Welcome, everybody, to Pictures from the Gallery 7.
My name is Scott Wise, I'm a Senior Systems Engineer
in the US W est Coast and I'm joined today by my daughter Samantha.
-Hey. -Hey.
I wanted to ask you, you're a brand- new incoming college student.
What are you most concerned about for the future?
Well, to start, I'm pretty worried about negative effects on the environment
like deforestation and soil depletion and climate change.
Additionally, I'm worried about things like sexism in the workplace
and gender gap wages.
Wow, that's a lot to think about.
It got me thinking as well what we can do to make this world a better place.
To start off our presentation, I got three suggestions here.
If we stay curious, part of what we can do with that curiosity
is actually share with each other good data.
You all like to analyze data,
but sharing some good data would be a great idea.
and JMP has a data for green initiative where over the JMP community,
you can actually share what you think are meaningful views,
meaningful data collections, and we can analyze these things together.
The second thing you can do is to use your time.
So JMP and SAS have both partnered
with the IIASA, which is trying to actively measure
the amount of deforestation in the rainforest to help guide better policy.
So it's kind of a cool application
that lets you look at some of their satellite images and actually
help identify where you see development in human growth
in the rainforest and enable them to do a better measurements.
Lastly, user skills.
We all have great JMP skills
in practice, looking at analytics and building visualizations.
Our friends at WildTrack I think are some of the best examples where they're
using the footprints of many different species of endangered animals,
and by doing a little bit of JMP and a little bit of visualization,
they're able to help track in a non- invasive way these endangered animals
to help us again create better policy.
That's Sky and Zoe, and definitely I'll put a link in here
where you can check out their work and get inspired.
All right, so without further ado,
here is the pictures for the gallery for this version.
And in our version, I am going to dedicate every view to something around
environmental green data. We can start that conversation.
But as usual, I am showing you some things
that are new into Graph Builder, such as the first five years I show you
have been brand new things in JMP 16, as well as I'm going to show you
just a couple of tips and tricks you probably have never seen before.
All right, I've got the first chart here,
and the first chart is going to address equality
and it's on the gender wage gap.
It's going to show you a new interval type chart that's available in JMP 16
that I call the Dumbbell Chart.
Now I'm going to give you this journal, and why I'm pointing this out is
if you want to recreate this view,
you not only have a picture of what it looks like
and tips on how to set your data up,
to make this chart, but I give you the steps.
In order, not only that, I give you the data,
and within the data, you can just click on the script to regenerate the view.
It's all there for you.
I'm going to build this one from scratch. What is this data?
This comes from the International Labor Organization
and it is looking at the nominal mean earnings of males and females.
But it has it normalized by US dollars.
It would be nice to see if that gap' s getting smaller,
like with Sammy's concern about the gender wage gap.
So I used to think you'd have to create a formula
which actually took the delta, but to graph it, you do not.
You just need to have both columns you're wanting to compare.
I'm going to go to Graph Builder.
Everything today is going to be in Graph Builder.
And I'm going to take the female monthly and the male monthly.
I'm going to put them both on the x- axis,
but I'm also going to put them on the interval landing spots.
And I'm going to take year and put year on the Y.
Now it looks really busy and it's because there's many countries
represented here over this span.
If I go under the red triangle,
I like to call this a hot spot, and add a local data filter.
We'll just look at it by one country. Let's pick out France.
Now I get a pretty good view.
It might be better to clean up the view a little bit.
I can right- click right on the female monthly marker,
and I can take this marker size up a little bit.
I'm going to make it a 10.
And now I can do the same thing with the male monthly.
I'll make that 10 as well.
I can right- click right here into the graph and go to Customize.
And I want to make that intersection bar.
I want to make it a different color. It's the second air bar in that list,
and I'm going to make it gray and maybe make it a bigger width of three.
Now I got the view I like.
Now you can kind of tell why I call it a Dumbbell Chart,
because if anybody likes to work out, you know, at the gym, you have weights
and the ends of the weights is where the heaviness of it is on the ends.
In the middle, you have a bar to lift.
That's why a lot of people called this a Dumbbell Charts.
Now, a couple of cool things I can show you.
Number one, we generally don't read from bottom up, we read from top down.
You can right- click here in your Axis Settings,
and I can reverse this order just by clicking on this little box right here.
Now I'm going 2010 to 2019.
Also, I might want to put a reference line on the X-axis to help my eyes.
So I'm going to right- click go to Axis Settings.
And I think about 3,500 would make a good little reference line.
It's going to put one on the X- axis, and there we go.
Now I can kind of gauge,
is the gap closing, is the wage increasing for both sexes, that type of thing.
