1

是否可以按行而不是按列对数据集的值进行舍入而不是旋转？标准方法是使用mutate_ator now with对列进行舍入，across但我更愿意按特定行进行：

library(tidyverse)

set.seed(10)
df <- data.frame(let = sample(letters, 20),
                 x = rnorm(20),
                 y = rnorm(20) + 10,
                 z = rnorm(20) + 30)
df

#for rows where "let" = i, j, k, h round to 2 decimal places using ~round(., 2)
round_2 = c("i", "j", "k", "h")

#for rows where "let" = a, b, c, d, e round to  decimal places using ~round(., 0)
round_0 = c("a", "b", "c", "d")

#typical type approach with columns
#df %>% 
#  mutate(across(matches("x|y"), ~round(., 0))) 
#df %>% 
#  mutate(across(matches("z"), ~round(., 2)))

也许rowwise可以在这里使用？

谢谢

4

2 回答 2

1

由于letcolumn 在 mutate 函数中可用，您可以执行以下操作：

df %>%
  mutate_at(c('x', 'y', 'z'), ~round(., case_when(
    let %in% round_0 ~ 0, 
    let %in% round_2 ~ 2
  )))

对于dplyr >= 1.0.0：

df %>%
  mutate(across(matches('x|y|z'), ~round(., case_when(
    let %in% round_0 ~ 0, 
    let %in% round_2 ~ 2
  ))))

结果：

   let     x     y     z
1    k -0.71  9.87 29.94
2    i -0.37  9.22 29.81
3    j -1.32 10.09 31.01
4    p    NA    NA    NA
5    l    NA    NA    NA
6    h  1.69 11.77 30.82
7    g    NA    NA    NA
8    s    NA    NA    NA
9    o    NA    NA    NA
10   r    NA    NA    NA
11   x    NA    NA    NA
12   t    NA    NA    NA
13   w    NA    NA    NA
14   b  0.00 10.00 29.00
15   u    NA    NA    NA
16   v    NA    NA    NA
17   q    NA    NA    NA
18   f    NA    NA    NA
19   n    NA    NA    NA
20   c  1.00 11.00 31.00
于 2020-09-07T11:42:07.167 回答
1

data.table解决方案

library( data.table )
setDT(df)
cols = c("x", "y", "z")
df[ let %in% round_2, (cols) := round( .SD, digits = 2), .SDcols = cols][]
df[ let %in% round_0, (cols) := round( .SD, digits = 0), .SDcols = cols][]

#     let            x         y        z
#  1:   k -0.710000000  9.870000 29.94000
#  2:   i -0.370000000  9.220000 29.81000
#  3:   j -1.320000000 10.090000 31.01000
#  4:   p  1.280597456 10.064564 30.07160
#  5:   l  0.667415054 10.115208 31.36119
#  6:   h  1.690000000 11.770000 30.82000
#  7:   g  0.001261409  9.186451 30.52619
#  8:   s -0.742461312  9.910625 29.57074
#  9:   o  0.609684421 10.314817 30.16474
# 10:   r -0.989606379  7.939459 29.22681
# 11:   x -0.034848335  9.400197 29.62148
# 12:   t  0.847159906  9.046434 31.65189
# 13:   w  1.525498006 10.557506 28.32389
# 14:   b  0.000000000 10.000000 29.00000
# 15:   u  0.210143002 10.973221 29.42507
# 16:   v -0.081721617 10.101948 31.08997
# 17:   q  0.013249400  8.119381 30.12649
# 18:   f -1.203106335  8.456234 29.27683
# 19:   n -0.261154359  9.766260 31.42620
# 20:   c  1.000000000 11.000000 31.00000
于 2020-09-07T11:33:48.847 回答