R/epi_plot_all.R

In AntonioJBT/episcout: Quickly Clean, Explore and Visualise Large Epidemiological Datasets

# # ////////////
# # Parallelising plots ----
#
# # Get numeric variables:
# num_vars <- list()
# for (i in colnames(data_f)) {
#     if (epi_clean_cond_numeric(data_f[[i]])) {
#         num_vars <- c(num_vars, i)
#     }
# }
# num_vars
# # ////////////
#
#
# # ////////////
#
# # ===
# # Boxplot for one ----
# epi_plot_box(df = data_f, var_y = 'EDAD')
# # ===
#
# # ===
# # Boxplots for all in a loop ----
# # i <- "EDAD"
# num_list <- epi_plot_list(vars_to_plot = num_vars)
# for (i in names(num_list)) {
#     num_list[[i]] <- epi_plot_box(df = data_f, var_y = i)
# }
# # ===
#
# # ===
# # Save plots in a loop ----
# # Plot 4 per page or so:
# per_file <- 4
# jumps <- seq(1, length(num_list), per_file)
# length(jumps)
#
# # i <- 2
# for (i in jumps) {
#     # infile_prefix
#     file_n <- 'plots_box'
#     suffix <- 'pdf'
#     outfile <- sprintf(fmt = '%s/%s_%s.%s', infile_prefix, file_n, i, suffix)
#     # outfile
#     start_i <- i
#     end_i <- i + 3
#     my_plot_grid <- epi_plots_to_grid(num_list[start_i:end_i])
#     epi_plot_cow_save(file_name = outfile, plot_grid = my_plot_grid)
# }
# # ===
# # ////////////
#
# # ////////////
# # ===
# # Boxplots for all in parallel ----
# # TO DO:
# # test functions, add code tests, test examples
# # generate_bar_plot_list <- function(vars_to_plot,
# #                                    data,
# #                                    custom_palette,
# #                                    n_cores = num_cores
# #                                    ) {
# #   library(parallel)
# #   bar_list <- mclapply(vars_to_plot, generate_bar_plot, data = data, custom_palette = custom_palette, mc.cores = n_cores)
# #   # mclapply() should run with shared memory on *nix
# #   names(bar_list) <- vars_to_plot
# #   bar_list
# # }
# # ===
#
# # ===
# # Save all in parallel ----
# save_bar_plot_grids <- function(plot_list,
#                                 results_subdir,
#                                 file_n = "plots_bar",
#                                 per_file = 4, # per page
#                                 suffix = "pdf",
#                                 n_cores = num_cores
#                                 ) {
#   library(cowplot)  # For plot grids
#   library(parallel)
#
#   jumps <- seq(1, length(plot_list), per_file)
#
#   save_plots <- function(start_i) {
#     end_i <- min(start_i + per_file - 1, length(plot_list))
#     plot_grid <- plot_grid(plotlist = plot_list[start_i:end_i], ncol = 2)
#
#     outfile <- sprintf(fmt = '%s/%s_%s.%s', results_subdir, file_n, start_i, suffix)
#     ggsave(outfile, plot_grid, device = "pdf", width = 15, height = 15)
#   }
#   # mclapply() should run with shared memory on *nix
#   mclapply(jumps, save_plots, mc.cores = n_cores)
# }
# # ===
# # ////////////
#
#
# # ////////////
# # How to run ----
#
# # ===
# # Input data and variables
# data_f <- data.frame()  # Your data goes here
# results_subdir <- "path/to/results"  # Ensure this directory exists
# fact_cols <- c("SEXO", "OTHER_FACTOR")  # Replace with your factor column names
# custom_palette <- c("red", "blue")  # Replace with your custom palette
#
# # Detect number of cores
# num_cores <- detectCores() - 1
# # ===
#
# # ===
# # Step 1: Generate bar plots in parallel
# bar_list <- generate_bar_plot_list(vars_to_plot = fact_cols,
#                                    data = data_f,
#                                    custom_palette = custom_palette,
#                                    n_cores = num_cores
#                                    )
# # ===
#
# # ===
# # Step 2: Save bar plots to files
# save_bar_plot_grids(plot_list = bar_list,
#                     results_subdir = results_subdir,
#                     per_file = 4, # per page,
#                     n_cores = num_cores
#                     )
# # ===
# # ////////////