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.
- Insheet, import a CSV file
- R data.table::fread()
- R advantages: faster for huge files, usually guesses what you want, can work with files from internet or zipped files
- Replace y if x something
- DT[, y := x] #initialize a new variable equal to existing variable x in the datatable called DT
- DT[x>5, y := 5] # now censor the y version
- DT[is.na(x), y := 0] # now code the missing x to be 0s in the y variable
- Rename variables:
- setnames(DT,old=c(“blah”,”blah_blah”), new=c(“x1″,”x2”)) renames blah to be x1 and blah_blah to be x2. all other variables keep their names.
- The old variables can be just a range of columns as in old=5:12
- gsort x -y: setorder(DT,x,-y)
- DT1 <- merge(DT1,DT2,by=c(“id1″,”id2”),all.x=TRUE) is like if in Stata merge 1:1 id1 id2 using DT2.dta followed by drop if _merge==2
- if you don’t want to drop anything, use all =TRUE
- R’s version of merge allows for the id vars to have different names in DT1 and DT2, e.g. suppose your country code is “iso” in DT1 but “iso3” in DT2. similarly “year’ and “yr”. then you write DT1 <- merge(DT1,DT2,by.x=c(“iso”,”year”),by.y=c(“iso_o’,”yr”),all.x=TRUE)
- Collapse and egen: these are the two commands i use the most and i still call them by their Stata names
- collapse (mean) x_i = x_it, by(year) would be DTc <- DT[,.(x_i =mean(x_it)),by=year]
- egen x_i = mean(x_it), by(year) would be DT[,x_i := mean(x_it),by=year]
- if there are more than one by variables, both commands use .(iso,year)
- Note1: the collapse must be “sent” to a data.table. If we don’t need the original DT, we could <- it to DT.
- Note 2 The “:=” is essential in the egen-equivalent. It does something called “modification by reference”
- Reshape long or wide. While the syntax of reshape is often said to be hard to remember, data.table’s melt and dcast are even harder to keep straight but they are very flexible and powerful functions.
- reshape long MFN_, i(iso_d year) j(product) string
- DTl <-melt(DTw,id.vars=c(“iso_d”,”year”),measure=patterns(“^MFN_”),value.name=”MFN”,variable.factor = FALSE)
DTl[,product := substr(variable,5,nchar(variable))] # extract code
DTl[,variable := NULL] # we don’t need “variable” anymore
- the reshape wide is done with dcast()
- DTw2 <- dcast(DTl,iso_d+year~product,value.var = “MFN”) # the variables in the formula before the “~” are id vars that stay as rows. the variable to the right of “~” is the column variable.
- in contrast to “reshape wide” dcast on a single value.var will name the columns after the “product” variable
- Drop/keep conditionally: In data.table you drop by selectively keeping.
- Drop if x==. | y==0
- DT <- DT[!is.na(x) & y!=0]
- Save/use: The first thing you need to do when getting started is go to Rstudio’s preferences settings and make sure you NEVER save or restore the workspace in .Rdata. Furthermore, you should make a practice of restarting the R session fairly regularly. But that’s just an aside. I save data as RDS forms which is automatically compressed.
- use is done by DT <-readRDS(“path_to_file/file.rds”)
- save is saveRDS(DT,”path_to_file/file.rds”)
- reghdfe (AKA AKM). The pioneer was Simon Gaure’s excellent lfe::felm(). But for most purposes you should probably use Berge’s fixest::feols(). Not only is it faster, but it will make it easier to transition to various GLM in item 11.
- res.ols <- fixest::feols(log(y) ~ educ + age+ I(age^2) | worker_id +firm_id,data=DT)
- ppmlhdfe res.ppml <- fixest::fepois(y~educ+age +I(age^2) | worker_id +firm_id,data=DT)
Setting combine.quick to FALSE is useful when you specificy interactive fixed effects (fixest has a great notation for this) want to do something later with the Fixed effects and need to know which country-year FE they correspond to. fepois is a special case of feglm, which can be used for non-poisson specifications.
- esttab (LaTeX table of regression results) There are many options for latex tables in the R world with Stargazer probably the best known but I have found the built in etable() function to be quite adequate fixest::etable(res.ols,res.ppml,sdBelow = TRUE,digits=3,fitstat=~sq.cor+pr2,
tex=TRUE,file=”Tables/AKM_regs.tex”,signifCode = “letters”,cluster=”worker_id”,replace = TRUE)