https:/.../9077db8e-b57d-4825-a472-ad9601830d80-6177ebff-599e-4e9f-bc3d-ad9c006c9db8.mp4?invocationId=994e94ce-a50f-ec11-a9e9-0a1a827ad0ec

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Welcome back. This video,

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I'm going to walk you through some of the different types of charts that we're going to be learning how to create outcomes or

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be able to identify the appropriate type of chart for data in a question and understand key rules to avoid common errors.

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I'm not going to be showing the detailed code for these chart types in the video.

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You're going to be able to find that in the documentation link from here. And also,

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I'm going to be preparing a notebook that demonstrates various of these charting

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types with the actual code to create them using the software we discussing.

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So common software for this or Seabourne and matplotlib, those are going to be the primary ones that we're working with this semester.

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When I'm showing the function names, Seabourne is commonly input imported S.A.S.

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So as an ascot function is going to be a seabourne function PLDT,

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the function is going to be a matplotlib of function and also showing the function you can use in plot nine or Ares g.G plot too,

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if you want to use those instead. I often use plot nine for a lot of my graphics.

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That's just for reference though. We're not going to be getting into much detail on Plot nine in the course of this course.

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So there's a variety of different types of charts. Some of them are showing relative proportions.

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Some of them are showing how different amounts relate to each other. Some of them are showing positions and an x y coordinate space.

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A bar chart is a very common type of chart that shows numeric values grouped by a categorical or ordinal variable.

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Sometimes they're grouped by New America as well.

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But usually our x axis is a categorical variable of some kind best with a moderate number of categories.

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We can use a second categorical variable to say color the bars.

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So this chart shows the survival rates of Titanic passengers or the X axis is the passage class for second or third class.

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And then the bars are colored based on the gender of the of the passenger.

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And so we can see the different survival rates.

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The y axis on a bar chart is often a mean or a sum or a count within the cap of the group determined by our categorical variables.

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Sometimes these will be horizontal. So the horizontal bar chart, the categorical is on the Y and the bars run horizontally.

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This also shows some whiskers that come from a confidence interval.

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It's very easy to generate a default, relatively good confidence interval with Seabourne so tough to pluck to plot

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these Seabourne has the count plot function which lets which does a quick,

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basically categorical histogram. How many observations are in each are in each category.

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The cap plot variable will plot by default a mean value for each category.

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And if you have it, do the mean plotting. It will also compute. Ninety five percent confidence intervals.

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That's what's being shown in this in this plot here.

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And then you can also use the bat, the bar function or the plot nine Geon Bar.

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So if you rules about bar charts first is never start the Y axis on a bar chart.

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Anything but zero. And so the reason for this we can see here is that.

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So the top one. So these are these are looking at the mean average ratings.

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We take you to movies, mean rating, and then we compute the mean of the average ratings within a genre.

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What is that? So if we look here, the difference between horror and IMAX, it's a notable difference, but it's a difference of about point five or so.

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The difference between sci fi and short is a difference of a little under one, probably.

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But when we start the Y axis at 2.5 instead of zero,

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what happens is the differences look much larger than they are because the human eye, naturally it's not.

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We not only want to see the difference, but we want to it's very natural for us to compare the difference to the bar length because these are bars.

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They have length, they have an area since they're all the same with the length is proportional to the area.

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Braking length area. Proportionality is a good way to confuse your readers,

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but it looks like IMAX movies have twice as high an average rating as horror movies because the bar is twice as high, but they don't.

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It's really a shift from about 2.8 to three point three or three point four.

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And so it creates a distortion that makes the different like it highlights the differences, but it makes the differences look larger than they are.

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So when I talked about integrity and avoiding deception, when I was introducing statistical graphics,

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this is what I was talking about, the differences there. It's just not as big as it looks like it is.

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And we truncate our bar charts.

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So if you have the general rule here to generalize beyond bar charts is if something has a length that varies based on the data,

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that length needs to actually represent the value, not the value, minus something because you started the axis somewhere else.

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So if you're including Whiskers, like I did in the previous chart,

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define how they're computed and also as one thing to just be careful of seaboard's cat platen count plot.

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If you aren't using the color for a second variable, they will just make every bar a different color for no particular reason,

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which it creates something that's different when it doesn't need to be so.

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It causes the reader to look for a difference that isn't actually their best avoided.

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You can fix that by just specifying the color.

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We saw histograms last week in a histogram as a bar chart, but a categorical was Binz or ranges of a numerical value.

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Also, though, if we have a bar chart that's showing the relative frequency of categorical variables that can also be called a histogram,

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the Y axis is either the number or the fraction of occurrences in this case.

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So we can that. The key thing, though, is the different heights of the bars that I see visually, the relative frequency of different values.

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So it really makes it visually clear how the data is shaped.

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We can see Skewes and things like that. Is there one way to graphically describe a distribution?

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A scatterplot shows two numeric variables. So each observation is a dot.

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Each observation has two numeric variables. And we put the one variable on the x axis.

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The other variable on the Y axis and put the dot at where its variable values would intersect.

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This is really useful for seeing how two variables relate. Does one increase with the other Duplin?

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Do points clump or cluster in an interesting way? Other interesting patterns.

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It helps us find outliers. So this this is scatterplot is showing the tip versus the total bill for a bunch of restaurant bills.