Now I'm going to bring a lot of pictures in as examples for our data,
and I'm going to show you that if you just take a picture and you just put it
into your graph, it will put the picture as a background.
Now, it's sized horribly here.
It's easy, you just right- click go to Image, go to Size and Scale,
and save Fill Graph. There we go.
And this is pretty cool.
Now, I know the female symbol here in the background map is red,
so maybe I'll go right up here
to my legend and I will change the colors around here.
Pretty easy to do.
A lso I'm going to right- click back into the graph
and in my image, I'm going to make that background a little more transparent,
maybe like a 0.3 here.
Now I've got a really cool view.
Now, one word of warning, it's locked into this scale ,
and you're going to get a different scale for each country
because some countries pay more than others.
I know Germany pays very well. You can see it changed my picture,
so you'd have to right- click
and go to Image again and Fill the Graph to pull it back correctly.
You might want to move your reference line.
The background maps are not great if you're going to change your scale a lot.
I do have a version here that's a multiple view version without a picture
and if you right- click on this one,
you can see here I was comparing over the same scale.
I fixed the scales but made them a little bigger.
What was the difference in France, Germany and Sweden,
and you can see that in France,
the wage gap doesn't look so bad on this scale,
but Germany has a bigger wage gap but pays higher.
Maybe you want to be in Germany.
And I noticed in Sweden that the females make more than the males.
There you go. There's a lot of differences out there.
This is some fun data to play with, so definitely see what views you like.
All right, second picture we're going to look at is a Word Cloud.
This was the second most popular thing that got requested.
And you might have seen in JMP, there is a Text Explorer platform
that allows you to look at unstructured text data that you might have.
And Word Cloud was one of the views it gave you just with a click of a button.
But how do you do that in Graph Builder?
Well, let's take a look.
In Graph Builder, all you need is the unstructured text.
In this case, I have a column of words and you need some sort of counterweighting.
Here I have the weight and where this data came from.
This was a study run during COVID of what are the top five things
teachers were worried about. Of course they were dealing with a lo t.
Remote teaching, sick students, sick teachers,
a big change to curriculums just to get through the year.
So here, the highest weight was anxious.
Twenty respondents all mentioned being anxious.
So what I've done is I have sorted
the words by weights and then I just put a order here.
So the highest weight got an order of one
and the next highest got an order of two and so forth.
That's how I got the weight column and that's how I got the order column.
And I also created some random data because you can have a sorted,
ordered word cloud, but you can also have one that just looks like a cloud.
To generate that one...
You might not have known this, but if you go and open a new column in JMP
with this initialized data,
you can put in random data and you can put in things like random normal data.
Okay, I have already done that.
Let's just go to Graph Builder and see how this works.
Well, the first thing I'm going to do, I'm going to put weights.
There we go on the Y- axis,
and I am going to size by weight, but I don't want points.
And here's a little trick in JMP 16,
under the red triangle, under the points elements panel here
that's on your bottom left hand side of the Graph Builder,
I could set a shape column.
When I do that, I can substitute for points the actual word.
And now you're starting to see the words
and as you start to move around your graph,
you can see what's going on with those words and that is very cool.
This right here is your first check of doing a word cloud.
Now I can color, by the way, to give this thing some color.
Now, I'll want to make it as cloud-like as possible.
I can move this random over here.
A gain, playing around with the data set,
I can get the view that I like.
Maybe I'll say done here, maybe I'll go to the legend position,
maybe I'll put it on the inside left.
Maybe I'll go under the legend settings.
Maybe I'll turn off all but just the color code .
And now I got a pretty nice- looking word cloud.
Now as well, if I wanted it to be in sorted order,
because I know anxious is the most important.
That's the biggest- sized word,
I love that to kind of be on top and then the next, and then the next.
So to do that one, I'll open my control chart panel back up.
I will swap out the order for the random.
Now you can see all the big words are on the bottom,
so I'm going to right- click here under Axis Settings,
and I'm going to do that reverse order again.
N ow all the big stuff is up at the very top that I have.
And Jittering, what you didn't see before
was you were getting a centered grid jitter.
And that's actually what's automatically in your points jittering.
If I do a positive grid now, I get things in order.
Anxious, constant stress and tired, whatever it has room for on the line.
But it is in that row order, which is pretty cool.
But it's so much on the left- hand axis that's a little weird.
So I can right- click here on the X-axis.
Even though there's nothing down here, you can still play with the settings.
And I can go and maybe make this a negative 0.5 for the minimum.
It's going to add a little bit of space over here.
And if it did a nice job, did a really nice job.
Now you can get an ordered word cloud.
