functionality is  that  if  I  were  to,  let's  say… While  the container is  still  selected,  right-click, I  can  actually  change  the  container. Let's  say,  I  don't  want  an  H list  box. I  want  an  outline  box. Maybe  that  H list  box  was  better. I  can  also  change  that  container. That  works  from  the  workspace, and  in  addition, it  works  from  the  Objects  panel. You'll  notice  that  if  I  right-click over  the  item  in  the  Objects  panel, I  can  also  change  the  container, I  can  add  a  container,  and  so  on. Now,  in  certain  circumstances, there  is  an  additional piece  of  functionality, and  that  is  the  ability to  remove  a  container. I  can  remove  a  container  anytime it  is  not  the  lowest- level  container. For  example, if  I  were  to  put  this  in  another… Let's  add  an  outline  box  to  that. Now,  what  I  can  do… Let  me  just  move  this so  this  is  out  of  the  way of  the  other  items. Now  that  I've  got  that  outline  box with  a  horizontal  list  box and  then  all  my  items, I  can't  remove  that  horizontal  list  box because  it's  the  lowest  level, but  if  I  were  to  right-click on  the  Outline  box,  I  can  remove  that. Again,  sometimes  it's  easier  to  just  grab the  items  and  apply  the  container  box to  those  items  rather  than  dropping  them into  the  container  box. Let  me  recap some  of  these  tips  that  we  talked  about. Again,  I  can  organize that  source  panel  items by  either  grouping  them  or  alphabetically. I  don't  think  I  showed  that, but  just  let  me  point  out that  if  you  go  onto  the  hotspot of  the  Application B uilder, go  to  Source  Panel. If  I  were  to  change  that  group  by  column, they're  in  groups. If  I   would  prefer  them  alphabetically, I  have  that  option  available  to  me. However  you  find  it  easier to  recognize,  to  find  those  objects, I  can  reorganize  that  source  panel. Work  from  a  template to  make  things  easier  and  faster, not  having  to  change  properties for  items  that  you  always  change. Applying  the  container  to  the  objects rather  than  dropping  the  objects into  the  container is  often  much  quicker. The  Object  panel  is  there  to  do  selection, to  add  containers, remove  containers,  and  so  on. Copy  and  paste  works  for  a  single  item or  for  multiple  items  as  well. Okay. Let  me  move into  some  of  the  scripting  concepts. To  do  that, I  have  my  pre-built  dialog  box  here. I've  got  everything  organized the  way  I  want  it  to  be  organized. I  don't  have any  scripts  associated  with  it. Now  there's  a  couple  of  things that  I  need  to  point  out  visually that  really  are  implemented via  the  scripting, but  they're  important  to  know  about. Not  every  property  from  every  item is  available  to  change  interactively. For  example, you'll  notice  that  this  list  box, I  still  have  an  item  in  it. Well,  what  if  I  wanted  to  start that  list  box  with  absolutely  no  items? As  it  turns  out,  interactively, there's  no  way to  get  rid  of  that  last  item. I've  got  to  do  that  using  a  message. Same  thing  goes  for  setting. If  I  want  to  set  this  check box, if  I  want  to  set the  first  selected  item  to  be  JMP  files, that  has  to  be  done  through  messaging. That's  not  available  interactively. The  place  where  I  do  that, I'm  going  to  go  to  the  scripting  tab. I've  got  namespaces. I've  got  different  options for  application,  my  module. We'll  talk  a  little  bit about  the  namespacing in  the  next  example. But  anytime  I  generate  a  module, I'm  going  to  have a  different  module  container for  my  script, and  then  I'm  going  to  have  one for  the  overall  application. I'm  going  to  go  to  the  module, and  what  I  want  to  do is  I  want  to  set  that  list  box  to  be  empty when  it  starts. Let  me  go  back  and  select  that  list  box. I  see  that  the  name that  I'd  given  to  that  list  box is   lbfiles. I'm  going  to  have  to  message  that. Now  it's  important  to  know that  when  I  message an  object  that  is  visible, I  can  only  do  so after  those  objects  are  created. That  message  has to  appear  after  that  line. That  is  a  line  in  which the  visual  items  are  instantiated. After  they're  instantiated, I'm  going  to  say   lbfiles,  Set  Items, and  we're  just  going  to  leave  it  blank. That's  just  basic  JSL  scripting that  you  should  know how  to  do  or  you  should  be  familiar  with. Now,  another  important  point is  that,  let's  say  at  this  point, I  want  to  test  it  to  make  sure that  that  is  working  properly. When  I  run,  when  I  debug  a  script, I  don't  do  it  from  my  Edit  menu, nor  do  I  do  it  from  my  options if  I  have  a  run or  debug  script  available  in  my  icons. All  the  running  and  debugging has  to  happen  from  the  hotspot. I  have  two  options  here. I've  got  the  Run  and  Debug  Application. This  is  how applications  are  run  and  are  debugged. Great,  I  ran  the  application. It  looks  like it's  removed  that  initial  item, so  things  are  good  to  go. Now,  let's  talk  about  getting  scripts into  things  like button  boxes  or   checkboxes. What  if  I  wanted  to  associate  a  script? Let's  start  with  this  Cancel  button. One  of  the  things that  the  Cancel  button  does in  the  application is  just  dismisses  the  dialog  box. I've  got  two  places where  I  can  put  that  script. I  can  put  that  script in  the  Scripts  window where  we  saw  before  where  I  had that  initial  setting  of  my  list  box. Or,  again,  I'll  select  my  Cancel  button. If  I  scroll   into  the  properties down  in  the  Properties  panel, I  see  this  item. I'm  going  to  hover  and  you  should  see where  it  says  Edit  Script. I've  got  this  area where  I  can  write  my  own  script. If  I  want a  little  bit  larger  area  to  work  with, like  the  Formula  Editor, I  can  click  on  this and  I  can  write  my  script  here. I'm  going  to  go  ahead  and  do  that. We'll  talk  a  little  bit about  the  namespaces, but  what  I  need  to  know  is  that this  is  called  thisM oduleI nstance, and  I  have  to  use  the  message  Get  Box. That'll  return  a  link  to  the  window that's  created  by  the  module, and  then  Close  Window. That  should  work. Whenever  I  use  scripting  in  that  fashion, whenever  I  don't  have  a  function  name associated  with   a script, if  you  go  to  the  documentation, it  will  be  referred to  as  an  anonymous  script. Again,  if  I  want  to, I  might  want  to  test  that. Let's  go  ahead  and  run  the  application. I'll  hit  the  Cancel  button  and  that  works. That's  one  place  I  can  put  my  script. I  had  mentioned  that  I  can  also put  the  script  in  the  Script  tab. Now,  whether  you  put  it in  the  Properties  window  or  the  Script  tab is  really  a  personal  preference. I  have  a  tendency to  avoid  the  Properties  window simply  because  I  forget that  I  put  scripts  in  there and  I  can't  remember where  a  particular  script  is. What  I'm  going  to  do  now is  just  Control C, I'm  going  to  copy  this  item. Let  me  just  go  ahead and  clear  this  box  out  for  now. Anytime  I  want  to  create  a  script for  any  of  the  objects  to  do  things, quickest  way  for  me to  get  that  script  into  the  Script  tab is  to  hover  over  it,  right-click, and  you'll  notice  at  the  very  bottom, there  will  be  one  or  more  selections. Most  of  the  objects within  the  Application B uilder only  do  one  thing:  the  Text Edit. Number  Edit  Boxes do  a  couple  of  different  things and   Mouse  Box  does a  whole  bunch  of  different  stuff. Here,  I'll  select  the  script  I  want, and  this  is  what  happens when  I  press  the  button. You'll  notice  that  as  soon  as  I  do  that, it's  going  to  generate  my  function. It's  going  to  give  me a  little  stub  of  a  function. It's  going  to  give  it  a  name based  on   the  variable  name I  gave  the  button, and  it's  going  to  give  me just  a  little  fill-in  for  some  code. Here  is  where  I  would  put the  script  that  I  showed  you  earlier. Let  me  just  paste  that  in  there. Okay. Couple   things  to  point  out  is  that  first, the  default  argument  of  a  link of  a  pointer  to  the  object  is  supplied when  you  generate  a  script  this  way. You  don't  necessarily  need  it, but  it's  given  to  you  by  default. Also,  the  default  local  is  used, which  means that  anything  defined  within  the  function is  local  to  that  function. If  you  want  to  change  that, you  need  to  get  rid  of  the  default  local. Again,  let's  go  ahead  and  test  this  out. That  works  as  well. Again,  two  options  here  in  terms of  where  you  can  put  that  script. You  can  either  put the  script  in  the  Script  window or  you  can  put the  script  in  the  Properties  panel. One  final  note about  scripts  in  the  Script  tab is  that  they  don't  have  to  appear after  the  objects  are  instantiated. You  can  actually  put  them anywhere  in  the  script. You  don't  need to  have  the  script  available. You  don't  need to  have  the  objects  available before  that  function  is  created. You  can  put  that  anywhere. As  a  matter  of  fact, I  often  will  separate  all  my  functions. I'll  put  those  at  the  top  of  the  script, and  then  I'll  put all  my  messages  to  objects obviously  below  this  object  instance. That's  about  all  I  wanted to  cover  with  this  particular  example. I  want  to  move  on  to  the  second  example. In  this  example,  I  want to  cover  a  couple  of  visual  elements. As  I  had  mentioned,  I  want  to  talk  to  you about  how  you  deal  with  data  tables when  you  have a  table  that  you  want  to  use. I  want  to  talk a  little  bit  about  tab  boxes, so  when  you  have  a  tab  display. Some  folks  have  a  tendency of  finding  them  a  bit  confusing. So I  want  to  talk  about  tab  display. Finally,  I  want  to  touch on  two  important  scripting  notes. One  is,  how  do  I  pass information  from  one  module  to  another? Often  I  will  have  one  module, like  a  dialog  box  that  passes information  to  another  dialog, and another  module, which  is  a   Report window, so  I  need  to  know  how  to  do  that. I  also  need  to  know a  little  bit  about  the  namespaces that  the  Application B uilder  generates. Let  me  start  with  demonstrating what  this  example  looks  like. In  this  example,  you  get  a  dialog  box. You'll  notice that  my  Run  button  is  grayed  out until  I  drop  things into  the  list  box  on  the  right. I'm  going  to  go  ahead  and  click  Run. Two  JMP  platforms  are  used. The  distribution  platform is  in  the  first  tab and  the  scatter plot  matrix in  the  second  tab. Now,  the  beauty  of  the  way this  application  was  set  up is  that  it  doesn't  matter the  data  table  that  I  use, it  doesn't  matter  the  number  of  columns, which  columns  I  pick,  and  so  on. If  I  were  to  rerun  this, different  set  of  columns, different  column  names… Let  me  just  minimize  that. My  computer  is  having a  little  bit  of  problem redrawing   the  screen. There  we  go. It  works. It  works  regardless  of  the  data  table. Let  me  go  ahead  and  close  that. I  am  going  to  start with  a  fresh  application to  illustrate  some  of  the  visual  aspects. I'm going to say  New, A pplication. Again,  I'm  going  to  start with  a  blank  application. What  you'll  see  is  that  some  of  the  items in  that  source  panel  are  not  available. There  are  actually  seven  items that  are  not  available and  only  become  available when  you  open  up  a  data  table. If  I  have an  application  that  I'm  generating, and  that  application is  going  to  be  using  a  data  table, what  I  like  to  do is  to  start  with  something from  the  sample  data  directory. In  this  example,  I  mean,  this  is  not from  the  sample  data  directory, but  what  I  would  do  if  I  were  creating an  application  for  general  use is  that  I  would  open  something  up from  the  sample  data  directory, I  would  build  my  report, and  then  I  would  use  that as  the  basis  for  my  application. The  reason  I  do  that is  that  when  I  start  dropping  in some  of  these  items that  require  a  data  table— in  this  case, I've  got  this  column  list  box  all— you'll  notice  that  it's  going to  add  that  data  table  to  my  application. Now,  with  these  types  of  applications, I  need  something  there, at  least  as  a  stand-in. The  advantage  of  having something  from  the  sample  data  directory is  that  it's  unlikely  to  change  names, it's  unlikely  to  change  locations, and  it's  unlikely  to  be  deleted. It's  a  safe  bet  in  terms of  what  data  table  to  use. You'll  also  notice that  I  can  specify  the  path  here. I  can  give  it  a  different  label. I  don't  have  to  start with  the  current  data  table, but  that  is  the  default. That's  what  I  find  that  I  use  most  often, is  I'm  building an  application  that's  for  general  use that's  going  to  be  used on  the  current  data  table. I'm  going  to  go  ahead  and  change. I  have  a  stock  variable  name that  I  use  for  that,  so  we'll  change  that. That's  the  way I  will  typically  start  an  application that  requires  a  data  table. Before I  move  into  the  scripting, let  me  talk a  little  bit  briefly  about  the  tab  boxes. If  we  scroll  up  to  the  containers, we  see  we've  got  two  different  tab  boxes. We've  got  a  tab  box  and  a  tab  page  box. As  it  turns  out, the  only  one  I  really  need  is  the  tab  box. If  I  were  to  drop  that  tab  box  in  there, and  let  me  just  to  call  out the  tab  box  in  my  Objects  panel, and  I  were  to  drop any  item  in  that  tab  box, and  let's  just  drop  a  border  box  in  there, you'll  notice that  it  automatically  generates a  tab  page  box. I  do  not  need to  drop  something  in  a  tab  page  box and  then  drop that  tab  page  box  into  my  tab  box. All  I  have  to  do  is  work  with  the  tab  box. If  I  want  to  add  tabs, all  I  need  to  do  is, again,  select  that  tab  box, make  sure  I've  got  it  selected  here, right-click, and  I  have  the  option  to  insert  tabs either  before  the  currently  selected  tabs or  after  the  currently  selected  tab, or  if  I  want  to,  I  can  also  delete  tabs. Now, one  of  the  questions  I  often  hear  as  well. I've  got  my  tabs  set  up  a  certain  way. How  do  I  move  tabs from  one  space  to  another? Currently,  there's  no  interactive  way to  do  it,  unfortunately. What  you  would  have  to  do is  you  would  have to  manually  add  a  tab  page and  move  the  contents of  those  tab  pages  around. You  might  be  tempted to  save  the  script  to  a  Script  window and  change  things  that  way, but  I  often  discourage  folks from  working  with  the  saved  script or  making  alterations  to  the  saved  script just  because  there  are  a  lot  of  elements within  that  saved  script that  are  not  documented, that  are  particular to  the  Application  Builder. I  would  stay  away from  making  changes  to  that  saved  script. Let's  do  this. To  talk about  the  scripting  elements  of  my  box, I  have  pre-built  an  application. Actually,  before  I  move  on, let  me  point  out  a  couple  of  things that  occasionally  will  happen  to  folks who  wonder, "Well,  why  won't  the  display  box  work?" One  of  the  things,  if  you've  had some  experience  with  Application  Builder, one  of  the  things  you  might  have  heard  of is  a  parameterized  application. Let  me  go  to  pass  number  one. I've  got  my  dialog  box  built. What  I've  done  is  I  have  opened  up a   Report window,  a   data  table, generated  a  couple  of  reports and  just  dropped those  reports  into  the  blank  area. For  example,  what  I  did  in  this  case was  I  had  my  cars  data  table  opened  up. I  had that  distribution  platform  generated, and  all  I  did  was  drop  that in and  worked  with  the  report. Now,  if  I  were  to  stop  there and  use  that  for  my  report, what  would  happen… Let  me  close  out  my  items. I've  actually  done  that, and  I've  saved  that  here. What  would  happen  is  that  unless  I  use the  data  table  I  started  with, I  will  not  get  any  results. Here,  I've  got  my  air  traffic. I'm  going  to  use  that  saved  script. Everything  looks  good  so  far. I  get  nothing. If  I  were  to  switch  back  to  my  cars and  try  the  same  thing, and  I'm  going  to  grab  different  columns, run, I  get  the  same  columns  I  started  with. The  reason  is, anytime  I  use  a   Report window, if  I  want  to  change those  columns  that  get  used, if  I  want  to  change the  data  table  that  gets  used, I  have  to  use  a  parameterized  version. Let  me  go  to  the  example of  the  parameterized  version, and  I  will  point  out  the  differences. It's  not  that  one. My  parameterized  version  is  here. Here's  my  parameterized  version. Dialog  box  looks  the  same. My  report  looks  slightly  different in  the  sense  that if  I  were  to  select  on  the  reports, you'll  notice  in  the  very  bottom, I've  got  these  roles, and  these  are  filled  out. If  I  do  the  same  thing  with  scatter plot, select  the  report, my  roles,  that's  filled  out. Now  when  I  run  the  application, I  can  change  the  data  table. But  in  most  circumstances, I  am  fixed  to  the  number  of  columns that  I  use  when  I  built  the  application. Let  me  show  you  an  example  of  that. Let  me  close  this  out. Let's  go  with  the  air  traffic  data  table. Here's  my  second  example, my  parameterized  example. You'll  notice that  when  I  call  up  the  dialog  box, I've  got  a  space  for  the  three  columns that  I  initially  used  for  distribution. I've  got  a  space  for  the  columns I  used  for  my  scatter plot  matrix, but  that's  what  I'm  limited  to. As  it  turns  out, if  I  am  dealing  with  platforms that  come  from  the  Multivariate  menu, then  I  can  use a  variable  number  of  columns. Anything  else,  I  am  fixed to  the  specific  number  of  columns that  I  used  when  I  built  the  application. Here's  the  workaround, here's  the  solution. Let  me  do  this. I'm  going  to  go  back  to  my  cars  data  table and  open  up  my  final  example. Again,  dialog  box  looks  the  same. Report  does  not  have  anything  in  it. I  just  have  placeholders to  put  my  built  application, my  built  platforms. Here's  where  the  ideas of  namespaces  and  passing  variables becomes  very  important. When  an  application  is  created, there  are  two  different  namespaces that  you  have  to  be  aware  of. One  is  the  thisA pplication  namespace. That  is  variables  that  get  created that  can  be  shared  by  any  of  the  modules. If  we  look  in  the  Objects  panel, I've  got  my  applications  here, these  three  variable  names, they  are  in  the thisA pplication  namespace. Let  me  actually  type  that  out. The  name  of  the  variable is  thisA pplication. It's  in  that  namespace. The  other  namespace that  I  have  to  be  aware  of occurs  in  each  one  of  my  modules. Each  one  of  my  modules  gets their  own  thisM odule Instance. You've  already  seen  this when  we've  instantiated  objects on  the  visual  parts  of  the  application. I  have  got  a thisA pplication and  thisM oduleI nstance. How  do  I  pass  data from  one  module  to  the  other to  make  sure  things  get  done  correctly? Well,  where  we  do  it  in  this Application is  in  this  spot  right  here. Here  I'm  referencing  that  second  module, that  report  module. Let  me  also  point  out that  I've  got  some  options  to  change whether  or  not  it  gets  launched  at  start. Obviously, I  don't  want  this  to  be  launched until  the  user  picks  columns. I've  got  my  report  module  referenced. I'm  going  to  use  Create  Instance to   create  that   Report window, and  I'm  going  to  pass  the  items that  were  put  in  the  list  box  this  way. Now  on  the  report  module  side, the  business  gets  done in  the  OnM oduleL oad. On  OnM oduleL oad, everything  defined  within  OnM odule is  local  to  the  function. What  I  need  to  do  is  I  need  to  create a  variable  specific  for  the  module that's  going  to  store that  data  that  gets  passed  in. I've  done  that  right  here. A fter  that,  I'm  just  using somewhat  standard  JSL  to  be  able to  generate  my  platforms using  the  platform  calls. Everything  that  you're  seeing  here, this  is  standard… A  bit  on  the  complex  side, but  standard  JSL in  order  for  me  to  do  that. One  more  thing  I  want  to  briefly  explain that  you  might  have  noticed that  I  know  folks  always  ask  about, and  that  is  if  I  go  to, let's  say,  one  of  my  modules, there's  a  whole  bunch of  different  module  types that  I  can  pick  from. Dialog  box  is  exactly what  you'd  think  with  no  menu. Dialog  box  with  a  menu is  a  window  with  a  menu. A  modal  dialog  box  is  exactly  that. It's  a  modal  dialog  box. A   Report window. The  difference  between  a   Report window and  say,  a  dialog  with  a  menu is  that  you  can  save a  report  to  a  journal,  a  JRN  file. The  other  two  items  that  are  in  there, there  is  a  Launcher  item, which  is  internally  different than  a  dialog, but  externally  the  same,  more  or  less. Then  display  box  is  used  as  a  template to  be  embedded  in  other  modules. It  does  not  generate  its  own  window, but  it's  used  to  embed  in  other  windows. Probably  a  good  place  for  me  to  stop. Lots  more  to  talk  about. As  I  mentioned, there'll  be  a  PDF  that  you  can  download. All  of  the  examples  that  we  saw, they'll  be  there. There'll  be  some  additional  examples and  much  more  detail in  terms  of  what  I  can  explain. Hopefully,  you  found   this  all  helpful, and  hopefully,  that'll  motivate  your  want to  use  Application B uilder  more  often. Thank  you.
