在由另一列分组的一列中添加取值差异的列答案

【问题标题】：Add column taking difference of values in one column grouped by other column在由另一列分组的一列中添加取值差异的列
【发布时间】：2020-11-28 00:35:02
【问题描述】：

我有一个名为 diff_colour_valid_int1 的 df：

> head(diff_colour_valid_int1)
# A tibble: 6 x 5
# Groups:   search_difficulty, cue_validity [3]
  search_difficulty cue_validity cue_colour           meanrt stdev
  <fct>             <fct>        <fct>                 <dbl> <dbl>
1 difficult         FALSE        Match (Color) cue     0.990 0.158
2 difficult         FALSE        Mismatch (Onset) cue  0.972 0.150
3 difficult         TRUE         Match (Color) cue     0.828 0.133
4 difficult         TRUE         Mismatch (Onset) cue  0.881 0.177
5 easy              FALSE        Match (Color) cue     0.813 0.132
6 easy              FALSE        Mismatch (Onset) cue  0.801 0.137
>

我想添加一个名为 cue_effect 的列，用于计算每个 cue_validity 对的均值之间的差异（例如，前两个 FALSE FALSE）。所以该列的前六个值将是：

cue_effect
<dbl>
0.018
0.018
-0.053
-0.053
0.012

感谢任何建议。谢谢。

【问题讨论】：

标签： r dplyr group-by tibble

【解决方案1】：

我们可以使用rleid来创建分组列

library(dplyr)
library(data.table)
diff_colour_valid_int1 %>%
    group_by(search_difficulty, grp = rleid(cue_validity)) %>%
    mutate(cue_effect = -diff(meanrt))

-输出

# A tibble: 6 x 7
# Groups:   search_difficulty, grp [3]
#  search_difficulty cue_validity cue_colour           meanrt stdev   grp cue_effect
#  <chr>             <lgl>        <chr>                 <dbl> <dbl> <int>      <dbl>
#1 difficult         FALSE        Match (Color) cue     0.99  0.158     1     0.018 
#2 difficult         FALSE        Mismatch (Onset) cue  0.972 0.15      1     0.018 
#3 difficult         TRUE         Match (Color) cue     0.828 0.133     2    -0.053 
#4 difficult         TRUE         Mismatch (Onset) cue  0.881 0.177     2    -0.053 
#5 easy              FALSE        Match (Color) cue     0.813 0.132     3     0.0120
#6 easy              FALSE        Mismatch (Onset) cue  0.801 0.137     3     0.0120

数据

diff_colour_valid_int1 <- structure(list(search_difficulty = c("difficult", "difficult", 
"difficult", "difficult", "easy", "easy"), cue_validity = c(FALSE, 
FALSE, TRUE, TRUE, FALSE, FALSE), cue_colour = c("Match (Color) cue", 
"Mismatch (Onset) cue", "Match (Color) cue", "Mismatch (Onset) cue", 
"Match (Color) cue", "Mismatch (Onset) cue"), meanrt = c(0.99, 
0.972, 0.828, 0.881, 0.813, 0.801), stdev = c(0.158, 0.15, 0.133, 
0.177, 0.132, 0.137)), class = "data.frame", row.names = c("1", 
"2", "3", "4", "5", "6"))

【讨论】：

【解决方案2】：

每当cue_validity 值发生变化时，您可以使用cumsum 和lag 创建一个新组，并计算每个组中的mean。

library(dplyr)

diff_colour_valid_int1 %>%
  group_by(search_difficulty, 
           group = cumsum(cue_validity != lag(cue_validity, 
                   default = first(cue_validity)))) %>%
  mutate(cue_effect = na.omit(lag(meanrt) - meanrt)) %>%
  ungroup() %>%
  select(-group)

#  search_difficulty cue_validity cue_colour           meanrt stdev cue_effect
#  <chr>             <lgl>        <chr>                 <dbl> <dbl>      <dbl>
#1 difficult         FALSE        Match (Color) cue     0.99  0.158     0.018 
#2 difficult         FALSE        Mismatch (Onset) cue  0.972 0.15      0.018 
#3 difficult         TRUE         Match (Color) cue     0.828 0.133    -0.053 
#4 difficult         TRUE         Mismatch (Onset) cue  0.881 0.177    -0.053 
#5 easy              FALSE        Match (Color) cue     0.813 0.132     0.0120
#6 easy              FALSE        Mismatch (Onset) cue  0.801 0.137     0.0120

【讨论】：