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Introduction to R

Week 2: Making figures

Louisa Smith

July 20 - July 24

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Let's make our data...

beautiful

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#goals

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Basic structure of a ggplot

ggplot(data = {data}) +
      <geom>(aes(x = {xvar}, y = {yvar}, <characteristic> = {othvar}, ...),
             <characteristic> = "value", ...) + ...
  • {data}: must be a dataframe (or tibble!)
  • {xvar} and {yvar} are the names (unquoted) of the variables on the x- and y-axes
  • {othvar} is some other unquoted variable name that defines a grouping or other characteristic you want to map to an aesthetic
  • <geom>: the geometric feature you want to use; e.g., point (scatterplot), line, histogram, bar, etc.
  • <characteristic>: you can map {othvar} or a fixed "value" to any of a number of aesthetic features of the figure; e.g., color, shape, size, linetype, etc.
  • "value": a fixed value that defines some characteristic of the figure; e.g., "red", 10, "dashed"
  • ... : there are numerous other options to discover!
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ggplot builds figures by adding on pieces via a particular "grammar of graphics"

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ggplot(data = nlsy)

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ggplot(data = nlsy) +
  geom_bar(aes(x = eyesight,
          fill = factor(eyesight)))

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ggplot(data = nlsy) +
  geom_bar(aes(x = eyesight, 
          fill = factor(eyesight))) +
  facet_grid(
    cols = vars(glasses),
    labeller = labeller(glasses = c(
      "0" = "Doesn't wear glasses",
      "1" = "Wears glasses/contacts")))

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ggplot(data = nlsy) +
  geom_bar(aes(x = eyesight, 
          fill = factor(eyesight))) +
  facet_grid(
    cols = vars(glasses),
    labeller = labeller(glasses = c(
      "0" = "Doesn't wear glasses",
      "1" = "Wears glasses/contacts"))) +
  scale_fill_brewer(palette = "Spectral",
                    direction = -1)

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ggplot(data = nlsy) +
  geom_bar(aes(x = eyesight, 
          fill = factor(eyesight))) +
  facet_grid(
    cols = vars(glasses),
    labeller = labeller(glasses = c(
      "0" = "Doesn't wear glasses",
      "1" = "Wears glasses/contacts"))) +
  scale_fill_brewer(palette = "Spectral",
                    direction = -1) +
  scale_x_continuous(
    labels = c("Excellent", "Very Good",
               "Good", "Fair", "Poor"),
    breaks = c(1, 2, 3, 4, 5))

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ggplot(data = nlsy) +
  geom_bar(aes(x = eyesight, 
          fill = factor(eyesight))) +
  facet_grid(
    cols = vars(glasses),
    labeller = labeller(glasses = c(
      "0" = "Doesn't wear glasses",
      "1" = "Wears glasses/contacts"))) +
  scale_fill_brewer(palette = "Spectral",
                    direction = -1) +
  scale_x_continuous(
    labels = c("Excellent", "Very Good",
               "Good", "Fair", "Poor"),
    breaks = c(1, 2, 3, 4, 5)) +
  theme_minimal()

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ggplot(data = nlsy) +
  geom_bar(aes(x = eyesight, 
          fill = factor(eyesight))) +
  facet_grid(
    cols = vars(glasses),
    labeller = labeller(glasses = c(
      "0" = "Doesn't wear glasses",
      "1" = "Wears glasses/contacts"))) +
  scale_fill_brewer(palette = "Spectral",
                    direction = -1) +
  scale_x_continuous(
    labels = c("Excellent", "Very Good",
               "Good", "Fair", "Poor"),
    breaks = c(1, 2, 3, 4, 5)) +
  theme_minimal() +
  theme(legend.position = "none",
        axis.text.x = element_text(
      angle = 45, vjust = 1, hjust = 1)
    )

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ggplot(data = nlsy) +
  geom_bar(aes(x = eyesight, 
          fill = factor(eyesight))) +
  facet_grid(
    cols = vars(glasses),
    labeller = labeller(glasses = c(
      "0" = "Doesn't wear glasses",
      "1" = "Wears glasses/contacts"))) +
  scale_fill_brewer(palette = "Spectral",
                    direction = -1) +
  scale_x_continuous(
    labels = c("Excellent", "Very Good",
               "Good", "Fair", "Poor"),
    breaks = c(1, 2, 3, 4, 5)) +
  theme_minimal() +
  theme(legend.position = "none",
        axis.text.x = element_text(
      angle = 45, vjust = 1, hjust = 1)
    ) +
  labs(title = "Eyesight in NLSY",
       x = "Eyesight quality",
       y = NULL)

