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如何在R/dplyr中最多取几列

dplyr r

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mmyoung77 · 技术社区 · 5 年前

id <- c(1:5)
VolumeA <- c(12, NA, NA, NA, NA)
VolumeB <- c(NA, 34, NA, NA, NA)
VolumeC <- c(NA, NA, 56, NA, NA)
VolumeD <- c(NA, NA, NA, 78, NA)
VolumeE <- c(NA, NA, NA, NA, 90)

df_now <- tibble(id, VolumeA, VolumeB, VolumeC, VolumeD, VolumeE)
df_now

# A tibble: 5 x 6
     id VolumeA VolumeB VolumeC VolumeD VolumeE
  <int>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
1     1      12      NA      NA      NA      NA
2     2      NA      34      NA      NA      NA
3     3      NA      NA      56      NA      NA
4     4      NA      NA      NA      78      NA
5     5      NA      NA      NA      NA      90

Volume[label] 列,但在每一行中我只需要其中一个:最大的一个。所以我想创建一个值最大的新变量:

Volume <- c(12, 34, 56, 78, 90)
df_desired <- cbind(df_now, Volume)
df_desired

  id VolumeA VolumeB VolumeC VolumeD VolumeE Volume
1  1      12      NA      NA      NA      NA     12
2  2      NA      34      NA      NA      NA     34
3  3      NA      NA      56      NA      NA     56
4  4      NA      NA      NA      78      NA     78
5  5      NA      NA      NA      NA      90     90

在看了dplyr文档之后,我尝试了这个。。。

library(tidyverse)
df_try <- df_now %>%
  mutate(Volume = across(contains("Volume"), max, na.rm = TRUE))

…但是得到了一堆数据,而不是一列。有人能告诉我怎么做吗?

gather 和 spread 我的数据。我想用条件句 mutate .)

1 回复 | 直到 5 年前

1

2

akrun 5 年前

pmax (第一次发布解决方案)。注意,相对改善是非常小的 do.call

library(dplyr)
library(purrr)
df_now %>%
    mutate(Volume = reduce(select(., starts_with('Volume')), pmax, na.rm = TRUE))
# A tibble: 5 x 7
#     id VolumeA VolumeB VolumeC VolumeD VolumeE Volume
#  <int>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>  <dbl>
#1     1      12      NA      NA      NA      NA     12
#2     2      NA      34      NA      NA      NA     34
#3     3      NA      NA      56      NA      NA     56
#4     4      NA      NA      NA      78      NA     78
#5     5      NA      NA      NA      NA      90     90

或与 c_across max (仅使用 tidyverse 方法)

df_now %>%
   rowwise %>%
   mutate(Volume = max(c_across(starts_with('Volume')), na.rm = TRUE))
# A tibble: 5 x 7
# Rowwise: 
#     id VolumeA VolumeB VolumeC VolumeD VolumeE Volume
#  <int>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>  <dbl>
#1     1      12      NA      NA      NA      NA     12
#2     2      NA      34      NA      NA      NA     34
#3     3      NA      NA      56      NA      NA     56
#4     4      NA      NA      NA      78      NA     78
#5     5      NA      NA      NA      NA      90     90

基准

system.time({df_now %>% mutate(Volume = reduce(select(., starts_with('Volume')), pmax, na.rm = TRUE))})
#   user  system elapsed 
#  0.023   0.006   0.029 

system.time({df_now %>% rowwise %>% mutate(Volume = max(c_across(starts_with('Volume')), na.rm = TRUE))})
#   user  system elapsed 
#  0.012   0.002   0.015 

system.time({df_now %>% mutate(Volume = do.call(pmax, c(select(., starts_with('Volume')), na.rm = TRUE)))})
#   user  system elapsed 
#  0.011   0.001   0.011

