R: t-test over all columns

Question

I tried to do t-test to all columns (two at a time) of my data frame, and extract only the p-value. Here is what I have come up with:

for (i in c(5:525) ) {

t_test_p.value =sapply( Data[5:525], function(x) t.test(Data[,i],x, na.rm=TRUE)$p.value)

}

My questions are: 1. is there a way to do it without a loop? 2. how to capture the results of the t-test.

Answer 1

I would recommend to convert your data frame to long format and use pairwise.t.test with appropriate p.adjust:

> library(reshape2)
> 
> df <- data.frame(a=runif(100),
+          b=runif(100),
+          c=runif(100)+0.5,
+          d=runif(100)+0.5,
+          e=runif(100)+1,
+          f=runif(100)+1)
> 
> d <- melt(df)
Using  as id variables
> 
> pairwise.t.test(d$value, d$variable, p.adjust = "none")

    Pairwise comparisons using t tests with pooled SD 

data:  d$value and d$variable 

  a      b      c      d      e   
b 0.86   -      -      -      -   
c <2e-16 <2e-16 -      -      -   
d <2e-16 <2e-16 0.73   -      -   
e <2e-16 <2e-16 <2e-16 <2e-16 -   
f <2e-16 <2e-16 <2e-16 <2e-16 0.63

P value adjustment method: none 
> pairwise.t.test(d$value, d$variable, p.adjust = "bon")

    Pairwise comparisons using t tests with pooled SD 

data:  d$value and d$variable 

  a      b      c      d      e
b 1      -      -      -      -
c <2e-16 <2e-16 -      -      -
d <2e-16 <2e-16 1      -      -
e <2e-16 <2e-16 <2e-16 <2e-16 -
f <2e-16 <2e-16 <2e-16 <2e-16 1

P value adjustment method: bonferroni