Event has ended
Pictures from the Gallery 8: Innovative Graph Builder Views - (2023-US-30MP-1386)
Monday, October 16, 2023
A picture is said to be worth a thousand words, and the visuals that can be created in JMP Graph Builder can be considered fine works of art in their ability to convey compelling information to the viewer. This journal presentation features how to build popular and captivating graph views using JMP Graph Builder. Based on the popular Pictures from the Gallery journals, the Gallery 8 presentation highlights new views and tricks available in the latest versions of JMP. We feature several popular industry graph formats that you may not have known could be easily built within JMP. Views such as integrated tabular graphs, satellite mapping, formula-based graphs and more are also included, helping you breathe new life into your graphs and reports!     All  right . Welcome ,  everybody . My  name  is  Scott  Wise , and  we  are  going  to  talk about  pictures  from  the  Gallery  8 . Every  year  we  come  up  with  some fantastic  views  that  you  can  do  in  JMP that  you  might  not  have  known because  there  are  some  tips and  tricks  involved . We  definitely  are  excited to  show  you  our  next  release . Before  we  begin , I  always  like  to  start  off with  something  interactive  here . I  got  inspired  by  a  recent  trip I  took  with  my  daughter . We  went  to  the  National  Video  Game  Museum . It 's  up  in  Dallas ,  Texas , and  it  walked  through the  development  of  video  games . They  had  a  game that  I  used  to  love  to  play . It  was  called  Battle  Zone . You  kind  of  felt  like you 're  on  this  strange  planet , everything  was  in  3D and  you  felt  like  you 're  inside  a  tank . It  was  really  cool . To  make  this  game ,  they  had  to  overcome a  big  problem  with  graphics , which  is ,  if  you  have  3D  graphics , how  do  you  know  where  you  are in  relation  to  an  object ? If  there 's  a  wall  in  front  of  you or  are  you  in  the  wall   or  are  you  in  the  back  of  the  wall ? Are  you  in  front  of  the  wall ? It 's  obviously  something  they 've  overcome because  a  lot  of  the  games now  are  3D  and  first -person and  so  give  you  that  perspective . Well ,  actually ,  I  can  represent this  problem  in  Graph  Builder and  I 'm  going  to  challenge  you and  maybe  if  you  learn  this  trick on  how  they  solve  this  problem , it  can  help  you  maybe  win  a  bet sometime  down  the  road  here . Or  you  can  challenge  people  for  fun . What  I 'm  going  to  do is  I 'm  going  to  show  you  a  basic  shape and  there 's  going  to  be  two  points , a  point  A ,  a  point  B , I  want  to  know  if  point  A  is  inside or  outside  that  shape . If  point  B  is  inside or  outside  that  shape , and  you  are  going to  have  just  three  seconds . So  maybe  grab a  little  scrap  piece  of  paper and  a  pencil  so  you  can  write  this  down . I 'm  going  to  only  leave  it  up for  three  seconds . The  first  shape  is  going  to  be  a  polygon . All  right . Again ,  I 'm  going  to  show  this  to  you . I 'm  going  to  count  to  three , and  you  tell  me  if  point  A is  inside  or  outside  the  shape or  if  point  B  is  inside or  outside  the  shape . All  right . Are  you  ready ? All  right ,  here  we  go . All  right . Well ,  what  do  you  think ? Was  A  inside  the  shape  or  outside ? What  about  B ? Let 's  pull  t`his  back  up . Now  I  guarantee  probably   everybody  got  this  in  three  seconds . A  really  looks  like  it 's  inside this  little  U -shape  to  me . We  can  click  into  shapes  in  JMP we  can  color  the  background  of  shapes , and  that  makes  it  just  really  easy . So  A  is  inside  and  B  is  outside . Okay . I  think  you  got  this  down . Well ,  what  if  we  make this  a  little  more  challenging ? I  have  another  shape . In  this  shape , it  seems  it 's  going  to  be  a  polygon . I  think  it  looks  like  a  spiral . I 'm  going  to  do  the  same  thing . We  got  point  A ,  point  B . Tell  me  if  it 's  inside  the  shape of  the  spiral  or  outside . Same  thing  with  point  B . All  right . Ready ?  I 'm  going  to  going  to  launch  it and  I 'm  going  to  give  you  three  seconds . All  right . What  do  you  think ? Is  point  A  inside  the  shape  or  outside ? What  about  point  B ? All  right . Probably  didn 't  have  enough  time . Maybe  some  of  you were  trying  to  use  your  fingers and  maybe  trace  the  shape . Three  seconds is  not  enough  time  to  do  that . Now ,  again ,  within  JMP because  we  can  click  on  points , I  can  click  on  this  point and  I  can  see  that  point  A  is  inside and  point  B  is  outside  of  the  shape . But  that 's  not  the  easiest  way  to  do  this . From  a  computer  programing  standpoint , they  needed  a  better  way . There  is  a  method  actually  out  there that 's  actually  going  to  help  us  do  this , and  it  is  called  Ray  Casting . It 's  pretty  simple . It  just  involves … From  whatever  point  you  care  about , you  just  draw  a  line  moving  away in  any  direction  from  the  point and  you  count  the  number  of  lines in  the  shape  it  intersects , walls  of  the  shape  so  to  say . If  it  crosses  an  odd  count  of  lines  as  it 's  moving  outside  the  shape , it 's  in . If  it  crosses  an  even  count  of  lines , it 's  outside . All  right .  Well ,  let 's  see how  that  works  in  practice . Here 's  the  U -shape . All  we  got  to  do  is  just  draw  a  line and  see  how  many  times  it  intersects . I 've  done  this  in  JMP . I 'm  going  to  show  you  a  little  later  how you  can  draw  these  confidence  intervals , raise  these  lines out  of  points  within  JMP . But  I 'll  go  ahead  and  open  one  up  here . Let 's  take  a  look  at  point  A . There 's  point  A . Let 's  go  ahead and  see  how  many  points  it  has before  it  exits  the  total  shape  here . There 's  a  one , there 's  a  two ,  there 's  a  three . There  were  three  walls it  crossed  before  it  went  out . That 's  odd  so  it 's  in . This  is  one  of  the  few  places , if  you 're  odd ,  you 're  in . You 're  in  the  shape  if  you 're  odd . But  what  about  B ? Well ,  it  doesn 't  matter  which  way  you  go  with  B , let 's  go  in  this  direction, it  hits  that  wall, it hits that  wall ,  it 's  even . If  it 's  even ,  it 's  out . It  works  whether  you  go  left  or  right . Very  cool . Well ,  what  about … That 's  okay . But  you  could  have  eyeballed  that  one . What  about  that  nasty  spiral  shape ? Well ,  let 's  take  a  look  at  it . Let 's  go  ahead  and  take  B . I 'll  come  down  here  to  B . Let 's  just  go  this  direction . One  wall ,  two  wall ,  that 's  even . Let 's  take  A . Way  down  here  at  the  bottom . All  right . Let 's  see . One  wall ,  two  wall ,  three  wall ,  four  wall . 1 ,  2 ,  3 . I  missed  one ,  five  walls . Five  walls  it  goes  by . So  it 's  odd   and  therefore ,  it 's  in  the  shape . That 's  how  it  works . Basically ,  this  algorithm  drives all  those  3D  video  games and  all  those  3D  images  that  you  see and  just  a  really  cool  thing  you  can  do . I 'm  going  to  talk  a  little  bit about  drawing  these  lines  in  a  graph in  our  pictures  from  the  gallery . But  I  thought  that was  a  fun  interactive  example to  get  us  started  with  our  talk  today and  maybe  give  you  something you  can  amaze  your  friends  with . All  right .  Let 's  talk  about the  pictures  from  the  gallery . We  show  six  advanced  views that  either  we  challenged  ourselves to  come  up  with , our  customers  using  JMP  challenged   just  us  to  come  up  with , or  what  we  just  saw look  so  cool  and  we 're  like , how  do  we  do  this  in  JMP   and  Graph  Builder  can  do  about  anything . In  this  case ,  we 're  going to  look  at  formula -based  graphs . I  have  an  actual  formula  in  a  column . Can  I  have  that  work  within  Graph  Builder ? What  about  tabular  data ? Can  actually  have  information lined  up  like  report ,  tab  data , lined  up  underneath  my  graphic  shapes ? That  would  be  really  cool . What  about  an  input -output  parallel  plot or  we 'll  call  that  a  flow  parallel  plot . That  might  be  really  cool . Forest  plots . Forest  plots  help  you  look  at  means and  confidence  intervals and  you  can  eyeball  control  them . This  is  really  popular in  health  and  life  science . We 're  going  to  look  at  that  one. Percent of Factor . Everything  scales  to  100 %. Nice  way  to  compare  things using  bars  that  go  from  0 -100 %. You  can  see  what  segments here  account  for  what . Last  but  not  least , mapping  and  this  is  doing satellite  drill  down  something  you  can  do . The  things  I 'm  going  to  show  you, this  Mapbox  mapping  in  the  tabular  data , these  are  actually  new  features  in  JMP  17 . The  others  could  be  done with  older  versions  of  JMP , but  we  definitely  want to  feature  a  couple  new  things that  have  come  out in  the  latest  release  of  JMP . All  right .  I 'm  going  to  give  you this  journal ,  that 's  the  reward for  attending  this  talk . When  you  get  this  journal ,  it 's  going to  have  all  the  information  you  need . It 's  going  to  have  a  picture of  what  we 're  trying  to  replicate . Why  it 's  good ,  tips . It 's  going  to  give  you  the  raw  steps on  how  to  create  these . You  will  also  include  the  data with  the  scripts  to  recreate  it  here . All  right .  This  first  one  is  actually bringing  in  a  formula  into  JMP , which  is  really  cool . There 's  tips  to  do  this . The  tip  is  you  must  have  a  formula  column  within  your  data  table  and  JMP . That  makes  sense . But  you  need  to  find  a  way  to  include all  the  elements  of  the  formula . If  you  have  an  X  and  a  Y  in  your  formula , the  X  and  Y  need  to  be  somewhere in  one  of  the  landing  zones within  the  Graph  Builder . A  better  way  to  put  it . All  right . You  have  those  steps  if  you  need  them . I 'm  going  to  do  this  one  just  from  the  data  in  the  journal . Let 's  see  what  we  have  here . This  was  real  data  that  my  father asked  me  to  help  him  with . He  was  actually  trying  to  decide on  buying  a  garden  hose . He  was  doing  a  lot  of  washing  of  his  patio and  his  siding  at  his  house , and  he  wanted  to  make  sure that  he  had  the  best  water  flow . Well ,  there  is  a  formula  for  water  flow , and  here  is  that  formula . It  matters ,  the  diameter  of  your  hose . You  have  a  three -fourths -inch  diameter , a  half -inch  diameter . It  matters  how  long  the  hose  is . I  guess  the  distance  between  the  spigot  and  the  spray  attachment or  the  end  of  the  hose . That  worries  what  kind of  water  pressure  you  have coming  out  of  your  initial  spigot . You  have  40  pounds ,  60  pounds ,  so  on . There 's  a  formula  in  here and  easy  to  create  formulas using  JMP 's  formula  editor I  have  other  information . I 'll  turn  on  these  little  header  graphs . It  looks  like  I  have  from  0 .75 ,  the  0 .5 even  have  a  0 .625  hose  diameter . Looks  like  I  got  40 ,  50 ,  and  60  water  pressures running  through  that  formula and  it  looks  like  we  collected  data  for , looks  like  from  25 -50 to  75 -100  hose  lengths . So  the  100  feet . All  right . I  have  all  this  information . Let 's  just  go  to  Graph  Builder and  let 's  start  to  fill  it  out . Here  are  all  my  landing  zones . I 'm  going  to  take  the  one  that has  the  formula  in  it  and  put  it  in  first . That 's  the  water  flow . I  put  it  in  the  Y  here . I  think  I 'll  put  the  length down  here  on  the  X . Looking  a  little  more  interesting . Maybe  diameter  would  be a  good  thing  to  overlay  by . I  overlay  and  so  you  can  see I  get  three  different  lines  there for  the  hose  diameter . I  knew  the  water  pressure and  he  was  probably  going  to  go to  the  water  pressure  he  had . I 'm  going  to  put  that  on  the  Group  X instead  of  having  three  panels  here , I 'm  going  to  right -click and  I 'm  going  to  go  Level  in  View and  I 'm  going  to  go , let 's  just  do  one  at  a  time . Now  we   can  flip through  these  and  see  them . Okay ,  now .  How  to  represent  this . It 's  okay  to  have  points , but  this  smoother  line  is  not  the  formula . It 's  just  doing  some  sort of  spline  smoother  through  here . That 's  not  really  helping  me . What  about  if  I  select , I  don 't  know ,  a  straight  line ? Well ,  that 's  not  really  reflecting . That 's  just  connecting  the  points . That 's  not  reflecting  the  formula . To  do  the  formula ,  I  can  select this  little  formula  icon  here , or  you  can  even  right -click in  here  and  just  go , hey ,  line ,  change  that  out  to  formula . Now ,  it  is  reading  the  formula . It  will  not  work  unless  all the  elements  of  the  formula  are  there . See  if  I  take  this  overlay  diameter  out , you  see  the  line  disappears . It 's  got  to  have  all  the  elements of  the  formula  somewhere accounted  for  in  a  graph  element . But  now  it 's  pretty  cool . Now  I  can  sit  there  and  see  that , oh ,  looks  like  high  diameter ,  0 .75 . The  shorter  the  hose  length , the  higher  the  water  flow is  going  to  be  at  40 . It  looks  like  it 's  holding  for  50  or  60 . Something  you 're  going to  see  me  do  a  lot  as  well  is I 'm  going  to  show  you how  you  get  that  little  picture  in  here . It 's  easy  if  you  have  a  picture just  like  a  jpeg . It 's  easy  just  to  drag  it right  into  your  graph . Now  I  have  it  dragged . If  you  right -click  into  the  graph , there 's  a  section  for  images and  you  can  size  it . I  usually  use  this  fill  graph . Then  you  can  right -click  again and  you  can  even  make  it  transparent . I  like  to  do  that  so  I  can  see  the  points , maybe  make  it  only  a  40 %  clear , so  I  can  see  the  lines popping  through  there . Now  that 's  a  cool  graph and  that 's  how  you  do  this  view . Now  we  can  go  and  pick the  right  size  hose  that  we  want  to  use no  matter  the  water  pressure  we  have . All  right . Again ,  you  have  that available  to  you  at  any  time . Remember ,  I  have  the  scripts saved  to  the  data . You  can  click  on  it and  you  can  recreate  it  at  any  given  time . All  right . That 's  the  first  one . We 'll  go  through as  many  as  we  have  time  for . I  think  we 'll  get  through  all  six  today . I  have  them  ordered  in  terms of  when  I 've  shown  this  before and  what 's  the  most  popular. The  next  most  popular  one is  this  tabular  data . This  is  something  that became  available  in  JMP  17 . Why  this  is  nice  is … It  used  to  be  when   I  made  a  nice  graph  here , I 've  got  box  plots  up  in  this  area . It  used  to  be  I  had  to  go and  create  in  a  separate  window , maybe  something from  Tabulate  to  create  a  table and  just  had  to  line  those  two different  graph  windows  up . But  what  if  I  wanted  it  right  underneath ? Well ,  we  now  have  that  capability and  it 's  going  to  be  actually using  new  features  and  caption  boxes that  are  in  JMP  17 and  it 's  going  to  help  us  with  tables . It 's  even  going  to  help  us with  recalculating  reference  lines . Well ,  that 's  cool . What  does  that  look  like ? All  right .  I  have  this  data  set  here . It 's  cool  when  it 's  got all  this  chemical  production . I  got  this  rate  of  reaction and  I  have  these  different  vendors . Say  I 'm  just  really  interested in  graphically  seeing a  difference  in  the  vendors by  the  rate  of  reaction  here . Put  that  on  the  Y , vendor  on  the  X . Maybe  points . It 's  not  as  interesting as  maybe  a  box  plot . Maybe  I 'll  color  by  rate  of  reaction . Maybe  I  will  come  up in  this  little  bottom  left -hand  side , we  call  this  the  panel  boxes  here and  I  can  go  under  this  box  pop -panel and  I  say ,  give  me  a  confidence  diamond . That 's  pretty  cool . I  know  the  middle . That  diamond  is  where  my  mean  is . What  if  I  make  this  even a  little  more  interesting ? You  can  hold  your  control  key  down , shift  key  down if  you 're  using  a  Mac  like  I  am , I 'm  going  to  put  this, I  click . I  got  points  on  top  of  the  box  plots . I 'm  going  to  come  down  to  where this  point  is  and  say  summary  statistic , I  don 't  want  to  see  them  all . Just  show  me  the  mean . Yes ,  the  point  is ,  in  the  middle of  the  diamond ,  that  makes  sense . Oh ,  I  can  even  help  it  a  little  bit . I 'm  going  to  do this  air  interval  selection . I 'm  going  to  do a  confidence  interval . Now  I  can  see  the  ends  of  the  diamond . Oh ,  that 's  really  cool . Maybe  I  want  to  shade  it  all  in and  there 's  an  interval -style  here called  Hash  Band  I  like . Now  it 's  all  instead  of  those  lines , just  those  little  whisker  lines , now  I  have  this  little  shaded -in  square  and  that 's  pretty  cool . That 's  telling  me  maybe Acme  has  a  lower  rate  of  reaction than  somebody  like  Green . Acme  is  over  here , bluish ,  green  is  higher  and  reddish , although ,  the  box  plot 's  showing  me , there 's  a  lot  of  data  in  between , a  lot  of  spread  of  the  data . There 's  a  lot  of  variation  here . But  what  if  I  want  to  now bring  in  what  is  the  mean ? Not  only  what  is  the  mean for  the  rate  of  reaction  overall , but  what  is  the  mean for  Acme ,  Bloom ,  Green ,  and  this  Rizen ? How  can  I  do  that ? What  I 'm  going  to  do is  I 'm  just  going  to  right  click  in  here and  I 'm  going  to  add  a  caption  box and  you 're  like ,  Scott ,  that 's  boring . I  knew  how  to  do  that  in  JMP  16 . It 's  just  sitting  right  up  top  of  here . Yes ,  but  there 's  a  new  thing you  can  do  with  caption  boxes . There 's  a  location  area  here . For  the  mean ,  I  can  actually  say , you  know  what ,   make  it  an  axis  reference  line . Now ,  it  is  right  here  in  my  data . That 's  really  cool . What 's  really  cool  about  this, and I 'm  going  to  right -click  over  here , I 'm  going  to  turn  on ... I 'm  going  to  go  under  redo . You  might  have  seen column  switchers  before and  say ,  I  want  to  switch  out  the  rate of  reaction  with  some of  the  other  type of  continuous  factors  here and  now  get  my  little selection  box  over  here . I 'm  clicking  on  agitation . Do  you  see  it  recalculates the  mean  for  agitation and  here 's  the  mean  for  inlet ? This  is  much  better  than  right -clicking and  go  in  under  axis  settings and  setting  a  static  reference  line because  that  won 't  change . But  this  will  change  if  the  axis  change if  what  you 're  calculated  from  changes . That 's  pretty  cool , let 's  leave  it  at  rate  of  reaction and  let 's  go  ahead and  let 's  do  one  more  thing . I 'm  going  to  add a  second  caption  box . I  right -click  in  here , I 'm  going  to  go  add and  you  can  add  two  or  more , one  or  more  elements . Now  I  add  a  second  caption  box . This  first  one  is  doing an  axis  reference  line . It  doesn 't  know  what  to  do with  the  second  one . It  has  it  overlaying  on  top of  the  other  caption  box . Then  I  can  just  say ,  you  know  what ? This  one  make  it  into  an  axis  table . You  see  now ,  oh ,  it 's  lined  up right  underneath  all  the  labels and  underneath  all  the  columns ,  so  to  say , for  all  my  categorical  levels  here . I  can  even  add  another  summary  statistic and  I  can  go  and  do  like  standard  error and  I  can  just  keep  adding  more and  now  I  can  build  out  a  nice  table , I  can  say  done  here . All  I  really  would  have  to  do  now is  maybe  just go  under  the  Graph  Builder  red  triangle and  clean  up  the  legend  here . I  can  go  to  the  settings  here and  I  don 't  need  all  these  little  legends . I  can  just  keep  the  one for  the  color  gradient and  down  here  as  well . If  you  want  to  get back  to  that  control  panel . You  say  so ,  control  panel . There  was  one  more  thing I  was  going  to  show  you  here . You  see ,  I 'm  carrying four  decimal  points  down  here . The  caption  box will  let  you  change  the  format and  I  can  do  like  fixed  decimal  two . Now  that  looks  really  nice . Even  to  make  it  even  nicer , I  found  out  this  is  a  nice  little  trick . I  found  out  you  can  change the  legend  position . I  can  put  it  at  the  bottom . If  I  right -click  in  here and  go  to  the  gradient , you  can  even  make  it  horizontal . I  love  this  kind  of  horizontal  views and  now  it 's  a  much  more  compact  view and  it 's  going  to  look  a  lot  better when  I  start  to  change  things  around . All  right . A  very  cool  graph . I  would  like  to  give  some  thanks  as  well to  Joseph  Reese ,  one  of  my  peers who  helped  me  create  this  chart and  figure  it  out . Thank  you ,  Joseph . All  right . What  are  we  going  to  look  at  next ? The  in  and  out  parallel  plots . This  one  here . It 's   cool  because  I  often  did  work where  I  had  like  a  project  budget and  you  had  so  much  money that  would  go  into  the  total  budget and  then  you 're  pulling  out to  make  expenditures or  you  have  inputs and  outputs  of  a  process . I  used  to  do  a  lot of  input -output  boxes . Well ,  this  is  a  parallel  plot which  is  showing  me with  the  size and  the  width  of  these  bands . How  much ,  in  this  case  money is  coming  from  jobs  here . But  it 's  all  going into  one  big  bucket  here and  then  out  of  that  bucket , I  have  outflows . That 's  really  cool . How  do  we  set  this  up ? We 're  going  to  do  something that  enables  us  to  actually  look at  combine  data  in  a  parallel  plot . It 'll  be  a  little  easier to  show  you  by  hand . Here  is  my  data . Now  setup  is  everything  on  this and  every  row  here  is  an  expenditure . You  see , I  have  a  separate  amount  for  that . But  sometimes  those  expenditures get  rolled  up  into  groupings . Like  here ,  I 've  got  a  lot of  these  are  going  into  job , so  I  have  a  column  for  inflow and  I 'm  putting  the  category  for  inflow and  I  have  a  lot  that ... All  these  line  items of  inputs  go  in  to  job . That 's   cool . I  got  tax  refund . I 've  got  side  hustle  here . There 's  the  total  bucket and  you  can  see  I  can  start  with  outflows . Here ,  I  can  look  at  all  the  savings  here and  I  have  20K  versus  savings  here and  you  can  see  I  can  even  have  a  second outflow ,  which  breaks  that  savings  down to  where  what  type  of  savings  it  went  to . Some went  to  401K, some  went to  investment . If  you  have  things  set  up  like  this now ,  I  have  everything I  need  to  make  this  chart . I 'm  going  to  go  to  Graph  Builder . Border . I 'm  going  to  just  take all  the  categorical  factors and  I 'm  just  going to  dump  them  on  the  x -axis . I  might  color  by  the  outflow  one and  I 'm  going  to  size  by  the  amount . Now  it's  stuck  on  point so  I will  change  to  parallel  plot . I 'll  make  this  a  little  bigger . Now ,  if  I  put  the  control  chart  down , it 's  looking  okay . But  what  it 's  doing  now , it 's  taking  my  inflow  boxes and  then  it 's  slowly  breaking  them  out . Why  only  want  the  breakout  to  happen  here ? I  want  to  see  what  comes  in from  the  outflow and  what  goes  out  from  that  section . To  do  that , if  it 's  in  the  second  section , if  I  click  on  