Running multiply Anova and/or T-Test on variables in one dataframe in R

I've the a data frame containing different items (and it's cost) and also it's subsequent groupings. I would like to run an Anova and/or T-Test for each item based on their groupings to see if their mean differs. Anybody knows how to do this in R?

A sample of the dataframe is as follow:

Item	Cost	Grouping
Book A	7	A
Book A	9	B
Book A	6	A
Book A	7	B
Book B	4	A
Book B	6	B
Book B	5	A
Book B	3	C
Book C	5	C
Book C	4	A
Book C	7	C
Book C	2	B
Book C	2	B
Book D	4	A
Book D	2	C
Book D	9	C
Book D	4	A

The output should be a simple table (or any similar table) as follows

Item	P-Value (from ANOVA/t-test) (H0: Mean same for all groupings)
Book A	xxx
Book B	xxx
Book C	xxx
Book D	xxx

Thanks in advance!

Solution

You could use anova_test from the rstatix package like this:

df <- data.frame(Item = c("Book A", "Book A", "Book A", "Book A", "Book B", "Book B", "Book B", "Book B"),
                 Cost = c(7,9,6,7,4,6,5,3),
                 Grouping = c("A", "B", "A", "B", "A", "B", "A", "C"))

library(dplyr)
library(rstatix)
df %>% 
  group_by(Item) %>%
  anova_test(Cost ~ Grouping)
#> Coefficient covariances computed by hccm()
#> Coefficient covariances computed by hccm()
#> # A tibble: 2 × 8
#>   Item   Effect     DFn   DFd     F     p `p<.05`   ges
#> * <chr>  <chr>    <dbl> <dbl> <dbl> <dbl> <chr>   <dbl>
#> 1 Book A Grouping     1     2   1.8 0.312 ""      0.474
#> 2 Book B Grouping     2     1   4.5 0.316 ""      0.9

^{Created on 2022-07-10 by the reprex package (v2.0.1)}