one way is the following:
X1 <- c(1:10)
X2 <- c(11:20)
X3 <- c(21:30)
X4 <- c(31:40)
X5 <- c(41:50)
DF <- data.frame(X1, X2, X3, X4, X5)
as.data.frame(sapply(DF, function (x) {
qx <- quantile(x)
cut(x, qx, include.lowest = TRUE,
labels = 1:4)
}))
You may also have a look at function cut2() from package Hmisc.
I hope it helps.
Best,
Dimitris
On 3/13/2011 4:49 PM, Ram H. Sharma wrote:
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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--
Dimitris Rizopoulos
Assistant Professor
Department of Biostatistics
Erasmus University Medical Center
Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
Tel: +31/(0)10/7043478
Fax: +31/(0)10/7043014
Web: http://www.erasmusmc.nl/biostatistiek/
______________________________________________
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and provide commented, minimal, self-contained, reproducible code.