this  combined  data  sets , it  restarts  on  the  second  bar , so  to  say ,  of  the  parallel  plots . If  I  say  done , you  can  play  with ,  which  is , whether  things  are  ascending or  descending with  clicking  on  these  arrows  up  here , I 'm  going  to  click  on  a  few  of  these . Now  it 's  very  easy  for  me  to  see how  jobs,  side  hustle and  tax  refund  make  up  my  total of  101K and  now  I  can  see  something  like  auto and  that 's  very  cool . You  can  see  here  my  auto  was  11K and my  total ,  101K and  I  can  see  if  I  make this  even  a  little  bigger , I  can  see  that  it  gets  broken  out  among my  car  payment ,  my  gas  and  my  upkeep . This  is  just  a  really  cool  chart  to  use and  there 's  other  things  you  can  do to  make  it ,  enhance  it  a  little  bit  more . You  can  play  with  the  colors ,  but  a  really cool  inflow -outflow  parallel  plot . All  right . See  how  we 're  doing  on  time ? We 're  doing  pretty  good . Let 's  move  on  to the  next  most  popular  views  here in  our  pictures  from  the  gallery . This  one 's  going  to  be  a  forest  plot . Forest  plots  are  going  to  enable  you to  like  plot  means and  put  a  confidence  interval  around the  mean  that  you  can  compare  to  other means  with  confidence  intervals on  the  same  chart . It 's  very  popular , especially  in  health  and  life  science place  where  you 're  doing  a  lot of  summarization . But  we  have  a  cool  little  example here  on  how  to  do  forest  plots . Here  it  is . We  are  going  to  go  out and  we  are  going  to  buy  a  diamond . Maybe  you 're  getting  engaged . Maybe  you 're  getting  married  here . Now ,  people  always  talk about  that  cut  color  and  clarity and  there 's  all  these other  little  different  levels  within  them . Do  they  really  matter ? What  really  drives  what  you  care  about , which  is  what 's  the  average price  for  this ? How  much  am  I  going  to  have to  pay  for  a  diamond ? I  want  to  impress whoever  I 'm  getting  engaged  or  marry . But  want  to  do  it as  efficiently  as  possible . Let 's  take  care  of  this . Let 's  go  ahead  to  our  diamonds '  data . Let 's  take  a  look . I 'm  going  to  open  up the  column  headers  here . It  all  comes  from  one  table that 's  not  that  interesting . But  you  see ,  I  have  summarized . Each  row  is  summarizing  the  mean and  the  standard , the  mean  and  the  lower  and  upper confidence  interval ,  the  standard  error . Just  some  summarized  metrics here  for  it  looks  like  a  combination of  color ,  a  color  in  level  here . I 've  got  color ,  levels ,  I 've  got  clarity , I 've  got  cut and  just  all  kinds of  levels  within  there  that  I  want . To  look  at  this  data , I 'll  just  go  ahead and  put  the  Graph  Builder . I 'll  put  my  mean  price  down  at  the  bottom . Don 't  have  to  worry  about  the ... I 'm  not  going  to  worry  yet  about  the confidence  interval  around  the  mean . We 'll  do  that  last . But  let 's  go  ahead  and  put  the  X  here . There 's  my  color  clarity . I 'm  even  going  to  color  by  the  x . I 've  got  points automatically  being  driven ,  that 's  fine . Now  level ,  I 've  got  the  different  levels of  cut ,  clarity  and  color . Yeah ,  I 'm  going  to  move  it  here . But  you  see ,  you  can  embed  in  here . I  have  the  level  nested  within  my  x , which  was  the  cut  color  of  clarity . That 's  pretty  cool . Maybe  to  make  it  easier  to  segregate those  three  different  aspects , I 'm  going  to  right -click  here , I 'm  going  to  go  to  the  axis  settings and  I 'm  going  to  reverse  the  order . I 've  got  clarity  first and  under  the  X  tab  up  here , I 'm  going  to  show  a  grid that 's  going  to  draw  lines  there . Now  I 'm  looking  over  three  sections . This  is  okay , but  I  hate  the  eyeball  these  points . What  can  I  do ? Let 's  take  this  lower  95 and  upper  side  percent confidence  interval and  let 's  bring  it to  that  interval  landing  zone . You  see  what  it  has  done is  it  is  created  error  bars constructed  around  that  lower  95 and  upper  side 95 %  confidence  interval  around  the  mean . If  you 've  got  a  column  form , you  can  bring  them  into  that  landing  zone . You  can  even  bring  in  a  one -sided  one if  you  only  have  an  upper  or  lower . But  I  like  this  kind  of  shows me  where  those  points  are . You  can  right -click  in  here and  you  can  mess  with  the  marker  sizes and  you  can  make the  marker  sizes  all  big or  I 'm  going  to  make  them a  marker  size  a  five . Now ,  if  I  look  at  it now ,  I  can  answer  some  good  questions about  what  I  should  look  for  in  a  diamond . What 's  really  driving  the  price ? Remember  the  further  it 's  going  on  x -axis the  more  expensive  the  diamond  is . If  I  look  at  clarity , this  does  not  make  sense because  some  of  my  clearest these  things  this  IF it 's  almost  like  flawless  clarity or  very, very, very  subtle differences  in  it . This  is  actually  costing  less  than the  stuff  that 's  supposed  to  be  a  better . That  one  makes  no  sense . What  about  color ? D  was  supposed  to  be  the  best . K  was  supposed  to  be  the  worst. But  I 'm  seeing there 's  two  groups  over  here . What 's  really  driving  it ? It  looks  like  it 's  cut  and  the  ideal cuts  are  the  most  expensive  ones . Then  excellent and  very  good ,  then  good . You 're  going  to  go  buy  the  diamond , forget  about  the  color  rating , forget  about  the  clarity  rating , really  focus  on  the  cuts and  that 's  what 's  going  to  drive  price . All  right . Very  cool  to  do  that  type  of  graph . That 's  a  forest  plots . Very  easy  to  do  in  JMP . All  right . What  is  our  next  view ? Our  next  view  is  actually  something  cool . It 's  good  to  do  with  ranked  or  scale  data . It 's  percent  of  factor . This  is  something  most  people didn 't  know  we  had  the  capability to  do  in  Graph  Builder . We 're  going  to  look at  some  coffee  shops . I  have  all  this  data  from  my  hometown  here where  I  live  is  in  Austin ,  Texas , and  I  got  all  these  coffee  shops and  it  looks  like  there  were  reviews and  they  gave  them  ratings . Whether  the  ratings  low  or  high , 4  or  5  stars ,  the  best . I  even  have  things  like  sentiment , like  how ,  what 's  the  vibe  of  the  place ? That 's  something my  daughter  likes  to  say . This  place  has  good  vibes  or  bad  vibes . One  of  our  favorite  things is  checking  out  coffee  shops . Where  should  we  go  this  afternoon ? We  want  to  get  some  coffee  around  Austin . What  we 're  going  to  do is  we 're  going  to  set  this  up . I 'm  going  to  go  over  here to  my  Graph  Builder . I 'm  just  going  to  put my  coffee  shop  name  in  here . Okay ,  that 's  pretty  cool . Now ,  what  I  can  do , I  can  put  some  type  of  scale  here . I  have  the  rating . I  can  put  the  rating  down  here at  the  bottom , instead  of  points , I  can  ask  for  bars  to  be  done . That 's  not  too  interesting this  side  by  side ,  but  I  can  do a  percent  of  total . I  think  I  have  this ... Think  I  don 't  have  this set  up  correctly  here . This  is  a  good  thing  you  do when  you  got  everything  saved  for ,  yeah . I  can  come  right  back  in  here so  have  coffee  shop  name and  I  have  counts so  I  had  the  wrong  thing  on  there . You  want  something  continuous  on  there . We  had  the  actual  count  of  the  data instead ,  I  think  I 'm  going to  use  the  raw  numeric  rating instead  of  the  one that 's  categorical  here . Let 's  take  a  look  at  that  again . Let 's  go  ahead and  put  my  coffee  shop  name  out  here . Let 's  go  ahead  and  put  that  either that  rating  or  the  counts . I  guess  I 'll  just  put  the  count up  here ,  down  at  the  bottom . I 'll  go  to  bars  and  that 's more  what  I  was  looking  for . Now  I  have  the  count -down  here . It 's  going  to  give  me  a  raw  count . Doesn 't  look  that  interesting . But  now  I  can  take that  categorical  rating , and  we  can  do  something  like overlay  by  it  and  take  a  look  at  it . I 'm  going  to  go  back  and  double  check and  see  what  I  had  overlaid  by  here . It  looks  like  overlaid  by  the  rating . I  will  do  the  same  on  our  graph . Now  take  that  rating , I  will  overlay  by  it . It  looks  like  a  real  mess  right  now , but  now  I  can  go  in . You  can  choose  different  types of  bars  here , but  the  one  I  am  going to  look  for  is  going  to  be  one that  utilizes  a  new  summary statistic  called  percent  of  total . I do  that  one,  percent of  total . I  can  take  a  look  at  this in  different  types  of  bar  configurations . Again ,  I  will  take  a  look  to  see  what  kind of  bar  configuration  I  had  used  here in  my  finished  graph  and  I 'm  going  to  look at  stat  percent  of  factor is  the  one  I 'm  going  to  do . Instead  of  percent  of  total , let 's  do  percent  of  factor and  now  let 's  do  the  stacked  one . There  we  go . That 's  the  view  I  wanted . Stacked  percent  of  factor . It 's  going  to  change that  kind  of  count  to  100 %. It  is  going  to  break  out  what  portion of  it  went  to  what  rating , which  is  really  cool . Other  cool  things you  might  not  have  known  you  can  do . I  can  right -click  in  here and  you  can  order  by  something . I  can  even  order by  something  that 's  other . Like  already  have  a  high  rating . Yes /no . I  knew  this  was  like  fours and  fives  versus  one ,  twos  and  threes . I 'm  going  to  select  that  one . Now  you  can  see  it 's  kind  of  did a  nice  job  putting  the  ones that  have  the  more  four  and  fives on  the  top  of  the  graph . Now  I  can  say , "Hey ,  we  might  want  to  hit  the  Saa -Ten " if  I 'm  saying  that  correctly . This  is  my  favorite  coffee  house is  flight  path , so  maybe  we  weren 't  going  to  hit  that  one . But  my  daughter  might  say , "What  about  the  vibe ?" Then  you  can  say ,  "Well ,  that 's  good . Let 's  just  do  a  little  local  data filter  and  let 's  bring  in  the  vibe ." Maybe  make  this  a  block  style  view . Now  let 's  go  and  select just  the  ones  that  were  two ,  threes and  four  is  on  vibes  now , or  maybe  threes  and  fours  on  vibe and  we 'll  do  two ,  threes . I 'll  do  one ,  two ,  threes  and  fours . Now  we  can  see , "Oh  the  Hideout  has  really  good  vibes ." Flight  path  still  down  here , but  maybe  we  want  to  go  and  check the  hideout  out  if  can  find  it . That 's  the  easy  thing  you  can  do . Why  I  really  like  using this  percent  of  factor . If  you  can  have  a  continuous  x and  you  can  even  overlay by  something  which  can  go  break that  count  up  of  by  some  section , this  is  a  great  chart  to  use . All  right . That 's  just  going  to  leave us  with  our  last  chart . Our  last  chart  is  probably the  most  photogenic  of  it . It 's  going  to  be  Map box  Mapping . This  is  something  new  in  17 that 's  really  powerful . We  had  the  ability  to  look  up to  use  building  maps . You  had  the  ability to  use  mapping  services  in  the  past . But  in  17  we  came  up  with  a  much better  type  of  mapping  service , same  one  you  might  see  like with  Google  Maps  and  it 's  Mapbox  Mapping . I 'm  going  to  look I  have  these  some  select  hotels I  used  to  stay  at  when  I  used to  run  around  the  country  for  JMP . I 've  I 've  got  all  kinds of  information ,  but  the  most  important , I 've  got  latitude  and  longitude in  the  hotel  name  and  the  counts . This  will  be  a  good  thing  to  map . I  know  I  can  go  in  the  graph and  Graph  Builder  and  bring  up  a  map . I  know  I  can  put  my  latitude  down and  my  longitude  down and  I  know  you 're  already  starting  to  see , the  shape  of  the  country , East  Coast ,  West  Coast  going  on  here . I  know  I  can  right -click now  under  graph and  go  to  that  background  map . Now  instead  of  a  street  map  service or  just  doing  any  of  these  other  things that  used  to  be  in  JMP , I  can  do  a  web  map  service and  it 's  going  to  allow  me to  pick  from  all  these  Mapbox  options and  like  Mapbox  dark  one is  pretty  cool . That  we  can  select . There  we  go . You  do  it ,  and  I 'll  go  ahead and  I 'll  show  that  again . It 's  under  background  map and  you 'll  select  a  street  map  service . You  won 't  select  this  service unless  you  want  to  interact  with  it by  specifying  a  layer , but  I don 't  worry  about  that . Street  map  service  and  then you  pick  from  the  Mapbox  selections and  this  is  what  a  dark  one  looks  like . It 's  the  nighttime  view  of  the  country . That 's  cool . I  saved  to  my  script  a  couple  of  others . This  one  is  looking  at  the  Map box , outdoor  one . So  if  you  want  to  see if  you 're  in  the  water , you  want  to  see  if  you 're  in  the  bay , you  want  to  see things  a  little  differently . I  can  even  look  at  a  street  view . This  is  the  Map box  Street  View . Now ,  to  do  this  one ,  I 'm  going  to … It 's  hard  to  see ,  but  you  do  have a  little  plus  or  minus  into  your  map that  you  can  select  a  drill  down . I  like  doing  it  through the  magnifying  element  up  here . So  I  switch my  pointer  out  for  the  magnifier , and  here 's  a  hotel I  stayed  at  in  Sacramento . I 'm  going  to  click  on  it . I 'm  going  to  click  on  it . It  was  called  the  Delta  King . What 's  up  with  this  one ? It  looks  like  it 's  a  boat . Well ,  I 'm  going  to  right -click  right  here . I 'm  going  to  go  into  that  background  map and  instead  of  Streets , I 'm  going  to  select  a  satellite . How  cool . Now  I  can  go  see  it  is  a  boat . The  Delta  King  is  a  very ,  very  cool , ferry  boat , a  historic  one  that  used  to  run  between , I  think ,  Sacramento  and  San  Francisco , and  they  made  a  hotel  out  of  it . Now  you  can  stay  in  the  old  town in  Sacramento  and  check  it  out , thanks  to  Bonnie  Rigo . When  I  work  with  my  team  right  now who  introduced  me  to  this hotel  really  cool  one  to  use . All  right . That 's  how  we  do  these , and  I 've  got  other  cool  ones  in  here . You  want  to  see … I 've  got  some  of  the  other  cool  places I 'd  stayed  here . Like  looking  at  a  cool  one in  Las  Vegas  here . Maybe  you  recognize  this  one ? This  one 's  that  old  Luxor  pyramid . That 's  why  silicon  in  Las  Vegas … Satellite  views  are  really  cool . We  even  looking  at  maps  of  Miami  Beach . Cool  things  where  there 's  a  lot  of  water and  that 's  the  Fontainebleau in  Miami  Beach . Man ,  I  wish  it  was  one of  my  boats  as  well ,  but  it 's  not . Very  cool . Very  cool  things  you  can  do  now  in  JMP . Graph  Builder  with  that  Mapbox . All  right . Let 's  go  and  take a  look  at  the  other  ones . Again ,  thank  you . Thank  you  to  Joseph  Reese for  helping  with  tabular  data . I  don 't  think  I  mentioned Jason  Wiggins  helped  me with  the  flow  parallel  plot . So  thank  you ,  Jason ,  for  that  as  well , and  I  always  include in  my  journal  a  bonus  one . I 'm  not  going  to  show you  how  to  make  this  one . I 'm  going  to  give you  a  little  incentive  to  go  out , and  try  out  the  instructions . They 're  there  for  you . This  is  a  painter  chart . This  is  something  that  is  new in  the  Pareto  platform  in  JMP  17 , but  found  out  in  Graph  Builder , I  can  make  this  all  along . It 's  a  combination  of  a  bar  chart , a  run  chart ,  and  Pareto  chart . It  was  very  popular  at  places  like  Ford , when  they  were  looking  at  defects , and  I 've  seen  it  used  a  lot in  semiconductor  and  high -tech , for  example . This  is  just  an  example of  how  to  create  this  type  of  combo  chart within  a  Graph  Builder . All  right ,  so  that 's  your  bonus . I 'm  going  to  leave  you  behind with  where  to  learn  more . I 'm  going  to  give  you  the  link  to  the other  seven  pictures  from  the  gallery . Time  six , there 's  another  42  really  cool  views . You  can  go  and  look at  an  additional  the  one  I  just  gave  you , and  that 's  on  our  JMP  community , our  source  for  everything  you  want , for  learning  JMP , for  past  discovery  talks , for  Q&A ,  for  whatever  you  need . Blogs  and  journals , we 've  made  some  cool  blogs out  of  a  lot  of  these  graphs , so  please  visit those  as  well  on  the  community . There  was  a  link  in  the  community  again to  a  lot  of  the  good  training so  you  can  learn  from  our  the  godfather of  the  of  the  Graph  Builder ,  Xan  Gregg . Thanks  to  him for  creating  the  Graph  Builder and  making  it  so  powerful , and  there  are  other  tutorials and  training  available  to  you , as  well  as  the  presentations , and  if  you  have new  views  that  you  want  to  try  within  JMP , you  can  email  me , challenge  me  to  recreate  it . Maybe  you  get  this  in  some  other  thing besides  JMP , or  you 've  done  it  in  a  spreadsheet , it  takes  a  long  time  to  make , and  you 're  like , "Can  I  just  do  this  in  Graph  Builder ?" Challenge  us  and  if  we  have  something that  we 're  not  capable  of  making , but  would  be  good  to  consider for  future  releases  of  JMP , we  have  JMP  18 coming  out  pretty  soon  as  well . We  would  love  to  hear  that  and  all  you have  to  do  is  go  to  the  community , go  to  the  JMP  wish  list , and  put  in  what  that  is , and  it  will  go  under  consideration for  adding  into  possibly the  next  version  of  JMP . All  right . That  is  my  talk . Thank  you  so  much . I  hope  you  enjoy  all the  talks  here  at  Discovery , and  please  let  us  know  next if  you  have  any  questions , and  have  fun  exploring  with  Graph  Builder .
Event has ended
Quantifying Your Organization's Analytical Maturity: Data-Driven Decision Making to Do Things Better - (2023-US-30MP-1380)
Monday, October 16, 2023
Data is everything. Every organization, big or small, collects data and knows the more insight they can gain from the data, the more competitive they will be. But what tools does the organization need? What skill sets are necessary for their most valuable asset, their employees? How do they quickly ascertain the level of competency their people have in order to achieve this end?   Enter the JMP Analytical Workflow Survey (JAWS). JAWS is an expeditious tool for organizations at all scales to rapidly and succinctly identify the competency of their people – their strengths and weaknesses and an estimate of time they spend to perform certain analytical tasks – to create a roadmap to achieve organizational goals. By deploying JAWS in your organization, you will gain insight into the current state of your analytical fitness, identify strengths within your organization, and develop targeted action items to address your weaknesses. Perhaps most importantly, it will identify areas in your analytical process that are overly time-consuming or ripe for automation, freeing up valuable brainpower to address more pressing issues.   In this presentation, we walk you through the steps of deploying the JAWS and highlight the incredibly valuable insights one can gain, which will allow your organization to make data-driven decisions efficiently to achieve the analytical ends you desire.   Learn More.     Hello.  My  name  is  Peter  Polito.   I'm  a  Senior  Systems  Engineer  at  JMP. Today  I'm  going  to  be  talking  to  you about  quantifying   your  organization's  analytical  maturity in  order  to  make   data- driven  decision  making so  you  can  do  things  better. I've  been  using  JMP  for  some  time. I  actually  learned  JMP  on  a  boot  leg  copy of  version  six   when  I  was  in  graduate  school. It's  been  a  part  of  my  life  for  a  long  time, and  being  able  to  use  it  to  help  others find  success  is  one  of  my  great  joys. Today,  I  have  a  lot  of  people   that  are  helping  support  this, primarily  is  Brady  Brady. He  is  a  Principal  Systems  Engineer   also  at  JMP. He  helped  craft  the  background  tool to  perform  all  the  analytics  that  I'll  be  presenting and  that  you  may be  able  to  take  advantage  of. Then,  of  course, I  want  to  thank  my  team. We  work  with  high  tech  companies   here  in  the  United  States, and  that  is  Ben  Ross, who  is  a  Strategic  Account  Manager, and  Kyle  Bickford, a  Senior  Account  Executive. The  goal  for  today  is  to  demonstrate how  you  can  collaborate   with  your  JMP  support  team to  quantify  your  organization's  analytical  maturity. By  quantifying  that, you'll  be  able  to  understand   where  people  are  spending  their  time and  how  competent  they  are, and  then  use  that  as a  benchmark  to  track  progress. By  working  with  your   JMP  support  team  in  this  effort, they  can  help  craft  the  support  necessary to  bring  your  team  from  where  they  are to  where  you  would  like  them  to  be. How  do  we  do  this? We  use  the   JMP Analytical Workflow Survey. I'll  refer  to  it  as  JAWS throughout  this  talk. I  feel  that's  a  little  catchier. The  tool,  it's  a  quantitative  tool to  just  measure  the  analytical  maturity  for  your  entire  organization. Despite  JMP being  in  the  name, it  is  not  just  for  JMP  users and  so  the  idea  is  that  you assess  an  entire  organization. You  can  break  it  down  by  department, by  job  title,  etc., and  then  use  this  by  annual  rerunning of  the  survey  to  understand if  you're  moving  in  the  right  direction and  what  areas  you  need  help  in. It  also  helps  you  identify  white  space where  maybe  analytics  is  heavily  used, so  that  you  can  bring   that  up  to  your  management or  whoever  it  might  be in  order  to  help  get   your  entire  organization  moving  in  mass in  the  direction   that's  going  to  drive  your  company towards  discovery,  efficiency  and  growth. How  does  it  work? It's  simple. It's  a  five  minute  anonymous  survey. It  measures  the  amount  of  time a  person  spends  performing a  particular  analytical  task. It's  not  just  one, but  all  of  their  analytical  tasks. It  understands  their self-professed  competency. By  doing  this  anonymously,  we  find that  people  tend  to  be  more  honest. Not  only  do  they  get  to  say, I  spend  two  hours  a  week  doing  data  visualization, but  they  can  also  say, I  don't  really  know  what  I'm  doing,   or  I  got  this. I'm  definitely  an  advanced  user. So  we  can  understand   how  much  time  they're  spending and  what  level  of  competency  they  have. Maybe  most  importantly, it'll  give  the  person  opportunity  to  say, I  need  to  know  how  to  do  this, and  I  need  help   because  I  don't  know  what  I'm  doing, or  I  need  to  do  this  to  complete  my  job, and  I'm  totally  competent  in  this. It's  time  spent, how  well  they  think   that  they're  able  to  do  it, and  where  do  they  need  support   to  do  their  job  better. If  you  think  about  it, those  three  things  for  management  to  know  that, I  mean,  that's  incredibly  valuable. This  is  a  very  easy  way   to  identify  this  information, and  we  present  it  in  a  way  that  has  great  visuals, easy  to  comprehend  and  digest, easy  to  share  with  upper  management to  help  build  that  roadmap for  getting  your  organization  from  where  they  are to  where  you  want  them  to  be. What  exactly  are  we  looking  at? Well,  if  you're  unfamiliar, this  is  called  the  JMP  analytical  workflow. It  works  left  to  right. On  the  