Ch. 28: Graphics for communication

Functions and notes:
  • labs() to add labels
    • common args: title, subtitle, caption, x, y, colour, …
    • for mathematical equations use quote and see ?plotmath
      • e.g. within labs() could do y = quote(alpha + beta + frac(delta, theta))
  • geom_text() similar to geom_point() but with argument label that adds text where the point would be
    • use nudge_x and nudge_y to move position around
    • use vjust (‘top’, ‘center’, or ‘bottom’) and hjust (‘left’, ‘center’, or ‘right’) to control alignment of text
    • can use +Inf and -Inf to put text in exact corners
    • use stringr::str_wrap() to automatically add line breaks
    • geom_label() is like geom_text() but draws a box around the data that makes easier to see (can adjust alpha and fill of background box)
    • ggrepel::geom_label_repel() is like geom_label() but prevents overlap of labels
  • geom_hline() and geom_vline for reference lines (often use size = 2 and colour = white)
  • geom_rect() to draw rectangle around points (controlled by xmin, xmax, ymin, ymax)
  • geom_segment() to draw attention to a point with an arrow, (common args: arrow, x, y, xend, yend)
  • annotate can add in labels by hand (not from values of dataframe)
  • scale_x_continuous(), scale_y_continuous(), scale_colour_discrete(), … scale_{aes}_{scale type}()
    • breaks and labels are key args (can set labels = NULL to remove values)
    • scale_colour_brewer(palette = "Set1")for color blind people
    • scale_colour_manual() for definining colours with specific values, e.g. scale_colour_manual(values = c(Republican = "red", Democratic = "blue"))
    • for continuous scales try scale_colour_gradient(), scale_fill_gradient(), scale_colour_gradient2() (two colour gradient, e.g. + / - values), viridis::scale_colour_viridis()
    • date scales are a little different, e.g. scale_x_date() takes args date_labels (e.g. date_labels = "'%y") and date_breaks (e.g. date_breaks = "2 days")
    • scale_x_log10(), scale_y_log10… to substitute values with a particular transformation
  • theme() customize any non-data components of plots
    • e.g. remove legend with theme(legend.position = "none") (could also have inputted “left”, “top”, “bottom”, or “right”)
  • guides() to control display of individual legends – use in conjunction with guide_legend() or guide_colourbar()
  • coord_cartesian() to zoom using xlim and ylim args
  • can customize your themes, e.g. theme_bw(), theme_classic()…, see ggthemes for a bunch of others
  • ggsave() defaults to save most recent plot
    • key options: fig.width, fig.height, fig.asp, out.width, out.height (see chapter for details)
    • other options: fig.align, fig.cap, dev (e.g. dev = "png")

28.2: Label

28.2.1

  1. Create one plot on the fuel economy data with customised title, subtitle, caption, x, y, and colour labels.

    mpg %>% 
  ggplot(aes(x = hwy, displ))+
  geom_count(aes(colour = class))+
  labs(title = "Larger displacement has lower gas mileage efficiency",
       subtitle = "SUV and pickup classes` tend to be highest on disp",
       caption = "Data is for cars made in either 1999 or 2008",
       colour = "Car class")

  2. The geom_smooth() is somewhat misleading because the hwy for large engines is skewed upwards due to the inclusion of lightweight sports cars with big engines. Use your modelling tools to fit and display a better model.

    mpg %>% 
  ggplot(aes(x = hwy, displ))+
  geom_count(aes(colour = class))+
  labs(title = "Larger displacement has lower gas mileage efficiency",
       subtitle = "SUV and pickup classes` tend to be highest on disp",
       caption = "Data is for cars made in either 1999 or 2008",
       colour = "Car class")+
  geom_smooth()

    You could take into account the class of the car

    mpg %>% 
  ggplot(aes(x = hwy, displ, colour = class))+
  geom_count()+
  labs(title = "Larger displacement has lower gas mileage efficiency",
       subtitle = "SUV and pickup classes` tend to be highest on disp",
       caption = "Data is for cars made in either 1999 or 2008",
       colour = "Car class")+
  geom_smooth()+
  facet_wrap(~class)

  3. Take an exploratory graphic that you’ve created in the last month, and add informative titles to make it easier for others to understand.

    Done seperately.

28.3: Annotations

28.3.1

  1. Use geom_text() with infinite positions to place text at the four corners of the plot.

    data_label <- tibble(x = c(Inf, -Inf), 
       hjust = c("right", "left"), 
       y = c(Inf, -Inf),
       vjust = c("top", "bottom")) %>% 
  expand(nesting(x, hjust), nesting(y, vjust)) %>% 
  mutate(label = glue::glue("hjust: {hjust}; vjust: {vjust}"))

mpg %>% 
  ggplot(aes(x = hwy, displ))+
  geom_count(aes(colour = class))+
  labs(title = "Larger displacement has lower gas mileage efficiency",
       subtitle = "SUV and pickup classes` tend to be highest on disp",
       caption = "Data is for cars made in either 1999 or 2008",
       colour = "Car class")+
  geom_text(aes(x = x, y = y, label = label, hjust = hjust, vjust = vjust),
            data = data_label)

  2. Read the documentation for annotate(). How can you use it to add a text label to a plot without having to create a tibble?