All right. And then, of course,
the ones I have in my data that you can play with, you can see I put in a nice
transparent apple background just by bringing in that picture,
which is really nice to play with the colors and all those type of things.
All right, that was a nice popular view everybody asked for.
The third most popular view was Line Charts.
Everybody likes to do line charts. In JMP 16, there's many new features
that actually fit lines through points
in a lot of different formats, as well as label your line interactively.
And we're going to look at tree cover loss.
Remember I showed you that link that would help you with folks
that were trying to save the rainforest.
Well, it's important to know how much we're losing around the planet.
We have some of that data, the under three here,
moving average smoother line chart.
I bring up my data here.
I've got tree cover loss in hectares.
By year, this should be pretty straightforward,
so I'll just go to my Graph Builder.
I will put my tree cover loss in hectares.
I will put my year down here on the X.
And you can see I've got points and smoother lines.
Not so exciting, maybe take drivers and overlay.
Getting more interesting, but I don't like these lines.
What other options do I have for other smoother lines?
Well, in JMP 16, they put things like moving average.
Maybe a moving average would be cool.
You can control the spread of that mover average with this local width.
And I'm going to do that one.
I'm going to say done.
And now it's looking pretty good. I'm actually going to open it
right back up, there's one thing I forgot to do.
You can actually put a confidence in around them.
Now I'll say done just fine, but I clicked this little button right there.
Very cool.
But I just want to look at kind of the big hitters.
So this might be a good place to go under the red triangle,
go to the local data filter.
Go ahead under drivers, and just take the top three drivers.
There we go.
This is a good chart, I'm very close.
But one thing that this legend' s kind of hard to read.
Wouldn't it be nice to put the name next to the line, maybe even on the line?
Oh, that would be awesome.
Well, you can do it, you might not know this is the place
you can do it, but if you just right- click right here on the legend where it says
Agricultural Shift, you can say what happens to the label.
You can add minimum values and first value, last value,
but just go click Name and you can see... Oh, look at that, drew it right in there.
I'm going to do the same thing with Commodity,
and I'm going to do the same thing with Forestry Driven.
Now I don't need that legend.
I can go under my red triangle versus Graph Builder.
I can turn off the legend because it's not adding any value.
Now, here's what's really cool, you don't have to leave it out here.
You can move it anywhere along the line
if you get close to the line,
it will try to take the slope of the line as its orientation.
I'll do this for Agricultural Shift, I'll put that one there.
Commodity, I'll put one there. And now I can move out the axis.
And now I've got a really cool chart.
By the way, on this chart, Agricultural Shift was the big haha.
It was something that we were definitely having a huge spike in,
but I think those efforts, of our friends trying to save
the rainforest have managed to pull it back a little bit.
A gain, you have that version scripted in your data
as well as a cool little background picture in the background.
What else do we have here?
Let's go to the bottom of our chart.
The next most popular chart was actually a Point Cumulative Summary Chart.
Very interesting, it's on safety data.
It's not using points, it's using, looks like a value of years.
That's pretty cool.
This data came from the Bureau of Transportation Statistics,
and why I liked this data was it not only gave us a index of crash rate
and injury rates, and this is all based off millions of miles driven,
but for each year, like in 1998 year,
it told us that the dual front airbags was the safety innovation
that came in that year.
T his would be cool to see what's going on with my line chart.
I'm going to go graph, Graph Builder.
We'll go ahead and take both crash and injury rate,
put it on the Y, put year on the X.
I'm going to turn off the smoother line here and just look at the points.
And can I tell any difference between crash rate and injury rate?
I really can't. This is where having a cumulative summary
would be really cool
because I can go under the summary statistic,
under the Points element and just change this out from none to cumulative summary.
Now, do you get a sense of the differences in the slope of the line?
You should, because the summary of the crash rate definitely has a steep line,
and I would expect this,
there's more people on the Earth now driving more miles .
But you can see that it looks like the injury rate has less steep slope
and seems to be flattening out.
And maybe that's because of these innovations.
Here under my Axis Settings for the X- axis, I can put in like in 1998,
there were the dual airbags.
And see if that might be an inflection point, a cause of cars
that are now protecting us more from injury.
That's pretty cool. The other cool thing to do,
you could as well under your points red triangle, set the shape column by year.
And even though it's continuous, it's just going to give you the value
in this case, that is really cool. So now I'm seeing it by year.
Very nice. Then I have a view in here
where I have gone through and added a whole bunch
of the safety innovations over time and put a nice more transparent
airbag background because airbags was a big deal.
But you can see when things like blind spot warnings came in,
anti- lock brake [inaudible 00:22:34] technology.
