Effective Visualizations

Guiding Questions

• No guiding questions today as we’re mostly learning some technical aspects of ggplot
• We have largely reversed our content and example lectures for data viz.

Starting up

Load up our murders data

library(dslabs)
library(ggplot2)
library(dplyr)
data(murders)

p <- ggplot(data = murders, aes(x = population, y = total, label = abb))

Log transformations

Last lecture, we re-scaled our population by 10^6 (millions), but still had a lot of variation because some states are tiny and some are huge. Sometimes, we want to have one (or both) of our axes scaled non-linearly. For instance, if we wanted to have our x-axis be in log base 10, then each major tick would represent a factor of 10 over the last. This is not the default, so this change needs to be added through a scales layer. A quick look at the cheat sheet reveals the scale_x_continuous function lets us control the behavior of scales. We use them like this:

p + geom_point(size = 3) +
  geom_text(nudge_x = 0.05) +
  scale_x_continuous(trans = "log10") +
  scale_y_continuous(trans = "log10")

A couple of things here: adding things like scale_x_continuous(...) operates on the whole plot. In some cases, order matters, but it doesn’t here, so we can throw scale_x_continuous anywhere. Because we have altered the whole plot’s scale to be in the log-scale now, the nudge must be made smaller. It is in log-base-10 units. Using ?scale_x_continuous brings us to the help for both scale_x_continuous and scale_y_continuous, which shows us the options for transformations trans = ...

This particular transformation is so common that ggplot2 provides the specialized functions scale_x_log10 and scale_y_log10 which “inherit” (take the place of) the scale_x_continuous functions but have log base 10 as default. We can use these to rewrite the code like this:

p + geom_point(size = 3) +
  geom_text(nudge_x = 0.05) +
  scale_x_log10() +
  scale_y_log10()

This can make a plot much easier to read, though one has to be sure to pay attention to the values on the axes. Plotting anything with very large outliers will almost always be better if done in log-scale. Adding the scale layer is an easy way to fix this.

We can also use one of many built-in transformations. Of note: reverse just inverts the scale, which can be helpful, log uses the natural log, sqrt takes the square root (dropping anything with a negative value), reciprocal takes 1/x. If your x-axis is in a date format, you can also scale to hms (hour-minute-second) or date.

Transforming data vs. transforming using scale_...

We could simply take the log of population and log of total in the call and we’d get something very similar. Note that we had to override the aesthetic mapping set in p in each of the geometries:

p + geom_point(aes(x = log(population, base=10), y = log(total, base=10)), size = 3) +
  geom_text(aes(x = log(population, base=10), y = log(total, base=10)), nudge_x = 0.05) 

This avoids using scale_x_continuous or it’s child function scale_x_log10. One advantage to using scale_x... is that the axes are correctly labeled. When we transform the data directly, the axis labels only show the transformed values, so 7,000,000 becomes 7.0. This could be confusing! We could update the axis labels to say “total murders (log base 10)” and “total population (log base 10)”, but that’s cumbersome. Using scale_x... is a lot more refined and easy.

Changing axis titles

We’ll use the labs(...) annotation layer to do this, which is pretty straightforward. ?labs shows us what we can change, and while it looks pretty basic, the real meat is in the ... argument, which the help says is “A list of new name-value pairs”. This means we can re-define the label on anything that is an aesthetic mapping. X and Y are aesthetic mappings, so…

p + geom_point(size = 3) +
  geom_text(nudge_x = 0.05) +
  scale_x_log10() +
  scale_y_log10() + 
  labs(x = 'Population', y = 'Total murders')

Now, let’s use an aesthetic mapping that generates a legend, like color, and see what labs renames:

p + geom_point(aes(color = region), size = 3) +
  geom_text(nudge_x = 0.05) +
  scale_x_log10() +
  scale_y_log10() + 
  labs(x = 'Population', y = 'Total murders', color = 'US Region')

We can rename the aesthetic mapping-relevant label using labs. Even if there are multiple mapped aesthetics:

p + geom_point(aes(color = region, size = total/population)) +
  geom_text(nudge_x = 0.05) +
  scale_x_log10() +
  scale_y_log10() + 
  labs(x = 'Population', y = 'Total murders', color = 'US Region', size = 'Murder rate')

Titles

In ?labs, we also see some things that look like titles and captions. We can include those:

p + geom_point(aes(color = region, size = total/population)) +
  geom_text(nudge_x = 0.05) +
  scale_x_log10() +
  scale_y_log10() + 
  labs(x = 'Population', y = 'Total murders', color = 'US Region', size = 'Murder rate',
       title = 'This is a title', subtitle = 'This is a subtitle', caption = 'This is a caption', tag = 'This is a tag')

Now that you know how, always label your plots with at least a title and have meaningful axis and legend labels.

geom_line

For a good old line plot, we use the line geometry at geom_line. The help for ?geom_line tells us that we need an x and a y aesthetic (much like geom_points). Since our murders data isn’t really suited to a line graph, we’ll use a daily stock price. We’ll get this using tidyquant, which pulls stock prices from Yahoo Finance and maintains the “tidy” format. You’ll need to install.packages('tidyquant') before you run this the first time.

library(tidyquant)
AAPL = tq_get("AAPL", from = '2009-01-01', to = '2021-08-01', get = 'stock.prices')
head(AAPL)

