R code

This section aims to cover the R estimation commands from various packages. Click on the navigation links at the bottom of the page to see detailed implementation code for a specific package.

Before we start, you can refer to the following glossary table for symbols:

Symbol	Description
\(id\)	panel id
\(time\)	time variable
\(y\)	outcome variable
\(treat\)	=1 if treated observation
\(\epsilon\)	error term

Data generation

All of the R code in this section will make use of the same fake dataset, which we generate below. This dataset will closely mimic the equivalent dataset used in the Stata examples. But it won’t be exactly the same because of different random seeds (important!). This means that you shouldn’t expect the same results when comparing the R and Stata examples on this website. (Unless, of course, you explicitly use the same dataset.)

set.seed(123456L)

# 60 time periods, 30 individuals, and 5 waves of treatment
tmax = 60; imax = 30; nlvls = 5

dat = 
  expand.grid(time = 1:tmax, id = 1:imax) |>
  within({
    
    cohort      = NA
    effect      = NA
    first_treat = NA
    
    for (chrt in 1:imax) {
      cohort = ifelse(id==chrt, sample.int(nlvls, 1), cohort)
    }
    
    for (lvls in 1:nlvls) {
      effect      = ifelse(cohort==lvls, sample(2:10, 1), effect)
      first_treat = ifelse(cohort==lvls, sample(1:(tmax+20), 1), first_treat)
    }
    
    first_treat = ifelse(first_treat>tmax, Inf, first_treat)
    treat       = time>=first_treat
    rel_time    = time - first_treat
    y           = id + time + ifelse(treat, effect*rel_time, 0) + rnorm(imax*tmax)
    
    rm(chrt, lvls, cohort, effect)
  })

head(dat)
#>   time id        y rel_time treat first_treat
#> 1    1  1 2.158289      -11 FALSE          12
#> 2    2  1 2.498052      -10 FALSE          12
#> 3    3  1 3.034077       -9 FALSE          12
#> 4    4  1 4.886266       -8 FALSE          12
#> 5    5  1 7.085950       -7 FALSE          12
#> 6    6  1 5.788352       -6 FALSE          12

The dataset is easier to understand visually, so here it is in plot form. I’ll use the lattice package instead of ggplot2, since the former comes bundled with the base R installation.

library(lattice)
# Some (optional!) plot theme settings
trellis.par.set(list(
  axis.line      = list(col = NA),
  reference.line = list(col = "gray85", lty = 3),
  superpose.line = list(col = hcl.colors(imax, "SunsetDark")),
  par.xlab.text  = list(fontfamily = "ArialNarrow"),
  par.ylab.text  = list(fontfamily = "ArialNarrow"),
  axis.text      = list(fontfamily = "ArialNarrow")
  ))

xyplot(
  y ~ time,  
  groups = id,
  type = c("l", "g"),
  ylab = "Y", xlab = "Time variable",
  data = dat
  )

With our dataset in hand, please click through to the individual pages in the Table of Contents below. Each of these explores a specific R package in more detail, by walking you through an implementation example using our test dataset. I plan to add more packages as time allows, but please feel free to contribute yourself via a PR.

R code

Data generation

Table of contents