Clin.data_summary table now shows the number of missing values for a…

…ll variables except grouping variable(s). If any grouping variable is missing for the given sample, this sample is excluded from the Clin.data_summary table.

regression.analyses.r

@@ -401,9 +401,9 @@ res.failed <- subset(res.failed, subset = grepl(Factor, pattern = "(^#.*|^Too (m
 
 dep.var.cats <- sub(dep.vars, pattern = "^.*:", replacement = "")
 dep.var.conts <- sub(grep(dep.vars, pattern = ".*:.*", value = T), pattern = ":.*$", replacement = "")
-# df <- df[df %>% dplyr::select(all_of(c(dep.var.conts, grep(dep.vars, pattern = ":", value = T, invert = T)))) %>% is.na %>% rowSums() == 0, , drop = F]
 
 if(all(sub.vars != "ALL_SAMPLES")) {
+  df <- df[df %>% dplyr::select(all_of(sub.vars)) %>% is.na %>% rowSums == 0, , drop = F]
   df <- df %>% mutate(across(.cols = c(dep.var.cats, sub.vars), .fns = as.factor))
   N <- with(df, aggregate(df %>%
                             dplyr::select(dep.var.cats, dep.var.conts, other.factors), 
@@ -422,12 +422,12 @@ if(all(sub.vars != "ALL_SAMPLES")) {
                                    by = sapply(sub.vars, FUN = function(x) {get(x)}, simplify = F), FUN = function(y) {if(is.factor(y)) {table(y, useNA = "always")} else {summary_f(y)}})) %>%
     as.matrix() %>% as.data.frame(stringsAsFactors = F) %>% mutate(across(.cols = !sub.vars, .fns = as.numeric))
 
-  valid.cols <- foreach(i = colnames(df.summary), .combine = c) %do% {
-     if(i %in% sub.vars) {i} else
-     if(is.na(sum(df.summary[i]))) {i} else
-     if(str_contains(x = i, pattern = sub(dep.var.conts, pattern = "(^.*$)", replacement = "\\1\\."), logic = "OR")) {i} else
-     if(sum(df.summary[i]) >0) {i}
-    }
+valid.cols <- foreach(i = colnames(df.summary), .combine = c) %do% {
+         if(!is.numeric(df.summary[,i])) {i} else
+         if(!grepl(i, pattern = "\\.NA('s)?$")) {i} else
+         if(sum(df.summary[,i]) >0) {i}
+     }
+
   df.summary <- df.summary[valid.cols]
 
   df.summary.fin <- cbind(df.summary, N)