You probably want to use the na.locf() function from the zoo package to carry the last observation forward to replace your NA values. Here is the beginning of its usage example from the help page: library(zoo) az <- zoo(1:6) bz <- zoo(c(2,NA,1,4,5,2)) na.locf(bz) 1 2 3 4 5 6 2 2 1 4 5 2 … Read more
Yes, it’s subassignment in R using <- (or = or ->) that makes a copy of the whole object. You can trace that using tracemem(DT) and .Internal(inspect(DT)), as below. The data.table features := and set() assign by reference to whatever object they are passed. So if that object was previously copied (by a subassigning <- … Read more
Any of the following will remove column foo from the data.table df3: # Method 1 (and preferred as it takes 0.00s even on a 20GB data.table) df3[,foo:=NULL] df3[, c(“foo”,”bar”):=NULL] # remove two columns myVar = “foo” df3[, (myVar):=NULL] # lookup myVar contents # Method 2a — A safe idiom for excluding (possibly multiple) # columns … Read more
We need to cover at least these aspects to provide a comprehensive answer/comparison (in no particular order of importance): Speed, Memory usage, Syntax and Features. My intent is to cover each one of these as clearly as possible from data.table perspective. Note: unless explicitly mentioned otherwise, by referring to dplyr, we refer to dplyr’s data.frame … Read more