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ggplot(data = nlsy) +
  geom_bar(aes(x = eyesight, 
          fill = factor(eyesight))) +
  facet_grid(
    cols = vars(glasses),
    labeller = labeller(glasses = c(
      "0" = "Doesn't wear glasses",
      "1" = "Wears glasses/contacts"))) +
  scale_fill_brewer(palette = "Spectral",
                    direction = -1) +
  scale_x_continuous(
    labels = c("Excellent", "Very Good",
               "Good", "Fair", "Poor"),
    breaks = c(1, 2, 3, 4, 5)) +
  theme_minimal() +
  theme(legend.position = "none",
        axis.text.x = element_text(
      angle = 45, vjust = 1, hjust = 1)
    ) +
  labs(title = "Eyesight in NLSY",
       x = "Eyesight quality",
       y = NULL) +
  coord_cartesian(expand = FALSE)

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ggplot(data = nlsy, aes(x = income,
    y = age_bir, col = factor(sex))
)

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ggplot(data = nlsy, aes(x = income, 
    y = age_bir, col = factor(sex))
) +
  geom_point(alpha = 0.1)

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ggplot(data = nlsy, aes(x = income, 
    y = age_bir, col = factor(sex))
) +
  geom_point(alpha = 0.1) +
  scale_color_brewer(palette = "Set1",
    name = "Sex",
    labels = c("Male", "Female"))

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ggplot(data = nlsy, aes(x = income, 
    y = age_bir, col = factor(sex))
) +
  geom_point(alpha = 0.1) +
  scale_color_brewer(palette = "Set1",
    name = "Sex",
    labels = c("Male", "Female")) +
  scale_x_log10(labels =
                  scales::dollar)

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ggplot(data = nlsy, aes(x = income, 
    y = age_bir, col = factor(sex))
) +
  geom_point(alpha = 0.1) +
  scale_color_brewer(palette = "Set1",
    name = "Sex",
    labels = c("Male", "Female")) +
  scale_x_log10(labels = 
                  scales::dollar) +
  geom_smooth(aes(
    group = factor(sex)),
    method = "lm")

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ggplot(data = nlsy, aes(x = income, 
    y = age_bir, col = factor(sex))
) +
  geom_point(alpha = 0.1) +
  scale_color_brewer(palette = "Set1",
    name = "Sex",
    labels = c("Male", "Female")) +
  scale_x_log10(labels = 
                  scales::dollar) +
  geom_smooth(aes(
    group = factor(sex)), 
    method = "lm") +
  facet_grid(rows = vars(race_eth),
    labeller = labeller(race_eth = c(
    "1" = "Hispanic",
    "2" = "Black",
    "3" = "Non-Black, Non-Hispanic")))

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ggplot(data = nlsy, aes(x = income, 
    y = age_bir, col = factor(sex))
) +
  geom_point(alpha = 0.1) +
  scale_color_brewer(palette = "Set1",
    name = "Sex",
    labels = c("Male", "Female")) +
  scale_x_log10(labels = 
                  scales::dollar) +
  geom_smooth(aes(
    group = factor(sex)), 
    method = "lm") +
  facet_grid(rows = vars(race_eth),
    labeller = labeller(race_eth = c(
    "1" = "Hispanic",
    "2" = "Black",
    "3" = "Non-Black, Non-Hispanic"))) +
  theme_minimal()

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ggplot(data = nlsy, aes(x = income, 
    y = age_bir, col = factor(sex))
) +
  geom_point(alpha = 0.1) +
  scale_color_brewer(palette = "Set1",
    name = "Sex",
    labels = c("Male", "Female")) +
  scale_x_log10(labels = 
                  scales::dollar) +
  geom_smooth(aes(
    group = factor(sex)), 
    method = "lm") +
  facet_grid(rows = vars(race_eth),
    labeller = labeller(race_eth = c(
    "1" = "Hispanic",
    "2" = "Black",
    "3" = "Non-Black, Non-Hispanic"))) +
  theme_minimal() +
  theme(legend.position = "top")

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ggplot(data = nlsy, aes(x = income, 
    y = age_bir, col = factor(sex))
) +
  geom_point(alpha = 0.1) +
  scale_color_brewer(palette = "Set1",
    name = "Sex",
    labels = c("Male", "Female")) +
  scale_x_log10(labels = 
                  scales::dollar) +
  geom_smooth(aes(
    group = factor(sex)), 
    method = "lm") +
  facet_grid(rows = vars(race_eth),
    labeller = labeller(race_eth = c(
    "1" = "Hispanic",
    "2" = "Black",
    "3" = "Non-Black, Non-Hispanic"))) +
  theme_minimal() +
  theme(legend.position = "top") +
  labs(title = "Relationship between income and age at first birth",
    subtitle = "by sex and race",
    x = "Income",
    y = "Age at first birth")

