Welcome everybody to Pictures from the Gallery 7.
So my name is Scott Wise.
I'm a Senior Systems Engineer in the US West Coast
and I'm joined with my daughter Samantha.
So Sammy, I wanted to ask you, as a 16 year old
growing up during a pandemic and still going to school
and trying to find your path in the world,
what are you most concerned about with the future?
to start, I'm pretty worried about how we're affecting the environment,
like deforestation, soil depletion, climate change.
Additionally, I'm kind of worried about
sexism in the workplace, and gender gap wages,
and things like that.
Okay, well, that's a lot to think about.
So you got me thinking
as well about what we can all do to help make this a better place.
So I thought I'd dedicate my presentation
to also emphasizing what we can do to save the planet.
You used to say to me, "Be curious...", right?
"and do something about it", right?
So we can use our curiosity, our time, and our skills.
So I'm going to challenge everybody that's seeing this video
and myself as well, that we share meaningful data.
So a good place to do that is with the Data for Green J MP website.
It's a good place to see
what kind of sources of data are out there
for us to understand things about our environment,
as well as to share any meaningful data that we have
or any meaningful graphs or reports that we're able to generate.
So please do check that out.
I'll leave the link in my Journal for you, as well as use your time.
So here's a cool use of your spare time.
Instead of maybe playing your cell phone games
is you could go out to this II ASA website
and on this website, they give you images of the rainforest.
They take it from the satellite
and they ask your help
in identifying where there's roads and structures,
but also where there's untouched portions of the rainforest.
And this feeds their artificial intelligence models
to help them do a better estimate
of the rate of deforestation in the rainforest.
So use your time and then definitely use your skills.
And if you're here, you've picked up some good JMP skills
to analyze, graph, and explore your data
and get inspired from our friends like at WildTrack,
where Sky and Zoe are doing a great job.
Not only helping protect our endangered species
with their non- invasive wildlife tracking methods,
but also using analytics, using JMP
in very novel and creative ways.
So definitely read up on the stories to see how we can do better
and we can inspire ourselves to also use our skills to do better.
So thank you, Sammy, for all that help.
And I'm going to continue on and to show the pictures from the Gallery.
And instead of just showing it on fun data,
I'm going to show our advanced pictures
on data that reflects
some data for good, Data for Green topics.
So what we got keyed up,
number one is this equality.
So some equality data on the gender wage gap,
which is we're going to do the interval charts,
also called a dumbbell chart.
That was rated number one in terms of selections this year.
Number two would be this pandemic data,
which we are looking at how teachers are affected by teaching in the pandemic.
And we're using a word cloud.
B et you didn't know you could do word clouds within Graph Build.
Third one is going to be looking at tree cover loss
over our planet
from this new feature, which is a smoother line which uses moving averages.
Last is going to be some safety data,
or next to last, which is the points cumulative sum chart.
And so this is a new way of quickly looking at cumulative figures
within your graph, even when you're just looking at the points element.
So another thing you can do in Graph Builder is now do some, I think,
better looking and more advanced box plots.
And so we're going to do that on climate change
and looking at some projections for city risk
going out to 2050.
And then if we have time,
I will show you a little bit about some things we can do
with summarizing vector data,
such as wind direction and speed, which comes into play a lot
when we got a lot of adverse weather due to climate change.
And a wind rose chart is what we're going to feature there.
So these are the pictures from the Gallery I will go through each of these
individually as time allows here.
just so you know, this is a Journal I'm giving to you.
It is already out there on the link.
You can download it.
You will have full pictures of the graph.
You will have tips and tricks.
You will have, as well, full steps on how to create that graph.
And I will leave you with the data.
And you will not only have the data, but you will have scripts in the data
to regenerate the graphs so you can build your own
and you can compare and see if your graphs look like the ones that we created.
All right, so let's get going.
So our first chart here is gender wage gap,
and it's a dumbbell chart.
And it's because we have these interval charts.
And with these interval charts,
you might see that we kind of have large points on the end of the interval.
So you're trying to make a comparison between two points,
and you can see the distance in between them as a bar.
And some people thought these looked a lot like weight lifting.
You go to the gym and you're lifting some free weights
and you're going to pick up a barbell or a dumbbell,
and it's going to have weights on the end with the bar in the middle that you grasp.
That's kind of what they think it looks like
some people also call this an interval chart.
So you can see it's easy to see that males make more
than females in France.
And there seems to be a large wage gap
that doesn't seem to be closing as you go through the years.
So let's see how to create this.
So I'm going to go back to the data table we just opened up from our Journal.
And the secret to making this is you want to have
two columns of information to compare.
