How can I summarize a column that shows the mean difference in R?

Question

tableData <- tibble(Fruits = sample(c('Apple', 'Banana', 'Orange'), 30, T),
                        Ripeness = sample(c('yes', 'no'), 30, T),
                        Mean = ifelse(Ripeness == 'yes', 1.4 + runif(30), 1.6 + runif(30))) %>% 
 add_row(Fruits = "Peach", Ripeness = "yes", Mean = 5)

get_t_test_pval <- function(formula){
  tryCatch({t.test(formula)$p.value}, error = function(cond) NA)
}

tableData %>% 
  group_by(Fruits) %>% 
  summarise(t_test_pval = get_t_test_pval(Mean ~ Ripeness))

The following code summarizes the table so that p-values are evaluated for each fruit. Is it possible to also add a column that shows the mean difference (ie mean of yes ripeness - mean of no ripeness) to each fruit? With a trycatch enabled as well?

Answer 1

One option is after grouping by 'Fruits' summarise by the taking the difference of mean of 'Mean' where 'Ripeness' is 'yes' with that of 'no', while applying the OP's function to get the 't_test_pval'

library(dplyr)
tableData %>% 
  group_by(Fruits) %>%    
  summarise(Meandiff = mean(Mean[Ripeness == 'yes'])- 
        mean(Mean[Ripeness == 'no']), 
       t_test_pval = get_t_test_pval(Mean ~ Ripeness))
# A tibble: 4 x 3
#  Fruits Meandiff t_test_pval
#  <chr>     <dbl>       <dbl>
#1 Apple     0.122       0.435
#2 Banana    0.167       0.327
#3 Orange   -0.306       0.216
#4 Peach   NaN          NA