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无法在带有tible的summary()之后的mutate()中生成子集

tibble subset dplyr r

Anonymous coward · 技术社区 · 7 年前

我不知道这是否是处理问题的独特行为 tibbles

library(dplyr)
library(gapminder)
df <- gapminder %>%
  group_by(year, continent) %>% 
  summarize(avg_life = mean(lifeExp))

这就产生了一个tibble, df .

# A tibble: 60 x 3
# Groups:   year [?]
    year continent avg_life
   <int> <fct>        <dbl>
 1  1952 Africa        39.1
 2  1952 Americas      53.3
 3  1952 Asia          46.3
 4  1952 Europe        64.4
 5  1952 Oceania       69.3
 6  1957 Africa        41.3
 7  1957 Americas      56.0
 8  1957 Asia          49.3
 9  1957 Europe        66.7
10  1957 Oceania       70.3
# ... with 50 more rows

我以为下一步会奏效,然后 this post 建议应该这样。

如果我以标准方式将其子集,它将生成预期的输出。

df$avg_life[df$year == 1952]
[1] 39.13550 53.27984 46.31439 64.40850 69.25500

如果我试着在一个小时内完成 mutate() ,它什么也得不到。

df <- gapminder %>%
  group_by(year, continent) %>% 
  summarize(avg_life = mean(lifeExp)) %>% 
  mutate(life_chg = avg_life - avg_life[year == 1952])

mutate\u impl(.data,dots)出错: 列 life_chg 长度必须为5(组大小)或1,而不是0

更改 == 到 > 0

0 .

df <- gapminder %>%
  group_by(year, continent) %>% 
  summarize(avg_life = mean(lifeExp)) %>% 
  mutate(life_chg = avg_life - avg_life[c(T, T, T, T, T, rep(F, 55))])

为什么这在一个小时内不起作用

df的结构:

str(df)
Classes âgrouped_dfâ, âtbl_dfâ, âtblâ and 'data.frame': 60 obs. of  4 variables:
 $ year     : int  1952 1952 1952 1952 1952 1957 1957 1957 1957 1957 ...
 $ continent: Factor w/ 5 levels "Africa","Americas",..: 1 2 3 4 5 1 2 3 4 5 ...
 $ avg_life : num  39.1 53.3 46.3 64.4 69.3 ...
 $ life_chg : num  0 0 0 0 0 0 0 0 0 0 ...
 - attr(*, "vars")= chr "year"
 - attr(*, "labels")='data.frame':  12 obs. of  1 variable:
  ..$ year: int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
  ..- attr(*, "vars")= chr "year"
  ..- attr(*, "drop")= logi TRUE
 - attr(*, "indices")=List of 12
  ..$ : int  0 1 2 3 4
  ..$ : int  5 6 7 8 9
  ..$ : int  10 11 12 13 14
  ..$ : int  15 16 17 18 19
  ..$ : int  20 21 22 23 24
  ..$ : int  25 26 27 28 29
  ..$ : int  30 31 32 33 34
  ..$ : int  35 36 37 38 39
  ..$ : int  40 41 42 43 44
  ..$ : int  45 46 47 48 49
  ..$ : int  50 51 52 53 54
  ..$ : int  55 56 57 58 59
 - attr(*, "drop")= logi TRUE
 - attr(*, "group_sizes")= int  5 5 5 5 5 5 5 5 5 5 ...
 - attr(*, "biggest_group_size")= int 5

1 回复 | 直到 7 年前

Anonymous coward 7 年前

joran ,你必须 ungroup

library(dplyr)
library(gapminder)

gapminder %>%
  group_by(year, continent) %>%
  summarize(avg_life = mean(lifeExp)) %>%
  ungroup(.) %>%
  mutate(life_chg = avg_life - avg_life[year == 1952])

# A tibble: 60 x 4
    year continent avg_life life_chg
   <int> <fct>        <dbl>    <dbl>
 1  1952 Africa        39.1     0   
 2  1952 Americas      53.3     0   
 3  1952 Asia          46.3     0   
 4  1952 Europe        64.4     0   
 5  1952 Oceania       69.3     0   
 6  1957 Africa        41.3     2.13
 7  1957 Americas      56.0     2.68
 8  1957 Asia          49.3     3.00
 9  1957 Europe        66.7     2.29
10  1957 Oceania       70.3     1.04
# ... with 50 more rows