#' Make Completions Part E (gender details)
#'
#' @param df A dataframe of student/degree information
#' @param ugender A boolean: TRUE means you are collecting and able to report
#' "another gender" for undergraduate completers, even if you have no (or few) such students. Set as FALSE if necessary
#' @param ggender A boolean: TRUE means you are collecting and able to report
#' "another gender" for graduate completers, even if you have no (or few) such students. Set as FALSE if necessary
#'
#' @importFrom rlang .data
#' @importFrom dplyr select group_by summarize ungroup arrange transmute n distinct
#' @importFrom utils write.table
#' @importFrom stringr str_to_upper
#'
#' @return A text file
#' @export
#'
make_com_part_E <- function(df, ugender, ggender) {
colnames(df) <- stringr::str_to_upper(colnames(df))
partE_counts <- df %>%
dplyr::select("UNITID",
"STUDENTID",
"DEGREELEVEL",
"GENDERDETAIL" #Binary = 1, 2; Unknown = 3, Another = 4
) %>%
#break into UG and GR levels
dplyr::mutate(UGPB = ifelse(.data$DEGREELEVEL %in% c(7, 8, 17, 18, 19), 'GR', 'UG')) %>%
dplyr::select(-"DEGREELEVEL") %>%
#deduplicate
dplyr::distinct() %>%
#aggregate and count
dplyr::group_by(.data$UNITID,
.data$UGPB,
.data$GENDERDETAIL) %>%
dplyr::summarize(COUNT = dplyr::n()) %>%
dplyr::ungroup() %>%
#sort for easy viewing
dplyr::arrange(.data$UGPB, .data$GENDERDETAIL)
#set up the final DF
partE <- data.frame(UNITID = unique(partE_counts$UNITID),
SURVSECT = "COM",
PART = "E",
CGU01 = 0, #UG detail reporting?
CGU011 = 0, #UG Unknown
CGU012 = 0, #UG Another
CGU02 = 0, #GR detail reporting?
CGU021 = 0, #GR Unknown
CGU022 = 0) #GR Another
#ugly way to get the right counts in each bit.
#I am sure there is a much nicer way to do it with a pivot
# and maybe with a dummy table for all values. I dunno. This works.
# #No UG in the data
if(!'UG' %in% partE_counts$UGPB){
partE$CGU01 <- 2 #not reporting another
partE$CGU012 <- -2 #not applicable
# #UG in the data, another not being reported, unknown might exist
} else if(ugender == FALSE){
partE$CGU01 <- 2
#if UNK exists
if(sum(partE_counts$UGPB == 'UG' &
partE_counts$GENDERDETAIL == 3) == 1){
#then calculate unknown count
partE$CGU011 <- partE_counts$COUNT[partE_counts$UGPB == 'UG' &
partE_counts$GENDERDETAIL == 3]
}
#and set the Another Gender to N/A
partE$CGU012 <- -2
# #UG in the data, another being reported, another/unknown might or might not exist
} else {
partE$CGU01 <- 1
#if UNK exists, calculate it
if(sum(partE_counts$UGPB == 'UG' &
partE_counts$GENDERDETAIL == 3) == 1){
partE$CGU011 <- partE_counts$COUNT[partE_counts$UGPB == 'UG' &
partE_counts$GENDERDETAIL == 3]
}
#if ANO exists, calculate it
if(sum(partE_counts$UGPB == 'UG' &
partE_counts$GENDERDETAIL == 4) == 1){
partE$CGU012 <- partE_counts$COUNT[partE_counts$UGPB == 'UG' &
partE_counts$GENDERDETAIL == 4]
}
#BUT -- New in 2023 - mask if < 5 and set initial inquiry as "small N"
if(partE$CGU012 < 5){
partE$CGU012 <- -2
partE$CGU01 <- 3
}
}
# #No GR in the data
if(!'GR' %in% partE_counts$UGPB){
partE$CGU02 <- 2 #not reporting another
partE$CGU022 <- -2 #not applicable
# #GR in the data, another not being reported, unknown might exist
} else if(ggender == FALSE){
partE$CGU02 <- 2
if(sum(partE_counts$UGPB == 'GR' &
partE_counts$GENDERDETAIL == 3) == 1){
partE$CGU021 <- partE_counts$COUNT[partE_counts$UGPB == 'GR' &
partE_counts$GENDERDETAIL == 3]
}
partE$CGU022 <- -2
# #GR in the data, another being reported, another/unknown might or might not exist
} else {
partE$CGU02 <- 1
if(sum(partE_counts$UGPB == 'GR' &
partE_counts$GENDERDETAIL == 3) == 1){
partE$CGU021 <- partE_counts$COUNT[partE_counts$UGPB == 'GR' &
partE_counts$GENDERDETAIL == 3]
}
if(sum(partE_counts$UGPB == 'GR' &
partE_counts$GENDERDETAIL == 4) == 1){
partE$CGU022 <- partE_counts$COUNT[partE_counts$UGPB == 'GR' &
partE_counts$GENDERDETAIL == 4]
}
#BUT -- New in 2023 - mask if < 5 and set initial inquiry as "small N"
if(partE$CGU022 < 5){
partE$CGU022 <- -2
partE$CGU02 <- 3
}
}
return(partE)
}
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