用dplyr中的另一组划分（和命名）一组列 [英] Divide (and name) one group of columns by another group in dplyr

问题描述

经过（非常令人恐惧的）dplyr管道，我最终得到了这样的数据集：

After a (very scaring) dplyr pipeline I've ended up with a dataset like this:

year   A    B     C  [....] Z  count.A    count.B     count.C [....] count.Z
1999  10    20    10  ...   6      3          5           67    ...      6
2000   3    5      5  ...   7      5          2            5    ...      5

一些要重现的示例数据：

Some example data to reproduce:

df <- data.frame(year = c(1999, 2000), 
                 A = c(10, 20), 
                 B = c(3, 6), 
                 C = c(1, 2), 
                 count.A = c(1, 2), 
                 count.B = c(8, 9), 
                 count.C = c(5, 7))

我真正需要的是将每一列与其对应的计数组合在一起，即

What I really need is to combine each column with its "count" counterpart i.e.

weight.A = A / count.A, 
weight.B = B / count.B

我必须以编程方式我有数百个lum有没有办法在dplyr管道中做到这一点？

I've to do that programmatically as I have hundreds of columns. Is there a way to do that in a dplyr pipeline?

推荐答案

不要在列名中存储变量。如果您对数据进行整形以使其整洁，则计算非常简单：

Don't store variables in column names. If you reshape your data to make it tidy, the calculation is really simple:

library(tidyverse)

df %>% gather(var, val, -year) %>%    # reshape to long
    separate(var, c('var', 'letter'), fill = 'left') %>%    # extract var from former col names
    mutate(var = coalesce(var, 'value')) %>%    # add name for unnamed var
    spread(var, val) %>%    # reshape back to wide
    mutate(weight = value / count)    # now this is very simple

#>   year letter count value     weight
#> 1 1999      A     1    10 10.0000000
#> 2 1999      B     8     3  0.3750000
#> 3 1999      C     5     1  0.2000000
#> 4 2000      A     2    20 10.0000000
#> 5 2000      B     9     6  0.6666667
#> 6 2000      C     7     2  0.2857143

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