I have a data frame:
structure(list(groups = c("A", "A", "A", "A", "B", "B", "B",
"B", "C", "C", "C", "C", "D", "D", "D", "D"), weight = c(50.34869444,
49.20443342, 50.62727386, 50.12316397, 49.84571613, 50.88337532,
48.23188285, 51.13725686, 51.19946209, 49.02212935, 50.00188434,
49.70067628, 50.50444172, 48.88528478, 49.2378029, 49.11125589
), height = c(149.5389985, 150.7241218, 149.6922257, 149.6660622,
150.2770344, 149.6382699, 150.1900336, 151.264749, 151.3418096,
149.9407582, 150.2397936, 149.3163071, 148.079746, 149.1675788,
147.5201934, 150.8203477), age = c(10.18377395, 8.388813147,
9.858806212, 9.859746016, 9.584814407, 9.081315423, 10.67367302,
10.26713746, 10.96606861, 11.58603799, 10.34936347, 9.93621052,
9.584046986, 8.413787028, 10.39826156, 9.977231496), month_birth = c(3.627272074,
1.989467718, 2.175805989, 1.095100584, 2.16437856, 1.215151355,
2.63897628, 0.942159155, 1.155299136, 0.404000756, 1.695590789,
2.739378326, 1.950649717, 1.312775225, 1.904828579, 1.325257624
)), class = "data.frame", row.names = c(NA, -16L))
I want to use wilcox test to compare columns within each group individually
What was I trying to do:
wilcox.fun <- function(dat, col,group.labels) {
c1 <- combn(unique(group.labels),2)
sigs <- list()
for(i in 1:ncol(c1)) {
sigs[[i]] <- wilcox.test(
dat[c1[i,],col],
dat[c1[i,],col]
)
}
names(sigs) <- paste("Group",c1[1,],"by Group",c1[2,])
tests <- data.frame(Test=names(sigs),
W=unlist(lapply(sigs,function(x) x$statistic)),
p=unlist(lapply(sigs,function(x) x$p.value)),row.names=NULL)
return(tests)
}
debug(test.fun)
tests <- lapply(colnames(data[,c(2:6)]),function(x) wilcox.fun(data,group.labels=c(2:6),x))
names(tests) <- colnames(data[,c(2:6)])
I want to use the wilcox test to compare not between groups, but within the same group between the selected columns.
You can try this code to apply wilcox.test for every combination of variables within each group.
wilcox.fun <- function(dat) {
do.call(rbind, combn(names(dat)[-1], 2, function(x) {
test <- wilcox.test(dat[[x[1]]], dat[[x[2]]])
data.frame(Test = sprintf('Group %s by Group %s', x[1], x[2]),
W = test$statistic,
p = test$p.value)
}, simplify = FALSE))
}
result <- purrr::map_df(split(data, data$groups), wilcox.fun, .id = 'Group')