The t_parapgraph() Function

The t_parapgraph() function runs a t-test and generates an output that can be copied and pasted into a word document. The code for the function is as follows:

# Create the function t_paragraph with arguments x, y, and measure
# x is the dependent variable
# y is the independent (grouping) variable
# measure is the name of dependent variable inputted as string

t_paragraph <- function (x, y, measure){
  
  # Run a t-test and store it as an object t
  
  t <- t.test(x ~ y)
  
  
  # If your grouping variable has labelled levels, the next line will store them for reporting at a later stage
  
  labels <- levels(y)
  
  # Create an object for each value to be reported
  
  tsl <- as.vector(t$statistic)
  ts <- round(tsl, digits = 3)
  tpl <- as.vector(t$p.value)
  tp <- round(tpl, digits = 3)
  d_fl <- as.vector(t$parameter)
  d_f <- round(d_fl, digits = 2)
  ml <- as.vector(tapply(x, y, mean))
  m <- round(ml, digits = 2)
  sdl <- as.vector(tapply(x, y, sd))
  sd <- round(sdl, digits = 2)
  
  # Use print(paste0()) to combine the objects above and create two potential outputs
  # The output that is generated will depend on the result of the test
  
  
  # wording if significant difference is observed
  
  if (tp < 0.05) 
    print(paste0("An independent samples t-test revealed a significant difference in ", 
                 measure, " between the ", labels[1], " sample, (M = ", 
                 m[1], ", SD = ", sd[1], "), and the ", labels[2], 
                 " sample, (M =", m[2], ", SD =", sd[2], "), t(", 
                 d_f, ") = ", ts, ", p = ", tp, "."), quote = FALSE, 
          digits = 2)
  
  # wording if no significant difference is observed      
  
  if (tp > 0.05) 
    print(paste0("An independent samples t-test revealed no difference in ", 
                 measure, " between the ", labels[1], " sample, (M = ", 
                 m[1], ", SD = ", sd[1], "), and the ", labels[2], 
                 " sample, (M = ", m[2], ", SD =", sd[2], "), t(", 
                 d_f, ") = ", ts, ", p = ", tp, "."), quote = FALSE, 
          digits = 2)
}

When using t_paragraph(), x is your DV, y is your grouping variable while measure is a string value that the name of the dependent variable. To illustrate the function I’ll use the mtcars dataset.

Applications of the t_parapgraph() Function

The mtcars dataset is comes with R. For information on it simply type help(mtcars). The variables of interest here are am (transmission; 0 = automatic, 1 = manual), mpg (miles per gallon), qsec (1/4 mile time). The two questions I’m going to look at are:

1. Is there a difference in miles per gallon depending on transmission?
2. Is there a difference in 1/4 mile time depending on transmission?

Before running the test it is a good idea to look at the data². Because we’re going to look at differences between groups we want to run descriptives for each group separately. To do this I’m going to combine the the descriptives() function which I previously covered here (also part of the desnum package) and the tapply() function.

The tapply() function allows you to run a function on subsets of a dataset using a grouping variable (or index). The arguments are as follows tapply(vector, index, function). vector is the variable you want to pass through function; and index is the grouping variable. The examples below will make this clearer.

We want to run descriptives on mtcars$mpg and on mtcars$qsec and for each we want to group by transmission (mtcars$am). This can be done using tapply() and descriptives() together as follows:

tapply(mtcars$mpg, mtcars$am, descriptives)
## $`0`
##       mean       sd  min  max len
## 1 17.14737 3.833966 10.4 24.4  19
## 
## $`1`
##       mean       sd min  max len
## 1 24.39231 6.166504  15 33.9  13

Recall that 0 = automatic, and 1 = manual. Replace mpg with qsec and run again:

tapply(mtcars$qsec, mtcars$am, descriptives)
## $`0`
##       mean       sd   min  max len
## 1 18.18316 1.751308 15.41 22.9  19
## 
## $`1`
##    mean       sd  min  max len
## 1 17.36 1.792359 14.5 19.9  13