Remove rows conditionally from a data.table in R
data.table, r
Solution
In this scenario it is not so different than `data.frame`
data <- data[ menuitem != 'coffee' | amount > 0]
Delete/add row by reference it is to be implemented. You find more info in this question
Regarding speed:
1 You can benefit from keys by doing something like:
setkey(data, menuitem)
data <- data[!"coffee"]
which will be faster than `data <- data[ menuitem != 'coffee']`. However to apply the same filters you asked in the question you'll need a rolling join (I've finished my lunch break I can add something later :-)).
2 Even without key data.table is much faster for relatively big table (similar speed for handful amount of rows)
dt<-data.table(id=sample(letters,1000000,T),var=rnorm(1000000))
df<-data.frame(id=sample(letters,1000000,T),var=rnorm(1000000))
library(microbenchmark)
> microbenchmark(dt[ id == "a"], df[ df$id == "a",])
Unit: milliseconds
expr min lq median uq max neval
dt[id == "a"] 24.42193 25.74296 26.00996 26.35778 27.36355 100
df[df$id == "a", ] 138.17500 146.46729 147.38646 149.06766 154.10051 100
Problem
I have a data.table with fields {id, menuitem, amount}. This is transaction data - so, ids are unique, but menuitem repeats. Now, I want to remove all entries where `menuitem == 'coffee'`. Also, want to delete all rows where `amount <= 0`; What is the right way to do this in data.table? I can use `data$menuitem!='coffee'` and then index int into data[] - but that is not necessarily efficient and does not take advantage of data.table. Any pointers in the right direction are appreciated.