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And each each observation is a bill.

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And then the x axis is the the under the total bill on the Y axis is the tip that the that the the customer added to the bill.

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And we a couple of refinements we can do here. We can color or change to the point tight by a categorical variable.

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So on this one, we've changed it so that the points are different color.

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So those dinners are blue circles and the lunches are orange AXA's.

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We could also add a trend line or some other kind of a line to show some context, for example, on this chart,

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we might want to plot a line that shows that the 20 percent point and that let us easily see where we're going over 20 percent,

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how that the tips are distributed relative to it to a 20 percent mark.

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We can also X can be a categorical variable when that happens. We call this a point plot or a strip plot.

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Functions for doing this are scatter scatterplot and then plotlines John Point,

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the Seabourne documentation has some examples of more of these align plot.

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It's like a scatterplot that we have to numeric variables. However, we it emphasizes the progression or continuity from one variable to the next.

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By combining them with a line, it really works best. We have one Y per X value that we want to plot.

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If we've got more than one, it really starts getting very, very jagged. It's very common for Time series.

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So this is another example from the Seabourne tutorial not labeled super well.

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I don't know what the value actually is, but it shows that we have some kind of a value that's changing over time and it's going negative.

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That was zero. The Y axis is at the top and the values otherwise our negative functions to create.

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These are line plot from seabourne, line from a matplotlib and Gyeom line from plot nine.

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A box plot shows the distribution of a numeric variable grouped by a categorical.

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So the bar chart just showed us, say, the average value, maybe with confidence interval.

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The box plot actually shows us the distribution and it does so in a way that's based on the median.

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So the median, the the horizontal line in the middle of the box is the median value, the top and bottom of the box.

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Are the first and third quarter close to the bottom of the first quartile and the top as the third quartile.

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And what that means is twenty five percent of the values are below the bottom of the box.

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Twenty five percent in the bottom half. Twenty five percent here and then twenty five percent above.

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We then show these these whiskers that extend out to the minimum,

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a maximum of the data and a number of plotting packages will do some kind of an outlier detection.

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This is using seabourne default outlier detection. So if the max is very high and what the rule it uses by default is it allows the whisker to be.

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So you've got the IQ are the inter quartile range. That's the height of the box. It allows the whisker to be one point five times that tall.

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And if you have any data points that are further away than that, it plots them as individual points, makes it easy to see outliers.

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You can change. It's that the whisker goes all the way up to the max, but it lets you quickly see and compare between different groups.

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The median, the first and third quartiles and the men in the max to the data.

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Very useful for comparing observations of a variable when you're grouped by some categorical functions

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for doing this or box plot from both Seabourne and matplotlib and then Gyeom block box from plot nine.

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A few more plots, a violin plot. It's like a box plot, except it's based around the mean and has curved sides.

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The swarm plot is a kind of another kind of a categorical scatterplot.

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It's usually best to avoid pie charts, especially 3D pie charts, or a lot of the of our software is not going to produce 3D charts very easily.

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Don't try to go make a 3D chart. They're almost always more confusing, especially like the 3D bars that you have from vintage PowerPoint.

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But even a pie chart, just because the human perception is not super great at accurately comparing angular areas.

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So usually a bar chart,

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restacked bar chart is going to be a better option than a pie chart or a donut chart is sometimes a better option where you've got to circle.

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This is one place where I disagree with the reading. The reading that I gave you recommends pie charts for showing relative proportions.

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I recommend usually avoiding those use a bar chart is a stacked bar chart if you need to show you

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want to show multiple proportions of different or relative proportions within different categories.

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There's another kind of plot that's not a plot on its own, but it's combined with other kinds of plots.

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That's a rug plot useful for just displaying distributions at a margin.

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So to learn more, I've gone I've taken a whirlwind tour through a number of different plot types, the class readings.

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So the paper that I assigned you to read, it talks through the use cases for a number of different plot types.

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I'm going to be providing tutorial notebooks that walk you through different plot types.

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The textbook talks about graph plotting and data visualization.

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The Seabourne and matplotlib docs are extensive. And for what?

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If you're using another plodding library, its documentation as well. Most plotting libraries also have a gallery student.

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Go through the gallery, look for a plot that has a feature you want in your plot or that you think might be useful for displaying your data.

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Click on it and they'll give you the code to show you how they made that plot.

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You might want to combine pieces from multiple plots. In practice, it takes a lot of trial and error to really get the hang of your plot and library

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and figure out how to make it show you the data in the way you really want it to.

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Learning one plotting library really deep is useful for a lot of the a lot of the python ones,

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especially the ones that are oriented towards static charts. They're built on top of matplotlib.

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So Seabourne is a convenience API on top of matplotlib. If you're using Seabourne,

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you're also going to need to use matplotlib calls a lot of the time when the seabourne gets you 90 percent of the way there,

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but not quite all the way. So to wrap up, there are many different types of charts that have different use cases.

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Learning graphics techniques takes time and practice takes some of the example notebooks that I'm providing.

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Take some of the galleries from the examples from, say, the Seabourne Gallery.

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Play with them, play with them with some data that I'm giving you, play with them with some data that you have elsewhere.

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But it takes time and practice and spend some time with the galleries of the of the the plotting libraries you're using.