Auresm
Reputation: 139
Is there a way to run multiple t.tests that produce results that can be easily stored in table format?
I'm working with a set of data that I want to subset and compare t-tests for. The end goal is to have an easily readable table as an output that can be presented to a reader.
Currently I am using individual t-tests that give results one at a time, such as the code below.
t.test(survey$numericDV[survey$condition == 0 & survey$partisan_guess == "Republican"], survey$numericDV[survey$condition == 1 & survey$partisan_guess == "Republican"])
$condition is a factor variable with 5 levels from 0 to 4,and $partisan_guess is a factor with 2 levels. The goal is to run t-tests comparing condition == 0 against the other 4 levels, with the ability to specify which level of partisan_guess to use.
Is there a way to run each of these tests simultaneously that stores the results in a table, i.e. I would get table that lists the result of a t-test of Condition 0 against Condition 1, Condition 0 against Condition 2, etc.
Thanks in advance for the help!
Minimum replicable data:
survey <- structure(list(numericDVedu = c(0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0,
1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,
0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1,
0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,
0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0,
0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1,
1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,
0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0,
0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,
1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0,
0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0), condition =
structure(c(5L, 4L, 2L, 5L, 1L, 1L, 2L, 3L, 2L, 2L, 5L, 2L, 3L, 1L,
5L, 4L, 5L, 2L, 4L, 4L, 1L, 2L, 3L, 5L, 2L, 4L, 5L, 4L, 5L, 5L, 5L,
2L, 1L, 4L, 3L, 5L, 2L, 5L, 1L, 4L, 2L, 3L, 2L, 5L, 1L, 2L, 1L, 2L,
3L, 1L, 2L, 4L, 3L, 5L, 3L, 4L, 1L, 5L, 1L, 2L, 4L, 2L, 2L, 3L, 4L,
3L, 1L, 2L, 3L, 2L, 4L, 2L, 1L, 5L, 4L, 1L, 3L, 5L, 4L, 3L, 2L, 4L,
5L, 3L, 4L, 2L, 4L, 2L, 4L, 3L, 5L, 2L, 3L, 1L, 1L, 1L, 3L, 5L, 5L,
3L, 1L, 3L, 2L, 3L, 4L, 5L, 2L, 2L, 1L, 1L, 5L, 5L, 2L, 4L, 5L, 3L,
1L, 4L, 5L, 3L, 4L, 1L, 5L, 3L, 1L, 2L, 1L, 3L, 5L, 3L, 1L, 2L, 4L,
4L, 1L, 3L, 4L, 5L, 3L, 3L, 5L, 4L, 2L, 3L, 5L, 4L, 1L, 5L, 3L, 4L,
2L, 4L, 5L, 3L, 4L, 2L, 4L, 5L, 3L, 2L, 1L, 2L, 4L, 1L, 3L, 5L, 2L,
1L, 3L, 4L, 1L, 2L, 4L, 5L, 2L, 2L, 3L, 3L, 5L, 1L, 2L, 5L, 2L, 3L,
4L, 2L, 4L, 1L, 3L, 4L, 1L, 4L, 1L, 5L, 4L, 2L, 2L, 5L, 1L, 4L, 5L,
3L, 1L, 1L, 4L, 5L, 3L, 2L, 1L, 1L, 5L, 1L, 4L, 5L, 3L, 4L, 5L, 3L,
1L, 5L, 2L, 4L, 5L, 1L, 4L, 1L, 3L, 2L, 4L, 3L, 5L, 5L, 1L, 4L, 1L,
3L, 4L, 5L, 1L, 3L, 1L, 3L, 4L, 2L, 3L, 3L, 2L, 3L, 2L, 4L, 5L, 2L,
4L, 5L, 1L, 2L, 5L, 3L, 2L, 3L, 5L, 4L, 1L, 3L, 4L, 5L, 1L, 2L, 5L,
5L, 3L, 1L, 4L, 5L, 3L, 2L, 1L, 1L, 4L, 5L, 1L, 2L, 1L, 3L, 1L, 5L,
2L, 2L, 5L, 1L, 3L, 4L, 3L, 1L, 3L, 2L, 1L, 2L, 5L, 3L, 1L, 4L, 2L,
3L, 1L, 2L, 3L, 4L, 1L, 3L, 2L, 5L, 1L, 4L, 5L, 1L, 2L, 1L, 2L, 4L,
5L, 5L, 3L, 5L, 4L, 2L, 4L, 3L, 5L, 2L), .Label = c("0", "1", "2",
"3", "4"), class = "factor"),
partisan_guess = structure(c(2L, 1L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L), .Label = c("Democrat", "Republican"
), class = "factor")), class = "data.frame", row.names = c(NA, -330L))
Upvotes: 0
Views: 75
Answers (1)
Ronak Shah
Reputation: 389115
We can write a function to apply t.test for every condition against condition 0.
run_t_test <- function(data, partisan_guess) {
data <- subset(data, partisan_guess == partisan_guess)
zero_data <- data$numericDV[survey$condition == 0]
purrr::map_df(1:4, function(x) broom::tidy(t.test(
zero_data, data$numericDV[survey$condition == x])), .id = 'condition')
}
run_t_test(survey, 'Republican')
# A tibble: 4 x 11
# condition estimate estimate1 estimate2 statistic p.value parameter conf.low
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 1 -0.0490 0.333 0.382 -0.588 0.557 132. -0.214
#2 2 -0.113 0.333 0.446 -1.32 0.188 128. -0.282
#3 3 0.0635 0.333 0.270 0.782 0.436 127. -0.0972
#4 4 0.0980 0.333 0.235 1.25 0.212 130. -0.0565
# … with 3 more variables: conf.high <dbl>, method <chr>, alternative <chr>
run_t_test(survey, 'Democrat')
# A tibble: 4 x 11
# condition estimate estimate1 estimate2 statistic p.value parameter conf.low
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 1 -0.0490 0.333 0.382 -0.588 0.557 132. -0.214
#2 2 -0.113 0.333 0.446 -1.32 0.188 128. -0.282
#3 3 0.0635 0.333 0.270 0.782 0.436 127. -0.0972
#4 4 0.0980 0.333 0.235 1.25 0.212 130. -0.0565
# … with 3 more variables: conf.high <dbl>, method <chr>, alternative <chr>
Upvotes: 1
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