Drop rows with missing values
####calculating percenatge of missing values
pMiss <- function(x){sum(is.na(x))/length(x)*100}
apply(data,2,pMiss) # by column
apply(data,1,pMiss) # by row
mean(is.na(x))*100 #for whole dataframe
drops <- c("f147","f119","f122")
cleanDF=merged[ , !(names(merged) %in% drops)]
cleanDF=cleanDF[-which(rowMeans(is.na(cleanDF)) > 0.25),]
library(tidyr)
merged %>% drop_na()