R: how can I create a table with mean and sd according to experimental group alongside p-values?

Question

I know how I can do all that for individual variables but I need to report this information for a large number of variables and would like to know if there is an efficient w

Answer 1

The tables package makes everything in this except the p-values easy, and the p-values are doable. Here is a quick example:

> library(tables)
> iris2 <- iris[ iris$Species != 'versicolor', ]
> iris2$Species <- factor(iris2$Species)
> tmp <- tabular( Petal.Width+Petal.Length + Sepal.Width+Sepal.Length ~ Species* (mean+sd), data=iris2 )
> 
> tmp.p <- sapply( names(iris2)[1:4], function(x) t.test( iris2[[x]] ~ iris2$Species )$p.value )
> 
> tmp

              setosa        virginica       
              mean   sd     mean      sd    
 Petal.Width  0.246  0.1054 2.026     0.2747
 Petal.Length 1.462  0.1737 5.552     0.5519
 Sepal.Width  3.428  0.3791 2.974     0.3225
 Sepal.Length 5.006  0.3525 6.588     0.6359

> tmp2 <- cbind(tmp, tmp.p)
> colnames(tmp2) <- c('Setosa Mean','Setosa SD', 'Virginica Mean','Virginica SD',
+ 'P-value')
> tmp2
             Setosa Mean Setosa SD Virginica Mean Virginica SD P-value     
Sepal.Length 0.246       0.1053856 2.026          0.2746501    3.966867e-25
Sepal.Width  1.462       0.173664  5.552          0.5518947    4.570771e-09
Petal.Length 3.428       0.3790644 2.974          0.3224966    9.269628e-50
Petal.Width  5.006       0.3524897 6.588          0.6358796    2.437136e-48

#### Edit ####

It looks like newer versions of tabular do more checks which makes the cbind approach not work any more (and this could be a good thing, since I am not sure that it was properly matching the values if the ordering was different). I did not find a simple way to still do this using cbind (though you could convert to a matrix, pad the rows for the headers, then cbind).

Here is another approach that works, it is still a bit of a kludge since it hardcodes the species variable in the function (and the function would therefore have to be updated specifically for each table it is used in):

library(tables)
iris2 <- iris[ iris$Species != 'versicolor', ]
iris2$Species <- factor(iris2$Species)
P.value <- function(x) t.test(x ~ iris2$Species)$p.value
tmp <- tabular( Petal.Width+Petal.Length + Sepal.Width+Sepal.Length ~ Species* (mean+sd) + P.value, data=iris2 )
tmp