Search code examples
rloopsdataframet-test

Creating a t-test loop over a dataframe using an index

So, let's say I have a 1000-row, 6-column dataframe, the columns are a1, a2, b1, b2, c1, c2. I want to run some t-tests using a's, b's, and c's and get an output df with 3 columns for the t-values of a-b-c and another three for the significance information for those values, making it a total of 6 columns. The problem I have is with rows, I want to loop over chunks of 20, rendering the output a (1000/20=)50-row, 6-column df.

I have already tried creating an index column for my inital df which repeats a 1 for the first 20 row, a 2 for the next 20 row and so on.

    convert_n <- function(df) {
    df <- df %T>% {.$n_for_t_tests = rep(c(1:(nrow(df)/20)), each = 20)}
    }
    df <- convert_n(df)

However, I can't seem to find a way to properly utilize the items in this column as indices for a "for" or any kind of loop.

Below you can see the relevant code for that creates a 1-row, 6-column df; I need to modify the [0:20] parts, create a loop that does this for 20 groups and binds them.

    t_test_a <- t.test(df$a1[0:20], dfff$a2[0:20], paired = T, conf.level 
    = 0.95)
    t_test_b <- t.test(df$b1[0:20], dfff$b2[0:20], paired = T, conf.level 
    = 0.95)
    t_test_c <- t.test(df$c1[0:20], dfff$c2[0:20], paired = T, conf.level 
    = 0.95)
    t_tests_df <- data.frame(t_a = t_test_a$statistic[["t"]], 
                             t_b = t_test_b$statistic[["t"]],
                             t_c = t_test_c$statistic[["t"]])

    t_tests_df <- t_tests_df %T>% {.$dif_significance_a = ifelse(.$t_a > 
                                   2, "YES", "NO")} %T>% 
                                  {.$dif_significance_b = ifelse(.$t_b > 
                                   2, "YES", "NO")} %T>% 
                                  {.$dif_significance_c = ifelse(.$t_c > 
                                   2, "YES", "NO")} %>% 
                                  dplyr::select(t_a, dif_significance_a, 
                                                t_b, dif_significance_b,
                                                t_c, dif_significance_c)

Thank you in advance for your help.

Solution

  • You can use split() and sapply():

    set.seed(42)

df <- data.frame(a1 = sample(1000, 1000), a2 = sample(1000, 1000),
                 b1 = sample(1000, 1000), b2 = sample(1000, 1000),
                 c1 = sample(1000, 1000), c2 = sample(1000, 1000))

group <- gl(50, 20)

D <- split(df, group)

myt <- function(Di) 
  with(Di, c(at=t.test(a1, a2)$statistic, ap=t.test(a1, a2)$p.value,
    bt=t.test(b1, b2)$statistic, bp=t.test(b1, b2)$p.value,
    ct=t.test(c1, c2)$statistic, cp=t.test(c1, c2)$p.value))

sapply(D, FUN=myt) ### or
t(sapply(D, FUN=myt))