【Reading】ggplot2: Elegant Graphics for Data Analysis Part 1 C1-C9

Ploting
Author

Tony Duan

Published

October 23, 2022

https://ggplot2-book.org/index.html

Chapter 1 Introduction

Grammar of graphics

In brief, the grammar tells us that a graphic maps the data to the aesthetic attributes (colour, shape, size) of geometric objects (points, lines, bars)

Chapter 2 First steps

data

mpg data. It includes information about the fuel economy of popular car models in 1999 and 2008, collected by the US Environmental Protection Agency.

Code 
library(ggplot2)
mpg
# A tibble: 234 × 11
   manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
   <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
 1 audi         a4           1.8  1999     4 auto… f        18    29 p     comp…
 2 audi         a4           1.8  1999     4 manu… f        21    29 p     comp…
 3 audi         a4           2    2008     4 manu… f        20    31 p     comp…
 4 audi         a4           2    2008     4 auto… f        21    30 p     comp…
 5 audi         a4           2.8  1999     6 auto… f        16    26 p     comp…
 6 audi         a4           2.8  1999     6 manu… f        18    26 p     comp…
 7 audi         a4           3.1  2008     6 auto… f        18    27 p     comp…
 8 audi         a4 quattro   1.8  1999     4 manu… 4        18    26 p     comp…
 9 audi         a4 quattro   1.8  1999     4 auto… 4        16    25 p     comp…
10 audi         a4 quattro   2    2008     4 manu… 4        20    28 p     comp…
# … with 224 more rows

  1. cty and hwy record miles per gallon (mpg) for city and highway driving.

  2. displ is the engine displacement in litres.

  3. drv is the drivetrain: front wheel (f), rear wheel (r) or four wheel (4).

  4. model is the model of car. There are 38 models, selected because they had a new edition every year between 1999 and 2008.

  5. class is a categorical variable describing the “type” of car: two seater, SUV, compact, etc.

Key components

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

  1. data

  2. A set of aesthetic mappings between variables in the data and visual properties, and

  3. At least one layer which describes how to render each observation. Layers are usually created with a geom function.

Colour, size, shape and other aesthetic attributes

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

Faceting

Code 
ggplot(mpg, aes(displ, hwy)) + 
  geom_point() + 
  facet_wrap(~class)

smooth line

Code 
ggplot(data=mpg, aes(displ, hwy)) + 
  geom_point() + 
  geom_smooth()

Code 
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

jitter

Code 
ggplot(mpg, aes(drv, hwy)) + geom_jitter()

boxplot

Code 
ggplot(mpg, aes(drv, hwy)) + geom_boxplot()

Histograms

Code 
ggplot(mpg, aes(hwy)) + geom_histogram()

Code 
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

freqpoly

Code 
ggplot(mpg, aes(hwy)) + geom_freqpoly()

Code 
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Bar charts

Code 
ggplot(mpg, aes(manufacturer)) + 
  geom_bar()

Code 
drugs <- data.frame(
  drug = c("a", "b", "c"),
  effect = c(4.2, 9.7, 6.1)
)
Code 
ggplot(drugs, aes(drug, effect)) + geom_bar(stat = "identity")

Code 
ggplot(drugs, aes(drug, effect)) + geom_point()

Time series with line and path plots

Code 
ggplot(economics, aes(date, unemploy / pop)) +
  geom_line()

Code 
ggplot(economics, aes(date, uempmed)) +
  geom_line()

Code 
ggplot(economics, aes(unemploy / pop, uempmed)) + 
  geom_path() +
  geom_point()

Code 
year <- function(x) as.POSIXlt(x)$year + 1900
ggplot(economics, aes(unemploy / pop, uempmed)) + 
  geom_path(colour = "grey50") +
  geom_point(aes(colour = year(date)))

Modifying the axes

Code 
ggplot(mpg, aes(cty, hwy)) +
  geom_point(alpha = 1 / 3)

change x label and y label

Code 
ggplot(mpg, aes(cty, hwy)) +
  geom_point(alpha = 1 / 3) + 
  xlab("city driving (mpg)") + 
  ylab("highway driving (mpg)")

no x label and y label

Code 
# Remove the axis labels with NULL
ggplot(mpg, aes(cty, hwy)) +
  geom_point(alpha = 1 / 3) + 
  xlab(NULL) + 
  ylab(NULL)

Output

Code 
p <- ggplot(mpg, aes(displ, hwy, colour = factor(cyl))) +
  geom_point()
p

summary plot info

Code 
summary(p)
data: manufacturer, model, displ, year, cyl, trans, drv, cty, hwy, fl,
  class [234x11]
mapping:  x = ~displ, y = ~hwy, colour = ~factor(cyl)
faceting: <ggproto object: Class FacetNull, Facet, gg>
    compute_layout: function
    draw_back: function
    draw_front: function
    draw_labels: function
    draw_panels: function
    finish_data: function
    init_scales: function
    map_data: function
    params: list
    setup_data: function
    setup_params: function
    shrink: TRUE
    train_scales: function
    vars: function
    super:  <ggproto object: Class FacetNull, Facet, gg>
-----------------------------------
geom_point: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity

Save ggplot to png picture

Code 
# Save png to disk
ggsave("plot.png", p, width = 5, height = 5)

Save ggplot rds and load it back

Code 
saveRDS(p, "plot.rds")
q <- readRDS("plot.rds")
q

3 Individual geoms

Code 
df <- data.frame(
  x = c(3, 1, 5), 
  y = c(2, 4, 6), 
  label = c("a","b","c")
)
p <- ggplot(df, aes(x, y, label = label)) + 
  labs(x = NULL, y = NULL) + # Hide axis label
  theme(plot.title = element_text(size = 12)) # Shrink plot title
Code 
p + geom_point() + ggtitle("point")

Code 
p + geom_text() + ggtitle("text")

Code 
p + geom_bar(stat = "identity") + ggtitle("bar")

Code 
p + geom_tile() + ggtitle("raster")

4 Collective geoms

5 Statistical summaries

6 Maps

7 Networks

8 Annotations

9 Arranging plots

Reference

[Book]ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham https://ggplot2-book.org/index.html