使用dplyr :: mutate（）对列的子集上的Row-wise cor（） [英] Row-wise cor() on subset of columns using dplyr::mutate()

问题描述

set.seed(8)
df <- data.frame(
  A=sample(c(1:3), 10, replace=T), 
  B=sample(c(1:3), 10, replace=T),
  C=sample(c(1:3), 10, replace=T),
  D=sample(c(1:3), 10, replace=T),
  E=sample(c(1:3), 10, replace=T), 
  F=sample(c(1:3), 10, replace=T))

一个dplyr mutate（）并进行逐行计算，例如 cor（）以获得列AC之间的相关性和DF，但不能弄清楚如何。发现了灵感 here ，<一个href =https://stackoverflow.com/questions/21818181/applying-a-function-to-every-row-of-a-table-using-dplyr>这里和此处，但无法生成可接受的代码。例如，我尝试这样：

Would like to pass a subset of columns into a dplyr mutate() and make a row-wise calculation, for instance cor() to get correlation between column A-C and D-F, but cannot figure out how. Found SO inspiration here, here and here, but nevertheless failed to produce an acceptable code. For instance, I tried this:

require(plyr)
require(dplyr)
df %>%
  rowwise() %>%
  mutate(c=cor(.[[1:3]],.[[4:6]]))

推荐答案

您可以尝试

df %>% 
   rowwise() %>% 
   do(data.frame(., Cor=cor(unlist(.[1:3]), unlist(.[4:6]))))

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