sbalci/ClinicoPathJamoviModule

#' @title Summary of Categorical Variables
#' @return Text
#'
#' @importFrom R6 R6Class
#' @import jmvcore
#' @importFrom magrittr %>%
#' @importFrom gtExtras gt_plt_summary
#'

reportcatClass <- if (requireNamespace('jmvcore')) R6::R6Class(
    "reportcatClass",
    inherit = reportcatBase,
    private = list(
        .run = function() {


            # # Error Message ----
            #
            # if (nrow(self$data) == 0) stop("Data contains no (complete) rows")
            #
            # if ( (is.null(self$options$vars) || is.null(self$options$facs)) && is.null(self$options$target) ) {
            #     # ToDo Message ----
            #     todo <- "
            #         <br>Welcome to ClinicoPath
            #                   <br><br>
            #                   This tool will help you form an Alluvial Plots.
            #                   "
            #     html <- self$results$todo
            #     html$setContent(todo)
            #
            # } else {
            #     todo <- ""
            #     html <- self$results$todo
            #     html$setContent(todo)
            #
            #
            #
            # }








            if (length(self$options$vars) == 0) {
                # ToDo Message ----

                todo <- "
                <br>Welcome to ClinicoPath
                          <br><br>
                          This tool will help you to write a general summary of variables
                              in your data.
                          <br><br>
                          Select the 'Variables' you want to include in the summary. Nominal, Ordinal and Categorical variables are allowed.
                          <hr><br>
                          "

                html <- self$results$todo
                html$setContent(todo)
                return()
            } else {

                todo <- ""
                html <- self$results$todo
                html$setContent(todo)



                if (nrow(self$data) == 0)
                    stop('Data contains no (complete) rows')



            mydata <- self$data

            # med <- self$options$med
            # cent <- self$options$cent
            # disp <- self$options$disp
            # ran <- self$options$ran
            # distr <- self$options$distr
            # lev <- self$options$lev
            # n_ch <- self$options$n_ch
            # mis <- self$options$mis

            formula <- jmvcore::constructFormula(terms = self$options$vars)

            myvars <- jmvcore::decomposeFormula(formula = formula)

            myvars <- unlist(myvars)


            # myreport <- mydata %>%
            #     select(myvars) %>%
            #     report::report(.,
            #                    median = FALSE,
            #                    centrality = TRUE,
            #                    dispersion = TRUE,
            #                    range = TRUE,
            #                    distribution = FALSE,
            #                    levels_percentage = FALSE,
            #                    n_entries = 3,
            #                    missing_percentage = FALSE
            # #                    median = med,
            # #                    centrality = cent,
            # #                    dispersion = disp,
            # #                    range = ran,
            # #                    distribution = distr,
            # #                    levels_percentage = lev,
            # #                    n_characters = n_ch,
            # #                    missing_percentage = mis
            #                    )
            #
            # results1 <- myreport



            # results1 <- mydata %>%
            #     explore::describe(.) %>%
            #     dplyr::filter(na > 0)


            # for (fac in facs)
            #     data[[fac]] <- as.factor(data[[fac]])



            # catsummary function
            catsummary <- function(myvar) {




                leng <- length(mydata[[myvar]])

                miss <- sum(is.na(mydata[[myvar]]))

                valid <- leng - miss

                nlev <- nlevels(as.factor(mydata[[myvar]]))

                summar <- summary(as.factor(mydata[[myvar]])) %>%
                    as.table() %>%
                    tibble::as_tibble(.name_repair = "unique") %>%
                    dplyr::filter(.[[1]] != "NA's") %>%
                    dplyr::arrange(dplyr::desc(n))

                summar$validtotal <- valid

                description <- summar %>%
                    dplyr::mutate(
                        percent = n/validtotal
                    ) %>%
                    dplyr::mutate(
                        description = glue::glue(
                            "{...1}: n = {n}, {scales::percent(percent)}. "
                        )
                    ) %>%
                    dplyr::select(description) %>%
                    dplyr::pull(.)


                sentence1 <- paste0(
                    myvar, " has ", leng, " observations and ", nlev, " levels. "
                )

                sentence3 <- paste0("There are ", miss, " missing values.")

                sentence <- c(
                    sentence1,
                    unlist(description),
                    sentence3
                )

                return(paste0(sentence, collapse = ""))

            }



            results <- purrr::map(.x = myvars, .f = catsummary)

            results1 <- unlist(results)

            self$results$text$setContent(results1)

            }



            plot_mydata <- mydata %>%
                gtExtras::gt_plt_summary()

            print_plot_mydata <- print(plot_mydata)

            plot_mydata <- htmltools::HTML(print_plot_mydata[["children"]][[2]])

            self$results$text1$setContent(plot_mydata)

        }


        )
)