How to loop through columns in R to do a T test?

Question

I have one column with a response variable and many columns with independent variables. Every Independent variable is just a binary 0 or 1, and I want to loop through each column to calculate the response variable mean of the 1's and the response variable mean of the 0's so that I can run a T test. I am new to R and don't know how to set the response variable column aside or how to assign all of the other columns to a variable.

Answer 1

You can use lapply to solve your problem:

preds <- a vector of all predictors
response <- "the_response"

lapply(preds, function(x) t.test(reformulate(x, response), data = your_data))

Example:

dat
     y x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
1  5.1  1  1  1  1  1  1  1  1  1   1
2  4.9  1  1  1  1  0  1  1  1  1   1
3  4.7  0  0  0  0  1  0  0  0  0   0
4  4.6  0  1  1  0  0  0  1  1  0   0
5  5.0  0  1  1  1  1  1  0  0  1   1
6  5.4  1  1  1  1  1  1  1  1  1   1
7  4.6  1  0  1  1  0  0  1  0  1   0
8  5.0  0  0  0  1  0  0  0  0  0   0
9  4.4  0  0  0  0  0  0  0  0  1   0
10 4.9  0  1  0  0  0  0  1  1  0   0
11 5.4  1  1  1  1  1  1  1  1  0   1
12 4.8  1  1  1  1  1  1  1  1  1   1
13 4.8  0  1  0  0  0  0  0  0  1   0
14 4.3  1  1  1  1  1  1  1  1  1   1
15 5.8  0  0  1  1  1  0  1  1  1   0

preds <- names(dat)[-1]
response <- "y"

lapply(preds, function(x) t.test(reformulate(x, response), data = dat, var.equal = TRUE))
[[1]]

    Two Sample t-test

data:  y by x1
t = -0.13376, df = 13, p-value = 0.8956
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.4900214  0.4328786
sample estimates:
mean in group 0 mean in group 1 
       4.900000        4.928571 


[[2]]

    Two Sample t-test

data:  y by x2
t = -0.088442, df = 13, p-value = 0.9309
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.5085418  0.4685418
sample estimates:
mean in group 0 mean in group 1 
           4.90            4.92 
 :