Dear R-Experts
I am sure this might look simple question for experts, at least is problem
for me. I have a large data frame with over 1000 variables and each have
different distribution( i.e. have different quantile). I want to create a
new grouped data frame, where the new variables where the value falling in
first (<25%), second (25% to <50%), third (50% to <75%) and fourth quantiles
(>75%) are replaced with 1,2,3, 4 respectively. The following example is
just to workout.
# my example:
X1 <- c(1:10)
> X2 <- c(11:20)
> X3 <- c(21:30)
> X4 <- c(31:40)
> X5 <- c(41:50)
> dataf <- data.frame(X1, X2, X3, X4, X5)
>
> # my efforts of the last week led me to this point
>
for (i along(length(dataf[1,]))) {
> qntfun <- function (x) {
> XQ <- as.numeric(as.matrix(quantile(x)))
> Q1 <- XQ[1]
> Q2 <- XQ[2]
> Q3 <- XQ[3]
> Q4 <- XQ[4]
> for (i in 1:length(x)){
> if (x[i] < Q2) {
> x[i] <- 1
> } else {
> if ( x[i] > Q2 & x[i] < Q3){
> x[i] <- 2
> } else {
> if ( x[i] >Q3 & x[i] <Q4) {
> x[i] <- 3
> } else {
> if (x[i] > Q4) {
> x[i] <- 4
> } else{
> x[i] <- 0
> }
> }
> }
> }
> }
> }
> apply(dataf, 1:length(dataf), qntfun)
> }
>
# I got error, I can not fix it. I would be glad to see a more slim
solution, but I could not think any.
Thanks in advance for your help.
Ram Sharma
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