How to apply t test on each row of an R data frame?

To apply t test on each row of an R data frame, we can use apply function along with t.test function. For Example, if we have a data frame called DF and we want to apply t test on each row of DF then we can use the command given below −

apply(DF,1,t.test)

Check out the below Example to understand how it works.

Example

Following snippet creates a sample data frame −

x<-rpois(10,5) y<-rpois(10,2) z<-rpois(10,1) a<-rpois(10,2) b<-rpois(10,5) df<-data.frame(x,y,z,a,b) df

The following dataframe is created

x y z a b 1 2 4 0 2 2 2 8 3 1 4 7 3 6 0 2 3 7 4 6 4 2 1 6 5 6 2 2 3 5 6 5 1 1 4 2 7 6 2 0 3 10 8 3 1 2 2 3 9 7 1 3 4 3 10 5 0 1 0 5

To apply t test on each row of an R data frame, add the following code to the above snippet −

x<-rpois(10,5) y<-rpois(10,2) z<-rpois(10,1) a<-rpois(10,2) b<-rpois(10,5) df<-data.frame(x,y,z,a,b) apply(df,1,t.test)

One Sample t-test

For the One Sample t-test, on the above created data frame, add the respective code to the above snippet −

[[1]]       One Sample t-test data: newX[, i] t = 3.1623, df = 4, p-value = 0.03411 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval:  0.2440219 3.7559781 sample estimates: mean of x    2 [[2]]      One Sample t-test data: newX[, i] t = 3.5703, df = 4, p-value = 0.02337 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval:  1.022801 8.177199 sample estimates: mean of x    4.6 [[3]]      One Sample t-test data: newX[, i] t = 2.7941, df = 4, p-value = 0.0491 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval:  0.02280072 7.17719928 sample estimates: mean of x    3.6 [[4]]       One Sample t-test data: newX[, i] t = 3.7262, df = 4, p-value = 0.02036 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval:  0.9685704 6.6314296 sample estimates: mean of x     3.8 [[5]]        One Sample t-test data: newX[, i] t = 4.4313, df = 4, p-value = 0.01141 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval:  1.344405 5.855595 sample estimates: mean of x     3.6 [[6]]      One Sample t-test data: newX[, i] t = 3.2004, df = 4, p-value = 0.03289 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval:  0.3444053 4.8555947 sample estimates: mean of x    2.6 [[7]]      One Sample t-test data: newX[, i] t = 2.4089, df = 4, p-value = 0.07365 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval:  -0.6408975 9.0408975 sample estimates: mean of x     4.2 [[8]]      One Sample t-test data: newX[, i] t = 5.8797, df = 4, p-value = 0.004181 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval:  1.161149 3.238851 sample estimates: mean of x     2.2 [[9]]      One Sample t-test data: newX[, i] t = 3.6742, df = 4, p-value = 0.02131 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval:  0.8796505 6.3203495 sample estimates: mean of x     3.6 [[10]] One Sample t-test data: newX[, i] t = 1.9005, df = 4, p-value = 0.1302 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval: -1.013968 5.413968 sample estimates: mean of x 2.2