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Basic example

ggplot(data = {data}) +
      <geom>(aes(x = {xvar}, y = {yvar}, <characteristic> = {othvar}, ...),
             <characteristic> = "value", ...) +
      ...
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Basic example

ggplot(data = nlsy) +
      <geom>(aes(x = {xvar}, y = {yvar}, <characteristic> = {othvar}, ...),
             <characteristic> = "value", ...) +
      ...

The data = argument must be a dataframe (or tibble)

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Basic example

ggplot(data = nlsy) +
      geom_point(aes(x = {xvar}, y = {yvar}, <characteristic> = {othvar}, ...),
             <characteristic> = "value", ...) +
      ...

geom_point() gives us a scatterplot

Other helpful "geoms" include geom_line(), geom_bar(), geom_histogram(), geom_boxplot()

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Image via https://nbisweden.github.io/RaukR-2019/ggplot/presentation/ggplot_presentation.html

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Basic example

ggplot(data = nlsy) +
      geom_point(aes(x = income, y = age_bir, <characteristic> = {othvar}, ...),
             <characteristic> = "value", ...) +
      ...

geom_point() requires an x = and a y = variable

Other geoms require other arguments

  • For example, geom_histogram() only requires an x = variable

Notice that the variable names are not in quotation marks

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Basic example

ggplot(data = nlsy, aes(x = income, y = age_bir, <characteristic> = {othvar}, ...)) +
      geom_point(<characteristic> = "value", ...) +
      ...

We could also put the aesthetics (the variables that are being mapped to the plot) in the initial ggplot() function

  • This will be helpful when we want multiple geoms (say, points and a line)
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ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir))

What if we want to change the color of the points?

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ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir),
             color = "blue")

When we put color = outside the aes(), it means we're giving it a specific color value that applies to all the points.

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ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir),
             color = "#3d93c8")

One of my favorite color resources: https://www.color-hex.com

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ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir,
                 color = eyesight))

When we put color = inside the aes() -- with no quotation marks -- it means we're telling it how it should assign colors.

Here we're plotting the values according to eyesight, where 1 is excellent and 5 is poor.

  • But they're kind of hard to distinguish!

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ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir,
               color = eyesight)) +
scale_color_gradient(low = "green",
                    high = "purple")

We can map the values of eyesight to a different continuous scale using scale_color_gradient()

You can read lots more about this function here, so you don't have to have such ugly color scales!

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ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir,
            color = eyesight))

Returning to the nice blues, we think: But wait! The variable eyesight isn't really continuous: it has 5 discrete values.

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ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir,
            color = factor(eyesight)))

Returning to the nice blues, we think: But wait! The variable eyesight isn't really continuous: it has 5 discrete values.

We can make R treat it as a "factor", or categorical variable, with the factor() function

We'll see lots more on factors later!

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ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir,
            color = factor(eyesight))) +
scale_color_manual(
    values = c("blue", "purple", "red",
               "green", "yellow"))

Now if we want to change the color scheme, we have to use a different function.

Before we used scale_color_gradient, now scale_color_manual.

  • There are a lot of options that follow the same naming scheme.

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ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir,
            color = factor(eyesight))) +
scale_color_brewer(palette = "Set1")

There are tons of different options in R for color palettes.

You can play around with those in the RColorBrewer package here: http://colorbrewer2.org

  • You can access the scales in that package with scale_color_brewer(), or see them all after installing the package with RColorBrewer::display.brewer.all()

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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"))

Each of the scale_color_x() functions has a lot of the same arguments.

Make sure if you are labelling a factor variable in a plot like this that you get the names right!

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1

Your turn...

Exercises 2.1: Make a fancy scatterplot showing the relationship between sleep on weekdays and on weekends.

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Facets

One of the most useful features of ggplot2 is the ability to "facet" a graph by splitting it up according to the values of some variable.

You might use this to show results for a lot of outcomes or exposures at once, for example, or see how some relationship differs by something like age or geographic region

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ggplot(data = nlsy) +
  geom_bar(aes(x = nsibs)) +
  labs(x = "Number of siblings")

We'll introduce bar graphs at the same time!

Notice how we only need an x = argument - the y-axis is automatically the count with this geom.

