R/prepare_data.R

In PanelMatch: Matching Methods for Causal Inference with Time-Series Cross-Sectional Data

#' prepare_data
#' The calculation of point estimates and standard errors first requires the calculation of a variety of different weights, parameters, and indicator variables. This function prepares the data within PanelEstimate() such that the estimates can be calculated easily. In practical terms, the function calls the lower level helpers to calculate W_its and D_its as described in Imai et al. (2023) and merges those results together with the original data to facilitate calculations.
#' @param data.in data.frame: the data to be used in the analysis
#' @param lead See PanelMatch() documentation
#' @param sets.att matched.set object containing ATT or ART matched sets.
#' @param sets.atc matched.set object containing ATC matched sets.
#' @param qoi.in See PanelMatch() documentation
#' @param dependent.variable string specifying the outcome/dependent variable.
#'
#' @return data.frame with the results of the lower level calculations
#' @keywords internal
prepare_data <- function(data.in, lead, sets.att = NULL,
                        sets.atc = NULL,
                        qoi.in, dependent.variable)
{
  
  if ( identical(qoi.in, "att") || 
       identical(qoi.in, "art") || 
       identical(qoi.in, "ate"))
  {
    if (!identical(qoi.in, "ate")) qoi.t <- qoi.in
    if (identical(qoi.in, "ate")) qoi.t <- "att"
    for (j in lead)
    {
      
      dense.wits <- getWits(lead = j, data = data.in, matched_sets = sets.att)
      data.in = merge(x = data.in, y = dense.wits, all.x = TRUE,
                   by.x = colnames(data.in)[1:2], by.y = c("id", "t"))
      colnames(data.in)[length(data.in)] <- paste0("Wit_", qoi.t, j)
      data.in[is.na(data.in[, length(data.in)]), 
              length(data.in)] <- 0 #replace NAs with zeroes
    }

    data.in[, paste0("dit_", qoi.t)] <- getDits(matched_sets = sets.att,
                                                data = data.in)
    colnames(data.in)[length(data.in)] <- paste0("dits_", qoi.t)
    data.in[, paste0("Wit_", qoi.t, "-1")] <- 0

  }
  if (identical(qoi.in, "atc") || 
      identical(qoi.in, "ate"))
  {

    for (j in lead)
    {
      
      dense.wits <- getWits(lead = j, data = data.in, matched_sets = sets.atc)
      data.in = merge(x = data.in, y = dense.wits, all.x = TRUE,
                   by.x = colnames(data.in)[1:2], by.y = c("id", "t"))
      colnames(data.in)[length(data.in)] <- paste0("Wit_atc", j)
      data.in[is.na(data.in[, length(data.in)]), 
              length(data.in)] <- 0 #replace NAs with zeroes
    }

    data.in$dit_atc <- getDits(matched_sets = sets.atc, data = data.in)
    colnames(data.in)[length(data.in)] <- "dits_atc"
    data.in$`Wit_atc-1` <- 0

  }
  data.in[, dependent.variable][is.na(data.in[, dependent.variable])] <- 0 
  #replace the NAs with zeroes.
  #This is ok because the dits should always be zero for corresponding entries
  # so the value is irrelevant.
  return(data.in)
}