This question follows another one on group weighted means: I would like to create weighted within-group averages using data.table. The difference with the initial question is that the names of the variables to be average are specified in a string vector.
The data:
df <- read.table(text= "
region state county weights y1980 y1990 y2000
1 1 1 10 100 200 50
1 1 2 5 50 100 200
1 1 3 120 1000 500 250
1 1 4 2 25 100 400
1 1 4 15 125 150 200
2 2 1 1 10 50 150
2 2 2 10 10 10 200
2 2 2 40 40 100 30
2 2 3 20 100 100 10
", header=TRUE, na.strings=NA)
Using Roland's suggested answer from aforementioned question:
library(data.table)
dt <- as.data.table(df)
dt2 <- dt[,lapply(.SD,weighted.mean,w=weights),by=list(region,state,county)]
I have a vector with strings to determine dynamically columns for which I want the within-group weighted average.
colsToKeep = c("y1980","y1990")
But I do not know how to pass it as an argument for the data.table magic.
I tried
dt[,lapply(
as.list(colsToKeep),weighted.mean,w=weights),
by=list(region,state,county)]`
but I then get:
Error in x * w : non-numeric argument to binary operator
Not sure how to achieve what I want.
Bonus question: I'd like original columns names to be kept, instead of getting V1 and V2.
NB I use version 1.9.3 of the data.table package.
Normally, you should be able to do:
dt2 <- dt[,lapply(.SD,weighted.mean,w=weights),
by = list(region,state,county), .SDcols = colsToKeep]
i.e., just by providing just those columns to .SDcols. But at the moment, this won't work due to a bug, in that weights column won't be available because it's not specified in .SDcols.
Until it's fixed, we can accomplish this as follows:
dt2 <- dt[, lapply(mget(colsToKeep), weighted.mean, w = weights),
by = list(region, state, county)]
# region state county y1980 y1990
# 1: 1 1 1 100.0000 200.0000
# 2: 1 1 2 50.0000 100.0000
# 3: 1 1 3 1000.0000 500.0000
# 4: 1 1 4 113.2353 144.1176
# 5: 2 2 1 10.0000 50.0000
# 6: 2 2 2 34.0000 82.0000
# 7: 2 2 3 100.0000 100.0000
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