R/f - Check cols not empty.R

In pieterov/generaltoolbox: General toolbox by Pieter Overdevest

#' @title Check whether columns are unique
#'
#' @description Checks whether columns are unique.
#'
#' @author Pieter Overdevest
#'
#' @param df.input -----
#' @param v.col.include ----- (default: NULL)
#' @param v.col.exclude ----- (default: NULL)
#' @param c.id -----
#'
#' @returns Nothing.
#'
#' @details -
#'
#' @export
#'
#' @examples
#' f_check_col_unique(
#'
#'     df.input,
#'     v.col.include = NULL,
#'     v.col.exclude = NULL,
#'     c.id
#' )

        #################################################################################
        # FUNCTION.
        #################################################################################

        f_check_cols_not_empty <- function(

                df.input,
                v.col.include = NULL,
                v.col.exclude = NULL,
                c.id
        ) {


        ######################################################################################
        # TEST
        ######################################################################################

        # df.input = data.frame(ID = c(1,2,3,4), pieter = c(1,2,NA,NA), bart = c(NA,NA,NA,4))
        # df.input = df.datachamp.baseline.source %>% filter(Status == "ACTIVE")
        # v.col.include    = v.feature.cannot.be.empty
        # v.col.include    = c("pieter", "bart")
        # c.id     = "ID"
        # f_check_cols_not_empty(df.input, v.col.include, c.id)

        # df.input = df.order
        # v.col.exclude = c(
        #
        #         "created.refund", "discount.code",
        #         "line.unit.price.refund",
        #         "line.total.price.refund",
        #         "order.discount",
        #         "order.refund",
        #         "order.net.payment",
        #         "line.refund.status",
        #         "line.tag")
        # c.id     = "id"

        ######################################################################################
        # ERROR CHECKS
        ######################################################################################

        # Check presence of columns in df.input.
        f_check_cols_present(df.input, c.id)

        if(!is.null(v.col.include)) f_check_cols_present(df.input, v.col.include)
        if(!is.null(v.col.exclude)) f_check_cols_present(df.input, v.col.exclude)

        # Check that c.id does not contain NA and is unique
        f_check_col_not_empty(df.input, c.id)
        f_check_col_unique(df.input, c.id)


        ######################################################################################
        # INITIALIZE
        ######################################################################################

        if(is.null(v.col.include))  v.col.include <- names(df.input)
        if(!is.null(v.col.exclude)) v.col.include <- setdiff(v.col.include, v.col.exclude)


        ######################################################################################
        # PROCESS
        ######################################################################################

        # Determine features with NA.
        df.temp <- df.input %>%

                # Select concerned columns
                select(
                        all_of(v.col.include)
                ) %>%

                # Determine number of NA per column
                f_summarize(

                        b.view   = FALSE,
                        b.return = TRUE
                ) %>%

                # Select columns with NA
                filter(
                        n.na > 0
                ) %>%

                # Create label.
                mutate(
                        n.label = paste0("'", feature, "' (", n.na, ")")
                )


        ######################################################################################
        # ERROR CHECK
        ######################################################################################

        if(nrow(df.temp) > 0) {

                # Comms.
                cat(paste0(
                        "We observe features with empty values (out of ",
                        nrow(df.input), "): ", f_paste(df.temp$n.label), ".\n\n"
                ))


                # Create label.
                v.temp <- lapply(df.temp$feature, function(c.temp) { # c.temp = df.temp$feature[1]

                        paste0(
                                "'", c.temp, "' (",

                                f_paste(

                                        df.input %>%

                                                select(all_of(c(c.id, c.temp))) %>%

                                                filter(is.na(get(c.temp))) %>%

                                                pull(all_of(c.id))
                                ), ")"
                        )

                }) %>% unlist()


                stop(paste0(

                        "Note, the following features contain one ore more NAs, see '", c.id, "': ",
                        f_paste(v.temp)
                ))
        }

        }