You want them to be over the same scale.
And so female and male monthly nominal
salaries is what we're comparing here.
They normalize all the countries' currencies in the US dollars.
And so I should be able to use those in our graph.
So I'm going to open up my Graph Builder.
Start with the blank Graph Builder.
I'm going to take female monthly and male monthly
and put them both on the X axis.
I'm going to put year on the Y axis.
Now I'm going to copy both female and male monthly,
and I'm going to move those into the interval.
Now it's actually doing the job, but it's hard to see
because we have so many countries represented for each year
and their intervals are all on top of each other.
So let's go under the red triangle where it says Graph Builder.
And let's go ahead and do a local data filter.
And let's go ahead and just look at a certain country
and we are going to look at France.
Now I've got France here and it's starting to look like my interval chart.
I'll say done here in the Graph Builder to close my little control panels.
Most of the things I'm going to have to change here
are going to be directly on the graph, such as
I'd like to get the ends of these intervals,
the points to be bigger.
And so to do that, I'm just going to click right on the legend,
right click, you go marker size.
I'm going to go other,
I'm going to make it a big ten.
So you see how much bigger it looks now.
I'm going to do the same thing with male monthly with marker size
I'll go other and I'll go 10.
Now, what about the bar?
I'm going to right click, I'm going to go customize,
and it's the second error bar you're going to see
is the one that's going to show up on top here.
And the line style is fine with me.
I'm just going to make it gray
and I'm going to increase the line width to a two or three.
I'll do this one on a two.
And now I get the view that I like.
Now, a couple of things I'm going to do in the X axis.
I'm going to right click on the axis settings,
and I'm going to put a pre average salary of $3,500
I'll say okay.
And now I have that line driven.
So I can kind of see if things are increasing or decreasing over the years.
But it looks like my years are going and kind of from bottom up.
I like them to go from top down.
So I'm going to right click under axis settings.
And I'm just going to reverse the order on the scale.
And you can see now I'm going for 2010 down to 2019.
I click on that.
And now I can truly judge what's going on.
Now, one other thing I thought was pretty cool.
If I switched the colors here,
if I switch them, if I make the female the red
and I make the male the blue,
then I can bring in a picture.
And I've got this kind of great picture
you can see a small little icon of it
that I think will look cool in the background so I click on it.
You can't really see what's going on.
It's there. All I have to do is right click, go to images,
size and scale, build graph.
And there it is.
I'm going to right click again and go back to images.
And I'm going to go transparency,
and I'm going to make that a point three.
And now you can see it's kind of more muted in the background.
So you don't want to bring in background pictures if you're going to
create a complicated chart
or you're going to switch between a lot of different maybe filter options
or have many different panes open
because it can get tedious to have to keep resizing the picture.
But it's great for stand alone chart.
So here we go.
We got our stand alone chart.
I am going to close this one down and show you
we have them available to you
directly as a link in your data table.
And the one I was looking at was without the picture.
But I did a couple side by side.
And this kind of let me look at France versus Germany versus Sweden.
And I can see in France, the gap is not as necessary,
as bad as I thought it was, there's a bigger wage gap in Germany,
but in Germany, everybody seems to be making more money.
But if you go to Sweden, it's the females that make more than the males.
And so I thought that was very interesting.
So here's some good information and data to play with.
This data came from the International Labor Organization.
And I will put all the links into the data table
so you can find them where they are housed publicly.
All right, that was our most popular view.
Let's take a look at our second most popular view.
That would be how do you get a word cloud out of Graph Builder
a lot of people asked about this.
And all you have to do is we're going to have to create some ordering columns
that's going to help us figure out, let JMP figure out how to display
my words within a cloud- like shape.
So I'll show you what's going on.
So I'll open up this educator for top five COVID words.
So they went out...
An education article went out in the state of Kentucky
and they just said,
"What are the top five words you think about when you hear COVID?"
to school teachers, right?
School teachers have been by far
adversely affected by the pandemic, having to try to teach the schools
as they close, as they reopen, as they do it with mixed media
you have to do some classes live, some classes on site, mixed classrooms.
So it's been very difficult.
And you can see some of the words that have popped out
get captured here in this column
and you can see how many respondents
said those words in their top five.
Like 20 of them basically said...
The word anxious showed up to 20 of the respondees.
As a top five word associated with the pandemic.
So I have that information.
So I created two columns.
The first column is a random column
and it's just random normal information being assigned to the rows.
And I got it just from creating a column, right clicking, going into the column info
initializing some random data versus initialized data.
You can do random.
You can pick a random normal.
I figure if you have a mound shape, right,
a bell shape distribution, it's kind of like a cloud.
