r - dplyr 在变量子集上使用 pivot_longer 和 pivot_wider

Question

有没有办法使用pivot_longer和 pivot_wider在变量的子集上？这是一个例子。首先，我将创建一个具有所需起始结构的数据框。

library(tidyverse)

# Assume this as starting df
arrests <- USArrests %>% 
  as_tibble(rownames = "State") %>% 
  pivot_longer(-State, names_to = "Crime", values_to = "Value") %>% 
  group_by(State) %>% 
  mutate(Total = sum(Value)) %>% 
  ungroup()

arrests
# A tibble: 200 x 4
   State   Crime    Value Total
   <chr>   <chr>    <dbl> <dbl>
 1 Alabama Murder    13.2  328.
 2 Alabama Assault  236    328.
 3 Alabama UrbanPop  58    328.
 4 Alabama Rape      21.2  328.
 5 Alaska  Murder    10    366.
 6 Alaska  Assault  263    366.
 7 Alaska  UrbanPop  48    366.
 8 Alaska  Rape      44.5  366.
 9 Arizona Murder     8.1  413.
10 Arizona Assault  294    413.
# ... with 190 more rows

所以我们使用 arrest数据框。现在我想把“Total”折叠成“Crime”，这样“Total”就是Crime中的一个值，就像“Murder”一样。

我也想反过来。 “Total”折叠成“Crime”后，我想用pivot_wider关于“犯罪”，但仅适用于 Crime == "Total" 的值.

这些行动可行吗？

Answer 1

一种选择是 add_row .在按“状态”进行分组后，循环遍历 list与 map并添加一行( add_row from tibble )与“Total”列的第一个值并删除“Total”列

library(dplyr)
library(purrr)
library(tibble)
arrests2 <- arrests %>%
         group_split(State) %>%
         map_dfr(~ .x %>% 
               add_row(State = .$State[1], Crime = 'Total',
                        Value = .$Total[1]) %>%
                select(-Total))
arrests2
# A tibble: 250 x 3
#  State   Crime    Value
# * <chr>   <chr>    <dbl>
# 1 Alabama Murder    13.2
# 2 Alabama Assault  236  
# 3 Alabama UrbanPop  58  
# 4 Alabama Rape      21.2
# 5 Alabama Total    328. 
# 6 Alaska  Murder    10  
# 7 Alaska  Assault  263  
# 8 Alaska  UrbanPop  48  
# 9 Alaska  Rape      44.5
#10 Alaska  Total    366. 
# … with 240 more rows

或者另一种选择是 summarise使用“总计”值，然后执行 bind_rows

arrests %>% 
   group_by(State) %>% 
   summarise(Crime = 'Total', Value = first(Total)) %>% 
   bind_rows(arrests %>% select(-Total), .)  %>% 
   arrange(State)

或使用 pivot_longer

library(tidyr)
arrests %>%
    pivot_longer(cols = Value:Total) %>% 
    mutate(Crime = replace(Crime, name == 'Total', 'Total')) %>% 
    select(-name) %>%
    distinct()
# A tibble: 250 x 3
#   State   Crime    value
#   <chr>   <chr>    <dbl>
# 1 Alabama Murder    13.2
# 2 Alabama Total    328. 
# 3 Alabama Assault  236  
# 4 Alabama UrbanPop  58  
# 5 Alabama Rape      21.2
# 6 Alaska  Murder    10  
# 7 Alaska  Total    366. 
# 8 Alaska  Assault  263  
# 9 Alaska  UrbanPop  48  
#10 Alaska  Rape      44.5
# … with 240 more rows

如果我们需要做相反的事情，然后按'State'分组，通过提取与'Crime'对应的'Value'作为'Total'来创建'Total'列，然后filter出犯罪为“总”的那一行

arrests2 %>%
    group_by(State) %>% 
    mutate(Total = Value[Crime == 'Total'])  %>%
    filter(Crime != 'Total')
# A tibble: 200 x 4
# Groups:   State [50]
#   State   Crime    Value Total
#   <chr>   <chr>    <dbl> <dbl>
# 1 Alabama Murder    13.2  328.
# 2 Alabama Assault  236    328.
# 3 Alabama UrbanPop  58    328.
# 4 Alabama Rape      21.2  328.
# 5 Alaska  Murder    10    366.
# 6 Alaska  Assault  263    366.
# 7 Alaska  UrbanPop  48    366.
# 8 Alaska  Rape      44.5  366.
# 9 Arizona Murder     8.1  413.
#10 Arizona Assault  294    413.
# … with 190 more rows