## # A tibble: 6 × 8
##   symbol date        open  high   low close     volume adjusted
##   <chr>  <date>     <dbl> <dbl> <dbl> <dbl>      <dbl>    <dbl>
## 1 AAPL   2009-01-02  3.07  3.25  3.04  3.24  746015200     2.75
## 2 AAPL   2009-01-05  3.33  3.43  3.31  3.38 1181608400     2.87
## 3 AAPL   2009-01-06  3.43  3.47  3.30  3.32 1289310400     2.82
## 4 AAPL   2009-01-07  3.28  3.30  3.22  3.25  753048800     2.76
## 5 AAPL   2009-01-08  3.23  3.33  3.22  3.31  673500800     2.81
## 6 AAPL   2009-01-09  3.33  3.34  3.22  3.23  546845600     2.75

Now, we can plot a line graph of the Apple closing stock price over the requested date range. We want this to be a time series, so the x-axis will be the date and the y-axis will be the closing price.

ggplot(AAPL, aes(x = date, y = close)) +
  geom_line() +
  labs(x = 'Date', y = 'Closing price', title = 'Apple stock price')

In geom_line, R will automatically sort on the x-variable. If you don’t want this, then geom_path will use whatever order the data is in. Either way, if you have multiple observations for the same value on the x-axis, then you’ll get something pretty messy because R will try to connect, in some order, all the points. Let’s see an example with two stocks:

AAPLNFLX = tq_get(c("AAPL","NFLX"), from = '2021-01-01', to = '2021-08-01', get = 'stock.prices')
ggplot(AAPLNFLX, aes(x = date, y = close)) +
  geom_line() +
  labs(x = 'Date', y = 'Closing price', title = 'Apple and Netflix stock price')

That looks kinda strange. That’s because, for every date, we have two values - the NFLX and the AAPL value, so each day has a vertical line drawn between the two prices. This is nonsense, especially since what we want to see is the history of NFLX and AAPL over time.

Aesthetics to the rescue! Remember, when we use an aesthetic mapping, we are able to separate out data by things like color or linetype. Let’s use color as the aesthetic here, and map it to the stock ticker:

AAPLNFLX = tq_get(c("AAPL","NFLX"), from = '2021-01-01', to = '2021-08-01', get = 'stock.prices')
ggplot(AAPLNFLX, aes(x = date, y = close, color = symbol)) +
  geom_line() +
  labs(x = 'Date', y = 'Closing price', title = 'Apple and Netflix stock price')

Well there we go! We can now see each stock price over time, with a convenient legend. Later on, we’ll learn how to change the color palatte. If we don’t necessarily want a different color but we do want to separate the lines, we can use the group aesthetic.

AAPLNFLX = tq_get(c("AAPL","NFLX"), from = '2021-01-01', to = '2021-08-01', get = 'stock.prices')
ggplot(AAPLNFLX, aes(x = date, y = close, group = symbol)) +
  geom_line() + 
  labs(x = 'Date', y = 'Closing price', title = 'Apple and Netflix stock price')

Similar result as geom_line, but without the color difference (which makes it rather hard to tell what you’re looking at). But if we add labels using geom_label, we’ll get one label for every point, which will be overwhelming. The solution? Use some filtered data so that there is only one point for each label. But that means replacing the data in ggplot. Here’s how.

Using different data with different geometries

Just as we can use different aesthetic mappings on each geometry, we can use different data entirely. This is useful when we want one geometry to have one set of data (like the stock prices above), but another geometry to only have a subset of the data. Why would we want that? Well, we’d like to label just one part of each of the lines in our plot, right? That means we want to label a subset of the stock data.

To replace data in a geometry, we just need to specify the data = argument separately:

ggplot(AAPLNFLX, aes(x = date, y = close, group = symbol)) +
  geom_line() +
  geom_label(data = AAPLNFLX %>% group_by(symbol) %>% slice(100),
             aes(label = symbol),
             nudge_y = 20) + 
  labs(x = 'Date', y = 'Closing price', title = 'Apple and Netflix stock price')

In geom_label, we specified we wanted the 100th observation from each symbol to be the label location. Then, we nudged it up along y by 20 so that it’s clear of the line.

R also has a very useful ggrepel package that gives us geom_label_repel which takes care of the nudging for us, even in complicated situations (lots of points, lines, etc.). It does a decent job here of moving the label to a point where it doesn’t cover a lot of data.

library(ggrepel)
ggplot(AAPLNFLX, aes(x = date, y = close, group = symbol)) +
  geom_line() +
  geom_label_repel(data = AAPLNFLX %>% group_by(symbol) %>% slice(100),
             aes(label = symbol)) + 
  labs(x = 'Date', y = 'Closing price', title = 'Apple and Netflix stock price')

Now, we don’t lose a lot of space to a legend, and we haven’t had to use color to separate the stock symbols.

Multiple geometries

Since this section is about adding geometries, we can combine points and lines. Since lines connect points, it will look like a giant connect-the-dots.

library(ggrepel)
ggplot(AAPLNFLX, aes(x = date, y = close, group = symbol)) +
  geom_line() +
  geom_point() + 
  geom_label_repel(data = AAPLNFLX %>% group_by(symbol) %>% slice(100),
             aes(label = symbol)) + 
  labs(x = 'Date', y = 'Closing price', title = 'Apple and Netflix stock price')