search for: tmp_df

...means causing some sort of bias. So I want the summary stat for the NA's also. Here is a simple example session (edited to remove the typos I made, comments added later): > tmp_a <- 1:10 > tmp_b <- rep(1:5,2) > tmp_c <- rep(1:2,5) > tmp_d <- c(1,1,1,2,2,2,3,3,3,4) > tmp_df <- data.frame(tmp_a,tmp_b,tmp_c,tmp_d); > tmp_df$tmp_c[9:10] <- NA ; > tmp_df tmp_a tmp_b tmp_c tmp_d 1 1 1 1 1 2 2 2 2 1 3 3 3 1 1 4 4 4 2 2 5 5 5 1 2 6 6 1 2 2 7 7 2 1...

...ibrary(tidyverse) dat %>% names() %>% expand.grid(., .) %>% apply(1L, \(x) { tmp <- dat[rowSums(dat[x]) > 0, ] tmp2 <- cor.test(tmp[[ x[1L] ]], tmp[[ x[2L] ]]) c(tmp2$estimate, P = tmp2$p.value) }) %>% t() %>% as.data.frame() %>% cbind(tmp_df, .) %>% na.omit() The second is, in my opinion the one that makes more sense. If you see the results, cor is symmetric (as it should) so the calculations are repeated. If you only run the cor.tests on the combinations of names(dat) by groups of 2, it will save a lot of work. But the outp...

...;% > expand.grid(., .) %>% > apply(1L, \(x) { > tmp <- dat[rowSums(dat[x]) > 0, ] > tmp2 <- cor.test(tmp[[ x[1L] ]], tmp[[ x[2L] ]]) > c(tmp2$estimate, P = tmp2$p.value) > }) %>% > t() %>% > as.data.frame() %>% > cbind(tmp_df, .) %>% > na.omit() > > > The second is, in my opinion the one that makes more sense. If you see > the results, cor is symmetric (as it should) so the calculations are > repeated. If you only run the cor.tests on the combinations of > names(dat) by groups of 2, it will sa...

...> apply(1L, \(x) { > > tmp <- dat[rowSums(dat[x]) > 0, ] > > tmp2 <- cor.test(tmp[[ x[1L] ]], tmp[[ x[2L] ]]) > > c(tmp2$estimate, P = tmp2$p.value) > > }) %>% > > t() %>% > > as.data.frame() %>% > > cbind(tmp_df, .) %>% > > na.omit() > > > > > > The second is, in my opinion the one that makes more sense. If you see > > the results, cor is symmetric (as it should) so the calculations are > > repeated. If you only run the cor.tests on the combinations of > > na...

...> tmp <- dat[rowSums(dat[x]) > 0, ] > > > tmp2 <- cor.test(tmp[[ x[1L] ]], tmp[[ x[2L] ]]) > > > c(tmp2$estimate, P = tmp2$p.value) > > > }) %>% > > > t() %>% > > > as.data.frame() %>% > > > cbind(tmp_df, .) %>% > > > na.omit() > > > > > > > > > The second is, in my opinion the one that makes more sense. If you see > > > the results, cor is symmetric (as it should) so the calculations are > > > repeated. If you only run the cor.tests on th...

Hi Rui, You are always very helpful!! Thank you, I just modified your R codes to remove a row with zero values in both column pair as below for my real data. Ding dat<-gene22mut.coded r <- P <- matrix(NA, nrow = 22L, ncol = 22L, dimnames = list(names(dat), names(dat))) for(i in 1:22) { #i=1 x <- dat[[i]] for(j in (1:22)) { #j=2 if(i == j) { #

...tmp <- dat[rowSums(dat[x]) > 0, ] > > > tmp2 <- cor.test(tmp[[ x[1L] ]], tmp[[ x[2L] ]]) > > > c(tmp2$estimate, P = tmp2$p.value) > > > }) %>% > > > t() %>% > > > as.data.frame() %>% > > > cbind(tmp_df, .) %>% > > > na.omit() > > > > > > > > > The second is, in my opinion the one that makes more sense. If you see > > > the results, cor is symmetric (as it should) so the calculations are > > > repeated. If you only run the cor.tests...

...> > > tmp2 <- cor.test(tmp[[ x[1L] ]], tmp[[ x[2L] ]]) > > > > > c(tmp2$estimate, P = tmp2$p.value) > > > > > }) %>% > > > > > t() %>% > > > > > as.data.frame() %>% > > > > > cbind(tmp_df, .) %>% > > > > > na.omit() > > > > > > > > > > > > > > > The second is, in my opinion the one that makes more sense. If you see > > > > > the results, cor is symmetric (as it should) so the calculations are > >...

...> tmp2 <- cor.test(tmp[[ x[1L] ]], tmp[[ x[2L] ]]) > > > > > c(tmp2$estimate, P = tmp2$p.value) > > > > > }) %>% > > > > > t() %>% > > > > > as.data.frame() %>% > > > > > cbind(tmp_df, .) %>% > > > > > na.omit() > > > > > > > > > > > > > > > The second is, in my opinion the one that makes more sense. If you see > > > > > the results, cor is symmetric (as it should) so the calculations are...