Most of the stuff is going to be in the middle and less is going to be out
toward the end toward the tails.
So I did that and that's how I came up with this random data.
I'll go ahead and delete that one.
For the order column, it was pretty easy.
I just sorted by weight and you can see so anxious was 20, then 17, then 17.
So basically the order is just a row sorted order.
So anxious is number one because it was biggest
and that's going to help me later make the word cloud.
So let's go ahead, click on our...
Let's go to...
Before we do anything with the word,
let's actually go to the random.
And let's put random on the Y and it's doing what we expected.
It's distributing the rows out randomly on the Y axis,
just kind of separating them out.
What if we swapped those points for the word itself?
So if you go to points under the red triangle
and you click on it,
let's go ahead and select set shape column.
When I click on that one,
I'll go ahead and click on the words
and there's the words.
I'm going to take the weight of the words and I'm going to size it.
Oh that's starting to look like a word cloud, right?
Now I'm going to take that weight again
and maybe give it some sort of scale coloring there.
So I guess the more red,
the more frequency we had in that word, which is kind of cool.
Now what's going on is it's doing a jittering
and it's doing centering grid jittering.
That's actually the automatic default is the center grid so that looks pretty good
and look when you're done as you move it around, it will...
it will try to adjust the words within that shape
and try to hold that type of scale.
That's really easy to do.
I'm going to right click under the red triangle for Graph Builder
and under legend position I'm going to go inside bottom right.
It may be inside bottom left. I think I like that one better.
I'll put it right to the inside bottom left
and I'm going to go to legend settings
and I'm just going to keep the bit that talks about the color
so that's pretty cool and that's how you would do the random word cloud.
How I would do the ordered one
is I'll go back to the red triangle under the Graph Builder
I'll say show control panel.
I will swap out random for order.
Now all the big ones are on the bottom and the smaller ones are at the top.
That makes sense.
Maybe I'll right click on this axis setting.
You saw me do this earlier.
Go back and reverse the order
so it's going from 0-30,
with the ones and the twos and the threes being more on the top.
There we go. That's exactly what I wanted to see
and now I have an ordered one.
Now with ordered low, a lot of times it might make sense.
Here if I really wanted to have it in order ed order,
I need to use a certain jittering
that's going to kind of justify the ordering.
So I'm going to show back the control panel.
I'm going to go to the jitter style instead of center grid, I do positive grid
and now it really is ordered, going from left to the right,
top to bottom
with the number one thing, then the number two,
then the number three
and then the next line, the number four, and so on.
And sometimes people prefer this kind of word cloud, because...
it makes it easier to judge
which one's bigger than the other if the size and the color are similar.
Now you can make a judgment.
Now it's looking good
from what I want it to do, but it's not looking good on the graph.
What are we going to do about this one?
So I really would like to move this whole thing over to the right.
So to do that one, I'll take my control panel back open.
I'll go under this X axis. And even though there's nothing there,
I'll right click.
I'll say access settings,
and I will do a negative point three for the minimum.
And now it's moved over.
And now this looks a little more like an ordered word cloud.
And of course, you can bring in pictures as well.
And I did that on this one.
I brought in a nice apple
with a little picture with a little bit of transparency on it
and make the words pop out on top of it.
All right, so instructions are there if you want to try this one.
But I can see a lot of people having,
again, a bunch of words, a bunch of categories, a bunch of phrases.
And if you got a weight on them, got to count on them,
you can make a work cloud.
So let's see here where we are.
We are ready to go to the moving average smoother chart.
And this one's pretty cool.
It's a new smoothing line option that lets you kind of look for trends.
And a popular way to look for trends is a moving average.
When there's a lot of noise in...
the things that you're plotting over time, then sometimes you want to smooth it.
You see that blue line here is smoothed
in between the ups and downs of those points
that are being collected over the years.
The other thing it's doing here, as you can see
I don't have a legend. I've actually labeled the lines.
I'm going to show you how you can do that well as well.
All right, so here I'm going to click on this tree loss.
What we're looking at here is reasons for loss of tree cover or deforestations.
This came from the Global Forest Watch, and they have Tree Loss in hectares.
So we'll go ahead and show that one.
Here's tree loss in hectares and I'm going to throw my year down here.
And I've got both points and smoothers automatically showing.
So all I really do is just got to take the drivers and overlay by the drivers.
That's pretty cool.
Now I've got just the s pline method
and I'm not as big on that one format showing a trend.
So if I click on the options,
here's where you have more options in JMP 16
and I can do the moving average.
I'm going to do the moving average.
I'm going to move back this little local width toggle
so it fills out the graph.
