I want to find correlations, p-values and 95% CI between one specific column and all other columns in a dataframe. The 'broom' package provides an example how to do that between two specific columns using cor.test with dplyr and pipes. For mtcars and, say, mpg column we can run a correlation with another column:
library(dplyr)
library(broom)
mtcars %>% do(tidy(cor.test(.$mpg, .$cyl)))
estimate statistic p.value parameter conf.low conf.high
1 -0.852162 -8.919699 6.112687e-10 30 -0.9257694 -0.7163171
The output is a single-row dataframe. I would like to run cor.test for mpg with each column so that that the output for each column goes into a separate row. The desired output would look like this, where mpg column was paired with every other column.
estimate statistic p.value parameter conf.low conf.high
cyl -0.852162 -8.919699 6.112687e-10 30 -0.9257694 -0.7163171
disp -0.8475514 -8.747152 9.380327e-10 30 -0.9233594 -0.7081376
hp -0.7761684 -6.742389 1.787835e-07 30 -0.8852686 -0.5860994
drat 0.6811719 5.096042 1.77624e-05 30 0.4360484 0.832201
wt -0.8676594 -9.559044 1.293959e-10 30 -0.9338264 -0.7440872
qsec 0.418684 2.525213 0.01708199 30 0.08195487 0.6696186
vs 0.6640389 4.864385 3.415937e-05 30 0.410363 0.8223262
am 0.5998324 4.106127 0.0002850207 30 0.3175583 0.784452
gear 0.4802848 2.999191 0.005400948 30 0.1580618 0.7100628
carb -0.5509251 -3.61575 0.001084446 30 -0.754648 -0.2503183
Note the added row names, which show what column was paired with mpg for the cor.test. Ideally, I'd like to do that with dplyr and pipes.