---
title: "Get Started"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{DiDforBigData}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

There are only 3 functions in this package:

1. `SimDiD()`: This function simulates data.
2. `DiDge()`: This function estimates DiD for a single cohort and a single event time.
3. `DiD()`: This function estimates DiD for all available cohorts and event times. 

We now demonstrate the simplest application of the 3 functions.

Detailed documentation for each of these function is available from the Reference tab above.

## 0. Installation


```{r echo=T, eval=FALSE, message=FALSE}
devtools::install_github("setzler/DiDforBigData")
```

```{r echo=T, eval=T, message=FALSE}
library(DiDforBigData)
```


## 1. Prepare Data

I provide a simple data simulator as follows:

```{r echo=T, eval=T, message=FALSE}
sim = SimDiD(sample_size = 400, seed=123)

# true ATTs in the simulation
print(sim$true_ATT)

# simulated data
simdata = sim$simdata
print(simdata)
```

Your real data needs to have this "long" format, i.e., there need to be variables for the individual identifier (e.g. `id`), the time variable (e.g. `year`), the cohort at which treatment begins (e.g. `cohort`), and the outcome variable (e.g. `Y`). No other variables are required. These variables can have any names you prefer.

Before going to the estimation, we need to prepare a list of the variable names:

```{r echo=T, eval=T, message=FALSE}
varnames = list()
varnames$time_name = "year" 
varnames$outcome_name = "Y"
varnames$cohort_name = "cohort"
varnames$id_name = "id"
```



## 2. Estimate DiD for a Single Cohort 

We choose an event time (+3) and a cohort of treated units (2010), then estimate DiD:

```{r echo=T, eval=T, message=FALSE}
did_2010 = DiDge(inputdata = simdata, varnames = varnames, 
             cohort_time = 2010, event_postperiod = 3)

print(did_2010)
```


Comparing this estimate to the true ATT above, we see that the estimation performed well.

Note that it used -1 as the base year by default. This is easy to change.



## 3. Estimate DiD for All Cohorts and Event Times

Suppose we want to estimate the ATT at each event time from -3 to +5. We can do so as follows:

```{r echo=T, eval=T, message=FALSE}
did_all = DiD(inputdata = simdata, varnames = varnames, min_event = -3, max_event = 5)
```

The output of DiD() is a list. One object in the list is results_average, which includes the average ATT across cohorts:


```{r echo=T, eval=T, message=FALSE}
print(did_all$results_average)
```


The other output from DiD() is results_cohort, which includes all combinations of event times and cohorts. It is too large to print here, so let's just print the results for event times 1 and 2:

```{r echo=T, eval=T, message=FALSE}
print(did_all$results_cohort[EventTime==1 | EventTime==2])
```

Note: the simulated data ends in 2013, so event time 2 is not available for treatment cohort 2012.

To take an average across multiple event times, use the `Esets` argument. It accepts a list, in which each item is a vector of event times over which to average:


```{r echo=T, eval=T, message=FALSE}
did_all = DiD(inputdata = simdata, varnames = varnames, min_event = -3, max_event = 5, 
              Esets = list(c(1,2), c(1,2,3)))
```

```{r echo=T, eval=T, message=FALSE}
print(did_all$results_Esets)
```



.