注意:时间上没有太大的差别

2

3

r2evans 5 年前

“还有很多 Volume[label] “列” ,任何对每行有效的解决方案( rowwise )或在每列上单独显示(带 reduce 或 Reduce )会比必要的慢很多。

df_now %>%
  mutate(Volume = do.call(pmax, c(select(., starts_with('Volume')), na.rm = TRUE)))
# # A tibble: 5 x 7
#      id VolumeA VolumeB VolumeC VolumeD VolumeE Volume
#   <int>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>  <dbl>
# 1     1      12      NA      NA      NA      NA     12
# 2     2      NA      34      NA      NA      NA     34
# 3     3      NA      NA      56      NA      NA     56
# 4     4      NA      NA      NA      78      NA     78
# 5     5      NA      NA      NA      NA      90     90

相对改善的证明:

使用 减少 或 purrr::reduce 或者任何会对每列进行迭代的内容(好吧,使用 nc nc-1 次数):

mypmax <- function(...) { message("mypmax"); pmax(...); }
df_now %>%
  mutate(Volume = reduce(select(., starts_with('Volume')), mypmax, na.rm = TRUE))
# mypmax
# mypmax
# mypmax
# mypmax
# # A tibble: 5 x 7
#      id VolumeA VolumeB VolumeC VolumeD VolumeE Volume
#   <int>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>  <dbl>
# 1     1      12      NA      NA      NA      NA     12
# 2     2      NA      34      NA      NA      NA     34
# 3     3      NA      NA      56      NA      NA     56
# 4     4      NA      NA      NA      78      NA     78
# 5     5      NA      NA      NA      NA      90     90

有什么事吗 吵闹的 每行执行一次,可能更糟(假设数据中的行多于列:

mymax <- function(...) { message("mymax"); max(...); }
df_now %>%
  rowwise %>%
  mutate(Volume = mymax(c_across(starts_with('Volume')), na.rm = TRUE))
# mymax
# mymax
# mymax
# mymax
# mymax
# # A tibble: 5 x 7
# # Rowwise: 
#      id VolumeA VolumeB VolumeC VolumeD VolumeE Volume
#   <int>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>  <dbl>
# 1     1      12      NA      NA      NA      NA     12
# 2     2      NA      34      NA      NA      NA     34
# 3     3      NA      NA      56      NA      NA     56
# 4     4      NA      NA      NA      78      NA     78
# 5     5      NA      NA      NA      NA      90     90

去做吧一旦

mypmax <- function(...) { message("mypmax"); pmax(...); }
df_now %>%
  mutate(Volume = do.call(mypmax, c(select(., starts_with('Volume')), na.rm = TRUE)))
# mypmax
# # A tibble: 5 x 7
#      id VolumeA VolumeB VolumeC VolumeD VolumeE Volume
#   <int>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>  <dbl>
# 1     1      12      NA      NA      NA      NA     12
# 2     2      NA      34      NA      NA      NA     34
# 3     3      NA      NA      56      NA      NA     56
# 4     4      NA      NA      NA      78      NA     78
# 5     5      NA      NA      NA      NA      90     90

在这种规模下,基准比较小,但在更大的数据下,基准比较引人注目:

microbenchmark::microbenchmark(
  red = df_now %>% mutate(Volume = reduce(select(., starts_with('Volume')), pmax, na.rm = TRUE)),
  row = df_now %>% rowwise %>% mutate(Volume = max(c_across(starts_with('Volume')), na.rm = TRUE)),
  sgl = df_now %>% mutate(Volume = do.call(pmax, c(select(., starts_with('Volume')), na.rm = TRUE)))
)
# Unit: milliseconds
#  expr    min      lq     mean  median      uq     max neval
#   red 4.9736 5.36240 7.240561 5.68010 6.19915 70.7482   100
#   row 4.5813 5.02020 6.082047 5.34460 5.70345 63.1166   100
#   sgl 3.8270 4.18605 5.803043 4.43215 4.76030 65.7217   100