單變數分佈

R 的探索式資料分析

Andrew Bray

Assistant Professor, Reed College

邊際 vs. 條件

ggplot(cars, aes(x = hwy_mpg)) +
  geom_histogram()

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Warning message:
Removed 14 rows containing non-finite values (stat_bin).

第 2 章圖 2.003

ggplot(cars, aes(x = hwy_mpg)) +
  geom_histogram() +
  facet_wrap(~pickup)

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Warning message:
Removed 14 rows containing non-finite values (stat_bin).

第 2 章圖 2.006

cars2 <- cars %>%
  filter(eng_size < 2.0)

ggplot(cars2, aes(x = hwy_mpg)) +
  geom_histogram()

cars %>%
  filter(eng_size < 2.0) %>%
  ggplot(aes(x = hwy_mpg)) +
  geom_histogram()

cars %>%
  filter(eng_size < 2.0) %>%
  ggplot(aes(x = hwy_mpg)) +
  geom_histogram()

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

第 2 章圖 2.011

cars %>%
  filter(eng_size < 2.0) %>%
  ggplot(aes(x = hwy_mpg)) +
  geom_histogram(binwidth = 5)

第 2 章圖 2.013

cars %>%
  filter(eng_size < 2.0) %>%
  ggplot(aes(x = hwy_mpg)) +
  geom_density()

第 2 章圖 2.015

cars %>%   
  filter(eng_size < 2.0) %>%
  ggplot(aes(x = hwy_mpg)) +
  geom_density(bw = 5)

第 2 章圖 2.017

R 的探索式資料分析