recode

I want to recode a bunch of variables with as few function calls as possible. I have one data.frame where I want to recode a number of variables. I create a named list of all variable names and the recoding arguments I want to execute. Here I have no problem using map and dpylr . However, when it comes to recoding I find it much easier using recode from the car package, instead of dpylr 's own recoding function. A side question is whether there is a nice way of doing the same thing with dplyr::recode . As a next step I break the data.frame down into a nested tibble. Here I want to do specific

The overal goal is to stratify quantitative variables based on their percentile. I would like to break it up into 10 levels (e.g. 10th, 20th, ...100th percentile) and recode it as 1 if it falls into the 10th percentile, 2 if it falls into the 20th percentile, etc. This method needs to be applicable across any data set I plug in and I want this process to be as automated as possible. Below I have generated some test data: data test (drop=i); do i=1 to 1000; a=round(uniform(1)*4,.01); b=round(uniform(1)*10,.01); c=round(uniform(1)*7.5,.01); output; end; stop; run; The following macro is used to

This question already has an answer here: Fastest way to replace NAs in a large data.table 8 answers Let DT be a data.table: DT<-data.table(V1=sample(10), V2=sample(10), ... V9=sample(10),) Is there a better/simpler method to do multicolumn recode/sub-assign like this: DT[V1==1 | V1==7,V1:=NA] DT[V2==1 | V2==7,V2:=NA] DT[V3==1 | V3==7,V3:=NA] DT[V4==1 | V4==7,V4:=NA] DT[V5==1 | V5==7,V5:=NA] DT[V6==1 | V6==7,V6:=NA] DT[V7==1 | V7==7,V7:=NA] DT[V8==1 | V8==7,V8:=NA] DT[V9==1 | V9==7,V9:=NA] Variable names are completely arbitrary and do not necessarily have numbers. Many columns (Vx:Vx) and one

I run across this often enough that I figure there has to be a good idiom for it. Suppose I have a data.frame with a bunch of attributes, including "product." I also have a key which translates products to brand + size. Product codes 1-3 are Tylenol, 4-6 are Advil, 7-9 are Bayer, 10-12 are Generic. What's the fastest (in terms of human time) way to code this up? I tend to use nested ifelse 's if there are 3 or fewer categories, and type out the data table and merge it in if there are more than 3. Any better ideas? Stata has a recode command that is pretty nifty for this sort of thing, although