And I'm even going to include this confidence fit.
So this is looking pretty good.
So I'm going to say done here.
Now I'm going to go under the red hotspot for Graph Builder.
I'm going to go to the local data filter.
I'm going to add drivers and I'm just going to look at the first three drivers.
Okay, so these are looking pretty good.
Now I've got this legend way over here and it's not really adding to the graph.
I would like to do something better.
Well, I'm going to right click right on the legend line
for in this case the agricultural shift.
And I'm going to go to label, and this is brand new in 16.
See, I can do min maximum values, first values.
But I can also do a name. Look where it puts it.
It looks it to the right in the graph.
I'm going to do the same thing with each of them.
Each of the ones I have open.
Now I'm going to go under the red triangle for the Graph Builder
and go to show and turn off the legend. I don't need it anymore
and I don't want them on the side.
Look what happens when I start to move it back.
You can see I can move it into the body of the graph.
And I'm going to stretch this out a little bit on our screen
and you can see agricultural shift.
If you put it close to the line, it tries to hug...
the slope of the line which is really cool.
I'm going to put agricultural shift there. Maybe I'll put commodity driven
just over here and maybe forest driven
right in this area here.
And now you don't need the legend.
Now you can just see what's going on with the light
and kind of let people's eyes
go where they have interest in the agricultural shift.
Here was the real story,
how this was more of an assignable cause of loss of tree cover,
but it has gone down a little bit here in recent times.
All right, so pretty cool graph as well.
So smooth line moving average.
Points cumulative summary chart.
So this was cool data.
This was actually data on driving safety.
And kind of the idea was if we could point out
over the years times when there's been major releases in car safety,
are we getting safer?
As more vehicles are on the road, we expect there to be some more crashes
just from the volume increasing.
But are we actually having less injuries
as our cars helping us with airbags and anti lock brakes and these things?
And we did this with a points chart.
And we really benefited from using
cumulative sum options that are new in JMP 16 right off the points chart.
You can always create a cumulative sum column very easily in JMP.
But we think this is better.
I'll show you why.
So if I go to this motor vehicle safety
and this came from the US Bureau of Transportation Statistics,
crash rates and I have injury rates.
And the rates basically have taken
the total divided by the vehicle- miles in millions.
So you kind of get an index.
So I'm going to go open up Graph Builder.
I'm going to take crash rate and injury rate
and put it on the Y axis.
I'm going to put year on the X axis.
I'm going to pull off the smoother line and just leave the points.
And you can see why this is not so great,
because what's going on right now is I see a trend.
I see a trend of crash rates kind of going down over time, which is good news.
But is it different than the slope of the rate of change going on by injury rate?
Scale can make it hard for us to make that judgment.
And you're just trying to compare patterns here
and they're not next to each other.
They're ones above the other one.
It's kind of hard to see.
So with one selection here under summary statistic,
there's a new cumulative sum.
Now tell me which one you think has a steeper slope.
And the crash rate is going up at a pretty good cumulative growth rate.
But you can see the injury rate is kind of leveling out a little bit.
It is going up, but not as steeply.
So I could summarize that the vances are definitely...
We're still getting into a lot of crashes
because there's more cars in the road. But it seems like we are...
saving ourselves some injuries here and that's a good thing.
And so the fun thing I was able to do, you can see back in the data,
I was able to put in some...
So like in 1996, we had side impact testing.
We had dual front airbags in 1998.
So we can go in and go to our a xis setting.
And this is a good place for a reference line,
1998, we'll put a dashed line,
maybe a dash gray line, and we will say air bags.
We'll add those and there you go.
And now we can see where the air bags came in and what the performance was.
Now the other thing we can do is instead of using points,
we can go to the red triangle where the points are.
And we can use the shape column here as well
for something that's not even categorical, for something that's continuous, like year
and it will still bring in those values.
I'll say done.
I will make these a little bigger with marker size.
make them pop out a little bit more on our screen
and make that a little bigger, maybe even tuck in on the legend position.
Put this on the inside left, and by the way, you see where these
highlighted areas are on my screen,
you can put them in the corners
or you can put them again out on the side of the graph.
Just drag them there. But there we go.
And now we can kind of see how these early advancements
have really done the job to protect us,
so even though there's more of us and there's more traffic
and there's more cars
and there's going to be more accidents, just hopefully less serious ones.
Very cool. All right.
So hopefully you're enjoying these like I am.
Remember, I'm giving you this Journal so you can go and recreate them at any time.
We will show you the fifth most popular chart,
which is the advanced box plot.
Now, on this chart,
this is using some JMP 16 new features that allow us to really help
the Graph Builder graphics
to give us a lot of ways to visualize the box plots.
