Make a scatterplot

Make a scatter plot of the relationship between hours of sleep on weekends and weekdays. Color it according to region (where 1 = northeast, 2 = north central, 3 = south, and 4 = west). Use the code from the example below to get started!

ggplot(data = nlsy) +
  geom_point(aes(x = income, y = age_bir, color = factor(eyesight))) +
  scale_color_brewer(palette = "Set1", name = "Eyesight",
                     labels = c("Excellent", "Very Good", "Good",
                                "Fair", "Poor"))

Jittering

Using your plot from the previous question, replace geom_point() with geom_jitter(). What does this do? Why might this be a good choice for this graph? Play with the width = and height = options within geom_jitter(). This site may help: https://ggplot2.tidyverse.org/reference/geom_jitter.html

Shapes

Use the shape = argument to map the sex variable to different shapes. Change the shapes to squares and diamonds. (Hint: how did we manually change colors to certain values? This page might also help: https://ggplot2.tidyverse.org/articles/ggplot2-specs.html)

Explore!

Using a structure like this, you can explore changing what’s in the <>.

• Use either the resources in the slides (now also on the resources page), or just start typing e.g., geom_ or scale_color and choose one of the autocompletes.
• Depending on what you choose, different arguments might be required/allowed. For example, geom_point() requires both x = and y =, but geom_histogram() only x =. You can use help(geom_point) for guidance, under the “Aesthetics” section.
• What happens if you include color = both within and outside of the aes() argument? What happens if you use an actual color, like "blue" inside? Or a variable name outside?
• It might be helpful for you to keep a script with all the different things you try, with a comment about whether or not it worked and what you did.

ggplot(data = nlsy) +
  geom_<>(aes(x = <>, y = <>, color = <>, ...), color = <>, ...) +
  scale_color_<>(name = <>, ...)

Density histograms

When we’re comparing distributions with very different numbers of observations, instead of scaling the y-axis like we did with the facet_grid() function, we might want to make density histograms, where instead of the count of observations along the y-axis, we have the density (i.e., the histogram is scaled so that its entire area adds up to 1). Use google to figure out how to make a density histogram of income. Facet it by region.

Color your histograms

Make each of the regions in your previous histogram a different color. (Hint: compare what col = and fill = do to histograms).

scale_x_...

Instead of a log-transformed x-axis, make a square-root transformed x-axis. See what other options for the x-axis you have, using the autocomplete in RStudio.

Label your x-axis

Doing the last part squishes the labels on the x-axis. Using the breaks = argument that all the scale_x_...() functions have, make labels at 1000, 10000, 25000, and 50000.

Using ggsave()

Using the plot above as guidance, change at least 3 elements, if not more! Then save it using the code below. These are probably not the right dimensions for your plot, so experiment! You can also change from pdf to e.g., png by changing the file name. Look in the files pane to see where your saved plot shows up.

ggplot()

ggsave(filename = "my_plot.pdf", height = 8, width = 4)

Storing your plot as an object

That function will automatically save the last plot you made. If you’re making lots of plots, you should store them and refer to them by name. Try that with a new plot here. Notice what happens in the plots pane, and in the environment pane, when you do so.

new_plot <- ggplot()

ggsave(plot = new_plot, filename = "another_plot.png")

Challenge: Recreate the plot from the slides

This will definitely require some Googling (reminder: I have to Google something for almost every plot I make!) so is a challenge! See how close you can get. We’ll also be working on this in lab, so don’t worry if you don’t get very far!