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ggplot(data = nlsy) +
  geom_bar(aes(x = nsibs)) +
  labs(x = "Number of siblings") +
  facet_grid(cols = vars(region))

The facet_grid() function splits up the data according to a variable(s).

Here we've split it by region into columns.

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ggplot(data = nlsy) +
  geom_bar(aes(x = nsibs)) +
  labs(x = "Number of siblings") +
  facet_grid(rows = vars(region))

Since this is hard to read, we'll probably want to split by rows instead.

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ggplot(data = nlsy) +
  geom_bar(aes(x = nsibs)) +
  labs(x = "Number of siblings") +
  facet_grid(rows = vars(region),
             margins = TRUE)

We can also add a row for all of the data together.

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ggplot(data = nlsy) +
  geom_bar(aes(x = nsibs)) +
  labs(x = "Number of siblings") +
  facet_grid(rows = vars(region),
             margins = TRUE,
             scales = "free_y")

This squishes the other rows though! We can allow them all to have their own axis limits with the scales = argument.

Other options are "free_x" if we want to allow the x-axis scale to vary, or just "free" to combine both.

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ggplot(data = nlsy) +
  geom_bar(aes(x = nsibs)) +
  labs(x = "Number of siblings") +
  facet_wrap(vars(region))

We can use facet_wrap() instead, if we want to use both multiple rows and columns for all the values of a variable.

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ggplot(data = nlsy) +
  geom_bar(aes(x = nsibs)) +
  labs(x = "Number of siblings") +
  facet_wrap(vars(region),
             ncol = 3)

It tries to make a good decision, but you can override how many columns you want!

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Wait, these look like histograms!

When we have a variable with a lot of possible values, we may want to bin them with a histogram

ggplot(nlsy) +
  geom_histogram(aes(x = income))

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stat_bin() using bins = 30. Pick better value with binwidth.

We used discrete values with geom_bar(), but with geom_histogram() we're combining values: the default is into 30 bins.

This is one of the most common warning messages I get in R!

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ggplot(data = nlsy) +
  geom_histogram(aes(x = income), 
                 bins = 10)

We can use bins = instead, if we want!

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ggplot(data = nlsy) +
  geom_histogram(aes(x = income), 
                 bins = 100)

Be aware that you may interpret your data differently depending on how you bin it!

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ggplot(data = nlsy) +
  geom_histogram(aes(x = income), 
                 binwidth = 1000)

Sometimes the bin width actually has some meaning

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ggplot(data = nlsy) +
  geom_histogram(aes(x = income),
                 bins = 100) +
  scale_x_log10()

There are a lot of scale_x_() and scale_y_() functions for you to explore!

The naming schemes work similarly to the scale_color ones, just with different options!

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2

Your turn...

Exercises 2.2: Make a fancy histogram showing the distribution of income in this data.

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Finally, themes to make our plots prettier

You probably recognize the ggplot theme. But did you know you can trick people into thinking you made your figures in Stata?

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p <- ggplot(data = nlsy) +
  geom_boxplot(aes(
    x = factor(sleep_wknd), 
    y = sleep_wkdy,
    fill = factor(sleep_wknd))) +
  scale_fill_discrete(guide = FALSE) +
  labs(x = "hours slept on weekends",
       y = "hours slept on weekends",
       title = "The more people sleep on weekends, the more they\n sleep on weekdays",
       subtitle = "According to NLSY data")
p

Let's store our plot first.

Plots work just like other R objects, meaning we can use the assignment arrow.

Can you figure out what each chunk of this code is doing to the figure?

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p +
  theme_minimal()

We can change the overall theme

Since we stored the plot as p, it's easy to add on / try different things

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p +
  theme_dark()

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p +
  theme_classic()

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p +
  theme_void()

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p +
  ggthemes::theme_fivethirtyeight()

Other packages may contain themes.

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p +
  ggthemes::theme_excel_new()

In case you miss Excel....

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p +
  ggthemes::theme_gdocs()

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p +
  louisahstuff::my_theme()

You can even make your own!

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Finally, save it!

If your data changes, you can easily run the whole script again:

library(tidyverse)
dataset <- read_csv("dataset.csv")
ggplot(dataset) + 
  geom_point(aes(x = xvar, y = yvar))
`ggsave`(filename = "scatterplot.pdf")

The ggsave() function will automatically save the most recent plot in your output.

To be safe, you can store your plot, e.g., p <- ggplot(...) + ... and then 

ggsave(filename = "scatterplot.pdf", plot = p)
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More resources

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3

Your turn...

Exercises 2.3: Recreate this plot!

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Let's make our data...

beautiful

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