left  you  have  where  is  data  coming  from? For  example,  in  the  survey, it's  going  to  ask  how  much  of  your  week is  spent  interacting  with  files  or  documents  or  databases  or  web  APIs. Your  data  comes  into  some  analytical  tool, preferably  JMP,   but  it  might  be  something  else. Then  people  spend  time  doing  tasks. From  accessing  data to   performing  basic  data  analysis  and  modeling. Maybe  they're  doing  reliability, consumer  research. Maybe  their  job  is  more  focused on  building  automations for  the  organization. They're  doing  something  or  a  series  of  some  things that  take  some  time   and  requires  competency. Then,  of  course, they  need  to  share  that. It'd  be  a  shame  if  all  of  our  hard  work  just  lived  in  our  hard  drive, we  presented  it  in  a  PowerPoint in  a  meeting,  and  then  it  just  goes  away. We  want  data  to  come  in, we  want  something  to  be  done  to  that  data, and  then  we  want  data  to  be  shared with  the  entire  organization so  that  people  can  learn. The  longer  I  am  in  this  position, the  longer  I  recognize  there's  no such  thing  as  a  one-off  problem. Everything  comes  back  in  some  shade of  gray  relative  to  where  it  started. If  we  have  an  area  or  a  way  to  query all  of  the  problems   an  organization  has  solved, it's  going  to  save  a  lot of  time  in  the  future because  people  aren't  going  to  be starting  at  ground  zero. W e  want  to  measure  all  of  these  things. Where  are  they  spending  their  time? How  capable  do  they  feel they're  at  doing  that? And  where  do  they  need support  to  do  that  better? The  first  step  is  collecting  the  data. This  is,  as  I  mentioned, about  a  five  minute  survey. The  first  three  questions are c ompletely  customizable to  fit  your  organization. This  is  anonymous,  but  we  want  to  know  some  things. We  want  to  know   where  are  these  people  located? For  some  of  you,  it  might  be  we're  all  in  one  place. Maybe  they  work  in  the  office, they  work  from  home. Who  knows? Maybe  they  work  in  the  United  States. Maybe  they  work  in  Europe. Maybe  they  work  in  Asia. Maybe  they're  just  spread  out in  different  sites  across  the  US. But  we  can  customize  that  to  fit your  o rganizational  design. Then  we  want  to  know  what  department  they're  in and  what  are  their  job  roles. This  allows  us  to  slice  and  dice  that  data once  that  survey  data  is  collected to  better  understand  where  are  things working  well  and  where  do  we  need  support? You  may  have  an  R&D  department  that's in  one  location  that's  just  crushing  it. They  are  very  competent. They're  very  capable. They're  not  spending  too  much  time because  they  built  automation. Then  their  peers  at  maybe a  newer  location  are  way  behind. They're  the  ones  that  need  support   by  designing  it  in  such  a  way that  we  can  slice  and  dice  it   by  department,  by  job  title,  by  region. We  can  really  get to  the  heart  of  where  support  is  needed or  really  pat  ourselves  in  the  back because  we're  doing  things  well. We  are  where  we  thought  we  would  be. But  I've  administered   the  survey  many  times, and  every  single  time  I  hear, I  thought  we  were  better, so this  is  a  great  way  to  measure  that. From  there,  we're  going  to  look  at  how  much  time do  people  spend  doing  particular  things. You'll  see,  none  of  this  is  JMP  specific. It's  really  designed  for  anyone. Anyone  working  with  data needs  to  get  that  data. Anyone  working  with  data  needs  to  clean  that  data, put  it  into  a  position   where  they  can  analyze  it, look  for  outliers, visualize  it,  whatever  the  case  may  be. Additionally,  we'll  have  something  very  similar  to  this to  collect  data  on  what  their  competency  is. Then,  of  course,  as  I  mentioned, we  have  the  opportunity  for  them  to  say, I  need  to  access  data. It  is  critical  to  my  task, and  I  am  not  good  at  it. I'm  inefficient, I  don't  know  how  to  query  our  database, I  don't  know  how  to  bring  in  55  CSV  files  efficiently, I  need  advanced  training. Or  I'm  really  good  at  this,   or  I  don't  even  need  this, someone  just  emails  me a  file  and  I  do  my  work. It  allows  them  to  tell  you  what  do  I  need  to  be  better  at, so  that  I  can  be  better  at  my  job. At  the  end  of  the  day, most  of  our  people   want  to  do  their  job  well. They  want  to  be  successful. They  want  to  advance  in  the  company. They  want  to  show  that  they have  value  and  worth. This  is  an  opportunity  for  them to  tell  you  where they  think  they  need  help. There's  been  a  few  instances   where  I've  talked  to  a  management  team, and  they're  like,  this  department doesn't  know  how  to  do  that. Then  the  survey  results  come  back and  the  manager  is  like,  my  goodness, they  all  feel  like  they  need to  know  how  to  do  this  better. I  have  no  idea. This  is  really  an  eye- opening  opportunity for  a  lot  of  people   when  they  see  these  results to  really  fully  understand  exactly  where  their  people  are versus  where  they  think  they  should  be. We  collect  this  data, your  JMP  support  team   will  analyze  this  data, and  then  they'll  be able  to  present  it  to  you. Now  we're  going  to  walk  through   what  this  data  looks  like, so  you  can  get  a  sense   of  what  will  I  learn. We'll  go  at  a  couple of  different  views. At  the  10,000  foot  view, we  get  these  heat  maps  that  show  where  are  people  spending  their  time. I've  broken  this  up by  organizational  wide  on  the  upper  left, by  job  title  on  the  upper  right, and  by  department  at  the  bottom. If  we  just  look  at  the  upper  left, we  can  see  that  people  are  predominantly interacting  with  files, probably  Excel  files  or  databases, and  they're  doing  a  lot  of  data  exploration, a  lot  of  basic  data  analysis  modeling, very  little  reliability  analysis, a  little  bit   of  quality  process  engineering, and  they're  primarily  sharing  images. If  we  look  at  job  title, it's  a  similar  story, but  this  particular  job  title is  maybe  doing  a  little  bit  more  time running  design  experiments. Then  by  department, maybe  this  is  an  analytics  department or  a  chemistry  department, but  they're  doing  a  lot  of  DOE, a  lot  of  basic  data  analysis, a  lot  of  data  base,   and  then  sharing  images. As  a  leader  in  your  organization, you  might  ask  yourself,  are  images the  best  way  to  share  this  data? If  not,  this  shines  a  light  on  the  fact that  your  company  is  spending a  lot  of  time  sharing  images. Maybe  A,  this  could  be  automated, or  B,  maybe  we  want  to  push  people in  a  different  direction sharing  some  other  data  format,  writing  particular  reports  or  etc. But  it  just  shines  a  light on  the  things  that  are  going  on. Then  you  and  the  support  team  will  work  together  to  better  understand, they'll  probably  have  lots  of  questions, is  this  what  you  want? Is  this  what  you  expected? Should  you  have  people  doing  more  quality if  you're,  say,  a  manufacturing  firm? Do  you  want  people  to  be  quality  minded or  do  you  have  a  quality  department? Those  sorts  of  questions  will  start to  flesh  themselves  out and  they'll  help  you  craft how  they  might  be  able to  provide  that  support  to  you. Or  you  might  just  take  these results  and  say,  thank  you  so  much. Now  we're  going  to  go do  what  we  think  we  need  to  do. Going  a  little  bit  deeper. We'll  call  this  the  maybe  8,000  foot  view. This  example  is  broken  up  by  department  and  location, and  it's  showing  how  much  time  people are  spending  doing data  exploration  and  visualization. We  can  see  in  the  manufacturing  department and  in  the  fermentation  PD  department, a  few  people  are  spending  over  eight  hours  a  week. Whereas  over  at  R&D,  they're  spending  considerably  less  time. Maybe  that's  fine, maybe  that  isn't  fine. But  again,  it  just  helps  you  see  exactly how  your  people  are  spending  their  time. If  you  have  someone  located  in  the  east  in  the  firm  department and  they're  spending  zero  time doing  data  exploration and  visualization, that  might  be  a  problem. I  would  think  they  would  need  to  share their  data  and  look  at  that  data. So  you  might  have  some  questions and  you  go  ask  that  team to  better  understand   exactly  how  they're  doing  things. Then  we  get  to  go  a  little  bit  deeper, and  these  are  my  favorite  images. What  we're  looking  at  is  proficiency  on  the  left and  usage  on  the  bottom. Y  versus  X. Then  these  cells  are  colored by  how  many  people  are  spending  time or  grafted  with  their  competency. If  we  look  on  the  left, we  see  quality  process  and  engineering. For  this  particular  organization, by  and  large,  people  are  not doing  much  quality  work. This  is  where  I  always  ask, do  you  want  your  company   to  be  quality  minded, or  is  quality  focused  on  a  single  department? If  you  want  your  people  to  be quality  minded,  this  might  be  a  red  flag. No  one  is  an  advanced  user. People  are  spending  predominantly   less  than  an  hour  a  week, and  the  majority of  the  people  aren't  doing  it  at  all. This  would  be  a  situation   where  I  might  come  in  and  say, can  I  teach  your  people  about  the  quality  tools  in  JMP so they  can  better  understand how  to  build   and  interpret  a  control  chart? How  they  might  look at  metrics  like  CPK  and  PPK. Are  we  hitting  spec  limits? Are  we  not  hitting  spec  limits? How  do  we  understand  that  more  deeply so  we  can  make  more  intelligent  decisions to  solve  problems  and  understand  things. Contrasting  that  on  the  right with  this  basic  data  analysis  and  modeling  image, we  see,  I  would  call,  more  maturity. There  are  very  few  people  that  aren't  performing  this  task  at  all. The  majority  of  the  people   are  intermediate  too  with  some  advance, and  there's  also  a  lot  of  beginners. But  what  I  see  here  is  critical  mass. I  see  that  this  organization  has  enough  competence and  enough  people that  understand  it  well  enough that  they  can  help  draw  those  novice  users down  to  the  intermediate  level and we  have  some  advanced  users that  can  bring  the  intermediate  down  to  their  level. We  also  have  people  spending, predominantly  1-4  hours. Not  that  many  people  are  spending   20 %  of  their  week  performing  this  task. When  I  see  that,  I  ask, could  this  be  automated? We'll  get  to  more of  that  in  just  a  moment. This  really  helps  people understand  where  they  are. Do  they  have  maturity  in  this  analytical  capability or  do  they  need  support? Are  they  where  they  thought  they  would  be or  do  they  need  to  move  their  people  through  support to  a  more  advanced understanding  and  competency? This  is  probably  one of  my  favorite  images. What  we're  looking  at  here  is on  the  left  on  the  Y  axis, is  the  amount  of  time  people  are  using a  particular  capability  per  week. On  the  X  are  those  different  capabilities that  we  saw in  the  JMP  analytical  workflow. For  now,  you  can  just  ignore  the  color. Those  are  color  coded  by  the  amount of  time  they've  been  using  JMP. That's  one  of  the  only   JMP  specific  questions in  the  entire  survey. But  what  we  see  here, and  I  want  to  draw  your  attention  right  here, is  there  are  six  people  spending  eight  plus  hours  a  week performing  data  access. If  you  have  1  person  spending 8   hours  a  week, that's  20 %  of  the  week. That  means  five  dots equate  to  one  annual  salary. Is  one  annual  salary   how  you  want  to  be  spending… Do  you  want  to  be  spending that  amount  of  time  on  data  access? Probably  not. I  mean,  it's  not  cheap  to  hire  someone. It's  not  cheap  to  support  them, provide  benefits  and  training, and  keep  them  motivated  and  keep  them growing  within  the  organization. This  is  a  very  impactful  image  because it  shows  us  where  can  we  automate. Clearly,  you  can  see   up  in  the  very  upper  left, that  green  dot,  someone  has already  automated  data  access. Whereas  we're  spending 1.2  annual  salaries  on  data  access, and  many  of  them  are  new  users. Half  of  them  are  only  1-3  years. So  could  we  come  in   and  teach  people  about  automation? Could  the  person   that  has  already  automated  this sit  down  with  these  other  six  people and  teach  them  how  they have  automated  their  process? Because  if  you  can  free  up   an  entire  annual  salary, think  of  what  you  can  do. I've  worked  with  people that  are  in  hiring. I've  worked  with  people  that  manage  teams. The  common  thread  I  hear is  we  need  more  people. Either  A,  we  don't  have  the  budget, or  B,  and  right  now  in  this  environment, it's  just  sometimes  hard  to  hire  people. If  you  can  liberate  an  entire  person from  a  particular  task, just  think  of  what  more  you  could  do. They  could  solve  more  problems, they  could  help  bring automation  elsewhere. They  can  automate  data  access for  everybody  potentially. This  is  a  really  useful  thing. On  the  flip  side, if  we  look  at,  say, predictive  modeling  and  machine  learning, it's  right  here  in  the  center, we  can  see  there  are  only  two  people spending  any  time  at  all: one,  one to  four  hours  a  week   and  one  less  than  an  hour  a  week. We're  spending  a  fraction  of  the  time, particularly  compared  to  data  access, on  predictive  modeling   and  machine  learning. Perhaps  this  is  not  necessary in  your  organization. Perhaps  it's  very  necessary if  you  are  trying  to  understand why  aren't  we  hitting  our  manufacturing  KPIs? Why  are  we  having  these issues  in  our  process? We're  not  able  to  understand  exactly  why, despite  having  everything  set  up the  way  we  think  should  work, we're  not  hitting  our  metrics. Well,  again,  this  is   a  ripe  opportunity  for  support. Yet  on  the  other  flip  side, so  I  think  we're  looking at  a  triangle  here,  a  prism  here, basic  data  analysis  and  modeling, I  see  they're  doing  fantastic. They  have  a  lot  of  people that  are  performing   basic  data  analysis  and  modeling. We  have,  as  we  saw  earlier, some  good  there. We're  not  spending  a  lot  of  time. These  are  one  of  the  tasks   where  automation  may  not  be  possible. It  might  be,  but  it  might  be  people  are dealing  with  problems  that  are  unique  every  single  time. Again,  this  is  an  opportunity  where  I, as  someone  trying  to  support  a  customer, might  be  starting  to  ask  some  questions and  understand  if  automation is  even  possible. But  by  and  large,  this  is  a  good  vertical  in  this  particular  graph, as  well  as  over  on  the  far  right, sharing  and  communicating  results. That's  another  one  that  is  very  easy  to  automate, but  the  majority of  people  are  sharing  results. I  might  have  some  questions about  why  there's  maybe   about  40 %  that  aren't and  is  that  important  to  you? But  this  just  really  puts  the  entire  story in  one  image  that  really  helps  you  understand where  opportunity  is  to  A,  automate,  B,  train, and  C,  say,  great,  we're  doing  well, we  don't  need  to  spend  time  on  that. Then  finally,  where  do  your  people   think  they  need  support? On  the  left, we  have  those  capabilities. On  the  bottom,  we  have  four  questions. Is  this  critical  to  my  task and  training  is  needed? This  is  critical  to  my  task and  basic  training  is  needed. I  don't  need  this, or  maybe  I'm  just  interested. I've  organized  these  based   on  the  majority  of  people or  the  number  of  people  that  feel  that  advanced  training  is  needed. This  is  from  a  different  organization. But  I  imagine  if  you   are  a  tech-driven  company, an  analytically- driven  company, you  would  probably  think that  basic  data  analysis  and  modeling is  not  something that  you  need  to  worry  about. But  here,  the  majority  of  the  people are  saying  this  is  critical  to  my  task and  I  need  advanced  training. Again,  it's  just  shining  a  spotlight on  the  areas  where  you  might  need to  support  your  organization, where  you  might  need  to  support  your  people because  they're  saying very  clearly,  I  need  help. Whereas  mass  customization, automation  and  scripting, reliability  analysis,  these  aren't  things that  people  need  as  much  support  on. You  can  know  that  I  don't  need to  spend  time  in  this  area  or  that  area. I  need  to  focus  up  here. It  turns  out  a  lot  of  these  are  fairly  basic  tasks that  I  think  a  lot  of  people  think that  people  are  fully  capable  and  competent  of, but  they're  clearly  saying,   no,  no,  I  need  some  help. The  benefits  of  the  JAWS  is, it  identifies  strengths  and  areas for  improvement  within  your  organization. You're  able  to  work  with  your  JMP  support  team and  provide  support  in  those  areas. Your  support  team  has  the  training, has  the  tools,  has  the  backing  support of  a  large  organization  that  is  focused solely  on  expanding  the  use  of  JMP to  come  in  and  guide  your  people in  whatever  support  you  need. We  are  here  to  help  you  out. Then  the  beauty  of  this  is  if  you administer  this  survey  annually, you're  going  to  start  to  be  able  to  track  progress. You'll  be  able  to  see  we  needed a  lot  of  help  in  design  of  experiment. A  year  later,  we  see  improvement. The  support  has  worked, and  we  just  need  to  go  a  little  bit  farther. We  can  say  DOE  is  now  doing  great. Let's  focus  our  attention  elsewhere. Some  best  practices. This  is  very  practical,   but  what  we  have  learned  is, don't  allow  a  long  time for  the  survey  to  be  filled  out. We  say  send  it  out  on  Monday, send  a  reminder  email  on  Wednesday, and  close  the  survey  out  on  Friday. How  this  would  work  is  you  would  work   with  your  JMP  support  team to  craft  those  questions  about  region, about  department,  about  job  title, and  then  they'll  just   provide  you  the  survey., You  send  that  survey  out and  people  fill  it  out, we  collect  the  data,  it's  all  anonymous, and  then  we  analyze  those  results and  then  come  back  and  share  those  results  with  you. But  keep  it  short. Don't  allow  people  a  long  time  because people  get  busy  and  they  just  forget. It's  really  important,  I  think, to  get  those  three  questions  right. We  don't  want  to  be  too  much  of  a  grouper because  then  you  don't  have the  level  of  understanding that  you  might  want. You  want  to  be  a  splitter. Really  dig  down,   get  those  departments  right, get  those  job  titles  right, get  those  regions  right, because  you  can  always   group  things  together  later to  understand  the  survey  results, but  you  can't  split  them once  you've  collected  that  data. This  one  is  probably  the  most  important, is  it's  incredibly  valuable   to  get  management  buy-in and  then  develop  a  team   to  help  administer  that  survey. You  need  people  that  people are  going  to  listen  to. If  you  have  someone  in  your  company who  is,  for  lack  of  a  better  term, not  well  liked  and  they  send  out  a  survey, people  probably  aren't  going  to  be  as  likely  to  participate as  if  you  have  a  team  of   3-5  people that  are  leaders  within  the  departments or  leaders  within  their  organizations, and  people  are  going   to  hear  that  and  listen  to  that. It  helps  even  more  when  you  have management  saying,  you  need  to  do  this. The  flip  side  of  this  is  when  you  get  the  data  back, you  want  management  to  be  involved. You  want  someone  that  has   some  decision- making  capabilities to  see  the  results and  understand  what's  going  on so  that  they  can  help   craft  the  big  picture. It's  great  when  JMP  usage  expands from  the  bottom  up, but  when  you're  trying  to  drive  something at  an  organizational  level, you  really  need  people  that  are  higher  up to  help  drive  the  usage  from  the  top  down. We  strongly  encourage  that  you administer  this  organization  wide. Ignore  JMP  usage. A  lot  of  our  companies  have email  list  group   of   strictly  their  JMP  users. But  really,  that's  often  just a  snippet  of  the  company. We  have  found  people  and  human  resources that  when  they  see  what  JMP  can  do, like  I've  got  to  have  that. We  don't  always  think  of  an  analytical software  tool  as  being  something that  maybe  HR  would  want   or  would  gain  benefit  from,  but  they  do. By  understanding   where  your  entire  organization  is, you're  going  to  be  able  to  make  better  decisions, you're  going  to  be  able  to  make better  support  calls, and  you're  going  to  be  able  to  move  your  entire  organization versus  just  moving a  single  department  or  a  single  job  title. Lastly,  I'm  sorry,  second  to  last, lean  on  your  JMP  support  team. They  administer  these  surveys  frequently. They  know  how  to  interpret  the  results. They  know  how  to  help  you. Even  if  you  don't  want  to  use  them to  provide  that  support, if  you  have  an  internal  education, maybe  you  want  to  build   internal  education  up as  a  result  of  the  survey, but  lean  on  them  to  help  guide  you  because  this  is  what  we  do. We  support  companies  like  yours to  help  them  build  analytical  excellence. Lastly,  and  I've  said  this  a  few  times, administer  the  survey  annually, so  that  you  can  actually  track  your  progress. It's  one  thing  to  have  an  analytical  snapshot. It's  a  lot  better  to  have an  analytical  time  series. Collecting  that  data  annually is  going  to  really  help  you gage  is  this  successful? Do  we  need  to  change  things? Is  what  we're  doing  working. Maybe  you  go  to  your  support  team and  they  lead  the  training and  you  don't  see  growth. So  you  turn  to  your  internal  education  team, or  maybe  the  flip  side  is  true. We  just  want  you  to  be  better, we  want  to  be  collaborators  with  you. We  want  to  support  you in  whatever  you  think  is  the  best  way to  execute  that  plan. I  will  close  with  a  call  to  action   [inaudible 00:24:26]   survey. It  can  really  help  an  organization. I've  seen  it  help  an  organization. I've  been  able  to  administer  close to  a  dozen  of  these, and  all  of  them  have  resulted  in... I'm  going  to  close with  a  call  to  action. Connect  with  your  JMP  support  team and  complete  the   JMP Analytical Workflow Survey. Being  able  to  understand   where  you  are  as  an  organization versus  where  you  want  to  be   is  incredibly  valuable. At  the  end  of  the  day, our  goal  is  to  help  you   democratize  analytics, help  you  have  one  version  of  the  truth, help  you  make   analytically- driven  decisions, and  from  that,  gain  efficiency, quicker  discovery, and  save  money,  save  time. This  survey  is   an  incredibly  powerful  tool to  help  you  achieve  those  ends. We  have  the  expertise  to  help  you  not  only  administer  the  survey, but  interpret  the  survey  and  create  a  plan to  then  make  decisions   for  training  support, and  help  drive  you  from  where  you  are  to  where  you  want  to  be. Thank  you  very  much.
