R: repeat linear regression for all variables and save results in a new data frame

Question

I have a data frame named “dat” with 10 numeric variables (var1, var2,var3,var4 , var5,…var 10), each with several observations…

dat

   var1 var2 var3 var4 var         


        

Answer 1

dat <- structure(list(var1 = c(12L, 3L, 13L, 17L, 9L, 15L, 12L, 3L, 
13L), var2 = c(5L, 2L, 15L, 11L, 13L, 6L, 5L, 2L, 15L), var3 = c(18L, 
10L, 14L, 16L, 8L, 20L, 18L, 10L, 14L), var4 = c(19L, 6L, 13L, 
18L, 8L, 17L, 19L, 6L, 13L), var5 = c(12L, 13L, 1L, 10L, 7L, 
3L, 12L, 13L, 1L), var6 = c(17L, 17L, 17L, 17L, 17L, 17L, 17L, 
17L, 17L), var7 = c(11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L
), var8 = c(16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L), var9 = c(18L, 
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L), var10 = c(10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L)), class = "data.frame", row.names = c("1", 
"2", "3", "4", "5", "6", "7", "8", "9"))

We first write a function to obtain all the statistics you need. Note, rsq is the square of the correlation coefficient. So you don't need the linear model. The coefficient you get from the model is the slope.

STATS = function(x,y,DATA){
 COR = cor.test(DATA[,y],DATA[,x])
 MODEL = summary(lm(DATA[,y]~DATA[,x]))
 data.frame(
 VAR=x,
 PEARSON_COR=as.numeric(COR$estimate),
 PVAL=COR$p.value,
 RSQ=as.numeric(COR$estimate^2),
 SLOPE = MODEL$coefficients[2,1],
 stringsAsFactors=FALSE
 )
}

We test it on var2

STATS("var2","var1",dat)

     VAR PEARSON_COR      PVAL      RSQ     SLOPE
1 var2   0.5668721 0.1114741 0.321344 0.5251232

We do it for example on var2,var3,var4 and combine them into a data frame. Note I did not try var 6 to 10 because it's only 1 value

results = do.call(rbind,
lapply(c("var2","var3","var4"),function(i)STATS(i,"var1",dat)))
results

    VAR PEARSON_COR        PVAL       RSQ     SLOPE
1 var2   0.5668721 0.111474101 0.3213440 0.5251232
2 var3   0.7328421 0.024699805 0.5370575 0.8630573
3 var4   0.8450726 0.004127542 0.7141477 0.7660377

If you are familiar with tidyverse and purrr, you can do the following:

library(dplyr)
library(purrr)
c("var2","var3","var4") %>% map_dfr(STATS,"var1",dat)