    • function adds geoms, but not mapped from variables of a dataframe, so can pass in small items or single labels
    mpg %>% 
  ggplot(aes(x = hwy, displ))+
  geom_count(aes(colour = class))+
  labs(title = "Larger displacement has lower gas mileage efficiency",
       subtitle = "SUV and pickup classes` tend to be highest on disp",
       caption = "Data is for cars made in either 1999 or 2008",
       colour = "Car class")+
  annotate("text", x = Inf, y = Inf, label = paste0("Mean highway mpg: ", round(mean(mpg$hwy))), vjust = "top", hjust = "right")

  3. How do labels with geom_text() interact with faceting? How can you add a label to a single facet? How can you put a different label in each facet? (Hint: think about the underlying data.)

    data_label_single <- tibble(x = Inf, y = Inf, label = paste0("Mean highway mpg: ", round(mean(mpg$hwy))))

data_label <- mpg %>% 
  group_by(class) %>% 
  summarise(hwy = round(mean(hwy))) %>% 
  mutate(label = paste0("hwy mpg for ", class, ": ", hwy)) %>% 
  mutate(x = Inf, y = Inf)

mpg %>% 
  ggplot(aes(x = hwy, displ))+
  geom_count(aes(colour = class))+
  labs(title = "Larger displacement has lower gas mileage efficiency",
       subtitle = "SUV and pickup classes` tend to be highest on disp",
       caption = "Data is for cars made in either 1999 or 2008",
       colour = "Car class")+
  facet_wrap(~class)+
  geom_smooth()+
  geom_text(aes(x = x, y = y, label = label), data = data_label, vjust = "top", hjust = "right")

  4. What arguments to geom_label() control the appearance of the background box?

    • fill argument controls background color
    • alpha controls it’s relative brighness
    best_in_class <- mpg %>%
  group_by(class) %>%
  filter(row_number(desc(hwy)) == 1)

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_label(aes(label = model), data = best_in_class, nudge_y = 2, alpha = 0.1, fill = "green")

  5. What are the four arguments to arrow()? How do they work? Create a series of plots that demonstrate the most important options.

    b <- ggplot(mtcars, aes(wt, mpg)) +
  geom_point()

df <- data.frame(x1 = 2.62, x2 = 3.57, y1 = 21.0, y2 = 15.0)

b + geom_curve(
  aes(x = x1, y = y1, xend = x2, yend = y2),
  data = df,
  arrow = arrow(length = unit(0.03, "npc"))
)

    • angle (in degrees), length (use unit() function to specify with number and type, e.g. “inches”), ends (“last”, “first”, or “both” – specifying which end), type (“open” or “closed”)
    • See 28.3.1.5 for more notes on line options (not specific to arrow())

28.4: Scales

28.4.4

  1. Why doesn’t the following code override the default scale?

    df <- tibble(x = rnorm(100), y = rnorm(100))

ggplot(df, aes(x, y)) +
  geom_hex() +
  scale_colour_gradient(low = "white", high = "red") +
  coord_fixed()

    • geom_hex uses fill, not colour
    df <- tibble(x = rnorm(100), y = rnorm(100))

ggplot(df, aes(x, y)) +
  geom_hex() +
  scale_fill_gradient(low = "white", high = "red") +
  coord_fixed()

  2. What is the first argument to every scale? How does it compare to labs()?

    • name, i.e. what the title will be for that axis/legend/… labs first argument is ... so requires you to name the input

  3. Change the display of the presidential terms by:

    1. Combining the two variants shown above.
    2. Improving the display of the y axis.
    3. Labelling each term with the name of the president.
    4. Adding informative plot labels.
    5. Placing breaks every 4 years (this is trickier than it seems!).
    presidential %>%
  mutate(id = 33L + row_number()) %>%
  ggplot(aes(start, id, colour = party)) +
  geom_point() +
  geom_segment(aes(xend = end, yend = id)) +
  geom_text(aes(label = name), vjust = "bottom", nudge_y = 0.2)+
  scale_colour_manual(values = c(Republican = "red", Democratic = "blue"))+
  scale_x_date("Year in 20th and 21st century", date_breaks = "4 years", date_labels = "'%y")+
  # scale_x_date(NULL, breaks = presidential$start, date_labels = "'%y")+
  scale_y_continuous(breaks = c(36, 39, 42), labels = c("36th", "39th", "42nd"))+
  labs(y = "President number", x = "Year")

  4. Use override.aes to make the legend on the following plot easier to see.

    diamonds %>% 
  ggplot(aes(carat, price)) +
  geom_point(aes(colour = cut), alpha = 1/20)+
  guides(colour = guide_legend(override.aes = list(alpha = 1)))

Appendix

28.3.1.5

Not arrow() function specifically, but other line end options

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_segment(aes(xend = displ +5, yend = hwy + 5), data = best_in_class, lineend = "round")

b <- ggplot(mtcars, aes(wt, mpg)) +
  geom_point()

df <- data.frame(x1 = 2.62, x2 = 3.57, y1 = 21.0, y2 = 15.0)

b +
 geom_curve(aes(x = x1, y = y1, xend = x2, yend = y2, colour = "curve"), data = df) +
 geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2, colour = "segment"), data = df)

b + geom_curve(aes(x = x1, y = y1, xend = x2, yend = y2), data = df, curvature = -0.2)

b + geom_curve(aes(x = x1, y = y1, xend = x2, yend = y2), data = df, curvature = 1)

Grolemund, Garrett, and Hadley Wickham. 2017. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. 1st ed. O’Reilly Media.