使用dplyr::mutate()对列的子集进行行式cor()-Row-wisecor()onsubsetofcolumnsusingdplyr::mutate()

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))

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:

希望将列的子集传递到dplyr mutate()中，并进行逐行计算，例如cor()，以获得列a - c和D-F之间的相关性，但无法知道如何进行。在这里、这里和这里找到如此多的灵感，但仍然无法生成可接受的代码。例如，我尝试过:

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

1 个解决方案

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