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In this video, we’ll be taking a look at the line chart. Line charts show the change of data over a continuous time span. If the story you’re telling depends on showing trends in your data or changes in value, line charts are an effective choice.
Vocabulary
- Line chart: a chart that shows the change of data over a continuous time span. Line charts show trends or changes in value.
- Area chart: Similar to a line chart, but the area between lines and the x-axis is filled in
- Stacked area chart: An area chart in which one data set is stacked on top of another, demonstrating individual contributions to a whole.
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[MUSIC]
0:00
Early in this course we looked at a chart
from a textbook called Good Charts.
0:09
And we learned we didn't have enough
information about the audience to
0:14
determine whether this was
a good chart or not.
0:17
The chart in question is a vertical
column chart, and its rectangular
0:21
bars demonstrate changes in revenue
over time from 2010 to 2013.
0:26
And while it's certainly possible to
examine changes to your data over time
0:31
using a bar chart, as this chart has done,
0:36
there's another type of chart
that's built specifically to do so.
0:38
In this video,
we'll be taking a look at the line chart.
0:44
Line charts show the change of
data over a continuous time span.
0:47
If the story you're telling depends
on showing trends in your data or
0:52
changes in value over time,
line charts are an effective choice.
0:57
Line charts get their name from the line
use to connect individual data points.
1:02
For example,
if we wanted to chart Dennis Rodman's
1:07
total rebounding percentage
from year to year,
1:11
we could start by labeling the x axis for
each season Rodman played,
1:15
1986-87, 1987-88, 1988-89, and so on.
1:20
Next we draw a dot for each data point.
1:26
In 1986-87 Rodman's total
rebounding percentage was 15.5.
1:28
Followed by 18.6% the following season,
and 19.8% the year after that.
1:35
Once we've plotted Rodman's entire career,
we draw a line connecting our data points.
1:42
Now we can see two major
increases in Rodman's rebounding
1:48
proficiency: from his rookie
season to his second season, and
1:53
again from the 1990-91 season
to the 1991-92 season.
1:58
Line charts can make comparisons
by adding additional lines.
2:03
If we wanted to compare Rodman to
another great rebounder of his era,
2:08
such as Hakeem Olajuwon,
2:12
we could plot Olajuwon's total
rebounding percentage in a similar way.
2:14
However, be cautious about
making more than three or
2:19
four comparisons in a single graph, as too
many lines make a chart difficult to read.
2:22
Earlier in this course we learned it's
a bad idea to start your bar chart axis
2:29
anywhere but zero, as zooming in on
the data can tell a misleading story.
2:35
Is the same true for line charts?
2:40
The answer is it depends.
2:43
Here's a line chart from
the website 538.com comparing
2:45
trust in Black Lives Matter versus
trust in law enforcement from
2:50
June 2020 through March 2021.
2:55
And it's pretty misleading.
2:58
By scaling the y axis
from only 50% to 70%,
3:01
the graphic makes the gap in these
two data sets look exaggerated.
3:04
In the following line chart comparing
Black Lives Matter support by
3:10
white versus black Americans,
the y axis is scaled from 0 to 100%.
3:15
We get a more realistic look
at the trends in this data.
3:21
However, starting a line chart axis at
zero doesn't guarantee a meaningful story.
3:26
In 2015, a publication skeptical
of climate change called
3:33
the National Review tweeted
out a chart documenting annual
3:37
average global temperatures
from 1880 through 2015.
3:42
The problem with this chart, however,
is that the y axis scales from 0
3:47
degrees all the way up to 110 degrees,
even though every number in
3:52
the dataset is somewhere between 55 and
60 degrees Fahrenheit.
3:57
If the Earth's average annual
temperature came anywhere close to 0
4:02
degrees Fahrenheit or
110 degrees, we'd all be dead.
4:07
Zooming out this far makes
trends in the data or
4:12
changes in value impossible to see.
4:15
So the rule when providing axis labels for
4:19
a line chart is to include
realistic numbers.
4:22
It's not okay to adjust
the scale to exaggerate or
4:25
minimize the impact of the data.
4:29
But it is okay to start your axis from
a number other than zero if it makes
4:32
sense for the data you're using.
4:36
Now let's take a look at a few variations
of line charts you should be aware of.
4:40
One subtle variation is the area chart
in which the area between the line and
4:45
the x axis is filled in with color.
4:51
When plotting a single data set over time,
4:54
an area chart works very
similarly to a line chart.
4:57
When working with multiple data sets,
however, area charts are not a good
5:02
choice since the color of the top
layer hides the layers underneath.
5:06
Sometimes area charts
with multiple datasets
5:11
are presented using transparent colors,
but
5:14
I'd still argue this is more difficult to
understand then a traditional line chart.
5:17
However, combining multiple data sets
can be useful in what's known as
5:25
a stacked area chart, in which we view
how each member contributes to a whole.
5:29
Returning to my example of
the basketball team I'm coaching,
5:35
imagine I wanted to track scoring by my
five starters over the last four seasons.
5:39
A stacked area chart demonstrates
changes in data over time in the same
5:45
way a line or area chart does.
5:50
But in this case,
5:52
we can see how the team's scoring total is
impacted by each player's contribution.
5:53
Looking at our chart,
6:00
we can see that overall scoring by
my five starters is trending upward.
6:01
It also looks like Mike dominated the
scoring for the first two years shown, but
6:06
the scoring has grown more evenly
distributed over the past two years.
6:11
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