I have a data frame that looks like this, say this is called DF.

I want to perform a Mann-Whitney test between each subject and the subject's baseline (Base) in each category. For example, do a Mann-Whitney test for Joseph and Joseph (Base) for Avg_Score, EOTM, and FSO_1 separately so I get 3 p-values for the direct comparison between the two. My end goal is to essentially make a final data frame, DF2 like this:

Where [p-val] is the resulted p-value between the regular subject name and subject name (Base). (E.g. p-val for [p-value] for Avg_Score with Joseph is a whitney test comparing Joseph Avg_Score vs Joseph (Base) Avg_Score.

To do the mann-whitney test, I can use wilcox.test command. But in a large data set that have more than the rows/columns listed here, how could I make that perhaps as a for loop, if necessary? I would appreciate any help thank you. An example of the wilcox test is here.

Subject <- c("Joseph", "Joseph", "Joseph", " Joseph (B)", " Joseph (B)", " Joseph (B)", " Susie", "Susie", "Susie", "Susie (B)", "Susie (B)", "Susie (B)")
Avg_Score <- c(1.09, 0.89, 0.99, 0.76, 1.23, 1.11, 1.88, 1.9, 1.4, 1.1, 1.2, 1.4)
EOTM <- c(1.2, 1.9, 0.8, 0.8, 0.1, 0.22, 11.2, 10.9, 10.8, 10.8, 12.1, 11.22)
FS0_1 <- c(6.1, 6.8, 8.2, 8.9, 21.1, 26.1, 60.1, 63.8, 81.2, 84.9, 71.1, 76.1)
DF <- as.data.frame(Subject, Avg_Score, EOTM, FS0_1)

CodePudding user response：

Create a temporary column 'Sub' from 'Subject', then remove the spaces (\\s ) followed by the ( and any characters from the 'Subject' to use it as grouping column, loop across the numeric columns, subset the elements where the 'Sub' doesn't have ( followed by 'B' and the second subset with the rest, apply the wilcox.test and extract the pvalue ($p.value)

library(dplyr)
library(stringr)
DF %>%
   mutate(Sub = Subject) %>% 
   group_by(Subject = trimws(str_remove(Subject, "\\s \\(.*"))) %>% 
   summarise(across(where(is.numeric), ~ 
    wilcox.test(.x[str_detect(Sub, "\\(B")], 
      .x[str_detect(Sub, "\\(B", negate = TRUE)])$p.value), .groups = "drop")

-output

# A tibble: 2 × 4
  Subject Avg_Score  EOTM FS0_1
  <chr>       <dbl> <dbl> <dbl>
1 Joseph      0.7   0.121   0.1
2 Susie       0.121 0.507   0.4

base R - similar logic as stated above

by(DF, trimws(DF$Subject, whitespace = "\\s \\(.*|\\s*"), 
  FUN = \(x) {
    i1 <- grepl("\\(B", x$Subject)
    sapply(x[-1], \(u) wilcox.test(u[i1], u[!i1])$p.value) 
  })