How to run a paired t-test on different levels of a categorical variable?

I am trying to run a paired t-test on pre- and post-intervention results of three intervention types. I am trying to run the the test on each intervention separately using "subset" in t.test function but it keeps running the test on the whole sample. I cannot separate the intervention levels manually as this is a large database and I do not have access to the excel file. Does anyone have any suggestions?

Here's the codes I am using:

Treatment (intervention) levels:"Passive" "Pro" "Peer"

"Post" and "Pre" are continuous variables.

t.test(data$Post, data$Pre, paired=T, subset=data$Treatment=="Peer")
t.test(data$Post, data$Pre, paired=T, subset=data$Treatment=="Pro")
t.test(data$Post, data$Pre, paired=T, subset=data$Treatment=="Passive")

Solution

There is no subset argument (nor a data argument) for the t.test function when using the default method:

> args(stats:::t.test.default)
function (x, y = NULL, alternative = c("two.sided", "less", 
    "greater"), mu = 0, paired = FALSE, var.equal = FALSE, 
    conf.level = 0.95, ...)

You'll have to subset first,

with(subset(data, subset=Treatment=="Peer"),
         t.test(Post, Pre, paired=TRUE)
    )

There's also an easier way using dplyr and broom...

library(dplyr)
library(broom)

data %>%
  group_by(Treatment) %>%
  do(tidy(t.test(.$Pre, .$Post, paired=TRUE)))

Reproducible example:

set.seed(123)
data <- tibble(id=1:63, Pre=rnorm(21*3,10,5), Post=rnorm(21*3,13,5), 
                   Treatment=sample(c("Peer","Pro","Passive"), 63, TRUE))
data
# A tibble: 63 x 4
      id   Pre  Post Treatment
   <int> <dbl> <dbl> <chr>    
 1     1  7.20  7.91 Pro      
 2     2  8.85  7.64 Peer     
 3     3 17.8  14.5  Peer     
 4     4 10.4  15.2  Peer     
 5     5 10.6  13.3  Passive  
 6     6 18.6  17.6  Passive  
 7     7 12.3  23.3  Pro      
 8     8  3.67 10.5  Peer     
 9     9  6.57  1.45 Pro      
10    10  7.77 18.0  Passive  
# ... with 53 more rows

Output:

# A tibble: 3 x 9
# Groups:   Treatment [3]
  Treatment estimate statistic p.value parameter conf.low conf.high method     alternative
  <chr>        <dbl>     <dbl>   <dbl>     <dbl>    <dbl>     <dbl> <chr>      <chr>      
1 Passive      -2.41    -1.72  0.107          14    -5.42     0.592 Paired t-~ two.sided  
2 Peer         -3.61    -2.96  0.00636        27    -6.11    -1.10  Paired t-~ two.sided  
3 Pro          -1.22    -0.907 0.376          19    -4.03     1.59  Paired t-~ two.sided