We've said over and over throughout this section that our goal with any chart or graph is just to display
the data or conclusions that we're trying to communicate as clearly as possible.
Part of what that means is, of course, being aware of some common plot pitfalls, like, for example,
a misleading vertical axis, which is something that unfortunately is too common.
In this chart here, we clearly have a bar graph and at first glance it looks like there's a huge increase
between 2022 and 2023.
And so we might expect some dramatic change between these two years.
But when we look at the Y axis, what we see is that the y axis not only starts at 34%, but tops out
up here at 42%.
So the difference between these two years is actually not that significant at all.
In 2022, it looks like we're sitting at about 35% maybe.
And in 2023, it looks like we're sitting at about maybe 40%.
So an increase from 35% to 40% is really not that significant at all.
Imagine instead, if we started this y axis at zero and it went all the way to 100, the bars representing
these two years would look almost identical, and we certainly wouldn't interpret the bar chart as if
there was a big change between 2022 and 2023, but sketched out this way with this particular y axis,
it looks like the increase is dramatic.
So we have to be really careful about the scale of this vertical axis.
In this case, the scale is probably too small, but we can also have the opposite scenario where the
scale is too big.
And so a change between bars is actually dramatic, but it doesn't appear dramatic because the scale
of the vertical axis is too big.
So we have to be aware of the scale of any axis in any chart.
We also need to think about where it makes sense to start and end the axis like we talked about here
in this particular chart, when we're talking about percentages, it probably makes more sense to start
the vertical axis at zero and ended at 100 instead of starting it at 34 and ending it at 42.
In fact, it's usually a best practice to start the vertical axis at zero unless we have really good
reason for not doing so.
If we think the data is actually more clear, if we don't start at zero, maybe we have negative values
in our data and we need to start at some negative value.
So we just need to be really careful about the start and end points, the scale.
We also need to think about a consistent scale across the axis.
So for instance, with this chart here, if we look at the horizontal axis, we have years across the
horizontal axis.
So this is 1960, 1970, 1980, and then all of a sudden we jump to the year 2000 and then the year
2020, which makes this horizontal axis really confusing.
It feels compressed over here on the left side.
And then like there's bigger jumps over here on the right side.
It's hard for us to envision where we are in time as we move along this horizontal axis because we don't
keep the scale consistent.
We also have a problem here where the horizontal axis isn't labeled very well.
Yes, we say here that the horizontal axis represents year, but because we're crossing over from the
1900s to the two thousandths to start at just 60, 70, 80, and then suddenly show just zero zero and
then 20, it just makes this axis more difficult to interpret.
So whenever we're building a chart or a graph, not only do we want to be correct, we don't want to
have data that's wrong in our graph or in our chart, or we don't want the chart to be misleading,
but we also want to do everything we can to make it clear.
So even if there was nothing wrong about this horizontal axis right, we could fill it in here with
90 and then ten.
Even doing that would help make things a little more clear.
It also might make things more clear to say here 1960 and then show the year 2000.
Just making those couple of changes makes it a lot easier to read the horizontal axis.
So we want to do everything we can to first, of course, not be wrong, but then second, be as clear
as possible with how we're displaying information.
So this axis wasn't labeled really well in this chart over here, nothing's labeled well at all.
We don't have a chart title.
We don't even know what's being shown here in the chart.
And we probably be helped if we maybe displayed the percentage at the top of each bar.
Something like this, maybe 35.5%, let's say.
And then up here, let's say 40.2%.
If those are the real values, because of the scale of this vertical axis, it's maybe a little hard
to tell the exact value of these bars.
Just adding these values here brings extra clarity to this particular chart.
We should probably also have here a title like we said, so we know what the chart is displaying.
We just want to make sure everything's labeled as well as it can be.
This line plot over here on the right is another example of a chart that's misleading.
The title of the graph said that college is no longer worth it and tries to show that based on.
On the discrepancy between the rising cost of tuition and the relatively stable earnings that can be
expected after a person completes college and earns their degree.
The problem is not only am I giving you extra information there because the legend here only says earnings,
it doesn't say annual earnings or specify some other period.
But in addition to that problem, what the chart is actually showing is total cost of tuition over the
course of the entire college program.
So let's say that's over four years.
The mean total cost has risen to close to $95,000.
The graph is comparing that value to mean earnings of that graduate, but on an annual basis.
So it's showing that the graduate can expect to earn about $50,000 per year after they graduate.
But this line plot makes it look like there's some huge discrepancy in the cost.
