r - 使用 mutate 对数值变量进行分类

Question

我想对我的data.frame对象中的数字变量进行分类dplyr（并且不知道该怎么做）。

没有dplyr，我可能会做类似的事情：

df <- data.frame(a = rnorm(1e3), b = rnorm(1e3))
df$a <- cut(df$a , breaks=quantile(df$a, probs = seq(0, 1, 0.2)))

它会完成的。但是，我非常喜欢dplyr在mutate我chain对data.frame.

Answer 1

该ggplot2软件包有 3 个功能可以很好地完成这些任务：

• cut_number()：使 n 组具有（大约）相同数量的观察
• cut_interval()：使 n 组具有相等的范围
• cut_width: 使宽度宽度组

我的首选是cut_number()因为这使用均匀间隔的分位数进行分箱观察。这是一个带有倾斜数据的示例。

library(tidyverse)

skewed_tbl <- tibble(
    counts = c(1:100, 1:50, 1:20, rep(1:10, 3), 
               rep(1:5, 5), rep(1:2, 10), rep(1, 20))
    ) %>%
    mutate(
        counts_cut_number   = cut_number(counts, n = 4),
        counts_cut_interval = cut_interval(counts, n = 4),
        counts_cut_width    = cut_width(counts, width = 25)
        ) 

# Data
skewed_tbl
#> # A tibble: 265 x 4
#>    counts counts_cut_number counts_cut_interval counts_cut_width
#>     <dbl> <fct>             <fct>               <fct>           
#>  1      1 [1,3]             [1,25.8]            [-12.5,12.5]    
#>  2      2 [1,3]             [1,25.8]            [-12.5,12.5]    
#>  3      3 [1,3]             [1,25.8]            [-12.5,12.5]    
#>  4      4 (3,13]            [1,25.8]            [-12.5,12.5]    
#>  5      5 (3,13]            [1,25.8]            [-12.5,12.5]    
#>  6      6 (3,13]            [1,25.8]            [-12.5,12.5]    
#>  7      7 (3,13]            [1,25.8]            [-12.5,12.5]    
#>  8      8 (3,13]            [1,25.8]            [-12.5,12.5]    
#>  9      9 (3,13]            [1,25.8]            [-12.5,12.5]    
#> 10     10 (3,13]            [1,25.8]            [-12.5,12.5]    
#> # ... with 255 more rows

summary(skewed_tbl$counts)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>    1.00    3.00   13.00   25.75   42.00  100.00

# Histogram showing skew
skewed_tbl %>%
    ggplot(aes(counts)) +
    geom_histogram(bins = 30)

# cut_number() evenly distributes observations into bins by quantile
skewed_tbl %>%
    ggplot(aes(counts_cut_number)) +
    geom_bar()

# cut_interval() evenly splits the interval across the range
skewed_tbl %>%
    ggplot(aes(counts_cut_interval)) +
    geom_bar()

# cut_width() uses the width = 25 to create bins that are 25 in width
skewed_tbl %>%
    ggplot(aes(counts_cut_width)) +
    geom_bar()

^{由reprex 包（v0.2.1）于 2018 年 11 月 1 日创建}

Answer 2

set.seed(123)
df <- data.frame(a = rnorm(10), b = rnorm(10))

df %>% mutate(a = cut(a, breaks = quantile(a, probs = seq(0, 1, 0.2))))

给予：

                 a          b
1  (-0.586,-0.316]  1.2240818
2   (-0.316,0.094]  0.3598138
3      (0.68,1.72]  0.4007715
4   (-0.316,0.094]  0.1106827
5     (0.094,0.68] -0.5558411
6      (0.68,1.72]  1.7869131
7     (0.094,0.68]  0.4978505
8             <NA> -1.9666172
9   (-1.27,-0.586]  0.7013559
10 (-0.586,-0.316] -0.4727914