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