Event has ended
Graph Builder and Tabulate Applications in Clinical Trials - (2023-US-30MP-1376)
Monday, October 16, 2023
Flexible visualizations allow for easy exploration of clinical trial data. JMP Clinical uses many different options in Graph Builder and Tabulate to allow for dynamic views and publication-ready output. From stacked bar charts to display adverse events by severity to line graphs to show change from baseline over time to swimmer plots graphing disease response in oncology studies, JMP Clinical uses many different Graph Builder elements. Paired with most graphs is Tabulate, which displays the statistics reflected in the graph. New Tabulate features in JMP 17 such as stack, pack, and unique ID, help make these tables ready for publication. This pairing of Graph Builder and Tabulate gives users a quick way to visualize the data through a graph and then dig further into the numbers.     Hi,  I'm  Sam  Gardner, and  I'm  presenting  today   with  Rebecca  Lyzinski. We're  going  to  be  talking  about how  we  use  Graph  Builder   and  Tabulate  in  applications for  visualizing  data  from  clinical  trials. We  have  a  product  called  JMP  Clinical, and  it's  a  focused  and  specialized  product for  clinical  trial  data  review. We  give  users  straight  out  of  the  box functionality  to  do   a  thorough  review of  clinical  trials  at  the  study  site and  subject  level. It  is  a  product  that's  used  across the  pharmaceutical  industry and  at  several  regulatory  agencies   across  the  world. It's  built  on  top  of  JMP  Pro, utilizing  JMP  scripting  language, and  it  provides  a  user  interface   for  data  management,  configuration, and  standardized  reports. We  utilize  data  standards   as  part  of  JMP  Clinical. We  utilize  the  CDISC study  data  tabulation  model and  analysis  data  model   as  required  input  formats for  the  data   that  you're  going  to  visualize. This  allows  us  then   to  develop  standardized  tools. In  general, if  you  want  to  develop  standardized  tools, having  a  data  standard  to  follow   for  the  input  data really  enables  you  to  do  that. We  use  Graph  Builder  and  Tabulate across  almost  all  of  our  reports that  we  have  in  JMP  Clinical. We  want  to  show  you   how  we  create  the  graphs that  we  use  in  JMP  Clinical so  you  can  learn  more  about   these  important  platforms, Tabulate  and  Graph  Builder, and  how  you  could  use  them   for  your  analysis  work and  if  you  wanted  to  make   standardized  reports  yourself. We're  going  to  show  you several  of  the  analysis  reports   that  we  have  in  JMP  Clinical: our  adverse  events  distribution   that  utilizes  Graph  Builder  and  Tabulate, our  adverse  events  risk  report, which  uses  a  multipanel  Graph  Builder that  also  utilizes  virtual  joins   between  data  tables, and  it  applies  some  data  filters. We  also  are  going  to  show  you   our  findings  time  trend  result, which  also  uses   a  complicated  graph  builder, a  column  switching  and  virtual  joins. Then  we'll  finish  up by  showing  you  an  adva nced-level collection  of  graph  builders that  we  use  for  our  patient  profiles. One  of  our  most  popular  reports   is  the  Adverse  Event  Distribution  Report. This  report  shows  a  bar  chart  at  the  top and  a  tabulate  underneath  it. The  bar  chart  has  dictionary- derived term  on  the  X- axis, which  is  a  way  of  grouping  adverse  events, and  planned  treatment  group  on  the  Y- axis. Each  bar  represents   a  count  of  the  adverse  events for  a  given   dictionary-derived term. Underneath  we  have  a  tabulate that  is  also  showing   dictionary-derived term, but  it  also  groups  the  adverse  events by  body  system  or  organ  class. We  have  two  different  grouping  variables. The  columns  represent   one  column  for  each  treatment  group, one  for  Nicardipine  in  the  study, and  another  for  placebo, as  well  as  a  total  column. The  ends  represent  counts and  the  percents  are  the  percents  of  subjects  with  an  adverse  event. Down  at  the  bottom,  there's  an  all  row, which  represents  any  subject  that  had an  adverse  event  in  the  given  column. For  example,   we  have  882  subjects  in  this  study that  had  at  least  one  adverse  event. One  of  the  options   that  JMP  Clinical  includes is  a  way  to  stack  the  bar  chart in  the  table. For  example,   you  can  stack  by  severity  or  intensity. This  creates  a  stacked  bar  chart with  green  bars  representing  mild  events, yellow  for  moderate,  and  red  for  severe. It  also  splits  out  the  table   by  mild,  moderate,  and  severe  events. In  order  to  recreate  these, we  can  pop  out  the  data  table. For  the  graph,   we  go  to  Graph  and  Graph  Builder. We  select  our  planned  treatment  group and  put  that  on  the  group  Y. Make this  a  little  bigger. There  are  a  lot  of  columns   in  this  data  table, so  we're  going  to  search for   dictionary-derived term and  we'll  grab  that   and  place  it  on  the  X- axis. This  is  pretty  close   to  what's  on  the  report, except  for  the  ordering. If  you  right-click  on  the   X-axis   and  go  to,  Order  By, we  can  select  Count  Descending, and  now  it's  ordered  the  same  way   that  it  was  in  the  report. If  we  want  to  also  add  that  stacking, we  can  search  for  severity and  drag  that  variable   over  to  the  overlay. That  gets  us  close  to  what  we  saw, but  the  bars  are  stacked   side-by-side. In  order  to  change  that, we  can  go  to  the  control  panel and  instead  of   side-by-side, select  Stacked  for  bar  style. Now  we're  back  to  our  original  graph. Next,  if  we  want  to  recreate  the  tabulate, we  go  to  Analyze  Tabulate. Again,  select  Plan  Treatment  Group for  our  columns. This  time  we're  going  to  select dictionary-derived term and  put  that  in  the  first  grouping  column. But  we  also  want  to  select   Body  System  or  Organ  Class and  put  that   as  a  grouping  column  as  well. By  default,   they  show  up  as  two  separate  columns. We're  going  to  select  both  columns, right-click   and  go  to  Stack  Group  and  Columns. That  allows  them  to  both be  concatenated  into  one  column. To  add  to  our  table, we  are  going  to  drag  the  little  N  over underneath  our  treatment  group so  that  we  get  our  counts   and  the  percent  next  to  the  N. By  default,   these  are  two  separate  columns. In  order  to  create  one  column, we're  going  to  first  drag  the  sum  up  above so  that  Tabulate  knows  both  the  N and  percent  are  supposed  to  be  sums. We  again  select  both  columns   and  right-click. Under  Pack  Columns,  go  to  Pack. Now  we  see  the  account and  the  percent  in  one  column. The  percent  has  no  formatting   on  the  number  of  decimal  places. If  we  go  to  Change  Format   and  go  to  the  percent, we  can  change  from  best to  one  decimal  place. Now  we  have  a  better  formatted  table. One  other  thing  that's  missing is  we  have  a  group  N that  we're  going  to  stick  underneath our  treatment  groups. That  way  we  can  see  how  many  subjects were  in  each  treatment  group. You'll  also  notice  by  default that  these  rows  for  the  body  system   organ  class  are  missing  values. In  order  to  fill  those  in, we're  going  to  select   Add  Aggregate  Statistics. Now  all  our  values  have  been  filled  in. Now  we  just  need  to  do  a  little  cleanup. Add  Aggregate  Statistics adds  all  columns  for  each  of  our  different  columns. The  first  step   is  we're  going  to  delete  some  of  these just  to  clean  it  up and  we'll  get  rid  of  this  all  at  the  end. In  order  to  get  the  columns  back  together, we  again  have  to  drag  the  sum  up  above and  the  N  and  percent   are  now  back  in  one  column. We  can  also  right-click  on  the  group N and  remove  column  label and  right-click  on  the  sum and  we're  just  going  to  change   the  item  label  to  be  missing so  that  it's  a  little  cleaner. One  final  thing. You'll  notice  that  these  ends are  a  little  bit  bigger   than  what  was  on  the  report. The  reason  for  that  is  automatically the  top  grouping  variable   is  a  sum  of  all  of  the  rows  underneath  it. This  is  not  what  we  want  for  this  report because  these  categories are  not  mutually  exclusive. A  subject  could  have  both a  vasoconstriction  event and  a  hypertension  event. But  we  don't  want  to  count  them  twice   under  vascular  disorders. We  only  want  to  count  them  once. In  order  to  do  that, we're  going  to  grab   the  unique  subject  identifier and  drag  it  into  the  ID. Now  you'll  see  those  numbers reduced  quite  a  bit. Now  they  represent   a  unique  subject  count for  that  type  of  event. One  other  thing  to  point  out   on  this  report is  that  we  do  on  this  data  table have  a  reference   by  unique  subject  identifier  to  TADSL. What  that  means  is  that  the  table  is  being  virtually  linked to  this  TADSL  table, which  allows  us  to  filter  on   demographic  variables that  are  found  in  the  ADSL  table. Now  I'm  going  to  hand  it  over  to  Sam so  he  can  talk  about  the  risk  reports and  how  we  use  virtual  joins   to  filter  those  tables. Thanks,  Rebecca. That  was  a  really  nice  description   of  that  report and  how  it  was  put  together. I'm  going  to  show  you  another  report that  Rebecca   is  responsible  for  developing. This  one  is  our  adverse  event  risk  report. What  this  report  will  do is  it  will  go  through   all  of  the  reported  adverse  events, and  it  will  count   the  number  of  events  by  treatment  group and  calculate  a  percent  or  a  rate. It  displays  on  the  graph  two  things. It  displays  two  different  graphs. On  the  left-hand  side, it  shows  the  rates for  each  treatment  group. You  can  see  that  the  color  key  there shows  which  treatment  group is  being  displayed  there. For  pruritus, we've  got  a  rate  of  pruritus   in  the  placebo  group  of  9.3%. We  have  a  rate  of  pruritus in  the  low-dose  group  of  27.381%, and  in  the  high-dose  group  of  30.92%. Usually,   what  we're  interested  in  in  this  case is  the  difference  in  the  rates   compared  to  the  placebo  group. What's  shown   on  the  right-hand  side  of  the  graph is  the  calculation  of  the  differences for  each  treatment  group and  the  placebo  group. There  are  three  treatment  groups, so  there  are  two  computed  differences: the  high  dose  and  the  low  dose   compared  to  placebo. We  also  display   a  confidence  interval  around  those  points. We  make  this  report  filterable. One  thing  we  can  do  is, let's  say  we  only  want  to  look  at adverse  events   that  occur  at  a  certain  rate, maybe  higher  than  5%  of  the  time, and  we  can  narrow  that  down quite  a  bit  and  reduce  it. We  also  have  a  corresponding   tabulate  table that's  very  similar  to  the  tabulate that  Rebecca  showed  earlier. What  I  want  to  do  is  show  you  how to  recreate  this  Graph  Builder  graph because  there's  lots  of  interesting  things you  can  do  in  Graph  Builder that  you  might  not  be  aware  of. The  first  thing  I'm  going  to  do  is I'm  going  to  open  up   the  data  tables  that  we  use, and  we  actually  use three  different  data  tables. Let  me  clear  this report  filter  selection  first. There  we  go. Now  I'm  going  to  open  up the  data  tables  that  we  use. The  first  one  is  the  data   that's u sed  to  actually  make  the  plot. We  call  that  the  risk  plot  table. The  second  table  is  the  term  ID  table, and  it's  just  the  list  of  unique  terms that  we  want  to  display  in  the  graph on  the  Y-axis. Then  the  last  table  is  a  filter  table. It's  what  we  use   to  actually  filter  the  report. I've  got  all  those  three  open. Let's  start  out  by  making  the  graph of  the  risks  and  the  risk  differences. I've  got  a  Graph  Builder. We're  going  to  take   the  dictionary-derived  term, drag  that  onto  the  Y-axis. Make  this  bigger. Then  we're  going  to  take  the  calculation, in  this  case, the  variable  we  just  labeled  percent, and  we're  going  to  put  that onto  the  X-axis. Then  what  we  want  to  do  is  we  want to  overlay  by  the  active  treatment. Just  want  to  put  that  on  there. The  next  thing  we  want  to  do is  we  want  to  introduce a  little  bit  of  customization of  the  way  the  points  are  presented. You  can  see  the  points, there's  a  bar  here   where  you  can  control  the  points and  the  way  they're  drawn. One  thing  is  we  want  to  make  sure that  they  have  some  jitter  in  them. In  this  case,  the  jitter  is  set  to  auto, but  we  can  set  that   type  of  jitter  that  we  want. I'm  going  to  change  it  to  center  grid. Another  thing  that  we  want  to  do is  we  want  to  change  the  way the  points  are  drawn. Under  the  Graph  menu that  you  can  access through  right-clicking  in  the  graph, I'm  going  to  right-click,  Select  Graph, and  go  to  Marker  Drawing  mode, and  I'm  going  to  choose  outlined. That  changes  those  points   to  have  an  outline,  a  more  sharp  outline, and  it  really  makes  them  stand  out   much  more  significantly. That's  this  plot  of  the  percent   or  the  rates of  each  of  those  adverse  events. One  thing  I'm  also  going  to  do is  I'm  just  going  to  add  a  grid  line  o n  the  Y-axis, which  you  can  just  look  across  then so  you  can  see  where the  individual  points  line  up and  match  that  to  the  grouping  level for  the  dictionary-d erived  term. That's  for  the  rates. The  next  thing  I  want  to  do   is  I  want  to  look  at  the  differences. We  have  a  risk  difference  variable and  I'm  going  to  drag  that and  I'm  going  to  drag  that  down   onto  the  X-axis  but  to  the  right. What  it  actually  does   is  it  creates  another  graph. Now  I  have  a  graph of  the  risk  differences. I  also  want  to  put an  interval  around  those. What  we  do  is  we  have  two  variables that  have  the  upper  and  lower  limit  for  the  intervals. I'm  going  to  select  those   and  drag  them  to  the  interval  zone. You   notice  what  happens  when  I  do  that. It  actually  draws  intervals  around   all  the  points  on  all  the  graphs that  I've  added  in  here. I  don't  want  that   on  the  points  for  the  percent  variable. For  the  interval  style   or  the  error  interval, I'm  going  to  set  that  to  be  none. It  turns  those  off. Essentially  what  we've  done is  we've  recreated  the  graph. This  is  a  little  busy because  there's  lots  of  categories  here. We  could  just  filter  this  table. We  could  go  here   and  we  could  select  the  local  data  filter. Let's  say  we  wanted  to  just filter  on  risk  difference. We  could  say, let's  only  look  when  the  risk  difference  may be  greater  than  3%  in  absolute  value. What  I'm  going  to  do  is   I'm  going  to  set  a  limit that  it's  between  three  and  three and  then  invert  that. Then  that  reduces  the  number. Maybe  I  want  that   to  be  a  little  bit  more  restrictive, so  make  that  5, - 5 . There  we  go. That  reduces  the  number  that  are  selected. But  the  thing  is,  it's kind of  weird. It  only  filtered  the  values   for  the  risk  difference and  it  didn't  filter   the  values  for  the  percent. It  didn't  filter  everything the  way  we  wanted  it  to. We  needed  to  do  something a  little  bit  different. I'm  going  to  clear  that. What  I'm  going  to  do  is I'm  going  to  link  this  table,   the  left-hand  table, which  is  used  to  make  the  graph, to  the  table  of  all  of  the  unique  levels that  are  displayed  on  the  Y-axis, all  of  the   dictionary-derived terms. The  way  we  do  that  first  is for  the   dictionary-derived term, we  have  to  make  sure that  the  link  ID  property  is  set. What  that  tells  JMP  is  that   if  I  link  to  this  table, this  is  the  variable  that  I'm  going  to  use for  matching  to  the  table   that  I'm  linking  it  to. I  set  that  to  be  the  link  ID. Then  over  here   for  the  dictionary-derived  term, what  I  need  to  do  is   I  need  to  choose  the  link  reference, and  I'm  going  to  choose  the  actual  table,  this  actual  table  as  the  link  reference. Now  what's  happened  is these  two  tables  are  linked. The  last  thing  I  need  to  do   to  specify  this  virtual  linking  properly is  to  right-click,  select  Column  Info, and  under  the  link  reference  property, choose  the  row  states   that  I  want  to  broadcast from  the  middle  table   to  this  linked  table. I  want  to  accept  the  row  states from  the  reference  table. I'm  going  to  choose  the  selected, excluded,  and  hidden  row  states and  accept  those  from  this  center  table. Then  when  I  do  that, when  I  select  rows  in  this  data  table, I  end  up  selecting  rows in  the  corresponding  link  table. If  I  hide  and  exclude  those  rows, they  become  hidden  and  excluded   in  this  plot because  I've  hidden  and  excluded several  of  the  rows  in  this  linked  table. That's  one  level  of  linking. But  what  we  really  want  to  do is  we  want  to  filter  this  graph based  on  corresponding  measures  or  metrics that  are  calculated  in  this  table. These  are  based  on   the  differences  and  the  risks and  other  difference  measures between  the  placebo  group   and  the  treatment  group. What  I'm  needing  to  do  to  make  that  happen is  I  need  to  link  this  right-hand  table   to  the  center  table. I'm  going  to  do  that  as  well. I'm  going  to  go  here to  the  dictionary-d erived  term for  the  right-hand  table, and  I'm  going  to  choose  the  link  reference and  pick  that  table   that  I  want  to  link  to, which  is  this  one  displayed  here. Open  up  the  column  properties  here, go  to  the  link  reference  property. Now  what  I  want  to  do  is  I  want  to  dispatch  row  states, the  selected,  excluded, and  hidden  row  states, from  this  table  to  the  linked  table. What  I've  done  now   is  I've  linked  two  tables  together. I  have  the  table  on  the  left   is  linked  to  the  center  table, and  there's  a  many-to-one  relationship. There's  actually a  three-to-one  relationship. For  every  row  in  the  center  table, there  are  three  rows in  the  left-hand  table. I've  also  linked  the  table  on  the  left, which  has  a  two-to-one  relationship where  every  row  in  the  center  table, there  are  two  rows   in  the  right-hand  table. What  I  want  to  do  is   I  want  to  push  row  states from  the  right-hand  table   to  the  far  left-hand  table, and  having  this  linking  between  the  two and  pushing  row  states from  the  right-hand  table to  the  center  table, and  then  those  row  states  being  accepted   by  the  left-hand  table allows  me  to  do  that. Now  what  I  can  do   is  I  can  filter  on  this  table, and  I'll  just  open  up  the  data  filter, and  I'm  going  to  choose the  absolute  risk  difference. I'm  going  to  say   if  the  absolute  risk  difference  is  large, that those  are  the  points   that  I  want  to  display. Now  that  I  have  everything  linked  up, I  can  use  the  table  that's  on  the left-hand  side  to  filter  the  graph, which  is  based  on  the  data on  the  right-hand  side. If  I  go  and  open  up  a  local  data  filter, I'm  going  to  turn  on   show  and  include  as  the  options. I'm  going  to  choose   absolute  risk  difference because  that's  a  good  filtering  criteria just  to  find  things  that  have  a  large  absolute  risk  difference. Then  now  notice  it's  filtering  the  table. I'm  filtering  the  left-hand  table, pushing  it  through  to  the  middle  table, which  is  pushing  those  row  states   through  to  the  right-hand  table. Some  clever  virtual  joins. One  thing  we  do   in  addition  to  JMP  Clinical is  we  use  a  callback  function   when  we  use  our  filters. Actually,  there's  a  way  with  JMP  scripting you  can  write  a  little  function  that  will  run every  time  you  change  a  data  filter and  it  can  do  some  additional  things to  customize  the  graph  just  a  little  bit  more. If  you  see  it  in  our  product, it  does  it  a  little  bit  differently, but  it  looks  pretty  much  the  same as  what  I'm  doing  here. Now,  Rebecca  can  show  you  something. One  of  our  other   most  commonly  used  reports is  our  findings  time  trends. When  you  launch  findings  time  trends, you'll  get  two  graphs, but  I'm  just  going  to  focus   on  the  first  graph. The  first  graph  shows   the  mean  change  from  baseline for  a  given  lab  test   over  the  course  of  the  study, shown  by  visits. The  bottom  graph  shows the  number  of  records   at  each  visit  for  that  lab  test. If  we  want  to  recreate  this  table, we  can  pop  out  our  data  table. Again,  we'll  go  to  Graph and  Graph  Builder. We're  going  to  use Billy  Rubin  as  an  example. We'll  grab  the  Billy  Rubin  record   and  put  it  on  our   Y-axis. We  want  our  graph  plotted  by  visit. We'll  grab  visit  and  put  it  on  the   X-axis. By  default,  we  end  up  with  points, but  we  want  to  switch  it  over   to  a  line  graph. Once  we  have  our  line  graph, we  also  want  to  split  it  out   by  treatment  group. Treatment  group  is  not  actually  a  variable in  our  base  data  set, but  it  is  a  variable in  the  linked  data  set  to   TADSL. We  can  go  into  our  reference  data  set and  actually  grab   that  plan  treatment  value from  the  virtually  joined  one and  drag  it  over  to  our  overlay. That  splits  out  our  lines   into  two  different  treatment  groups. We  also  want  to  add  intervals. Under  error  interval, instead  of  using  auto, we'll  change  that  to  confidence  interval. Now  we  have  a  confidence  interval   for  each  of  our  visits for  each  of  our  treatment  groups. We  also  want  to  add  the  bar  graph. We're  going  to  grab  Billy  Rubin  again and  this  time  drag  it  over   into  the  bottom c orner  of  the   Y-axis so  that  we  now  have  two  separate  panels. By  default, we  end  up  with  just  a  duplicate of  the  panel  we  already  created. We're  going  to  change  this   by  right- clicking, going  to  the  line  and  change  to  bar. Now  we  have  a  bar  graph. We  can  go  ahead  and  remove   our  confidence  intervals  from  this  one by  selecting  none  under  error  interval. We  get  pretty  close  to  our  final  results with  just  these  two  panels. But  we  also   want  to  add  points   onto  our  top  panel. By  default,  we  get  a  point   for  every  single  row  in  the  data  table. We're  going  to  just  go  over  here and  change  our  summary  statistic  to  mean. Now   we're  just  going  to  clean  up  a  little  bit. We  can  change  the  labels   by  clicking  on  the  label and  then  typing  in  a  new  one, so number  of  records. On  the  top,  we'll  do  the  same  thing and  change  it  to   mean  change  from  baseline. We  can  also  clean  up   our  legend  a  little  bit. You'll  see,  because  we  have  two  panels it  actually  show   the  treatment  group  twice, we  don't  really  want  to  see  that. We  can  go  to  the  red  triangle, go  to  Legend  Settings, and  we'll  just  unclick  a  couple  of  these so  that  we  only  see the  treatment  groups  once. We're  back  to  our  panel   that  we  created  in  the  report and  we  can  add  our  column  switcher. If  you  go  to  the  red  triangle   and  go  to  Redo  Column  Switcher, we  can  change  out  the  Billy  Rubin  record. We  want  to  change  the  lab  test that's  being  shown  on  the   Y-axis. This  dataset  has   a  bunch  of  different  lab  tests from  alkaline  phosphatase all  the  way  down   to  partial  pressure  oxygen. We're  just  going  to  select   all  those  lab  tests  and  click  OK. Now  we  have  a  column  switcher and  you  can  actually  select  each  lab  test and  you'll  see  that  the  graph  changes to  show  you  the  lab  test  that's  selected. Now  I'm  going  to  hand  it  back  to  Sam so  that  he  can  show   some  more  complicated  graphing using  patient  profiles. Thanks,  Rebecca. Just  to  wrap  up  here, I  want  to  show  you  a  more  advanced collection  of  graphs  that  we  use called  our  patient  profile. This  is  a  way  just  to  visualize  everything that  happens  to  a  subject in  a  clinical  trial   that's  been  recorded  in  the  data. We  collect  things  like   when  did  they  do  their  study  visits and  what  day  did  that  happen   and  what  study  day? What  were  the  disposition  events that  they  had,  the  exposure  they  had? Did  they  have  adverse  events and  what  was  the  duration   of  the  adverse  events? When  did  they  take medications  during  the  trial? Vital  signs  that  were  recorded or  test  results  that  were  recorded   can  also  be  displayed. We  have  a  lot  of  ability to  customize  this  and  configure  it. This  is  all  scripted, so  I  can't  really  show  you, "How  would  I  build  all  this  just  with the  graphical  user  interface  and  JMP?" You  need  to  know   a  little  bit  of  JMP  scripting. But  in  reality, each  one  of  these  graphs is  just  a  graph  builder that  have  been  put  together   on  top  of  each  other and  then  a  little  bit  of  editing   of  the  report  layer to  remove  some  of  the  controls. That's  really  what's  happened. We've  also  added  some  custom, like  the  legend  here  is  a  custom  graphic that  we  add  in  to  display, and  it's  just  an  image   that  displays  in  the  report  window. But  really,  all  of  this  is  just Graph  Builder  for  the  most  part. We  can  switch  between  patients  here and  look  at  the  profile   of  each  different  patient  in  the  trial as  we  would  want  to. I  hope  you've  been  able  to  see during  this  presentation just  some  of  the  ways   you  can  use  Graph  Builder  and  Tabulate to  summarize  your  data. More  than  just  the  standard, I  did  a  few  clicks   and  this  is  the  result  I  get. With  a  little  bit  of  extra  knowledge of  how  to  customize  the  output for  the  graphs  and  the  tables that  you  have  displayed, you  can  actually  get   some  very  nice-looking  output that  you  can  use  for  presentation  within  JMP and  even  for  reporting. Some  of  this  that  we've  used has  been  driven  by  regulatory  guidelines that  say  they  would  like  to  see   tables  and  graphs  look  this  way. We've  been  able  to  achieve  that   using  some  of  these  features like  stacking   and  packing  columns,  and  tabulate, having  multipanel  displays, doing  the  filtering  in  a  unique  way, and  using  column- switching, to  make  it  not  just  a  reporting  tool, but  an  interactive  tool  as  well. Thanks  for  your  attention, and  we  are  happy  to  hear   any  feedback  you've  got or  questions  about  the  product or  about  what  we've  shown you  in  this  presentation.