And also, you're going to see that
this is a good way we can do some interactive labeling as well.
So I take this data that I found from Nestpick.
It's climate change city index.
And it was quite interesting.
They came up with a total climate change risk
and it went from 1-100.
And they base that off of climate shift,
of temperature shift, and potential sea rise, and...
...water stress, and that was the one I really hadn't thought of,
like, will there be water around your town in 30 years?
So in 2050, it said that these were going to be the cities based on total risk
that were the most going to have the most problems.
So Bangkok was number one, Marrakesh was number ten going on down.
So I turned on the labels for these rows
so it will pop up in our graph.
So let's take a look at it.
So I'm going to go Graph Builder.
I'm going to take the total climate change risk on the bottom.
You can see there's already starting to be some things getting labeled
from having asked for labels in my data on those rows .
I'm going to look at it by region.
So I got three areas here.
It's looking at it by region.
And now I can say, well, let's make this a different color.
Let's make this red.
I want to right click there, right on the points on the Legends
and make it a little bigger here.
Now the points are standing out a little bit.
Now let's hold our shift key down and let's add in a box plot.
Now this is a typical box plot. It's not that interesting.
You might notice Amsterdam showing up twice and Bangkok is.
That's because the box plot is labeling the outliers.
Well, the points are already represented so we can turn that outlier box off.
I don't like this view low.
I've always kind of liked the saw with views,
but I've got points showing up well with this open box plot.
But if I change the box type, not the box type.
Actually, the box style, to solid, all of a sudden it's gray,
it covers it up, and I don't see the fences of the whiskers
kind of where my normal spread of point should be, I lose that.
Well, not exactly. If I go to box plots now
and I just click on the red triangle, I can do notched and I can do fences.
So the notch actually kind of notches the figure right where the median is.
My boss likes to call this a torpedo.
Looks like a torpedo.
And then I can add the fences back to see
where the ends of my whiskers are from my box plot, which really does help.
Now I'm going to say done.
Now you're going to say, "Well, Scott, it's still covering up the points."
When I make this really big on our screen,
all you have to do is right click on the graph,
go to points and say, you know what, move that forward.
And there you go.
And now I can see where all...
my top ten at risk cities fall on this list.
And it was interesting.
I heard some surprises that I didn't expect.
Like, I thought that New Orleans here in the US,
because it's so low and so prone to floodings,
especially from natural disasters like Hurricanes,
it would come out higher.
2050 maybe Boston is not a great place to be.
So that was really interesting to me.
Fun data to play with.
And this is some fun things to do with points and box plots.
All right, so we are nearly out of time.
I will just quickly show you the last one is this wind rose chart.
And the wind rose chart is a way of looking at vector information.
In this case, you see all these little arrows here.
Those are all measured wind speeds
and directions that came out of looking at the Great Lakes area around Chicago.
And so they wanted to summarize it.
So they were able to come up with a special type of pie chart.
It's called a coxcomb chart that actually will let you kind of mimic a compass.
So the compass rose is kind of what you're used to seeing
on like compasses on the face, right?
Yo u know you get North, South, East, West.
Well, we've kind of doing the same thing with the wind rose.
So it's kind of summarized all this data.
So to make it, it's pretty easy,
we just go ahead and we've got our positions for our wind.
We have the speed of our wind, but we also have, in this case...
the direction of the wind.
And this one went into the 230th degree of the compass
so that would be a west southwest.
So when we go on the graph it all we have to do is take those sections
that we've identified, pop them on down here,
ask for the bar chart, ask for the coxcomb chart
and I'm going to take the speed and I'm going to put it in the overlay
and now it's easy to see that a lot of my data
is coming out of... I put that one right there.
Now I can see a lot of my data is coming out of the western section,
the northwest in particular, especially with the larger orange segments
where it's got a higher count
and of course I brought in a nifty...
background map and so you can make it look really cool
and if you want to learn how to draw those arrows
I've included those as well.
That is something you would do
under points, set shape, expression and I'm showing you
how you can put in just a little of JSL scripting
and you can draw these wind directions
and the length of the arrow is the strength of the wind.
That's pretty cool.
All right, so we are right at time
and I'm going to include in your Journal where to learn more
so you can learn from the other galleries,
you can learn from the other blogs and journals,
the other presentations as well as other tutorials
just off the JMP community
so please do learn more about Graph Builder
and please do share your data
at the JMP Data for Green.
All right so please email me or contact me
if you'd like to talk more about Graph Build
or see any of these views differently.
And I thank you and I hope you enjoy your discovery
and please do go help save the planet
and get curious and share your results.