Just came across a .do file that I need to translate into R because I don't have a Stata license; my Stata is rusty, so can someone confirm that the code is doing what I think it is?
For reproducibility, I'm going to translate it into a data set I found online, specifically the Milk Production dataset (p004) that's part of a textbook by Chatterjee, Hadi and Price.
Here's the Stata code:
collapse (min) min_protein = protein ///
(mean) avg_protein = protein ///
(median) median_protein = protein ///
(sd) sd_protein = protein ///
if protein > 2.8, by(lactatio)
Here's what I think it's doing in data.table syntax:
library(data.table)
library(foreign)
DT = read.dta("p004.dta")
setDT(DT)
DT[protein > 2.8,
.(min_protein = min(protein),
avg_protein = mean(protein),
median_protein = median(protein),
sd_protein = sd(protein)),
keyby = lactatio]
# lactatio min_protein avg_protein median_protein sd_protein
# 1: 1 2.9 3.162632 3.10 0.2180803
# 2: 2 2.9 3.304688 3.25 0.2858736
# 3: 3 2.9 3.371429 3.35 0.4547672
# 4: 4 2.9 3.231250 3.20 0.3419917
# 5: 5 2.9 3.855556 3.20 1.9086061
# 6: 6 3.0 3.200000 3.10 0.2645751
# 7: 7 3.3 3.650000 3.65 0.4949748
# 8: 8 3.2 3.300000 3.30 0.1414214
Is that correct?
This would be easy to confirm if I had used Stata in the past 18 months or if I had a copy installed--hoping I can bend the ear of someone for whom either of these is true. Thanks.
Your intuition is correct. collapse is the Stata equivalent of R's aggregate function, which produces a new dataset from an input dataset by applying an aggregating function (or multiple aggregating functions, one per variable) to every variable in a dataset.
Here's the output for that Stata command on the example dataset:
. list
+------------------------------------------------------+
| lactatio min_pr~n avg_pr~n median~n sd_pro~n |
|------------------------------------------------------|
1. | 1 2.9 3.162632 3.1 .2180803 |
2. | 2 2.9 3.304688 3.25 .2858736 |
3. | 3 2.9 3.371429 3.35 .4547672 |
4. | 4 2.9 3.23125 3.2 .3419917 |
5. | 5 2.9 3.855556 3.2 1.908606 |
|------------------------------------------------------|
6. | 6 3 3.2 3.1 .2645752 |
7. | 7 3.3 3.65 3.65 .4949748 |
8. | 8 3.2 3.3 3.3 .1414214 |
+------------------------------------------------------+