Event has ended
Harnessing the Power to Look Within: How We Used JMP Live to Understand the Health of JMP Use - (2023-US-30MP-1369)
Monday, October 16, 2023
JMP is a statistical software that can be harnessed to provide superpowers to enable effective and efficient organization, analysis, and reporting of data to save the world with data-driven decisions and process understanding. As Super Statisticians, we aim to grow the use of these superpowers in all areas of Regeneron.   However, with great statistics comes great responsibility, and it was time we used JMP to unleash our superpowers and look within. Is Regeneron using JMP effectively to channel its data superpowers? Are we providing the right support to overcome making anecdotal decisions? Come along with us on our quest to identify metrics to gauge the health of not only how the business is using JMP to flex its powers, but how our team is supporting the development of superpowers in the business. We demonstrate how to use JMP to connect to data sources, analyze data effectively, share metrics using JMP Live, and leverage the automation capability of JSL to update metrics faster than the speed of light.          Welcome  to  our  JMP  presentation on  Harnessing  the  Power  to  Look  Within: How W e  Used  JMP Live  to  Understand the H ealth  of  JMP  Use  at  our  company. Before  we  start  getting  into   the  real  content  of  our  presentation, we're  going  to  start  by  giving  you  guys a  little  background  on  ourselves. I  am  Isabel  and  this  is  Katie. We  both  work  as  statisticians at   Regeneron Pharmaceuticals at  their  industrial  operations   and  product  supply  site,  or  IOPS. The  primary  responsibility  of  our  site  is to  manufacture  the  medicine  for  patients. As  statisticians,  our  main  job  here is  to  enable  the  site  to  use  statistics. We  work  with  a  variety  of  groups, from  groups  like  process  scientists, manufacturing,  quality  control, and  quality  assurance. A s  you  guys  know,   JMP  is  a  powerful  statistical  tool. Over  the  years,  we  have  developed our  JMP  program  as  part  of  our  strategy for  expanding  statistical thinking  at  Regeneron. A  few  years  into  our  JMP  program, our  management  came  to  us and  asked  if  our  program  was  having   a  meaningful  impact  on  the  business and  are  we  being  successful? While  we  felt  that  we  were, we  realized  we  didn't  have  any  objective evidence  to  show  that  this  was  true. We  didn't  really  have  any  meaningful  metrics  to  gage  the  health  of  our  program. So  rather  than  just  take   a  couple  metrics  and  call  it  a  day, we  decided  to  do  this really  intentionally. We  took  a  step  back  and  realized that  we  needed  a  mission  and  vision statement  for  our  JMP  program. We  asked  ourselves, what  are  we  trying  to  accomplish? How  are  we  actually  aiding in  our  team  and  company  goals? First,  we  actually  reviewed   Regeneron's  mission  statement, which  is  to  repeatedly  bring   important  new  medicines to  patients  with  serious  diseases. Regeneron  does  this through  the  Regeneron  way. There's  some  more  information   about  that  in  the  presentation  right  here and  a  link  below   if  you  want  to  learn  more. But  after  reviewing   all  of  this  information, we  came  up  with  our  JMP Program  Mission  and  Vision  to  help to  accomplish  Regeneron's  mission by  providing  the  site  with  the  tools and  software  knowledge  to  effectively and  efficiently  organize,  analyze, and  report  data  that  will  guide  decision making  and  process  understanding. After  we  developed  our  mission  and  vision, it  was  time  to  start determining  our  metrics. We  started  by  asking ourselves  some  questions. The  first  one  being, what  benefits  are  we  actually  providing to  our  business  for  Regeneron? We  realized  that  the  biggest  benefit to  the  business  is  really  coming  from  how the  JMP  users  at our  company  are  using  JMP. So  then  we  started  to  ask  ourselves, "Well,  how  are  we  supporting   those  JMP  users?" Then,  what  must  we,  on  this  JMP  program  team, excel  at  in  order  to  support  these  users, and  how  should  we  develop  and  grow to  continue  to  support  the  company? This  resulted  in  us  finding these  four  different  areas: company  benefit,  JMP  user  benefit, internal  processes  learning  and  growth. We   realized  that   they  all  fit  into  each  other. A  bit  of  like  a  leading and  lagging  indicator  stuff  here. We  have  our  learning  and  growth of  our  JMP  team, which  is  going  to  help   our  internal  processes. If  our  internal  processes are  running  well, we  should  have  a  lot  of  benefits to  our  JMP  users. Finally,  if  our  JMP  users  are  using  JMP, well,  there  should  be  a big  benefit  to  the  company. So  with  these  four  different  areas, we  identified  different  objectives   that  we  want  to  work  on  or   monitor. We  mapped  out  similar  to  before,   the  relationship  between  these  objectives. The  ones  at  the  bottom   are  for  the  learning, the  pink  ones  in  the  middle are  for  our  internal  processes, the  green  are  for  our  JMP  users and  the  top  there  on  the  blue are  for  the  company  benefits. As  an  example  here, one  of  the  objectives  for  our  JMP  user benefits  is  providing  custom  JMP  tools. A portion  of  our  program  is  looking  at the  development  and  implementation of  JMP  custom  tools  through  JMP  scripts. Some  examples  of  some  of  the  scripts   we  do  might  be  doing  new and  new  outlier  tests  to  increase  the  functionality  of  JMP to  our  Regeneron  JMP  users  or automating  some  of  their  routine  analyzes. We  can  map  the  relationship how  this  directly  benefits  those  company area  of  ensuring  high  quality analysis  and  increased  efficiency. Once  we  have  our  objectives  in  place, we  sat  down  and  tried  to  figure  out the  different  measurements   or  metrics  for  each  objective. An  example  here,  again,  going  back  to that  provide  custom  JMP  tools  objective, we   started  to  look  at the  number  of  custom  scripts that  were  actually  available, the  number  of  script  uses  and  the  utilization  rate  of  our  script  users, what  percent  are  actively   using  the  scripts. Once  all  that  was  done, we  had  a  bunch  of  metrics and  we  started  to  review  those  metrics and  realized  we  didn't  have  the  data for  a  lot  of  these  metrics. This  actually  became  one  of  the  most time- consuming  parts  of  this  process, was  identifying  the  different  data  sources and  starting  to  collect  the  data to  make  sure  we  had  these robust  metrics  for  our  program. Then  finally  we  were  able  to, as  we  come  up  with   projects  and  initiatives, map  those  to  the  objectives   so  we're  able  to  make  sure that  these  projects  and  initiatives   are  actually  aiding  at  our mission  vision. Once  we  had  all  of  that  outlined, it  was  time  to  start  mapping  out how  we  want  our  metrics  to  look. Before  going  into  JMP, we  thought  it  would  be  best   to   come  up  with  a  game  plan of  how  we  want  things  to  look  at  the  end. We  actually  drew  out  the  way that  some  metrics  should  look. This  is  a  great  way   to  make  sure  everything is  going  to  look  good  in  the  end, fit  together. When  doing  this  part,  it's  going  to  be really  important  to  maybe  look  online and  review  some  good  practices when  it  comes  to  things  like  accessibility or  good  visuals  for  metrics, keeping  in  mind  things like  color  and  font  size and  the  directionality  of  text are  important. Just  make  sure  that  all  of  your  users  can view  your  metrics  and  understand  them. At  that  point,  we  had  our  game  plan for  how  we  want  our  metrics  to  look for  our  JMP  program. It  was  time  to  jump  over  to  JMP and  start  working  these  out. Katie's  going  to  demonstrate   how  our  flow  through  JMP getting  these  metrics  together   and  then  publishing  to   JMP Live, and  she's  going  to  be  using that  example  we  talked  about  earlier, which  is  looking  at   the  number  of  script  uses. All  right. Thanks,  Isabel. Now  that  Isabel's  given  an  overview  of   the  hard  part, the  time  where  we  have   to  do  a  lot  of  thinking to  make  sure  that  we're  coming  up   with  meaningful  metrics, I  get  to  do  the  fun  part, which  is  to  demo  how  easy  this  can  be from  start  to  finish  to  build  out our  dashboard  in  JMP. A  couple  of  things we're  going  to  touch  on, we're  going  to  touch  on  a  bunch  of different  tools  at  a  pretty  high  level. Hopefully,  it  brings  some  awareness to  some  things  that  are  available  for  you and  how  you  can  piece  it  all  together  to  get  from  start  to  finish, from  your  data  to  your   JMP Live  report. If  you  have  questions, I  guess  this  is  recording,  so   we'll  have  to  take  them  at  another  time. Some  of  the  things   we're  going  to  cover  in  the  overview is  the  connecting  to  the  data  sources. Like  Isabel  mentioned,   this  is  a  challenge, is  identifying  which  sources we're  going  to  connect  to. We're  going  to  connect  to a  couple  different  items  here, one  being  a  text  file   and  one  being  an  Excel  file. Data  cleanup. Once  we  pull  that  data  in, usually,  there's  some  kind  of   modification  and  cleanup  that's  involved. We  can  do  that  all  within the  JMP  data  table. Then  we'll  summarize and  visualize  the  data, create  the  graphics  that we  want  to  display  for  our  metrics, and  then  easily  publish  them  to   JMP Live so  that  we  can  share  them   with  other  users. Once  we  have  completed   that  process  and  workflow, there's  some  cool  tools  that  we  can  use so  that  we  can  recreate  that  process with  a  click  of  a  button  so  that  we  can generate  these  metrics  on  a  routine  basis. We  can  also  take  that  automated  script and  workflow  and  package  it  in  an  add  in so  that  we  can  share  it  with  other  users. I'm  going  to  start  off with  the  import  of  our  text  file. If  I  go  to  open  and  I  navigate   to  where  the  file  is  located, I  have  to  tell  JMP  that   I  also  want  it  to  display  text  files. I  can  select  the  file  that   I'm  interested  in  importing. The  first  one  that  we're  going to  pull  from  is  called  our  usage  log. We  have,  as  Isabel  mentioned, our  validated  JMP  scripts. These  are  scripts  that  people can  access  through  an  add- in. But  every  time   they  use  that  tool  in  the  add- in, it  will  log  the  use  of  that  script   in  a  controlled  file. So  this  is  called  our  usage  log. If  we  open  up  that  file, we  can  see  that  it's  imported   all  of  our  data,  as  we  would  expect, into  a  JMP  data  table. To  start,  we're  just  going  to  talk  through  some  of  the  columns that  are  included  in  our  data  set. We  have  the  script  name, we  have  the  username,  the  JMP  version, a  date,  timestamp,  and  the  data  tables. This  is  all  of  the  things  that  get  logged every  time  someone  executes a script in  our  validated script  environment. Some  things   we  might  want  to  clean  up  here. To  start,  I'm  going to  modify  the  script  name. I  know  that  there's  some  things that  have  changed with  the  naming  of  the  scripts throughout  their  use, so  I  just  want  to  clean  them  up   so  they  get  grouped  together  correctly and  we  can  look  at   for  any  other  little  errors  too, that  we  can  clean  up  along  the  way. Here  we  have  our  list  of  script  names and  then  if  we  need  to  recode  them, we  can  select  them  over  here  to  change  the  name. The  first  one  I'm  going  to  change is  this  Old  Outlier  Test  Name. We  had  originally  had a  name  for  a  script. We  changed  it  to  just  Outlier  Test. I  want  to  make  sure  that   all  those  get  counted  together because  those  were   technically  the  same  script. I  can  just  rename  it  here, rename  it  Outlier  Test. Now  it  will  indicate  that  those  two are  now  going  to  be  grouped  together. Also,  just  looking  through,  I  can  see that  there's  a  little  bit  of  a  typo  here. That  simulation  has  a  capital  I. I  don't  want  to  have   to  keep  cleaning  this  up all  throughout  the  data  table and  things  later  on. I'm  just  going  to  tell  it in  the  recode  that  I  want  it  to  correct that  to  be  a  lowercase  I. When  we're  happy  with  that,  we  can  hit Okay , you  can  see  it's  created  a  new  column  here for  the  script  names   of  their  cleaned- up  data  file. I  don't  love  the  name  script  name  too, so  I'm  just  going  to  grab  that and  change  it. Instead,  I  just  want  it  to  display  script descriptive  enough   and  I  don't  have  to  explain  why there's  a  two  in  front  of  it when  we're  looking  at  our  metrics. So  we'll  clean  that  up  now. Now  we're  still  looking  at  the  rows representing  each  use  of  our  script. We  could  just  summarize  this in  different  ways,  but  it  might  be  helpful when  we  go  to  do  some  digging   into  this  data  later  on to  also  have  some  information  related   to  the  different  departments that  we're  running  our  script. I  know  we  want  to  pull  in  that  information. We're  going  to  go  to  our  next  data  table. This  one  we're  going  to  import   is  an  Excel  file. I  will  say  that  when  we're  routinely running  these  metrics, we  do  actually  connect  directly to  the  data  mart  that  houses  this  data so we  can  pull  the  most  update  version  of  this  file  anytime. For  this  example,   we  had  to  pull  a  static  file so  that  we  could  recode for  privacy  and  security. To  import  that  file, I'm  going  to  click  on  the  file, and  I  don't  think  you   always  have  to  do  this, but  just  to  be  safe,  I'm  going  to  open  using  the  Excel  Wizard. One  of  the  nice  things   about  the  Excel  Wizard is it  gives  you  the  ability  to  preview   your  file  before  you  import. Now,  I  know  that  this  file   I  created  this  file, so  I  know  that  it's  a  nice  clean  file   with  the  right  headings and  the  data  all  ready  to  go  just as  I  would  want  it  to  import. But  just  in  case, a  lot  of  times  we  receive  files from  different  people  in  all  different formats  with  merged  headings. One  of  the  nice  things  about  this  tool is  you  can  make  some  of  those modifications  to  clean  up  the  data and  import  it  correctly  on  the  first  try. There's  a  couple  more  options if  we  click  Next. If  you  have  any  hidden  rows, you  can  tell  JMP   how  you  want  to  handle  it. Again,  all  this  is a  pretty  clean,  easy  file. When  I  know  that  it's  how  I  want, I'll  make  sure  my  sheet   is  selected  and  select  Import. Just  to  take  a  look  at  this  data  set, I  now  have  the  first  and  last  name of  the  users,  the  username, and  then  I  also  have  the  information  about their  location  and  their  department. It  would  be  helpful  if  I  could  get  this information  merged  into  my  usage  log  file so  that  I  can  break  down  my  analysis  on the  usage  by  the  location  and  department. I'm  going  to  take  advantage   of  the  update  tool. There's  a  bunch  of  different  tools  available  in  the  tables, the  Tables  menu  here that  allow  you  to  make modifications  and  manipulations to  your  data  table. I  suggest  browsing  through  it  when you  have  a  lot  of  data  cleanup  to  do or  trying  to  get  creative combining  different  files. But  this  one's  pretty  simple. We're  just  going  to  go  to  the  update because  we  would  like  to  update  this  file, the  usage  log  with  information from  the  HR  roster. So  I  selected  my  HR  roster, and  now  I'm  just  going  to  tell  JMP   that  I  want  to  update. With  selected  columns, I  don't  need  all  that  information. The  first  and  last  name  and  username  is  pretty  redundant. I'm  just  going  to  grab the  location  and  department. Then  from  the  main  table, I  don't  really  want  anything  to  update. I  want  that  to  be  left  alone. I  do  have  to  tell  JMP how  I  want  to  link  up  the  rows. I'm  just  going  to  indicate  that I  want  the  usernames  to  match  by  row and  we  can  hit  okay. You  can  see  now  that  the  usage  log  table that  we  imported  originally has  been  updated  to  include the  location  and  department. Now  that  I  have  the  information that  I  want  all  in  one  place, I  can  start  to  play  around  with   the  different  graphics  for  the  metrics. So  here  I'm  going  to  go to  Graph,  Graph  Builder, which  is  a  great  tool  to  drag and  drop  whether  you  know what  you're  looking  for  or  if  you  like to  explore  different  ideas. It's  really  quick  and  easy  to  make adjustments  and  changes to  get  a  feel  for  how  you  want   your  metrics  to  be  displayed. For  this  first  example,  we're  just going  to  summarize  by  department. I'm  going  to  grab  the  department  group and  put  it  down  here  at  the  X. Pretty  straightforward, just  a  simple  bar  chart. It'll  pull  up  by  default, but  I  can  see  overall  the  amount  of  use between  the  different  departments. Maybe  I  want  a  little  more  granularity to  this  or  more  information  on  this. Maybe  I  want  to  see  what this  looked  like  year  over  year. Has  this  been  consistent  year  over  year? A  quick  change  that  I'll  make  is  that I'm  going  to  add  an  overlay  by  year. Notice  we  don't  have a  year  column  available. One  thing  we  could  do  is  go  back to  our  data  table  and  create  it. But  we're  already  too  far  along. So  I  think  I'm  just  going  to   create  a  temporary  column, which  I  can  do  by  just  right  clicking   on  the  date  time  column. Going  to  date  time  and  selecting  year. Now  it  creates  a  temporary  column. Notice  it  didn't  add  it   to  the  full  data  table, but  I  can  use  it  here as  an  overlay  in  my  graph. So  quick  and  easy  click  modification. Now  I  just  have  a  little  bit  more   information  of  how  the  uses  compare from  one  year  to  the  next. You  can  see  that  the  overall  drop for  Department  2, there's  been  a  drop  for  Department  2 with  the  use  of  the  script  in  general. Maybe  we  would  dig into  that  a  little  bit  more. But  I  think  for  now,  I'm  happy with  how  that  graph  came  out so  I'm  just  going  to  hit  Okay   to  get  rid  of  that  control  panel and  the  first  one's  done. Another  thing  we  might  want  to  look at  is  the  use  of  the  scripts  over  time. I'll  go  back  to  my  Graph  Builder. Then  I'm  just  going  to  put… Before  I  just  throw  the  date  time   variable  on  the   x-axis, I'm  going  to  create  another  category. I'd  like  to   see  it  summarized by  week  over  year, year  and  then  the  week. So  I'm  going  to  go  to  the  date  time  again, create  a  temporary  column, and  select  your  week. If  I  take  that  and  put  it  on  the   x-axis, you  can  see  that  it's  in  sorted  order. We  have  the  amount  of  use  of  each of  the  scripts  for  each  week  of  the  year. So  a  total  count  of what  the  use  is. It's  getting  a  little  messy  with  the  bars. It  might  be  more  appropriate  to  use  a  line  in  this  case than  when  it's  like  the  line  icon. You  can  create  combinations of  these  if  you  want  some  overlaying. Sometimes  it  makes  your  charts  too  busy. We  have  a  lot  of  information on  here  already, so  I  think  I'm  going  to  leave  it  as  is, but  there's  a  lot  of   cool  options  up  there. Now  what  we  have  is  just  a  line that  shows  the  overall  use  of  the  scripts. But  what  I'd  really  like  to  know  is if  there's  any  trends  or  changes that  we're  seeing from  one  script  to  the  next. I  want  to  use  my  newly  created  column and  put  that  in  the  overlay. Now  I  have  a  separate  line   for  each  of  the  scripts  over  time, I'm  going  to  click  Done, because  I  think  I  have  everything laid  out  the  way  that  I  want. But  it's  also  four  years  worth  of  data. Maybe  that's  not  what  I  want  to  be looking  at  just  as  a  snapshot. To  give  myself  the  option to  filter  on  the  date, I  am  going  to  go  and  add in  a  local  data  filter. If  you  click  on  the  lyric,  L ocal  Data F ilter, we  can  choose  from   the  list  of  columns  that  we  have. But  again,  I'm  going  to  create  that temporary  column  to  include  the  year. If  I  click  the  year,  plus  sign, now  I  have  the  ability  to  filter. One  of  the  things  I  don't  like  is  that  it's   on  this  continuous  scale where  it'd  be  much  easier if  I  could  just  click  on the  bars  associated  with  each  year. I  can  actually  go  and  tell  JMP  that I  want  this  temporary  column to  have  a  modeling  type   that's  nominal  ordinal and  now  I  can  apply  the  filter   so  I  can  just  look  at  2023 if  I  want  to  look  over  the  past  year. If  I  want  to  look  over  the  past  two  years, I  can  select  both. But  it's  a  nice  feature  to   drill  down   into  the  different  sets  of  data. So  now  I  have   my  two  graphs  for  my  metrics, but  it's  not  super  helpful if  I'm  the  only  one  who  can  see  them. This  is  where  we're  going  to   publish  them  to   JMP Live so  that  we  can  share  with  other  users. To  publish,  you  just  go  to  the  file  menu, select  Publish  and  Publish  Reports to   JMP Live just  a  second  while  it  gets  things  ready, and  then  we  can  select, in  this  case,  I'm  going  to  publish  both of  these  reports  that  we  just  created. Next,  I  can  select  the  location where  I  want  them  to  go. I've  created  a  file  for  JMP  Discovery,   so  that's  where  we're  going  to  place  them. If  I  need  to  rename  anything, we  can  do  it  here. I'll  leave  them  default  for  now. Also,  if  you  wanted   to  add  any  descriptions, any  clarifying  information, a  lot  of  times  with  metrics, there  might  be  little  nuances  to   how  you  summarized  or  calculated  the  data. You  can  bring  clarity  to  any  of  that using  the  description  field. When  we're  ready,  we'll  hit  Publish. Now  it's  successfully published  the  reports. If  we  want  to  go  see  it, we  can  click  the  Open  buttons. We'll  do  that  just  to  go  take  a  look. Here  is  our  first  post  that  came  up. You'll  notice  that  it  matches   the  graph  that  we  just  created using  the  Graph  Builder. Now  it's  published  there for  everyone  to  see. If  anyone  wants  to  go  and  check  on  the  metrics  related  to  department that's  available  as  well. Copy  this. That's  the  workflow to  how  to  get  from  our  data  set all  the  way  to  our   JMP Live  report from  start  to  finish. But  typically  we  don't  just  do a  one- and- done  metric  analysis. It's  something  that  we'd  like  to  repeat on  a  routine  cadence and  make  sure  that   we  have  up- to- date  data and  are  discussing   with  our  team  routinely. So  ideally,  we'd  like  to  make this  super  easy  to  rerun that's  where  the  JMP  workflow   can  really  save  us  some  time. I'm  going  to  close  out  of  all  this just  to  clean  up  my  files  a  little  bit. Don't  need  to  save  it. One  of  the  things  that  I  did before  we  started  the  presentation was  open  up  a  new  workflow. If  you  open  up  a  workflow and  turn  on  the  Workflow  Builder by  just  clicking  the  red  button, it  will  actually  record  all  the  tasks that  you  perform  within  JMP in  the  sequence  that  you  performed  them. Pretty  neat  tool because  I  really   didn't  have  to  do  anything to  build  out  this  automated  workflow other  than  turn  on  the  button. To  create  a  new  one, you  would  just  go  File, N ew  and  Workflow. One  of  the  things  you  notice  that's  also a  nice  feature  is  this  JMP  Log  History. You  see,  I  have  a  blank  workflow  here because  I  haven't   actually  recorded  anything, but  maybe  I  had  performed  steps that  I  intended  to  record but  just  forgot  to  hit  the  button. It  actually  has  a  log  of  all of  your  activity  stored  down  below. You  can  always  go  in   and  then  drag  these  up  to  the  top if  you  actually  wanted  to  include  them   in  your  recording. I'm  going  to  close  out  of  this  one and  just  to  walk  through  quickly, this  is  a  summary  again  of  the  steps that  we  had  just  performed, including  the  recode. We  did  our  import,  we  did  our  recode, even  just  changing  the  name of  the  column  to  Script to  make  sure  that  that's  consistent. The  importing  of  the  HR  roster  from  Excel, updating  the  data  table,  building  out  our reports  and  then  publishing  to   JMP Live. We  even  have  closing  up  those  data  tables that  was  performed  at  the  end. One  of  the  nice  things  too, if  you  need  to  make   any  modifications  to  these or  you're  trying  your  hand  at  some   JMP Scripting,  you  can  double- click, it  will  actually  open  up   to  link  you  to  the  JMP  script that's  included in  order  to  perform  that  task. So  if  you  need  to  make   any  modifications  or  adjustments or  you  just  want  to  copy  out this  script  to  put  into  another  location, it's  all  available  right  here and  super  easy  to  pull  or  modify. The  last  thing  I  want  to  add  in  here is  that  if  you  do  have  any  custom  code that  you'd  like  to  include, you  can  click  on  the  drop- down  up here  and  say  Add  a  Custom  Action. Here,  you  can  type  out  any  code   that  you  would  like. Maybe  at  the  end, after  closing  all  these  tables, I  wanted  to  actually  open up  the   JMP Live  web  page. I  could  just  paste  in  the  web  function, paste  in  the  URL and  then  when  I  execute  this  step, it  will  actually  go  and  open  up   the  web  page  with  our  analysis. That's   building  it  out  start  to  finish. Then  one  thing  we  can  do  from  here, I  had  mentioned it  would  be  really  nice   if  we  could  package  this so  that  we  can  share  it  with  other  users. I  may  not  be  the  only  person  that  needs to  run  and  update  these  metrics. If  you  click  on  the  layer  up  here, you  can  save  all  of  this  entire  workflow  and  all  of  the  steps  associated  with  it into  a  single  script  file. You  can  either   save  this  file  and  share  it, or  you  can  take  advantage   of  the  JMP A dd-I ns and  package  the  script  right   within  the  Add-I n  Menu so  that  people  can  run  it   right  from  their  JMP  desktop. All  they  have  to  do  is  double- click to  install  the  Add-I n and  they  would  be  able  to  find  it along  with  all  the  other  menus. I  have  one  for  JMP  Discovery and  they  can  re- execute  the  metrics by  just  clicking  a  button. Those  go  through  all  the  steps. If  you  are  savvy  with   JMP Scripting, you  could  make  some  of  these flashing  tables  and  things  invisible. You  can  incorporate   a  lot  of  your  own  custom  items and  there  they  are,  brand  new  reports, updated  just  a  few  seconds  ago. There's  our  department  bar  chart and  then  our  use  over  time. I know  that  was  a  quick  overview, but  we  touched  on  a  lot of  different  things  at  a  high  level and  tried  to  illustrate  how   we  could  connect  them  all  together. Starting  with  our  data  source, cleaning  up  the  data  files, building  out  our  visualizations and  summaries,   and  then  publishing  to   JMP Live. Optional,  if  you  wanted   to  turn  on  that  workflow  builder, you  can  record  all  those  steps   so  you  can  repeat  them  easily  later  on, and  you  can  package  it  up  in  an  add- in so  that  you  could  share  with   other  users  to  run  the  metrics  as  well. Anything  to  add  before we  wrap  it  up,  Isabel? Nope. Thanks,  Katie. We  hope  you  guys  learned   a  lot  about  identifying  metrics and  actually  implementing   your  metrics  and  visuals, using  JMP  and  then  publishing  to   JMP Live. Thanks,  everybody.
