################################################################################
# Title: {ExclusionTable} a package for keeping track of exclusions
#        and inclusions
# Date: 2021-11-01
#
# Author: Joshua P. Entrop
# Website: joshua-entrop.com
################################################################################

# 1. Prefix --------------------------------------------------------------------

# Load packages
library(NHANES)  # NHANES dataset
library(dplyr)   # Data manipulation
library(remotes) # Install packages from GitHub

# Attach data
data("NHANES")

# Install {ExclusionTable} from GitHub
remotes::install_github("entjos/ExclusionTable",
                        ref = "main")

# Load package
library(ExclusionTable)

# 2. Use the ExclusionTable Package --------------------------------------------

#   2.1 Simple example with two inclusions and one  exclusion ==================
exclusion_table(NHANES,
                inclusion_criteria = c("Gender == 'female'",
                                       "Age    >= 65"),
                exclusion_criteria = "is.na(BMI)",
                keep_data = FALSE)

#   2.2 Adding labels for inclusion criteria ===================================
exclusion_table(NHANES,
                inclusion_criteria = c("Gender == 'female'",
                                       "Age    >= 65"),
                exclusion_criteria = "is.na(BMI)",
                labels_inclusion   = c("Get females",
                                       "Age is >= 65"),
                labels_exclusion   = "Missing BMI",
                keep_data = FALSE)

#   2.3 Keeping the new dataset ================================================
NHANES_ex_tab <-
  exclusion_table(NHANES,
                  inclusion_criteria = c("Gender == 'female'",
                                         "Age    >= 65"),
                  exclusion_criteria = "is.na(BMI)",
                  labels_inclusion   = c("Get females",
                                         "Age is >= 65"),
                  labels_exclusion   = "Missing BMI",
                  keep_data = TRUE)

# Extracting the new dataset
NAHANES_cleaned <- NHANES_ex_tab[["dataset"]]

#   2.4 Using objects in the enviroment in the ExclusionTable call =============
room_selection <- c(2, 4, 9)

exclusion_table(NHANES,
                inclusion_criteria = c("HomeRooms %in% obj$room_selection"),
                labels_inclusion   = c("2, 4, 9 rooms"),
                obj = list(room_selection = room_selection))

# //////////////////////////////////////////////////////////////////////////////
# END OF R-SCRIPT