如何自动修改间隔因子水平以更好地显示

Question

假设您的数据看起来像这样

df <- data.frame(income = rnorm(1000,77345,30569))

您添加一列来指示每个观测值所属的四分位数间隔因子

df$quant <- cut(df$income, quantile(df$income))

因子水平看起来像这样

Levels: (-4.48e+04,5.6e+04] (5.6e+04,7.69e+04] (7.69e+04,9.73e+04] (9.73e+04,1.64e+05]

如何以编程方式（而不是手动）更改间隔，以便它们在频率摘要中很好地打印出来？table？

df %>% count(quant)

打印如下：

               quant   n
1 (-4.48e+04,5.6e+04] 249
2  (5.6e+04,7.69e+04] 250
3 (7.69e+04,9.73e+04] 250
4 (9.73e+04,1.64e+05] 250

我希望它看起来像这样

              quant   n
1  (,800,,000] 249
2  (,000,,900] 250
3  (,900,,300] 250
4 (,300,4,000] 250

这仅用于打印目的（在 Rmarkdown 报告中）。我已经毫无问题地进行了所有计算和绘图。

Answer 1

cut2 可以接受一个 formatfun 参数

library(Hmisc)
library(scales)
df$quant2 <-  cut2(df$income,digits = 5, cuts = quantile(df$income), 
   formatfun = function(x) paste0("$", comma(x)), onlycuts = TRUE)

-输出

> head(df)
     income             quant2               quant
1  60657.97  [,485,,547) (5.55e+04,7.65e+04]
2  93747.88  [,547,,620) (7.65e+04,9.66e+04]
3  90172.46  [,547,,620) (7.65e+04,9.66e+04]
4  59504.10  [,485,,547) (5.55e+04,7.65e+04]
5 103251.01 [,620,8,251] (9.66e+04,1.78e+05]
6  85477.03  [,547,,620) (7.65e+04,9.66e+04]

如果我们要修改原来的cut列

library(tidyr)
library(stringr)
df <- df %>%
     mutate(quant = str_remove_all(quant, "\(|\]")) %>% 
     separate(quant, into = c('q1', 'q2'), sep=",", convert = TRUE) %>% 
     mutate(across(q1:q2, ~ dollar(.x)), 
     quant = glue::glue("({q1},{q2}]"), q1 = NULL, q2 = NULL)

-输出

> head(df)
     income              quant
1  60657.97  (,500,,500]
2  93747.88  (,500,,600]
3  90172.46  (,500,,600]
4  59504.10  (,500,,500]
5 103251.01 (,600,8,000]
6  85477.03  (,500,,600]

Answer 2

这是另一个解决方案：

q <- quantile(df$income)
qlbls <- sapply(1:4,function(i) paste0("(",scales::dollar(q[i]),",",scales::dollar(q[i+1]),"]"))
df$quant <- cut(df$income, q, labels = qlbls)

-输出

> head(df)
     income                    quant
1  43842.61  (,745.22,,569.49]
2  73176.84  (,569.49,,945.41]
3  85658.10  (,945.41,,013.99]
4  84613.72  (,945.41,,013.99]
5 130301.96    (,013.99,9,552]
6  61917.61  (,569.49,,945.41]

这个解决方案非常灵活，你可以把这个因素做得很漂亮。

Answer 3

chop_quantiles() 来自我的 santoku 包对此很有用：

library(santoku)
d <- rnorm(10)

# labelled by the quantiles:
chop_quantiles(d, c(0.1, 0.5, 0.9))
 [1] [0%, 10%)   [10%, 50%)  [50%, 90%]  [10%, 50%)  [10%, 50%)  (90%, 100%]
 [7] [50%, 90%]  [10%, 50%)  [50%, 90%]  [50%, 90%] 
Levels: [0%, 10%) [10%, 50%) [50%, 90%] (90%, 100%]

# by the raw values:
chop_quantiles(d, c(0.1, 0.5, 0.9), labels = lbl_intervals(raw = TRUE))

 [1] [-2.515, -1.633)  [-1.633, -0.8172) [-0.8172, 0.3274] [-1.633, -0.8172)
 [5] [-1.633, -0.8172) (0.3274, 0.4165]  [-0.8172, 0.3274] [-1.633, -0.8172)
 [9] [-0.8172, 0.3274] [-0.8172, 0.3274]
4 Levels: [-2.515, -1.633) [-1.633, -0.8172) ... (0.3274, 0.4165]

# format string passed to sprintf():
chop_quantiles(d, c(0.1, 0.5, 0.9), labels = lbl_intervals(raw = TRUE, fmt = "%.2f"))
 [1] [-2.52, -1.63) [-1.63, -0.82) [-0.82, 0.33]  [-1.63, -0.82) [-1.63, -0.82)
 [6] (0.33, 0.42]   [-0.82, 0.33]  [-1.63, -0.82) [-0.82, 0.33]  [-0.82, 0.33] 
Levels: [-2.52, -1.63) [-1.63, -0.82) [-0.82, 0.33] (0.33, 0.42]

# different kinds of labels:
chop_quantiles(d, c(0.1, 0.5, 0.9), labels = lbl_dash(" - ", raw = TRUE))
 [1] -2.515 - -1.633  -1.633 - -0.8172 -0.8172 - 0.3274 -1.633 - -0.8172
 [5] -1.633 - -0.8172 0.3274 - 0.4165  -0.8172 - 0.3274 -1.633 - -0.8172
 [9] -0.8172 - 0.3274 -0.8172 - 0.3274
4 Levels: -2.515 - -1.633 -1.633 - -0.8172 ... 0.3274 - 0.4165

# make your own:
chop_quantiles(d, c(0.1, 0.5, 0.9), labels = lbl_glue("{l} to {r}", raw = TRUE))
 [1] -2.515 to -1.633  -1.633 to -0.8172 -0.8172 to 0.3274 -1.633 to -0.8172
 [5] -1.633 to -0.8172 0.3274 to 0.4165  -0.8172 to 0.3274 -1.633 to -0.8172
 [9] -0.8172 to 0.3274 -0.8172 to 0.3274

如何自动修改间隔因子水平以更好地显示

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