Module 1.7: Plotting

Author
Affiliation

Alex Cardazzi

Old Dominion University

All materials can be found at alexcardazzi.github.io.

Plotting

An especially attractive feature of R (that does not differ between .R and .qmd) is its powerful graphics. Just Google “Best R Plots”, and you’ll see what I mean.

To start, we’ll learn some of the basics. We will begin by generating two scatter plots using data from ford.

  1. Plot mileage vs Price.
  2. Plot mileage vs cost_per_mile.
Code 
ford <- read.csv("https://alexcardazzi.github.io/econ311/data/ford_escort.csv")
colnames(ford)[2] <- "mileage" # change the second name
ford$mileage <- ford$mileage * 1000 # Multiply by 1000 and save/overwrite
ford$cost_per_mile <- ford$Price / ford$mileage # Create $/mi

plot(ford$mileage, ford$Price)

# This is the same as:
# plot(x = ford$mileage, y = ford$Price)
Plot

Scatterplot of mileage on the x-axis and price on the y-axis. There appears to be a negative relationship.

Below contains some examples of additional arguments for the plot() function.

  1. las = 1 rotates the text on the y-axis. Different numbers will rotate it more or less
  2. col sets the colors used in the plot. This can take a vector of colors.
  3. pch sets the type of point used.
  4. cex sets the size of the points. The default is 1.
  5. main sets the title of the plot.
  6. xlab sets the name of the x-axis
  7. ylab sets the name of the y-axis
Code 
plot(ford$mileage, ford$cost_per_mile, las = 1,
     pch = 23, cex = 1.2,
     col = "tomato", main = "Cost vs Mileage",
     xlab = "Mileage", ylab = "Price per Mile")
Plot

Scatterplot of mileage on the x-axis and price per mile on the y-axis. There is a strong, non-linear relationship.

Try tweaking some of these options in the following WebR chunk:

We can also add reference lines to the plot, and also make the colors a bit more complex.

Code 
# Set all colors as "tomato"
ford$point_color <- "tomato"
# If the Year is less than the mean year, color it "dodgerblue"
# Of course, these are therefore the "older" cars
ford$point_color[ford$Year < mean(ford$Year)] <- "dodgerblue"
plot(ford$mileage, ford$cost_per_mile, las = 1,
     pch = 19, cex = 1.2,
     col = ford$point_color, main = "Cost vs Mileage",
     xlab = "Mileage", ylab = "Price per Mile")
abline(h = 1) # horiz. line at Y = 1
abline(v = mean(ford$mileage)) # vert. line at the mean of X
Plot

Scatterplot of mileage on the x-axis and price per mile on the y-axis. There is a strong, non-linear relationship.

Of course, whenever you choose to add some differences in shapes, colors, etc., it’s helpful to add a legend to your plot. To do this, we can use the legend() function. This function accepts a few important arguments:

  • bty: setting this to "n" removes the box around the legend. I always use this option.
  • legend: this is the actual text to be displayed in the legend. It accepts a character vector, so if you colored your plot by men and women, you would use c("Men", "Women").
  • x, y: You can specify the exact coordinates of your legend, or you can specify things like: "topleft", "topright", "bottomleft", or "bottomright".
  • horiz: this accepts a boolean value, and turns the legend from vertical to horizontal.
  • Then, you will need to specify either pch or lty options to tell R if you want to display points or lines next to your legend.

Below is a plot with two legends (which is certainly redundant) to show off some of the different ways to customize the output.

Code 
plot(ford$mileage, ford$cost_per_mile, las = 1,
     pch = 19, cex = 1.2,
     col = ford$point_color, main = "Cost vs Mileage",
     xlab = "Mileage", ylab = "Price per Mile")
legend("topright", pch = 19, bty = "n", horiz = TRUE,
       legend = c("Old Ford", "New Ford"), cex = 1.5,
       col = c("dodgerblue", "tomato"))
legend("bottomleft", lty = c(1, 2), pch = c(2, 19),
       legend = c("Old Ford", "New Ford"),
       col = c("dodgerblue", "tomato"))
Plot

Same plot as before, but with one legend in the top right and another in the bottom left.

When generating figures, you will sometimes need to add data from a different source to the same set of axes. As an example, let’s simply plot the data above, but in two steps instead of one.

To do this, we will use points(). This function accepts nearly every argument plot() does, except you are unable to impact the axes/labels of the plot.

Code 
plot(ford$mileage[ford$point_color == "tomato"],
     ford$cost_per_mile[ford$point_color == "tomato"],
     las = 1, pch = 19, cex = 1.2,
     col = "tomato", main = "Cost vs Mileage",
     xlab = "Mileage", ylab = "Price per Mile")
points(ford$mileage[ford$point_color != "tomato"],
       ford$cost_per_mile[ford$point_color != "tomato"],
       pch = 19, cex = 1.2, col = "dodgerblue")
Plot

Plot of mileage on the x-axis and cost per mile on the y-axis.

Notice how I am subsetting the data when plotting. This is an important thing to learn!

Once you get the hang of using plot() and points() in tandem, you’ll find it convenient that points() does not impact the axes. However, to start, this will be annoying. For example, let’s switch the order of the data in plot() and points().

