The ordering above is basically random.
One thing Stata users miss in R is an easy way to do the following operation.
foreach x of varlist lnw age asq bmi hispanic black other asian schooling cohab married divorced //
separated age_cl unsafe llength reg asq_cl appearance_cl provider_second asian_cl black_cl hispanic_cl // othrace_cl hot massage_cl {
egen mean ‘x’=mean(‘x’), by(id)
_
gen demean ‘x’=‘x’ – mean ‘x’
drop mean*
}
This example is taken from Scott Cunningham’s super 
book, Causal Inference: the Mixtape. In the lower right of the picture you can see the demeaned variables being created one by one. So how does one perform an operation to create a large number of variables? My preferred method is to take advantage of the .SD functionality in data.table. Create a character vector like this: myvars <- c(“lnw”,”age”) with all the variables you want.
demean <- function(x) x- mean(x)
DM <- DT[,lapply(.SD,demean),by=id,.SDcols=myvars]
Note how general this is since demean could be replaced a great number of other operations.
Also note that DM won’t have all the “demean_lnw” variables. The demeaned variables in DM keep their names from before. i think that’s a plus for various reasons. if you want the variable names, Grant McDermott showed me that you just use the := as follows:
DT[, paste0(myvars, ‘_demean’) := lapply(.SD,demean), .SDcols=myvars, by=id]
However suppose you want to do something more along the lines of a foreach loop. then the following is more like the Stata code in terms of style and effects:
for(i in 1:length(myvars)) {
DT <- within(DT,{assign(paste0(“demean_”,myvars[i]),get(myvars[i])-ave(get(myvars[i]),id))})
}
The crucial function to learn is assign. Its first argument is string to be used as the name of the variable you’ll create and the second argument is the value you want to assign to that variabel. The get function just converts a string like “age” into an R object age.
Seeing today that more and more people are considering giving up on Stata based on their horrible pricing policies, I offer this collection of very frequently used Stata commands and how to do roughly the same thing using R. But not with dplyr and pipes and all that. This how-to uses only base R, data.table, and fixest.
A drawback of R compared to Stata and, within R, of data.table compared to tidyverse, is that documentation is spread out all over the place. You end up doing a lot of googling and spending time at Stack overflow. The problem is many R users now think the tidyverse and dplyr in particular is all there is. I started with base R and it is still what i use for many things. But when I began to switch my data “wrangling” from Stata to R, I got started on dplyr, only to learn (from Julian Hinz) that data.table is much faster. After initial struggles I realized that the syntax of data.table fit much better with my intuitions of the right way to do things. I am in the minority on this topic but not alone. In an effort to convince more people to take advantage of the powerful syntax and awesome speed of data.table, I am posting this set of resources.
PS. This now goes to 11! Here’s a great set of lecture slides from Grant McDermott. If you don’t follow him already, you should.
https://raw.githack.com/uo-ec510-2020-spring/lectures/master/05-datatable/05-datatable.html#1
These items are not ordered for beginners. I started with number 8 (which, as the title suggests, was probably not optimal!). But 4 and then 3 might be ideal starting points. According to my notes 9 (and its sequels) are good starting places.
I recently found out about this resource https://lost-stats.github.io/Data_Manipulation/data_manipulation.html which is really helpful for those interested in learning how to implement basic data and estimation tasks in both Stata and R. Unfortunately, the R implementations rely on dplyr, a part of the tidyverse. While I understand that many people prefer this approach, my concern is that new users of R will think dplyr is the only way to work with data in R. There is an alternative: data.table.
To give an example I wrote a short script (collapse) based on the code given on lost-stats for how to do the same thing in data.table. The task is to take the “storms” data set (provided in dplyr) and aggregate three variables by the storm name, year, month, and day. The original data set collapses from 10,010 observations to 2777 observations. This must be one of the most common operations most of us do with data.
I then benchmarked it and found data.table is 100 times faster on this task for this data set. I also show to use RStata to run Stata code in R (if you have Stata). Stata was also very fast, even running it from within R (about 50 times faster than dplyr). One think I like about the way R does it is that you continue to have in memory both the pre- and post-collapse versions of the data.
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