Compute one sample t-test for each column of a data frame and summarize results in a table

Question

Here is some sample data on my problem:

mydf <- data.frame(A = rnorm(20, 1, 5),
                   B = rnorm(20, 2, 5),
                   C = rnorm(20, 3, 5),
                   D = rnorm(20, 4, 5),
                   E = rnorm(20, 5, 5))

Now I'd like to run a one-sample t-test on each column of the data.frame, to prove if it differs significantly from zero, like t.test(mydf$A), and then store the mean of each column, the t-value and the p-value in a new data.frame. So the result should look something like this:

      A    B    C    D    E
mean  x    x    x    x    x
t     x    x    x    x    x
p     x    x    x    x    x

I could definitely think of some tedious ways to do this, like looping through mydf, calculating the parameters, and then looping through the new data.frame and insert the values.
But with packages like plyr at hand, shouldn't there be a more concise and elegant way to do this?

Any ideas are highly appreciated.

Answer 1

Try something like this and then extract the results you want from the resulting table:

results <- lapply(mydf, t.test)
resultsmatrix <- do.call(cbind, results)
resultsmatrix[c("statistic","estimate","p.value"),]

Gives you:

          A         B          C            D           E           
statistic 1.401338  2.762266   5.406704     3.409422    5.024222    
estimate  1.677863  2.936304   5.418812     4.231458    5.577681    
p.value   0.1772363 0.01240057 3.231568e-05 0.002941106 7.531614e-05

Answer 2

a data.table solution :

library(data.table)
DT <- as.data.table(mydf)
DT[,lapply(.SD,function(x){
         y <- t.test(x)
         list(p = round(y$p.value,2),
              h = round(y$conf.int,2),
              mm = round(y$estimate,2))})]

           A          B         C         D         E
1:        0.2       0.42      0.01         0         0
2: -0.91,3.98 -1.15,2.62 1.19,6.15 2.82,6.33 2.68,6.46
3:       1.54       0.74      3.67      4.57      4.57