将多个二进制列转换为单个分类列

时间:2015-03-24 07:32:47

标签: r categorical-data dummy-data

我有一个包含二进制变量的表,我想将其压缩为分类变量。

非常简单,我有一个像这样的数据框:

data <- data.frame(id=c(1,2,3,4,5,6,7,8,9), red=c("1","0","0","0","1","0","0","0","0"),blue=c("0","1","1","1","0","1","1","1","0"),yellow=c("0","0","0","0","0","0","0","0","1"))
data
  id   red   blue  yellow
1  1   1    0      0
2  2   0    1      0
3  3   0    1      0
4  4   0    1      0
5  5   1    0      0
6  6   0    1      0
7  7   0    1      0
8  8   0    1      0
9  9   0    0      1

我想回来的是:

  id   color 
1  1   red    
2  2   blue   
3  3   blue    
4  4   blue    
5  5   red    
6  6   blue    
7  7   blue    
8  8   blue    
9  9   yellow 

我希望有一个非常简单的答案。

2 个答案:

答案 0 :(得分:4)

您可以使用列namesas.logical来获取值。但是,因为你的&#34;二进制&#34;列是因素,你需要更多的箍:

> apply(data[-1], 1, function(x) names(x)[as.logical(as.numeric(as.character(x)))])
[1] "red"    "blue"   "blue"   "blue"   "red"    "blue"   "blue"   "blue"   "yellow"

将其与第一列(data[1])绑定,以获得所需的输出。

cbind(data[1], 
      color = apply(data[-1], 1, 
                    function(x) names(x)[as.logical(as.numeric(
                      as.character(x)))]))
#   id  color
# 1  1    red
# 2  2   blue
# 3  3   blue
# 4  4   blue
# 5  5    red
# 6  6   blue
# 7  7   blue
# 8  8   blue
# 9  9 yellow

或者,您可以尝试以下方法:

data[-1] <- lapply(data[-1], function(x) as.numeric(as.character(x)))
temp <- subset(cbind(data[1], stack(data[-1])), values == 1, c("id", "ind"))
temp[order(temp$id), ]

或者,您可以使用&#34; dplyr&#34;和&#34; tidyr&#34;,像这样:

library(dplyr)
library(tidyr)

data %>%
  group_by(id) %>%
  mutate_each(funs(an = as.numeric(as.character(.)))) %>%
  gather(color, val, -id) %>%
  filter(val == 1) %>%
  select(-val) %>%
  arrange(id)
# Source: local data frame [9 x 2]
# 
#   id  color
# 1  1    red
# 2  2   blue
# 3  3   blue
# 4  4   blue
# 5  5    red
# 6  6   blue
# 7  7   blue
# 8  8   blue
# 9  9 yellow

答案 1 :(得分:4)

这是使用max.col

的简单基础R矢量化解决方案
cbind(data[1L], color = names(data[-1L])[max.col(data[-1L] == 1L)])
#   id  color
# 1  1    red
# 2  2   blue
# 3  3   blue
# 4  4   blue
# 5  5    red
# 6  6   blue
# 7  7   blue
# 8  8   blue
# 9  9 yellow
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