根据值将一列逗号分隔的数字拆分为多列 [英] Split column of comma-separated numbers into multiple columns based on value

问题描述

我在数据框中有一个列 f ，我希望根据该列中的值将其分布到多个列中。例如：

I have a column f in my dataframe that I would like to spread into multiple columns based on the values in that column. For example:

df <- structure(list(f = c(NA, "18,17,10", "12,8", "17,11,6", "18", 
                           "12", "12", NA, "17,11", "12")), .Names = "f", row.names = c(NA, 
                                                                                        10L), class = "data.frame")

df
#            f
#  1      <NA>
#  2  18,17,10
#  3      12,8
#  4   17,11,6
#  5        18
#  6        12
#  7        12
#  8      <NA>
#  9     17,11
#  10       12

如何拆分列 f 分成多列，指示该行中的数字。我对这样的东西感兴趣：

How would I split column f into multiple columns indicating the numbers in the row. I'm interested in something like this:

          6    8    10   11   12   17   18    
1         0    0    0    0    0    0    0
2         0    0    1    0    0    1    1
3         0    1    0    0    1    0    0
4         1    0    0    1    0    1    0
5         0    0    0    0    0    0    1
6         0    0    0    0    1    0    0
7         0    0    0    0    1    0    0
8         0    0    0    0    0    0    0
9         0    0    0    1    0    1    0
10        0    0    0    0    1    0    0

我在想我可以在 f 列上使用 unique 来根据不同的数字创建单独的列，然后执行 grepl 确定特定数字是否在 f 列中，但我想知道是否有更好的方法。与<$ c $ tidyr 包中的价差或分隔类似。

I'm thinking I could useunique on the f column to create the seperate columns based on the different numbers and then do a grepl to determine if the specific number is in column f but I was wondering if there was a better way. Something similar to spread or separate in the tidyr package.

推荐答案

使用 tidyr :: separate_rows 的解决方案如下：

library(tidyverse)
df %>% mutate(ind = row_number()) %>%
  separate_rows(f, sep=",") %>%
  mutate(f = ifelse(is.na(f),0, f)) %>%
  count(ind, f) %>%
  spread(f, n, fill = 0) %>%
  select(-2) %>% as.data.frame()

   # ind 10 11 12 17 18 6 8
# 1    1  0  0  0  0  0 0 0
# 2    2  1  0  0  1  1 0 0
# 3    3  0  0  1  0  0 0 1
# 4    4  0  1  0  1  0 1 0
# 5    5  0  0  0  0  1 0 0
# 6    6  0  0  1  0  0 0 0
# 7    7  0  0  1  0  0 0 0
# 8    8  0  0  0  0  0 0 0
# 9    9  0  1  0  1  0 0 0
# 10  10  0  0  1  0  0 0 0

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