Graphics for communications | r4ds chapter 28

Label

labs()

Title

Figure title should be descriptive:

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) +
  labs(title = "Fuel efficiency generally decreases with engine size")

Subtitle and caption

subtitle adds additional detail in a smaller font beneath the title.

caption adds text at the bottom right of the plot, often used to describe the source of the data.

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) + 
  labs(
    title = "Fuel efficiency generally decreases with engine size",
    subtitle = "Two seaters (sports cars) are an exception because of their light weight",
    caption = "Data from fueleconomy.gov"
  )

Axis labels

ggplot(mpg, aes(displ, hwy)) +
geom_point(aes(colour = class)) +
geom_smooth(se = FALSE) +
labs(
  x = "Engine displacement (L)",
  y = "Highway fuel economy (mpg)"
)

Math equations

read about available options in ?plotmath

df <- tibble(x = runif(10), y = runif(10))
ggplot(df, aes(x, y)) + geom_point() +
  labs(
    x = quote(sum(x[i] ^ 2, i == 1, n)),
    y = quote(alpha + beta + frac(delta, theta))
  )

Annotations

Find the most fuel efficient car in each car class:

best_in_class <- mpg %>%
  group_by(class) %>%
  filter(row_number(desc(hwy)) == 1)

# equivalent as 
# best_in_class <- filter(group_by(mpg, class), row_number(desc(hwy)) == 1)
best_in_class

## # A tibble: 7 x 11
## # Groups:   class [7]
##   manufacturer model     displ  year   cyl trans  drv     cty   hwy fl    class 
##   <chr>        <chr>     <dbl> <int> <int> <chr>  <chr> <int> <int> <chr> <chr> 
## 1 chevrolet    corvette    5.7  1999     8 manua… r        16    26 p     2seat…
## 2 dodge        caravan …   2.4  1999     4 auto(… f        18    24 r     miniv…
## 3 nissan       altima      2.5  2008     4 manua… f        23    32 r     midsi…
## 4 subaru       forester…   2.5  2008     4 manua… 4        20    27 r     suv   
## 5 toyota       toyota t…   2.7  2008     4 manua… 4        17    22 r     pickup
## 6 volkswagen   jetta       1.9  1999     4 manua… f        33    44 d     compa…
## 7 volkswagen   new beet…   1.9  1999     4 manua… f        35    44 d     subco…

dplyr::desc function transforms a vector into a format that will be sorted in descending order
dplyr::filter function subsets a data frame, retaining all rows that satisfy your conditions

geom_label() draws a rectangle behind the text

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_label(aes(label = model), data = best_in_class, nudge_y = 2, alpha = 0.5)

ggrepel package automatically adjust labels so that they don’t overlap:

library("ggrepel")
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_point(size = 3, shape = 1, data = best_in_class) +
  ggrepel::geom_label_repel(aes(label = model), data = best_in_class)

Scales

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class))

automatically adds scales

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  scale_x_continuous() +
  scale_y_continuous() +
  scale_colour_discrete()

breaks

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  scale_y_continuous(breaks = seq(15, 40, by = 5))

labels

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  scale_x_continuous(labels = NULL) +
  scale_y_continuous(labels = NULL)

Plot y-axis at log scale:

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  scale_y_log10()

Plot x-axis in reverse order:

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  scale_x_reverse()

ColorBrewer scales are documentd online at http://colorbrewer2.org/
Available via RColorBrewer package

use scale_colour_manual() to use predefined mapping between values and colors

presidential %>%
  mutate(id = 33 + row_number()) %>%
  ggplot(aes(start, id, colour = party)) +
    geom_point() +
    geom_segment(aes(xend = end, yend = id)) +
    scale_colour_manual(values = c(Republican = "red", Democratic = "blue"))

use scale_colour_gradient() or scale_fill_gradient() for continuous colour
viridis::scale_colour_viridis()

df <- tibble(
  x = rnorm(10000),
  y = rnorm(10000)
)
ggplot(df, aes(x, y)) +
  geom_hex() +
  coord_fixed()

ggplot(df, aes(x, y)) +
  geom_hex() +
  viridis::scale_fill_viridis() +
  coord_fixed()

All color scales come in two variety:

scale_colour_x() for colour aesthetics
scale_fill_x() for fill aesthetics

Legends

Set legend position: "left", "right", "top", "bottom", none:

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) + 
  theme(legend.position = "left")

See following link for more details on how to change title, labels, … of a legend.

http://www.sthda.com/english/wiki/ggplot2-legend-easy-steps-to-change-the-position-and-the-appearance-of-a-graph-legend-in-r-software

Zooming

Without clipping (removes unseen data points)

ggplot(mpg, mapping = aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth() +
  coord_cartesian(xlim = c(5, 7), ylim = c(10, 30))

With clipping (removes unseen data points)

ggplot(mpg, mapping = aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth() +
  xlim(5, 7) + ylim(10, 30)

same as

mpg %>%
  filter(displ >= 5, displ <= 7, hwy >= 10, hwy <= 30) %>%
  ggplot(aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth()

ggplot(mpg, mapping = aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth() +
  scale_x_continuous(limits = c(5, 7)) +
  scale_y_continuous(limits = c(10, 30))

Themes

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) +
  theme_bw()

Saving plots

ggplot(mpg, aes(displ, hwy)) + geom_point()

ggsave("my-plot.pdf")
## Saving 7 x 5 in image

Cheat sheet

RStudio cheat sheet is extremely helpful.

Data visualization with ggplot2 (cont.)

MATH-7360 Data Analysis

Dr. Xiang Ji @ Tulane University

Sep 20, 2020

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