2  Introduction of ggplot2 package

#install.packages("ggplot2")
library(ggplot2)

head(mpg)

# A tibble: 6 × 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(l5)   f        18    29 p     compa…
2 audi         a4      1.8  1999     4 manual(m5) f        21    29 p     compa…
3 audi         a4      2    2008     4 manual(m6) f        20    31 p     compa…
4 audi         a4      2    2008     4 auto(av)   f        21    30 p     compa…
5 audi         a4      2.8  1999     6 auto(l5)   f        16    26 p     compa…
6 audi         a4      2.8  1999     6 manual(m5) f        18    26 p     compa…

class(mpg)

[1] "tbl_df"     "tbl"        "data.frame"

We can see that “mpg” is a data frame, and also a tibble. A tibble is like a data frame, but with other features. Tibbles are from the package “tidyverse”.

2.1 Scatter plot: good visualization for two numerical variables

2.1.1 basic

We draw a scatter plot between “cty” and “hwy”.

ggplot(data = mpg,
       mapping = aes(x = cty, y = hwy)) +
  geom_point()

2.1.2 colored by a third variable

We can color the points by another variable, for example, colored by “drv”. This allows us to see how “drv” influences the relationship between cty and hwy.

ggplot(data = mpg, 
       mapping = aes(x = cty, y = hwy)) +
  geom_point(aes(color = drv))

2.1.3 adding some labeling

Edit labels:

p1 <- ggplot(mpg, aes(x = cty, y = hwy)) +
  geom_point(aes(color = drv))
p1 + labs(title ="Hightway MPG VS City MPG",
          subtitle = "Colored by different types of drive train",
          x = "City Mileage",
          y = "Highway Mileage",
          color = "Driver Type")

2.1.4 colored and shaped simultaneously

Sometimes we want the points colored and shaped simultaneously by the same variable.

ggplot(mpg, aes(x = cty, y = hwy)) +
  geom_point(aes(color = drv, shape = drv)) +
  labs(title ="Hightway MPG VS City MPG",
       x = "City MPG",
       y = "HW MPG",
       color = "Drive type",
       shape = "Drive type")

Finally, it would be nice to change the legends texts in the above plot.

ggplot(mpg, aes(x = cty, y = hwy)) +
  geom_point(aes(color = drv, shape = drv)) +
  labs(title ="Hightway MPG VS City MPG",
       x = "City MPG",
       y = "HW MPG",
       color = "Drive Type",
       shape = "Drive Type") +
  scale_color_manual(labels = c("4wd", "front-wheel","rear wheel"),
                     values = c("red","green","blue")) 

The above picture is fine, but it would be better if we can only have ONE legend since it’s the same variable “drv”.

ggplot(mpg, aes(x = cty, y = hwy)) +
  geom_point(aes(color = drv, shape = drv)) +
  labs(title ="Hightway MPG VS City MPG",
       x = "City MPG",
       y = "HW MPG",
       color = "Drive Type",
       shape = "Drive Type") +
  scale_color_manual(labels = c("4wd", "front-wheel", "rear wheel"),
                     values = c("red","green","blue")) +
  scale_shape_manual(labels = c("4wd", "front-wheel", "rear wheel"),
                     values = c(16, 17, 15))

In the above code, we can see that value 16 corresponds to a solid circle, 17 corresponds to a solid triangle, and 15 corresponds to a solid square. At the end of this page, you will find all the correspondence between value and shape.

2.2 Save a plot

ggsave() is a convenient function for saving a plot. It defaults to saving the last plot displayed. Below is an example:

ggsave(“path/some_plot.png”, width = 7, height = 3.5)

This chapter is only an introduction to ggplot2 package. We will keep learning ggplot() in the next chapter.