Event has ended
Time-Efficient Strategy for Selecting a Test Set in the Validation of an Image Detection Algorithm - (2023-US-30MP-1362)
Monday, October 16, 2023
Advances in digital image analysis have created opportunities for quantitative histopathology assessments in rodent toxicology studies. Microscopic evaluation of rodent spleen is performed to assess for test article-induced immunotoxic effects but can be subject to inter- and intra-pathologist variability in characterization of differences between treatment groups and across studies.   To address this problem, an image detection algorithm was trained to quantify tissue compartments in histologic sections of rodent spleen. Our aim was to design a study to evaluate how the image detection algorithm compared to digital annotations performed by human raters for specific features of the spleen, while keeping within operational constraints (e.g., rater time and effort).   In this talk, we show how we used JMP Custom Designer, using data generated by the image algorithm as inputs, to select and allocate a test set across human raters. We used a response surface model, which is designed to select samples that fall on the boundaries and center of the input space. The resulting study design allowed us to strategically select a test set and create a balanced sampling plan for use across several pathologists from different institutions.     Hello  everyone. I'm  Caroll  Co.   I'm  a  statistician  at  DLH. Today,  I  will  talk  about   a  project  I worked on in  creating  a  time- efficient  strategy   for  selecting  a  test  set in  the  validation   of  an  image  detection  algorithm. This  work  was  done   in  collaboration with scientists from  the  National  Institute  of Environmental Health Sciences, pathologists  from  experimental   pathology laboratories, and  my  fellow  coworkers  at  DLH. In  rodent  toxicology, advances   in digital  image analysis offer  opportunities  for  quantitative   histopathology  assessments. An  example  of  where   digital  image  analysis  could  be  useful is in the evaluation of  test   article- induced immunotoxic effects in  rodent  spleens. Typically,  pathologists  use  a  microscope to  evaluate  whether  there  is   immunotoxicity in the spleen and  make  judgments  on  whether  spleens   from  animals that receive a treatment are  different  from  the  control  group. This  workflow  is  prone   to  inter-  and  intra-rater  variability in  characterizing  differences   within  the  study and  also  across  different  studies. Here's  an  example  of  a  zoomed- in   cross-section  view  of  a  rodent  spleen. Here,  I'm  just  pointing  out specific features  of  interest  to  our collaborators  that  we  want  to  capture. Our  problem. When  we  got  involved  in  this  project, our pathologist collaborators had already trained  an  algorithm that  measure  these  features  of  interest. The  question  that  was  posed  to  us  was   how  do  we  validate  this  algorithm? Validation  is  a  very  broad  term. To  narrow  our  focus, we  thought  about  what  are  some  of  the   main  questions  that  we  wanted  to  address. First,  the  algorithm  was  trained by a select few people, so  we  wanted  to  see  whether  different  pathologists from different laboratories would  agree  with  the  algorithm  output. Second,  there  were  multiple  features where  the  algorithm was  trained  to  measure. We  wanted  to  see   if  there  are  any specific features where  performance  is  better  or  worse. Then,  we  also  wanted  to  see if this algorithm  can  hold  up  to   against  a   wide  range  of  cases. If  there  are  any  blind  spots,   can  we  find  them? I'll  also  mention  that  from  here  onwards, I will  refer  to  the  image  algorithm   as  the  AI. One  solution  that  we  thought  of   was to have both humans and AI annotate  the  same  images   and  compare  the  output. This  is  how  it  would  work. A  tissue  sample  gets  scanned   so  it  turns  into  a  digital  image or a whole slide  image WSI, and  that  image  gets  fed   into  the  AI  for  processing. Then  for  humans,  they  view  the  image using  an  image software where  they  can  manually annotate  features  of  interest. One  of  the  questions  we  got  asked was how  many  images  do  we  need  to  validate? As statisticians, our  answer  is  always  as  many  as  you  can or  do  you  have  hundreds? Do you  have  thousands? But  after  talking  to  them, you realize that there are a number of   operational  constraints  to  implement  this. The  first  constraint  was  that there was a  need  for  each  image   to  be  evaluated  by  three  different  people. Having  three  people  was  useful so that  we  can  also  estimate the  variability  between  raters. It  also  turns  out  that  this  process of  annotation  is  very  time- consuming. After  talking  to  the  pathologists, the  maximum  number  of  images they were willing  to  annotate   was  about  24  per  person. Then,  lastly,  one  of  the  goal   of  this project is  to  get  buy- in  or  support from  pathologists  from  other  labs. It  meant  we  needed  participation from multiple labs and  multiple  people  from  each  lab. In  this  study,  we  got  participation from three centers and  we  had  three  pathologists   representing  each  center. In  total,  we  had  nine  pathologists recruited  in  the  study. Based  on  all  of  these  constraints, we've  determined  that  we  can  validate only  72  images. The  sampling  plan. Now  we  know  our  sample  size. How  do  we  select  the  72? Random  selection  is  okay, but  can  we  do  better? What  we  came  up  with  was since  the  cost  per  image  for  the  AI   is relatively low, we  got  the  AI  to  process a  larger  set  of  images and then from there, use  information  from  the  AI output to  better  select  our  72  samples. We  use  a  response  service  model   to  select  our  points and an RSM will select points towards  the  boundaries and  center  of  the  input  space. It's  a  model  containing  your  main  two- way interaction  effects  and  quadratic  effects. This  model  was  particularly  useful to  our  validation  problem because we wanted  to  look  for  areas  where agreement  fails  between  humans  and  AI. Generally,  bugs  tend  to  occur on  the  boundary  and  edges  of  this  space. This  type  of  model   fits  the  problem  that  we  had. Now,  we  have  a  plan  to  select  the  images. The  question  now  is  how  do  we  allocate these  72  images  across  nine  raters? We  expect  the  samples   to  have  a  wide  range  of   complexity. We  wanted  to  make  sure  that  everyone got  a  balanced  mix  of  slides. The  complexity  or  this  case  mix is determined  by  the  output that  we  got  from  the  AI. We  also  needed  to  satisfy the constraint of  making  sure  that  each  image   is  seen  by  three  different  people. Here  is  my  workflow. I  will  show  you  how  I  created   the  sampling plan that satisfied all  of  our  operational  constraints   in JMP. I  have  three  steps  in  this  workflow. First,  I'm  going  to  show  you how  we  selected  the  72  images from  a  larger  set. Second  is  how  we  replicated the  72  images  three  times, so we  have  216  runs. We needed to do this because we wanted each image to  be  seen  by  three  raters. The  last  step, I'll  show  you  how  we allocated  the 216 runs across nine raters so  that  each  person  has  exactly  24  images. In  each  of  these  steps, I  will  actually  be  using  the  DOE  platform. Now,  I'm  just  going  to  move  over  to  JMP. I  have  my  JMP  file  in  there. My   JMP journal. I  am  going  to  open  a  sample  data  set, and  I  say  sample  because  our  data   has  not  been  published  yet. For  the  purposes  of  this  demonstration, I  will  be  using  a  fake  data  set. This  data  set  has  the  same  features to   the  original  data  that  we  collected. This  is  what  the  data  looks  like. I  have  my  slide  ID,  which  is  just a  numeric  variable  going  from  1  to  100. Going  from  1  to  100  right  here. I  have  four  variables  that  I'm  looking  for. That's  been  collected  by  the  AI. Features  1,   2,   3, and  4. Three  of  them  are  continuous and  one  is  account  variable. If  you  look  at  under  Analyze,   Multivariate  Methods, Multivariate, if  we  just  show  the  scatterplot  matrix of all of the variables you'll see, all  of  them  are  uncorrelated. This  is   the  spread  of the  range  of  our  variables. Step  one. Under  DOE,  there's  a  Custom  Design, that's what I'm clicking. We  can  leave  the  responses  Y  here. Just  leave  it  alone. The  factors  here,  there's  actually   two  ways  to  do  this. One  way  is  to  click  on  this  button  here   that  says  Add  Factor. I  think  about  fifth  of  the  way and  there's  a  Covariate  selection. If  you  click  that, it'll  ask  you  which  columns  of covariates you  want  to  include  in  the  design. In  my  case,  it's  these  four  features. I'm  just   selecting  them and  you  click  Okay. JMP  will  automatically  populate the min and max for  each  of  these  variables. You'll  see  here  that   they're  all  treated  as  covariates. That  looks  good. I'll  just  quickly  close  this and  then  show  you  another  way   of  doing  the  same  thing. Again,  in  DOE  Custom  Design. What  I  showed  you  was  adding  it   using  this   way  here. Another  way  to  do  it   is  to  just  use  this  button  right  here which says Select Covariate  Factors and  it'll  give  you  the  exact  same  thing. Again,  I'm  picking  the  factors   that  I'm  interested  in and it automatically  populated this  Factors  window. I'm  just  scrolling  down  to  the  bottom   and  click  Continue. This  is  where  we  can put  in  the  model  that  we  want. For  us,  we  wanted  an  RSM or  response  service  model. There  is  again  a  shortcut button  right  here. I'm  just  going  to  click  that. What  that  did  is  it  added... We  already  have  our  main  factors  in  there, but  it  added  our  quadratics and  two- way  interaction  effects. Then,  lastly,  at  the  bottom in  here   where  it  says  number  of  runs, we  don't  want  100  because  we  actually just  want  to  select  72  out  of  100. I'm  just  going  to  change  this  to  72. This  all  looks  good  to  me. I'll  click  Make  Design. Let's  just  give  JMP  a  few  seconds   to  create  the  design  for  us. Here  we  go. This  is  the  design  that  it  created  for  us. As  you  can  see,  if  you  scroll  down, it  only  gave  us  72  rows, which  is  what  we  had  asked  for. I'm  just  going  to  say, turn  this  into  a  data  table. I'm  just  going  to  hit  Make  Table right  here  on  the  bottom  left. I'm  going  to  close  this  for  now. Now,  before  I  show  you what  this  design  looks  like, I  actually  want  to  go  back   to  the  original  table, just  so  you  could  see... We  can  compare  what  happened to the observations that were picked versus  observations  that  were  not  picked. If  you  go  back  to  the  original  data  table, the  ones  that  were  chosen, that  72  are  actually  highlighted. What  I'd  like  to  do  at  this  point is  I'd  like  to  create  a  new  column that  would  identify  which  rows   were  selected  and  which  ones  weren't. To  do  that,  you  go  under  Rows. Under  Row  Selection, there  is  the  last  option  in  here says  Name  Selection  in  Column. What  it'll  do  is it'll  label   the  currently selected rows and  save  whatever  values   you  assign  for  that  column. I'm  going  to  click  that. The  column  name. Y ou  get  to  create   a  name for that column. T he  rows  that  are  highlighted,   I'm  going to give them a value of 1. The  ones  that  were  not  selected, I  want  to  give  them  a  value  of  zero. I'm  going  to  press O kay, and  that  created  this  column  right  here. All  of  my  wants,  there's  72... There's  72  rows  for  the  ones   that  were selected and  then  28  for  the  ones that  were  not  selected. I  want  to  go  back  to  my  scatterplot  matrix to  see  the responses or  to  see  which  observations  were  picked   and  which  ones  weren't. But  before  we  do  that, I do want to color-code them so  that  when  you  look  at  the  graph, I  can  immediately  spot  which  ones were  picked  and  which  ones  weren't. A quick  way  of  doing  that  is  to  do  Rows, Color  or  Mark  by  Column. Then,  here,  I  want  to  use   this  column called Selected so  that  all  the  zeros  will  be  identified by an orange circle and  all  the  ones  will  be  identified   by  a  blue  circle. We  use  a  marker  as  well. That  might  make  it  easier to  identify  the  observations. I'll  just  click O kay. Now,  all  of  my  rows   are  marked   by  the  plus  or  the  circles. If I go back to my... If  I  go  back  to  my  scatterplot  matrix, again,  it's  under  Multivariate  Methods,  Multivariate,  and  I  just  hit  Recall. I'm  doing  the  same  thing as  I  did  initially. Let  me  click Okay. I'm  just  going  to  make  this   a  little  bit  bigger. You'll   now  see  that  the  blue  pluses are  the  observations  that  were  selected, and the  orange  circles   are  the  ones  that  weren't. The  ones  that  were  selected tend to  occur  more  on  the  boundary and  edges  of  our  space. This  is  exactly  what  we  wanted. This   looks  great  to  me. Let's close this. Just  to   convince  yourself that  the   model  is  doing what  it's  supposed  to  be  doing, I  actually  did  this  same  setup, but  this  time,  instead  of  picking  72, I'm  only  picking  24. Let's  make  it  a  little  bit  more  extreme. Just  to   show  you  an  example   of  what  that  looks  like. Again,  now  my  selection. Now,  there's  only  24  rows that  are  selected  in  here. Let's  do  the  same  thing. Making  this  a  little  bit  bigger. Now,  you  see  fewer  blue  pluses because  there  should  only  be  24. But  then  you'll  see  how   those  observations are getting picked versus  the  ones  that  were  not  picked. They  do  tend  to  occur  more   on  the outer boundaries, but  still  some  in  the  center. Let's  go  back  to  our  original  problem. We'll  just  close  this. This  was  our  original  data  table. The one that was created  by  the  design  is  this  one. What  do  we  have  here? We're  still  keeping  the  same  variables   that  we  asked  JMP to  include. We  have  our  features  1  2,   3, and  4. Why  our  response  is  still  missing? We  now  have  this  new  column that  says  Covariate  Row  Index. Basically,  this  just   links  you  back   to  your  original  table. If  this  says  88,  it  would  be  row  88 on  your  original  table  that  got  captured. This  one. This  row  here  should  be  the  same   as  this  row  right  here. Before  we  move,  I'm  actually  just going  to  rename  this  to  my  slide  ID. In  my  case,  the  slide  ID, which  is  just  a  number  from  1  to  100, it's  actually  just  the  same   as  the  Covariate Row  Index. That  was  all  for  step  one. Step  two  is  now  replicating  the  72  images. How  do  we  do  that? A  quick  way  of  doing  that  is  to  go  to  DOE. The  second  selection  is  augment  design. Then  this  window  will  pop  up  asking  you   what  are  your  responses  and  factors? I  know  why  we  don't  really   have a response, but  I  think  you  still  need  to  put  in   something  in  here. That's  okay. You  just  put  it  in  there   even  though  it's  all  missing  values. Then,  for  the  factors,  we're  going   to  select  our  features  1  to  4. This  time,  we  would  also need  to  select  our  Slide  ID. Click  Okay. This  new  window  pops  up with  all  of  our  factors . Again, JMP  auto- populates   the  ranges  of  all  of  our  variables. On  the  bottom  here,   under  Augmentation  Choices, there's  a  replicate  button, and  that's  exactly   what  we  need. Click  that  and  then  they'll  ask  you, how  many  number  of  times   do  you  want  to  perform  each  run? The  default  is  two, but  we  actually  want  it  to  be  three because  we  needed  each  image   to  show  up  three  times  in  our  design. Then  we  click  Okay. Now,  what  you'll  see  is  that   in  our design, instead  of  just  72, we  now  have  72  times  three. We'll  just  scroll   all  the  way  to  the  bottom. We  now  have  216  rows. I'll  just  click  Make  Table to  turn  that  into  a  data  table. We'll  close  this. A  few  more  things  that  I  want  to  check. A  couple  more  things  I  want  to  check before  we  move  on  to  step  three. That  is,   every  time  you're  doing   these  steps, you  want  to  make  sure  that  it's  actually doing  what  you  think  it  should  be  doing. In  this  case, I  wanted  to  check  that  each  slide  ID actually  occurs  three  times. We're  going  to  use  tabulate  to  do  that. Tabulate, S lide  ID,  and  I  should  have a  count  of  three  for  each  ID. That's  what  we  have. That  looks  great. That's  it  for  step  two. Let's  move  on  to  the  last  step, which  is  the  most  exciting  step. Now,  we  have  216  rows and  we  have  the  slide  IDs. Now,  we  want  to  distribute  this to  nine  different  people. How  would  we  do  that  in  a  way   that  each  person  gets   a  balanced  mix? A  cool  way  to  do  it  is  to use  DOE  again. DOE, click  on  DOE Custom  Design. Like  what  we  did  before, we're  still  going  to  use   our  covariate  as  factors. Just  select  features  1  to  4   and  our  slide  ID. JMP   will  auto- populate   the  mins  and  max. Now,  all  of  the  row  here  is they're  all  listed  as  covariate. But  at  this  point,  I  actually want  to  add  two  more  factors. One  factor  is  a  categorical  factor with three levels, and  that  is  the  center  or  the  laboratory because you  have  nine  pathologists, but  they're  all  coming   from  three  different  centers. I'm  just  going  to  rename  them  A,  B  and  C. Then  I  want  to  do... In each center, we  also  have   three  different  people  participating. I  want  to  add  another  categorical  factor   again  with  three  levels. This  is  going  to  be  our  rater. These  are  like  the  people. Let's  name  them  1,   2, and  3. Basically,  what  I'm  saying  is  that Center  A  will  have  Raters  1, 2, and 3, Center  B  has  Raters  1,  2, and   3, and  so  on. In  total,  we  have  nine  different  people, nine  different  combinations of  center  and  rater  in  here. I'm  going  to  minimize  this. This  actually  just  shows  the  data  table where  we  pulled  our  covariates  from. Then  hit  Continue  here. Now,  we  get  to  tell  JMP what  kind  of  model  do  we  want. There's  a  couple  of  things  in  here. First,  I  actually  wanted  to  add  in. JMP  will  automatically   put  in  your  main  effects. These  are  all  the  factors that  are  in  my  model. I  wanted  to  put  in  an  interaction  term between  center  and  rater, and  that's  because I  wanted  to  make  sure  that all combinations  of  center  and  rater   appear  in  our  design. That   guarantees  that. The  other  thing  to  know  is  that we actually  don't  have  enough runs  to  estimate  slide  ID. Remember,  slide  ID  goes  from... There's  72  distinct  slide  IDs  in  here, but  we  actually  don't  want  an  effect,  a  slide  ID  effect. We  just  want JMP  to   take  that  into  account when  it's  constructing  the  design. We  don't  really  want  to  estimate  slide  ID. Under Estima bility  here, you  can  change... The  estimability  for  slide  ID, from Necessary,  you  can  change  it  to  If  Possible. Then,  lastly,  the  number  of  runs. I  think  t he  number  of  runs they  calculated  for  me  was  18. But  we  actually  wanted   to  use  up  all  of  our  runs because  we  have  216  runs and  now  we're  just  looking  to  see... We wanted  to  get   JMP to  tell  us how  do  we  allocate  these  216. Make  Design. It  just  might  take  a  little  bit  longer. I'll  go  into  like, how do we check the design and  then  talk  a  little  bit   about  the  run  order. This  is  what  the  design  looks  like. We  have  our  original  features,   features  1  to  4  in  here,  the  slide  ID, and now it added  center  and  rater assigned   for  each  slide  ID. What  this  is  saying  is  that  rater   B1 would have to annotate slide number 27, A1  will  annotate  slide  96,  and  so  on. There  should  be  216  runs  in  here. That  looks  okay. The  last  part  here   under  Data  Table  Options, there  is  a  check mark   for  Include Run Order Column. I'm  going  to  click  yes   because  in  our case, for  annotation,  if  you  expect  there   to  be  some  type  of  time  effect in  whatever  process  that  you're  doing. In  our  case,  we  were  maybe   worried  about will  there  be  a  learning  curve, will  there  be  a  fatigue  effect? We  want  to  make  sure  that   not everyone is starting with a slide ID that's  like  the lower-numbered slide ID and  ending  with   the  higher- numbered  slide  IDs. I'm  just  going  to  click Make  Table. I'm  going  to  close  this  window  for  now. This  is  what  now  our  design  looks  like. Before  we  do  our  checks, I'm  just  going  to  create  a  new  column where  I  can  concatenate the  center  and  rater. I'm  just  highlighting   these  two  columns, right-click. Under New F ormula  Character, I  just  want  to  concatenate  them   with   a  comma. This  is  going  to  be our  Center, Rater  variable. There  you  go. Three  things  that  we're  checking  here. First  is  let's  do  tabulate. We  want  to  make  sure  that  each  person   has  exactly  24  images. That's  my  Center, R ater, and there's 24 here. That's  great. The  other  thing  that  we  want  to  check  for is  that  there  are  no  repeats. We  don't  want... For  example,  we  don't  want  slide  ID  1 being  assigned  to  the  same  person  twice because  that  would  not  be  fun   for  that  person. If  I  do  a  crosstab  of  slide  ID  by Center, Rater, I  should  see  just  a  column  of  1s. That  means  each  slide  ID  was  assigned   to  three  different  people. I'm  just  scrolling  through  here and   looking  at  this  table  to  see that  there  are  no   2s or 3s  in  here. That  looks  great. Then,  lastly,  I  do  want  to  check, how  does  the  case  mix  look  like across  these  nine  raters? One  way  of  thought  of   at  least checking that visually is  to  just  do  a  parallel  plot   using  Graph  Builder. I'm going to extend  this. I'm  going  to  highlight   features  1, 2, 3, and 4 because  those  are  my  original  variables. Then,  my  center  and  rater, I  am  dragging  it  to  here  on  my  x- axis. I'm  going  to  hit  this  parallel  plot  option right  here  on  the  top  right  icon. I  get  that. A ctually,  you  don't  want Center, Rater  in  there. I'm  just  going  to  turn  that  off. Maybe  what  I  want  is  Center, Rat er and  it's   own  panel. What  this  shows  us  is  we  have   the  assignments  from  the  nine  people, so  A1  all  the  way  to  C3. This  is  the  case  mix  of  the  images that  they  got  assigned  to. A gain,  we're  just  doing  this  visually. What  I'm  looking  for  is that  there are no... When  you  look  at  them   in total   that  they  all  look  about the same, that  they're  somewhat  blended   and  there's no clumping that's happening and  they  look  to  be  okay. Another  way  to  do  it  is  to, I  guess  you  can  overlay. It  gets  a  little  bit  hard  because   I  do  have  nine  different  colors  in  here. But  again,  you're  just  looking  for... You  don't  want  there  to  be  any  patterns  in  here. You  don't  want  there  to  be   clumps  of  green up here or down here or  wherever  in  this  space. You  could  also  check  it  by  center. I guess  my  colors  are  not  the  best. You  have  yellow,  blues,  and  greens. They  all  look  to  be  well- mixed. Then,  I  did  mention  about  the  run  order. What  it  is,  is  that   you  just  want to make sure that  when  you're  telling  people   how  to  do  their  annotation, you  want  to  make  sure  that   that  is  also  randomized so that if there is a time effect, that's  taken  into  account  already. What  I'm  plotting  here  is  the  slide  ID. A gain,  this  is  sequential. It goes from 1 to 100. Then  this  is  the  run  order, the  sequence  of  when  the  pathologist  would   rate  these  images. This  looks  random. We  could  also  plot  it  by  center  and  rater. This  is  for  each  individual  person. They  look  good. I'm just  going  to  close  that. Now,  I'll  just  go  back  to my PowerPoint slide and  just  end  with  some  conclusions  here. We  can  use  DOE  in  selecting   samples  or  test  cases where  you  have  prior information. If  you  have  data  or  covariates   that  you can use to inform the selection, why  not  use  them? A response  surface  model  is advantageous if  you're  interested in  the  boundary  or  edge  cases. Second  point  is  that  you  can  use augment  DOE replication if  you  have  a  situation  where  you  need  multiple raters per sample. The  job  or  the  design   already  gives you an opportunity to  factor  run  order  in  your  plan. That  could  be  really  useful   if  you expect there to be a time effect such  as  a  learning  curve  or  fatigue. I have  a  couple  of  links  here  to  a  blog that  talk  more  or  discuss  more  about  what is  a  covariate  in  design  of  experiments. Please  check  that  out  if  you  want to  learn  more  about  this  technique. Then,  lastly,  I  just  wanted  to  say thank  you  to  all  my  collaborators who  helped  make  this  project  possible. Thanks.
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Design Space Exploration employed for Warship Early-phase Design using the Concept of Negative Design (2023-KR-30MP-1566)
Sunday, November 12, 2023
Negative space in the discipline of art defines the space around and between the subject of an image. The use of negative space is an element of artistic composition, since it is occasionally used to artistic effect as the "real" subject of an image. In painting, it is a technique that negatively touches the background of an object to be expressed, so that it gives a feeling of unique texture and silhouette by touching unnecessary parts while leaving necessary parts. As in art, negative space in a design can also be useful to identify an image of infeasible design ranges with a straightforward view. Similarity between two disciplines leads to the introduction of the negative space concept for design space exploration. A rough design space exploration using statistics and visual analytics may support more efficient decision-making, and can provide meaningful insights into the direction of early-phase system design. For this, the approach guarantees dynamic interactions between visualized information and human cognitive systems. Visual analytics is useful to summarize complex and large-scale data. It is useful for identifying feasible design spaces, as well as for avoiding infeasible spaces or highly risky spaces. This presentation introduces the possible use of the negative space concept via an application example using JMP. 예술에서 네거티브 공간은 관심 있는 피사체 주위 공간과 여러 피사체 사이 공간으로 정의됩니다. 이것은 때때로 이미지의 "실질적" 주제로서 예술적 효과에 사용되기 때문에 표현하고자 하는 사물의 배경을 음각으로 터치하는 화법으로 필요한 부분은 남기고 불필요한 부분만을 터치하여 독특한 질감과 실루엣의 느낌을 주는 회화기법으로 말할 수 있습니다. 예술에서 이런 개념처럼 설계에서의 네거티브 공간은 기술적으로 실행하기 어려운 설계범위를 직관적으로 파악하는데 유용할 수 있습니다. 이런 두 분야의 유사성은 설계영역탐색에 네거티브 개념의 도입을 이끌었습니다. 통계분석과 시각화 분석을 도구로 하는 설계영역탐색은 더 효율적인 의사결정을 지원하고 초기단계 시스템 설계의 방향에 대한 의미 있는 통찰력을 제공할 수 있습니다. 복잡하고 많은 양의 데이터를 요약한 시각화된 정보는 인간의 인지시스템과 동적인 상호작용을 보장하기 때문입니다. 기술적으로 실행할 수 없거나 위험성이 높은 설계공간을 피할수 있을 뿐만 아니라 실행 가능한 설계공간을 정의하는 데 유용할 것입니다. 본 발표에서는 JMP 사용한 적용 예를 통해 네거티브 설계 개념 기반 설계영역탐색법의 활용 가능성을 소개하고자 합니다.
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JMP/Python 에코시스템 성공 사례 (2023-KR-30MP-1556)
Sunday, November 12, 2023
JMP/Python 에코시스템은 기본적으로 JMP의 기능과 Python의 기능을 상호 보완적으로 사용하는 에코시스템을 의미하며, 특히 머신러닝 관점에서 JMP Pro가 자체 보유하고 있는 머신러닝 기법과 더불어 Python이 보유하고 있는 scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch, Tensor Flow 등을 상호보완적으로 사용하여 머신러닝 예측 모형을 구축하고 최적화하는 JMP/Python 머신러닝 에코시스템을 우선적으로 구축해보았다. JMP/Python 에코시스템의 로드맵은 IGEO로 정의되며 IGEO 로드맵은 IG(Identify/Generate) 영역과 EO(Explore/Optimize) 영역으로 대별되며 IG 영역은 문제정의 영역과 실험/시뮬레이션 데이터를 생성하는 영역이며, JMP/Python 에코시스템의 코어인 EO 영역은 DEbO(Design Exploration based Optimization: 설계영역 탐색 기반 최적화)를 실행하는 영역이다. EO 영역에서는 MariaDB, JMP, Python을 통합하여 설계를 수행하게 되며 필요에 따라 Python/JMP GUI를 사용하여 GUI를 구축하여 자동화를 수행한다. 설계영역 탐색을 위해서는 실험계획법/전산실험계획법 생성, 머신러닝 모델 구축의 2가지 단계를 반복하게 되며 JMP와 Python 모듈들을 상황에 따라 결합하여 진행한다. JMP/Python 머신러닝 에코시스템의 성공사례로 Explore 영역 사례 및 볼베어링 고장 감지 모니터링 시스템 사례, CAE 머신러닝 최적화 사례를 정리해 보았으며, JMP와 Python의 결합으로 거의 모든 공학적인 분석을 수행할 수 있는 방향을 제시해보고자 한다.
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グラフビルダーとデータフィルタを駆使したドリルダウンで多変量解析の訴求力を向上する_細島 章(2023-JA-25MP-01)
Wednesday, November 15, 2023
グラフビルダーとデータフィルタは品質データを可視化して問題をクローズアップさせます。たとえば年度毎の不良台数を棒グラフで表示して不良内容や仕入れ先をグラフに重ね合わせます。不良内容と台数を年度毎にグラフ化すると改善点や悪化点の推移が明らかになります。年度と不良台数のグラフや不良内容と台数のグラフを基に、層別変数をグループX、グループYなどのゾーンや重な合わせゾーンにドロップします。特定セグメントを選択してドリルダウンするときに除外機能を使いますが、直接にテーブルを操作して除外すると記録が残らないため後で勘違いが起きます。私はデータフィルタを使ってスクリプトをデータテーブルに保存しながら行います。注目するセグメントのテーブルを調べる際はグラフをクリックして選択状態にしてからテーブル→サブセットでテーブルの詳細を確認します。モデルのあてはめやパティション分析の検証でもグラフビルダーを使います。あてはまりの状況を確認するときは、グラフビルダーを使って、主変数や温度をX軸にとって、他の変数をグループXなどにドロップします。これで非直線性が明らかになり交互作用も発見できます。
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日本における乳がん患者の経済的毒性に関連する要因： 患者と医師の観点からの比較_佐伯 澄人(2023-JA-25MP-04)
Wednesday, November 15, 2023
経済的毒性（FT）は、世界中の乳がん患者にとって注目すべき問題である。しかし、日本におけるFTの状況は十分に調査されていない。本研究では、日本の乳がん患者におけるFTについて検討し、班研究全体の知見の概要を発表したものである。 【方法】調査はQuestantアプリケーションを使用し、主に研究施設に通う乳がん患者と日本乳癌学会会員の医師を対象とした。患者のFTを定量化するために、omprehensive Score for FT（COST）の日本語版を用いた。アンケート項目は主成分解析を行い整理し、重回帰分析を用いて、日本の乳がん患者におけるFTに関連する因子を特定し、医療費に関する情報支援レベル（ISL）の充実度を評価した。すべての解析はJMP® 17.0.0（SAS Institute Inc.; Cary; NC; USA）を使用して行った。 【結果】患者からは1,558件、医師からは825件の回答を得た。FTに影響を与える要因としては、最近の支払いが最も影響が大きく、次いで病期（ステージ）、関連診療科がFTにプラスの影響を与えた。逆に、収入、年齢、家族支援などの因子は、FTにマイナスの影響を与えることがわかった。患者と医師の間には、情報サポートの認識において有意な相違が確認され、患者はしばしばサポートされていないと感じ、医師は十分なサポートを提供したと考えていた。さらに、医療費に関する説明の頻度や質問する機会についても、FTの毒性グレードによって違いが見られた。また、情報支援の必要性をよりよく理解し、医療費に関する知識が豊富な医師ほど、包括的な支援をより多く提供する傾向があることが示された。 【結論】本研究は、日本の乳がん患者におけるFTへの対応の重要性を強調し、経済的負担を軽減し、個々のニーズに合わせた個別化支援を提供するために、情報支援の充実、医師の理解、専門家間の協力の必要性を明らかにした。 【キーワード】経済的負担、乳腺腫瘍、医療サービスの利用しやすさ、多変量解析、日本乳癌学会班研究
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機械工作実習の安全教育がもたらす危険感受性向上効果の統計的検証_黒澤.拓未(2023-JA-25MP-07)
Wednesday, November 15, 2023
本発表では、高等専門学校の機械工作実習で実施した安全教育による効果の統計的検証をＪＭＰで行った内容について紹介する。様々な場所で安全教育は行われているが、教育による作業者の安全意識向上効果を統計的に検証した事例はあまり見られない。今回はそれを統計的な検証を行うことで、作業者が事故や怪我をせずに安全に作業ができるような安全教育の重要性を再認識することになると考える。安全教育の内容は、ＫＹＴ(危険予知トレーニング)テスト→座学教育→危険体感実習→ＫＹＴテスト、アンケート調査、統計解析という流れである。安全教育前後でＫＹＴテストの正解率の差異を二変量の関係で有意差の有無を検証する。また、個人の資質や性格等に関するアンケート結果がテスト結果に及ぼす影響を検証する。さらに、安全教育後の感想をテキストマイニングで可視化する。
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Pharmacovigilance 分野での JMP の活用事例の紹介_本田 主税-小柳 将(2023-JA-25MP-08)
Wednesday, November 15, 2023
Pharmacovigilance（PV）とは「医薬品の有害な作用または医薬品に関連するその他の問題の検出・評価・理解・予防に関する科学と活動」 であり、医薬品企業の重要業務である。製造販売後調査（PMS）は医薬品の特性を踏まえたPV活動の一種で、実施計画のもとに行われる。 PMSでは医師が調査票に診療情報を記載し、調査専用のデータベースに集積していく。それらの情報は、データマネジメント（DM）工程にてコーディング、論理チェック、再調査等を経て、解析部門で集計される。DMから解析に至る工程は多段階であり、想定外のデータが入ってきた場合、原因究明と対応に多くの時間を費やす。また、SASプログラムを用いて帳票化しなければ結果の確認が困難であった。そこでJMPの活用を開始した。JMPのインタラクティブな可視化・解析機能により、非解析部門の担当者とのデータモニタリング（イレギュラーの発見）や要因検討を簡便に行い業務の円滑化に寄与した。現在、PMSデータも含めた安全性情報全般を集積した副作用情報管理データベースのデータ品質向上を目的とし、ワークフロービルダーを用いた前処理自動化も試みている。本発表ではこれらの一端を紹介し、JMP活用の展望を述べたい。
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トヨタ自動車九州における粘土製造演習を教材とした、モノづくりとデータサイエンスと問題解決の習得_則尾 新一(2023-JA-25MP-12)
Wednesday, November 15, 2023
新入社員向け研修の中で、評価が最も低かった「SQC入門」に対して、「粘土製造演習」という模擬モノづくりを通じて、座学で学んだ内容を実践することで、多くのことを習得できるように設計された事例を紹介します。 1. モノづくり（QMS的観点、プロセス） 2. モノづくりに必要な組織と役割（人） 3. モノづくりの要素（5ME） 4. モノづくりの指標（KPI） 5. ばらつきと可視化（SQC） 6. トヨタの仕事の仕方（問題解決8Step思考） 7. チームワーク 8. 発表 受講生の声 - 「実務に近いモノづくりの流れを体験でき、本業務で自分が果たすべき役割の理解につながりました。」 - 「全員で役割を決め、それぞれが検討しながら最終的な製品を完成させ、問題点を洗い出すことができ、実際の業務でもSQCを活用できる有意義な講義だと感じました。」 - 「モノづくりの流れを原価などの費用面で考えるのは興味深かったです。」 - 「同期と議論や相談、協力しながら作業でき、非常に理解が深まりました。」 - 「各グループの発表に対するフィードバックを聞くことで客観的な目線で学べました。」 また、この演習を聴講した先生が小学生向けにカスタマイズした例もあり「モノづくりはヒトづくり」の参考になる内容です。
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PDCAサイクルに基づくコイン射撃を用いた能動型データサイエンス教育_小川 昭(2023-JA-PO-03)
Wednesday, November 15, 2023
近年は多種多量のデータの合理的処理の必要性が高まりデータサイエンスがその重要性を増している．この教育には座学により知識や分析方法を学ぶ受動型と，実技演習により問題解決や課題達成を学ぶ能動型の二つのタイプがある．これまでは主に受動型教育が推進され，すでに充実した成果が得られている．しかしながら能動型教育はこれからの課題とされそのためのアプローチの開発が強く要請されている． 実務の場では問題解決や課題達成に関して長年に亘り計画（Plan），実施（Do），検討（Check），処置(Act)のPDCAサイクルのアプローチが用いられて多くの成果をあげてきた．その本質は事実をデータという形で取集した上で分析し，得た結論に基づいて仮説を発想し，それを実行した上で結果をデータとして収集し仮説を検証するもので，これは取りも直さず能動型データサイエンスそのものである． 著者らは長年に亘りコイン射撃教材とPDCAサイクルの両者を用いた教育プログラムを開発するとともにそれを講義・講習会等で実施し，その内容を各種の場(論文，学会発表，講演等)で多数回の発表を行ってきた．本発表ではこれらを体系的に整理し能動型のデータサイエンス教育について報告を行う．
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済生会病院における医師の働き方に関する実態調査データの分析_佐村 紫帆(2023-JA-PO-05)
Wednesday, November 15, 2023
本研究は、医師の働き方を適正化するための2024年4月に施行される医師の働き方改革に向けて実施された調査データを分析したものである。本研究の調査対象病院は、日本の社会福祉法人としては最大の公的医療機関を運営する恩賜財団済生会（許可病床数22;191床）の81病院とした。そのうち76病院・1468名の常勤医師から無記名自己記入式質問紙調査で回答を収集した（回答率35%）。公開されている病床機能報告における病床機能区分やアンケート回答医師の分布に留意し、Classification and regression tree (CART)による回帰木を作成した。分析の結果として所属病院、医師の疲労の程度、休日の疲労回復の程度が最も職務満足度に影響を与えており、勤務時間や兼業病院の有無、当直やオンコールの回数など自己申告で把握可能な指標は直接的にはあまり大きな影響を与えていないことがわかった。医師の健康状態が保たれ、職務満足度を向上するとともに、質の高い医療を永続的な形で提供できるように労務管理を検討する必要があると結論づけた。
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Discovery Summit Japan 2023 - Handsout
Wednesday, November 15, 2023
※注意：このサイトが英語や日本語以外で表示されている方は、画面右上の言語設定で「日本語」、または、「私の設定」＞「Preference」から「日本語」を選んでください。   本サイトは、Discovery Summit Japan 2023の配布資料を公開しております。 サイト上部にあります各ファイルをクリックすると資料がポップアップされます。 矢印↓マークをクリックするとダウンロードできます。   全ての資料をダウンロードしたい場合は、「DSJ2023-handsout-all.zip」をダウンロードし、解凍してご利用ください。 尚、本年は配布資料を会場では配りませんので、PCや携帯電話を使用して表示するか、ご自身で印刷をお願いいたします。   【公開している配布資料のファイル名とセッション情報】※資料のないセッションもあります。 A-1_HOSOJIMA：グラフビルダーとデータフィルタを駆使したドリルダウンで多変量解析の訴求力を向上する（東林コンサルティング　細島 章） A-2_TAKYU：JMPからMYSQLサーバーへの検索スクリプト生成の効率化について（国士舘大学　田久 浩志） A-3_KAWAGUCHI：JMP/JSLでアプリケーション作成を行う上でのTips（日本ゴア合同会社　河口 雅彦） A-4_KUROSAWA：機械工作実習の安全教育がもたらす危険感受性向上効果の統計的検証（筑波大学　黒澤 拓未） A-6_NORIO：トヨタ自動車九州における粘土製造演習を教材とした、モノづくりとデータサイエンスと問題解決の習得（トヨタ自動車九州株式会社 則尾 新一） A-7_OTA：研究開発部門におけるJMPを活用した統計・データ解析普及への取り組み（株式会社レゾナック　太田 浩司 他） B-1_Y-TAKAHASHI：共変量を含む多因子実験データでの交互作用を考慮した探索的な変数選択（BioStat研究所株式会社　高橋 行雄） B-2_SAEKI：日本における乳がん患者の経済的毒性に関連する要因： 患者と医師の観点からの比較（がん研究会 有明病院　佐伯 澄人） B-3_ODAI：官能評価とJMPの活用（キリンホールディングス株式会社　小田井 英陽 他） B-4_HONDA：Pharmacovigilance 分野での JMP の活用事例の紹介（小野薬品工業株式会社　本田主税／イーピーエス株式会社　小柳 将） B-5_OGASAWARA：明日から使える！JMPテクニカルサポートへのお問い合わせ事例から見るJMPのTipsのご紹介（JMP 小笠原 澤)
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Mastering the Art of Scripting Seamless JMP-Like User Interfaces with Structured JSL
Thursday, March 7, 2024
Ballroom Ped 7
I create a lot of user-interfaces using JSL, some simple and some much more complicated, but most do follow similar principles: the scripts use similar templates, they have similar layout on the launcher, reports look similar, etc. In this presentation, I show how I use JSL to script JMP-like user interfaces, which type of templates I tend to use, and the decisions I make while developing such platforms. I also demonstrate different display boxes, how to create a user interface with JSL, script templates, the decision process I use when scripting user interfaces, and some tips and best practices for JMP scripting.   Welcome to my poster presentation regarding building user interfaces using structured JSL. A little bit background. I'm working for more of electronics in Finland and data integration and data science specialist in data integration team. One of my jobs is to create custom JMP applications, and how I create them, or rather how I structure my content in those, is topic for this poster. This was originally supposed to be a paper, so I had to go through it very quickly, but it should be fine. Let's get started here. I think most important thing in this poster is that you should try to mimic JMP platforms. A reason for this is that users are all very familiar with them. If you can organize your content in similar manner as JMP does, it's much faster for them to start using your tools. Here, we have a one example of JMP platform. In this case, it's a distribution as it's very simple one. On the left side, we have the launcher and the right side of the report. On the launcher and also in the report, we had icon of window title, JMP organizes content in panel boxes. On the left side, you generate column selectors in filter column selector. Below that, sometimes you have these extra options. On the middle, you have these buttons. Next two buttons, you have column list boxes, where user can collect the columns they want to analyze or perform actions with. This out-of-box name also tells you what these columns are used for. On the right side, you have the action buttons. In the reports, the structure is commonly very vertical. In this case, I have statutes as it's easier to see my opinion. Content is in a parent-child structure, a hierarchy structure, and it has been built common with outline boxes with these red triangles. We're trying this type of structure because users can launch architecture and analyzes from this red triangle things here. You have some organization with the content, craft boxes, table boxes, and so on. But this type of structures, users are familiar with,JMP users. When you are thinking about your tool and your users, think how they will use your tool. For this, you can use storyboards, for example. It doesn't have to look like this diagram here. You can have it in a comic type format if you want. We also has this type of view first, then they click something and next view and so on. When you are building this, it's helpful to think how JMP platform would work because then back to the familiarity. I won't go through this. This is just an example how it could be done. Few mockup examples. Quick demonstration or first initial design you could have for your interface. Here, I have very simple paper version. I like to start with this. They're very quick to modify, update if needed, and then you can recreate one, if there's something wrong with this. I usually quickly go to some JMP person. This is very simplistic one, but I can try it out. This works, and this doesn't, and modify, if needed. I can maybe use my template to make a bit more cleaner one to see how it would look in reality. Next mockup. Similar area, start with paper, then I have used a separate tool. In this case, this is called Excalidraw. Make a mockup. I could have additional notes here and so on and a bit more advanced JMP prototype, demonstration. This is already from the prototype application, but you could do this in part. Have data filter, then it has some graph builder. This can be totally separate. It has some images from somewhere. If you have just configured this layout, take those images, move them to separate tooling. I will use most likely PowerPoint. It's easy to move them around there. You can structure it there, and then get a better overview what the user interface could look like. Next, consider having templates and don't forget about user experience. I do have my own templates as scripting style. Why I have these and why I use them is to create apps faster. These are more robust, and they tend to look more professional. One additional note here is I do not design my user interfaces for any larger resolution or rather any smaller than full HD as most users are still using that for larger resolutions. The user experience. As JMP is very interactive tool, I think it's also important to consider user experience. Not just that interface you have. I have a few examples of bad user experience here. One is model windows. For example, for platform LAN sharing, it's a model window, user cannot go back to the data table and observe which columns they should use. They will first have to close your launcher, close the table, then rerun your launcher again. That's, in my view, a bad experience. Another one is the windows tend to resize if you have collapsible outline boxes and so on. I try to have static window sizes after the window has been initially created, and then a scroll box is to move around. Last thing here is that they do not hide toolbars and menus from user, because in JMP, you can launch separate platforms. You can have a platform open. Go to your toolbar menu and launch the next platform, which that makes doing analysis much faster. Comparing my platforms and JMP platforms. On top side, we have JMP platforms. In this case, it's a narrow platform and on bottom line. Quite similar looking here. Left side column selectors and then common listings where user adds them, actions on right side. I think the biggest difference here from the launcher, in this case, is that I have this table name, which is currently being analyzed. In maneuver platforms, I might have the application name also visible, not in this case. But there I have bigger differences than our reports. I don't have out my boxes, because I already have basically all the options defined for the user. They don't need to access the red triangle menus. In JMP platforms, you want access those. I can use that page boxes and sheet boxes. I think even the bigger thing is that I can… I do organize my content in horizontal because we have wider screens, and they are tall. Quite a few JMP platforms tend to go very long. I like to make wider because they are small, more estate that way. Also, here, the table is also visible here and application name. One example application I have here. We're going to have two examples, but I've gone through a little bit. This is basically that script user would run to run my application. I have separated different parts of the script with all, then own responsibilities. Configuration, utilities, and user interface are in their own script for us easier to manage in this way in my opinion. Then the script. This main script has a initial text if their table open, if the window is already open, and if everything, all of these paths, then the application is running. Depending a little bit on the check, I might throw a model window error to user. For example, if the window is already open, it's not a modal window, I will just bring the application to front, so a user can continue working with that. Here below is basically the main window after the application has been run. This creates the application window. These have been also separated here into separate expressions. Much easier to manage smaller expressions outside this new window than having very long, it could be hundreds of lines of new window. Difficult to manage usually. These are easier to handle. Sometimes you might have to do some evaluation and many abilities, but generally, I think this is better. You can find this example from community and a few extra examples from presentation I did in a Finnish user group in 2023. Some quick summary for this poster. Do mimic JMP platform launches and reports as users are already familiar with them. It's faster for them to get used to your tool. Consider how your user will use your application. Build storyboards, and create mockups to quickly to see how it could look like. Add some template. You can create robust and professional looping application. Get familiar with the most common display boxes and how to organize content in JSL. As JMP is very interactive tool, do not forget about the user experience because JMP interactive is very important. Consider user experience and that user interface. Thank you.
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