Chapter 3 Data Visualization

  1. Geometric shapes
  2. Multiple smoothing lines
  3. Statistical transformations

Today we are going to try some more code from Chapter 3 Data Visualization.

To start we will load the tidyverse. Note that ggplot2 is the first package loaded!

library(tidyverse)

We will continue to work with the mpg dataset that is in the ggplot2 package.

mpg
ABCDEFGHIJ0123456789
manufacturer
<chr>
model
<chr>
displ
<dbl>
year
<int>
cyl
<int>
trans
<chr>
drv
<chr>
cty
<int>
hwy
<int>
fl
<chr>
audia41.819994auto(l5)f1829p
audia41.819994manual(m5)f2129p
audia42.020084manual(m6)f2031p
audia42.020084auto(av)f2130p
audia42.819996auto(l5)f1626p
audia42.819996manual(m5)f1826p
audia43.120086auto(av)f1827p
audia4 quattro1.819994manual(m5)41826p
audia4 quattro1.819994auto(l5)41625p
audia4 quattro2.020084manual(m6)42028p

Make the scatterplot along with the smoothing line.

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

Multiple smoothing lines.

ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy, linetype = drv))

Statistical transformations

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut))

Proportions

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, y = ..prop.., group = 1))

Position adjustment

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge")

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