将多个不互斥的虚拟变量转换为单个分类变量,添加新行

Converting multiple dummy variables that are not mutually exclusive into single categorical variable, adding new rows

我有以下数据框:

data <- data.frame(id=c(1, 1, 2, 2), 
task=c(1, 2, 1, 2),
strategy1=c("1", "1", "0", "1"),
strategy2=c("0", "0", "1", "1"),
strategy3=c("0", "1", "0", "1"))

我的目标是将不同策略的虚拟变量组合成一个单一的分类变量,'strategy'。如果参与者在一项任务中使用了多种策略,则必须相应地创建具有相同 'id' 和 'task' 数字的新行,因为应该只有一个 'strategy' 变量。

对于给定的示例,数据框最终应如下所示:

data_single <- data.frame(id=c(1, 1, 1, 2, 2, 2, 2),
task=c(1, 2, 2, 1, 2, 2, 2),
strategy=c("1", "1", "3", "2", "1", "2", "3"))

谁能告诉我如何实现这个目标?

library(tidyr)
library(dplyr)
tidyr::pivot_longer(
  data, 
  cols = starts_with("strategy"),
  names_prefix = "strategy", 
  names_to = "strategy"
) %>%
  filter(value == 1) %>%
  select(-value)
# # A tibble: 7 x 3
#      id  task strategy
#   <dbl> <dbl> <chr>   
# 1     1     1 1       
# 2     1     2 1       
# 3     1     2 3       
# 4     2     1 2       
# 5     2     2 1       
# 6     2     2 2       
# 7     2     2 3  

我们也可以用data.table

library(data.table)
melt(setDT(data), measure = patterns('^strategy'), 
     variable.name = 'strategy')[value == 1, .(id, task, strategy)]