Apply t-test on many columns in a dataframe split by factor

dataframe, plyr, r

Solution

Maybe this produces the result you are looking for:

df <- read.table(text="Group   var1    var2    var3    var4    var5
1           3   5   7   3   7
1           3   7   5   9   6
1           5   2   6   7   6
1           9   5   7   0   8
1           2   4   5   7   8
1           2   3   1   6   4
2           4   2   7   6   5
2           0   8   3   7   5
2           1   2   3   5   9
2           1   5   3   8   0
2           2   6   9   0   7
2           3   6   7   8   8
2           10  6   3   8   0", header = TRUE)


t(sapply(df[-1], function(x) 
     unlist(t.test(x~df$Group)[c("estimate","p.value","statistic","conf.int")])))

The result:

     estimate.mean in group 1 estimate.mean in group 2   p.value statistic.t conf.int1 conf.int2
var1                 4.000000                 3.000000 0.5635410   0.5955919 -2.696975  4.696975
var2                 4.333333                 5.000000 0.5592911  -0.6022411 -3.104788  1.771454
var3                 5.166667                 5.000000 0.9028444   0.1249164 -2.770103  3.103436
var4                 5.333333                 6.000000 0.7067827  -0.3869530 -4.497927  3.164593
var5                 6.500000                 4.857143 0.3053172   1.0925986 -1.803808  5.089522

Problem

I have a dataframe with one factor column with two levels, and many numeric columns. I want to split the dataframe by the factor column and do t-test on the colunm pairs. Using the example dataset Puromycin I want the result to look something like this: ``` Variable Treated Untreated p-value Test-statistic CI of difference**** Conc 0.3450 0.2763 XXX T XX - XX Rate 141.58 110.7272 xxx T XX - XX ``` I think I am looking for a solution using PLYR that can an output the above results in a nice dataframe. (The Puromycin only contains two numeric variables, but the solution I am looking for would work on a dataframe with many numeric variables) UPDATE - I will try to clarify what i mean. I would like to go from data that look like this: ``` Grouping variable var1 var2 var3 var4 var5 1 3 5 7 3 7 1 3 7 5 9 6 1 5 2 6 7 6 1 9 5 7 0 8 1 2 4 5 7 8 1 2 3 1 6 4 2 4 2 7 6 5 2 0 8 3 7 5 2 1 2 3 5 9 2 1 5 3 8 0 2 2 6 9 0 7 2 3 6 7 8 8 2 10 6 3 8 0 ``` To a result dataframe that look like this: ``` "Mean in group 1" "Mean in group 2" "P-value of difference" "N" var1 ## ## ## ## var2 ## ## ## ## var3 ## ## ## ## var4 ## ## ## ## var5 ## ## ## ## ``` Maybe it is something with mapply I am looking for because i want to split up my dataframe into dataframe1 and dataframe2 by a two-level factor, and apply a function( t-test) to the first parts of dataframe1 and dataframe2, and then a t-test on the second parts of dataframe1 and dataframe2, and then a t-test to the third parts of dataframe1 and dataframe2, and so on on all the column-pairs generated by the split by factor.

Original source