Code 
plot(ford$mileage[ford$point_color != "tomato"],
     ford$cost_per_mile[ford$point_color != "tomato"],
     las = 1, pch = 19, cex = 1.2,
     col = "dodgerblue", main = "Cost vs Mileage",
     xlab = "Mileage", ylab = "Price per Mile")
points(ford$mileage[ford$point_color == "tomato"],
       ford$cost_per_mile[ford$point_color == "tomato"],
       pch = 19, cex = 1.2, col = "tomato")
Plot

Same plot as before, except this plot is significantly cutoff. All of the blue dots, the ones where the age is below the mean, are showing but many red dots are cut off.

The plot is different because when plot() is setting the axes, it doesn’t know that you’re planning on using points() next. So, it scales the axes so the data fed into plot() “fits” the space.

To overcome this issue, we can use the following trick. The idea is to plot the point (0, 0) (or any point, really!), but use type = "n" so the point is not displayed. Then, within this plot() call, we can set ylim and xlim equal to range() of the variables we’ll plot so axes fit the data perfectly.

Code 
plot(0, 0, type = "n",
     ylim = range(ford$cost_per_mile),
     xlim = range(ford$mileage), # range can include multiple vectors
     main = "Cost vs Mileage", las = 1,
     xlab = "Mileage", ylab = "Price per Mile")
points(ford$mileage[ford$point_color != "tomato"],
       ford$cost_per_mile[ford$point_color != "tomato"],
       pch = 19, cex = 1.2, col = "dodgerblue")
points(ford$mileage[ford$point_color == "tomato"],
       ford$cost_per_mile[ford$point_color == "tomato"],
       pch = 19, cex = 1.2, col = "tomato")
Plot

Plot of mileage on the x-axis and cost per mile on the y-axis.

Of course, this is a lot more coding than the initial plot’s code. The idea of showing you this is that, now, you can always make sure your data “fits”. This is one of the little things that I use constantly, but it took me a long time to figure out.

Finally, adding lines to a plot is very similar in that one needs to use lines(). To illustrate, we will examine panel data on cigarette consumption by state (documentation).

Read in the data, and plot sales on the y-axis and year on the x-axis below. Be sure to clean the data where appropriate.

Solution 
cig <- read.csv("https://vincentarelbundock.github.io/Rdatasets/csv/Ecdat/Cigar.csv")
cig$year <- cig$year + 1900
plot(cig$year, cig$sales, las = 1,
     ylab = "Sales", xlab = "Year")
Plot

Plot of cigarette pack sales on the y-axis and time on the x-axis.

This figure is very difficult to understand. Let’s trim it down to just a few states. In addition, we can add colors to the figure.

Unfortunately, the states seem to just be numbered instead of labeled, so we’ll just pick 1 through 5. In addition, there does not seem to be a state number 2.

Code 
cig <- cig[cig$state %in% 1:5,]
plot(cig$year, cig$sales, las = 1,
     # since state is a number,
     #  we can just use this as the color
     col = cig$state,
     ylab = "Sales", xlab = "Year")
Plot

Plot of cigarette pack sales on the y-axis and time on the x-axis. This time, however, only four states are displayed.

This plot can still be improved. It’d be a lot more natural to see the data as lines instead of points. To do this, we can use type = "l".

Code 
plot(cig$year, cig$sales, las = 1,
     col = cig$state, type = "l",
     ylab = "Sales", xlab = "Year")
Plot

Plot of cigarette pack sales on the y-axis and time on the x-axis. There are three diaganol lines that connect the last data point in the time series of one state to the first data point in the time series of another state.

Notice two things about this plot. First, there’s only a single color. In R, you should think of a line as a single point. R cannot color different parts of line differently, so it will just take the first color it’s given (here, it’s 1, which is black). Second, there are these three crazy diagonal lines that dash across the plot. This is because R is trying to connect each line into a single one. If you look closely, R is connecting the last year of one state to the first year of another state.

To fix this, we need to use lines like we used points before. This is another example case of a time where we’ll want to set up the axes before we plot anything.

Code 
# before, I plotted 0, 0
# now, I am simply keeping the data
#   in plot().
# this way, I don't need to set the axes
#   via ylim() and xlim()
plot(cig$year, cig$sales,
     las = 1, type = "n",
     ylab = "Sales", xlab = "Year")
lines(cig$year[cig$state == 1],
      cig$sales[cig$state == 1],
      col = 1)
lines(cig$year[cig$state == 3],
      cig$sales[cig$state == 3],
      col = 3)
lines(cig$year[cig$state == 4],
      cig$sales[cig$state == 4],
      col = 4)
lines(cig$year[cig$state == 5],
      cig$sales[cig$state == 5],
      col = 5)
legend("bottomleft", ncol = 2,
       legend = c("State 1", "State 3", "State 4", "State 5"),
       bty = "n", col = c(1, 3, 4, 5), lty = 1)
Plot

A correct time series plot of the first four states, each colored differently.

Next module, you’ll learn about “loops”, which will significantly cut down on the amount of code we need to write to generate these lines.