But if we really think about the annual earnings of the Graduate, if they earn 50,000 a year, then
in their first five years of employment, they can likely expect to earn at least 250,000, 50,000 a
year times five years, maybe more, if they get a small raise each year.
And that is compared to the total cost of the four year degree of close to.
95,000.
And when we compare those two figures, the cost of college looks a lot more reasonable.
Whereas if we compare them this way, the cost of college looks extremely unreasonable because of the
difference.
The discrepancy between this high cost of tuition and the apparent low earning potential of the graduate.
We'll also sometimes see in the real world charts like these ones.
This is a pie chart.
Let's say it's trying to represent support for different political candidates.
First of all, just like this bar chart, we have no title for the chart.
We have no legend, and including both of those would be really helpful if we had a legend here where
we said that Green was supports candidate A and Blue was supports candidate B, etc. that would help
us to interpret this pie chart.
We have another problem here with the pie chart.
The data is just flat out misleading.
A pie chart should always sum to 100% each piece of the pie.
When we add them all up, they should sum to 100%.
And here these percentages are summing to 165%, which makes the chart completely invalid and unreadable
entirely.
So not only is it just flat out wrong, but we don't really understand what it's saying because we have
no title, we have no legend.
And on top of that, pie charts are often not a great way of displaying most data in general, because
they can be hard to read in the sense that it's difficult to compare each slice against the other.
We can tell that this purple section here appears to be the biggest section, but looking at green versus
blue, if we don't have these labels at first glance, it's hard to interpret whether green is bigger
than blue or vice versa, or they're the same size.
So for something like this, a comparative bar chart might be just a better way to display the data
in general.
So the whole idea here is that whenever we're trying to display any kind of information, we've collected
data and now we want to communicate that data in some summarized way to somebody else, we really need
to think about choosing the best.
Chart type, right?
Is a bar chart the best option?
Should we use a line plot instead?
Should we use a box and whisker plot?
What kind of graph?
What kind of chart is the best way to display this information?
Once we've chosen the chart that we think best communicates the data that we have to display, then
we want to think about all of the aspects of the chart that affect the clarity of the information being
displayed.
So we should probably have a title.
We should probably label both axes, right?
We should probably have set up here that this horizontal axis represents a year.
We should label this vertical axis based on whatever it represents.
We should have a legend, if that's appropriate.
Over here, we had a legend for the line plot.
We should maybe give the units for each axis.
So over here we said that this vertical axis is dollars in units of thousands, so we can interpret
this 45.
Here's 45,000 over here.
This vertical axis, we know we have percentages because we see 34% here.
So we know we have percentages, but we don't know what this vertical axis is indicating.
So we want to think about labeling everything that we can.
We want to think about the scale of each axis and the scale of the data that we're displaying.
We don't want the scale to be too small.
We don't want it to be too big.
We want the scales to be consistent.
We want to start our scales at zero, unless some other starting point definitely makes more sense.
And we want to make sure that the point that we're actually communicating is a valid one.
So let's maybe say here.
Validity right in this chart here, we're communicating a significant increase across years when in
fact the increase isn't that significant at all.
Over here, we're communicating a significant difference between tuition across four years and then
earnings for just one year.
Maybe that's really not the most appropriate comparison.
Maybe we should compare cost of tuition to expected lifetime earnings or earnings over the first five
years or ten years.
In other words, we really might not be communicating the best conclusion based on the way that we're
displaying our data or what we're including in our data at all.
And then the last thing probably is just pure accuracy or correctness.
This pie chart here is just wrong.
A pie chart always has to sum to 100% by definition, and this one doesn't.
If the data were displaying is just incorrect.
We obviously want to fix that.
Maybe this 35.5% here was actually wrong in this bar.
Should have been all the way up here at 45%.
And after we build out our bar chart, we realize that and we can correct the bar and label it appropriately.
Maybe we had a mistake here where in our line plot, instead of having 85 right here, maybe we actually
had 90.
And that's just a mistake and we need to change it to 85.
So these are all things that we want to think about, starting primarily with just choosing the best
chart.
Working through each of these questions is my chart clear?
Is the point I'm trying to make even valid?
Is everything in the chart accurate?
And if we've done all that and we've built a chart or a graph that we feel accurately communicates the
data we've collected and shows the point that we're trying to make by summarizing the data in the chart
or the graph, then we can feel pretty confident that we've created a good chart or a graph or a plot
that's actually doing a good job displaying the conclusion that the data should reveal.