FillIn: an R function for filling in missing values of a variable from one data frame with the values from another variable. The function also prints the correlation coefficient between the two variables. You will need to have the data.table package installed to run FillIn.

FillIn.R

#' An R function for filling in missing values of a variable from one data frame with the values from another variable.
#'
#' \code{FillIn} uses values of a variable from one data set to fill in missing values in another.
#' 
#' @param D1 the data frame with the variable you would like to fill in.
#' @param D2 the data frame with the variable you would like to use to fill in \code{D1}.
#' @param Var1 a character string of the name of the variable in \code{D1} you want to fill in.
#' @param Var2 an optional character string of variable name in \code{D2} that you would like to use to fill in.
#' @param KeyVar a character vector of variable names that are shared by \code{D1} and \code{D2} that can be used to join the data frames.
#' 
#' @examples 
#' # Create data set with missing values
#' naDF <- data.frame(a = sample(c(1,2), 100, rep=TRUE), 
#'                    b = sample(c(3,4), 100, rep=TRUE), 
#'                    fNA = sample(c(100, 200, 300, 400, NA), 100, rep=TRUE))
#'
#' # Created full data set
#' fillDF <- data.frame(a = c(1,2,1,2), 
#'                      b = c(3,3,4,4),
#'                      fFull = c(100, 200, 300, 400))
#'
#' # Fill in missing f's from naDF with values from fillDF
#' FilledInData <- FillIn(naDF, fillDF, Var1 = "fNA", Var2 = "fFull", KeyVar = c("a", "b"))
#'

FillIn <- function(D1, D2, Var1, Var2 = NULL, KeyVar = c("iso2c", "year"))
{
  # Give Var2 the same name as var1 if Var2 is NULL
  if (is.null(Var2)){
    Var2 <- Var1
  } else {
    Var2 <- Var2
  }
  
  # Give var a generic name
  names(D1)[match(Var1, names(D1))] <- "VarGen"
  names(D2)[match(Var2, names(D2))] <- "VarGen.1"
  
  # Convert data frames to data.table type objects
  D1Temp <- data.table::data.table(D1, key = KeyVar)
  D2Temp <- data.table::data.table(D2, key = KeyVar)
  
  # Merge data.tables
  OutDT <- D2Temp[D1Temp]
  
  # Tell the user how many values will be filled in
  SubNA <- OutDT[, list(VarGen, VarGen.1)]
  SubNA <- subset(SubNA, is.na(VarGen) & !is.na(VarGen.1))
  print(paste(nrow(SubNA), "NAs were replaced."))
  
  # Fill in missing values from D1 with values from D2
  OutDT <- OutDT[is.na(VarGen), VarGen := VarGen.1]
  
  # Convert back to data frame
  OutDF <- data.frame(OutDT)
  
  # Tell the user what the correlation coefficient is between the variables
  SubNoNA <- subset(OutDF, !is.na(VarGen) & !is.na(VarGen.1))
  HowMany <- nrow(SubNoNA)
  CORR <- cor(SubNoNA$VarGen, SubNoNA$VarGen.1, use = "complete.obs")
  print(paste("The correlation between", Var1, "and", Var2, "is", round(CORR, digits = 3), "based on", HowMany, "shared observations." ))
  
  # Remove uncombined variable and return main variable's name
  names(OutDF)[match("VarGen", names(OutDF))] <- Var1
  Keepers <- setdiff(names(OutDF), "VarGen.1")
  OutDF <- OutDF[, Keepers]
  OutDF
}

Inspiration from JD Long and Josh O'Brien.

How would you handle filling in values of a binary output??

@rony23-byte I haven't looked at this code in a long time. Have you tried it with a binary variable?