CODEWITHSUNDEEP
rstatisticsanovat-testhypothesis-test
Bjorno
Bjorno

Reputation: 119

Test if group means are statistically significantly different in R

*(I asked this question earlier, but it got migrated to stackexchange and was labeled 'unclear' and I couldn't edit it, so I'm going to try to clean up the question and make it more clear).

I have the following data frame and need to determine if there are statistically significant differences among the means of Test Groups, and repeat this for each Task Grouping. :

set.seed(123)

Task_Grouping <- sample(c("A","B","C"),500,replace=TRUE)
Test_Group <- sample(c("Green","Yellow","Orange"),500,replace=TRUE)
TotalTime <- rnorm(500, mean = 3, sd = 3)

mydataframe <- data.frame(Task_Grouping, Test_Group, TotalTime)

For example, for Task A, I need to see if there are significant differences in the means of the Test Groups (Green, Yellow, Orange).

I've tried the following code, but something is wrong since the p.value is the same for each Test Group combination among different Task Groupings (i.e. every p-value is 0.6190578):

results <- mydataframe %>%
  group_by(Task_Grouping) %>%
  do(tidy(pairwise.t.test(mydataframe$TotalTime, mydataframe$Test_Group,
                 p.adjust.method = "BH")))

I'm also not 100% sure if a pairwise.t.test is the correct statistical test to use. To rephrase, I need to see if the Test_Group means are statistically different from one another. And then I need to repeat this analysis for each Task Grouping.

Upvotes: 0

Views: 450

Answers (1)

MrFlick
MrFlick

Reputation: 206207

Here's how you might do it using dplyr, purrr and broom

library(dply)
library(purrr)
library(broom)
mydataframe %>% 
  nest(data = c(Test_Group, TotalTime)) %>% 
  mutate(tidy=map(data, ~tidy(pairwise.t.test(.$TotalTime, .$Test_Group,
                                   p.adjust.method = "BH")))) %>%
  select(-data) %>% 
  unnest(tidy)

Note since we are using map, we use .$ rather than mydataframe$ to get the current group rather than the original table. See more examples at the broom and dplyr vignette

Upvotes: 4

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