Speed up fill function R

Question

I have a dataframe with missing values that I've written a function to fill using R 3.3.2

pkgs <- c("dplyr", "ggplot2", "tidyr", 'data.table', 'lazyeval')
lapply(pkgs, require, character.only = TRUE)

UID <- c('A', 'A', 'A', 'B', 'B', 'B', 'C', 'C')
Col1 <- c(1, 0, 0, 0, 1, 0, 0, 0)
df <- data.frame(UID, Col1)

Function to fill in Col1:

AggregatedColumns <- function(DF, columnToUse, NewCol1) {
  # Setting up column names to use
  columnToUse <- deparse(substitute(columnToUse))
  NewCol1 <- deparse(substitute(NewCol1))

  # Creating new columns 
  DF[[NewCol1]] <- ifelse(DF[[columnToUse]] == 1, 1, NA)
  DF <- DF %>% group_by_("UID") %>% sort(DF[[columnToUse]], decreasing = TRUE) %>% fill_(NewCol1)
  DF <- DF %>% group_by_("UID") %>% sort(DF$columnToUse, decreasing = TRUE) %>% fill_(NewCol1, .direction = 'up')
  DF[[NewCol1]] <- ifelse(is.na(DF[[NewCol1]]), 0, DF[[NewCol1]])

  DF
}

I've pulled out this part of the function since this is the piece that is slowing down the function. I'm very new to writing functions and any advice on how/if this can be sped up would be appreciated. I've isolated the speed issue down to the fill_ part of the function.

What I am trying to do is pass a dummy variable from Col1 to New_Column and then forward fills to other same ID's. For example:

UID             Col1
John Smith        1
John Smith        0

Should become

UID             Col1  New_Column
John Smith        1      1
John Smith        0      1

EDITED FUNCTION I edited the function to fit with @HubertL suggestion. The function is still fairly slow, but hopefully with these edits the example is reproducible.

AggregatedColumns <- function(DF, columnToUse, NewCol1) {
  # Setting up column names to use
  columnToUse <- deparse(substitute(columnToUse))
  NewCol1 <- deparse(substitute(NewCol1))

  # Creating new columns 
  DF[[NewCol1]] <- ifelse(DF[[columnToUse]] == 1, 1, NA)
    DF <- DF %>% group_by_("UID") %>% fill_(NewCol1) %>% fill_(NewCol1, .direction = 'up')
  DF[[NewCol1]] <- ifelse(is.na(DF[[NewCol1]]), 0, DF[[NewCol1]])

  DF
}

Desired output:

UID Col1 New
A    1    1 
A    0    1
A    0    1 
B    0    1
B    1    1
B    0    1
C    0    0
C    0    0

Speed up fill function R

2 Answers