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

Question

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.

Answer 1

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))

Answer 2

This is not the most pretty but i did a for loop like this:
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))
df_ttest <- data.frame(p_a = c(1:50),
t_a = c(1:50),
p_b = c(1:50),
t_b = c(1:50),
p_c = c(1:50),
t_c = c(1:50))
index <- 0:50*20
for(i in seq_along(index)) {
df_ttest$p_a[i] = t.test(df$a1[index[i] : index[i+1]])$p.value
df_ttest$p_b[i] = t.test(df$b1[index[i] : index[i+1]])$p.value
df_ttest$p_c[i] = t.test(df$c1[index[i] : index[i+1]])$p.value
df_ttest$t_a[i] = t.test(df$a1[index[i] : index[i+1]])$statistic
df_ttest$t_b[i] = t.test(df$b1[index[i] : index[i+1]])$statistic
df_ttest$t_c[i] = t.test(df$c1[index[i] : index[i+1]])$statistic
}
This gives a 50x6 dataframe with seperate columns of p and t values for every 20 row chunk of a, b and c.
You could even go further and make a nested for loop to cycle through each row in df_ttest to make this abit prettier.