1+2
## [1] 3
library(tidyverse)
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…
## # ℹ 224 more rows
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = class))
How to get help
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_wrap(~class, nrow = 2)
different visual object to represent data
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy))
ggplot(data = mpg) +
geom_smooth(mapping = aes(x = displ, y = hwy))
not every aesthetic works with every geom
two geoms in the same graph!
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point(mapping = aes(color = class)) +
geom_smooth()
local vs. global mappings This makes it possible to display different aesthetics in different layers.
specify different data for each layer
adjustments for bar charts
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge")
adjustments for scatterplots
switch x and y
set the aspect ratio correctly for maps
Polar coordinates reveal an interesting connection between a bar chart and a Coxcomb chart.
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut, fill = cut)) +
coord_polar()
The grammar of graphics is based on the insight that you can uniquely describe any plot as a combination of: