means/pvalue table from t.test in R

Question

Is there a way to extract the mean and p-value from a t.test output and create a table that includes the features, mean, and p-value? Say there are 10 columns put through t.test, and that means there are 10 means, and 10 p-values. How would I be able to create a table which only shows those specific items?

here is an example: data (iris):

    a.   b.  c.  d.   e. 
1   5.1 3.5 1.4 0.2 setosa
2   4.9 3.0 1.4 0.2 setosa
3   4.7 3.2 1.3 0.2 setosa
4   4.6 3.1 1.5 0.2 setosa
5   5.0 3.6 1.4 0.2 setosa
6   5.4 3.9 1.7 0.4 setosa

t.test(a)
t.test(b) #...ect we obtain the mean and p-value. 

this is the output im looking for:

feature mean p-val
col1  0.01 0.95
col2  0.01 0.95
.
.
.
coln

hope it makes sense!

Answer 1

Using the iris built in data set as an example

sapply(iris[, 1:4], function(i){
  t.test(i)[c(5,3)]
})

The sapply() function is iteratively performing that custom function - which performs a t-test on a variable and returns the estimate and p-value - through columns 1 to 4 of iris.

Beware multiple testing though...

Answer 2

We could do something like this. Here with an example dataset of 3 columns aa grouping column.

#example dataset
df <- mtcars %>% 
  mutate(vs = factor(vs)) %>% 
  select(mpg, disp, hp, vs)


means = colMeans(df[sapply(df, is.numeric)])

p.value= sapply(df[,1:3], function(i) t.test(i ~ df$vs)$p.value)


rbind(means, p.value)

                 mpg         disp           hp
means   2.009062e+01 2.307219e+02 1.466875e+02
p.value 1.098368e-04 2.476526e-06 1.819806e-06