Really cool to see how the industry is helping to save us from injuries.
All right, [inaudible 00:22:45] right along.
Our next to last chart, but still very popular review, is Advanced Box Plots.
There's a lot more you can do with box plots to integrate them
even with other elements like points and labels.
And we're going to look at some climate city risk.
And this is some really fun data that I found on looking out,
projecting out to 2050.
And it was coming up with this total climate change
risk index on a 1- 100 scale.
And it was looking at things like potential sea rise, shifts in temperature.
Shifts in climate is something very important for a lot of us,
especially us out in the West Coast, which is water stress or water scarcity.
And that's how I came up with this total climate change risk score.
If I want to see what that one is looking like on a box plot,
it would be pretty easy to just take my total climate change on the Y.
Take my... Well, actually, I'm going to put it on the X.
There we go.
And I'm going to do it by region on the Y.
Now that's going to allow me to then ask for some box plots
and here we go. It's not so interesting to me.
I'm going to hold my Shift key down and add back in the points.
Now, boring box plots don't have to be boring anymore because now I have
different types of box plot types I can do in styles.
Under style I got this Solid Style.
Now it colors it in, which is pretty cool.
And as well, you can go and notch them,
and you can go and add fences to them. By the way, it's got the Outlier selected
and since it looks like on this data set, you can see I've turned the labels on,
I've already sorted this by total climate risk.
I just want to label the top 10 that's already on there.
I can turn off this outlier and that takes any duplications out of there
and that's what we're looking at, which is pretty cool.
Maybe change this color.
By the way, if you cannot see your points in the boxplot,
sometimes if you put the box plot in last,
it would have moved it forward and it's over top of your points.
So then all you have to do is go into your points and move it forward.
Just right- clicking into your graph and just go into the right element
and bringing it forward.
That is pretty cool.
And you can see what's going on where we have the highest risk
of cities running in the climate change risk in 2050
and usually things coastal are at extreme risk of water shortage.
Very cool. All right. And as well,
I put a nice little background picture in the background on this one.
It moved the legend in a little bit, so that's all in the instructions as well.
All right, so we are to our last
beautiful pictures from the gallery review.
And that's a Wind Rose Chart, and I was thinking that
we're getting a lot of adverse weather
given the changes in our climate, and so we're always trying to get better
at predicting which way are the winds blowing, how strong,
where are hurricanes going, tornadoes, typhoons, all these types of things.
There is a cool view and this is not limited to JMP 16.
But it is a type of... The pie chart is actually a version
of a Coxcomb chart that will make a compass rose.
If I can get it labels that tell it like in a compass, what's north east?
What's north west? Those type of compass directions.
I can come up with a pretty cool pie chart
that lets me segment that chart by, in this case, wind speed.
So let's take a look at this data.
There we go. This is a day's worth of data
in the Great Lakes area.
If you take a look here at the 6th row, you can see that I not only have latitude
and longitude and the speed, where it starts, how strong the wind was,
what direction it was going, and then of course, I can get that
into a compass direction like west southwest.
With that I should be able to go
and just put that compass direction down on the X, ask for a pie chart,
but not only any kind of pie chart, I am going to ask for the Coxcomb chart
and I'm going to take the wind speed
and I'm going to overlay it by the wind speed.
You can play with these colors, I might move this in a little bit.
I might make the really fast winds red.
And now I can see that they were mainly in this direction on the compass.
That was where predominantly most of the wind was
and where some of the darker red was as well.
Very cool.
I have a couple of versions with this, you might have seen that I brought in
kind of an old type of wind direction map where they're drawing the wind vectors
onto a map, which is pretty cool,
and if you want to see how to do that one, I put this in the instructions as well.
And if I open the control panel back up,
and I go under the spread hot spot where points are,
and I go to Set Shape E xpression.
You can see that there's a formula behind it.
And what this formula is doing is it's looking at each point,
which is plotted by the latitude and the longitude on the map.
Then it is taking the wind speed, it is drawing an arrow,
and it's drawing a bigger arrow, of course, if there was a stronger speed.
That's kind of cool, and that's what draws those blue lines
that you're seeing involved right there.
All right, all those are in your instructions.
I'm just about out of time here, I will show you that I have put
into the journal that I'm giving you where to learn more.
You can see other galleries, you can see blogs in journals,
other presentations, and even great tutorials.
They're all from the JMP C ommunity, community.jmp.com.
Those are there for you.
Go have fun with your Pictures from the Gallery 7.
Go try to recreate these views on your own data so they can be nice and compelling,
and do use your curiosity, time, and skills to help save the planet.