# Sricp 1: Setting up Output Data Objects

## Version: "Imaginary Country" Start Year 2000 

> This setup file generates all country-specific objects used by the set of R simulation output analysis and visualization scripts. The output of this script is an image file "globals_for_analysis.RData" which is read by each of the thematic analysis scripts. This is the only R script which has to be adapted when porting the model to another country context.


Files in ../../Model/OutputScenarios/

- Output_ABC_2000_base.csv
- Output_ABC_2000_alt.csv
- Tables_ABC_2000_base.xlsx
- Tables_ABC_2000_alt.xlsx


```{r, message=FALSE, warning=FALSE}
####################################################################################################
#
#  DYNAMIS-POP Output Analysis File 1 - Generate all country-specific R objects
#  This file has to be run before performing all other output analysis steps
#  Last Update: Martin Spielauer 2019-04-14
#
####################################################################################################

####################################################################################################
# Clear work space and load required packages
####################################################################################################

rm(list=ls())

library("dplyr")
library("tidyr")
library("ggplot2")
library("ggthemes")
library("cowplot")
library("sf")
library("gdalUtils")
library("tmap")
library("readxl")



####################################################################################################
# Maps
####################################################################################################

f_shape <- st_read('../Data/ABC_2000/Shapes/shape_abc.shp')
dynxls_base <- '../../Model/OutputScenarios/Tables_ABC_2000_base.xlsx'
dynxls_alt <- '../../Model/OutputScenarios/Tables_ABC_2000_alt.xlsx'

f_4years <- c("2000", "2010", "2020", "2030")

dyn_base <- read_xlsx(dynxls_base)
sheet_names_base <- excel_sheets(dynxls_base)
df_sheets_base <- lapply(sheet_names_base, function(.sheet){read_xlsx(dynxls_base, .sheet)})

# We read all sheets except the first three, which only contain meta information
for (n in 4:length(df_sheets_base)) {
  s <- data.frame(df_sheets_base[n])
  s[1] <- NULL   # The first column contains no data; we drop it
  assign(paste0(sheet_names_base[n],'_base'), s)
}

dyn_alt <- read_xlsx(dynxls_alt)
sheet_names_alt <- excel_sheets(dynxls_alt)
df_sheets_alt <- lapply(sheet_names_alt, function(.sheet){read_xlsx(dynxls_alt, .sheet)})

# We read all sheets except the first three, which only contain meta information
for (n in 4:length(df_sheets_alt)) {
  s <- data.frame(df_sheets_alt[n])
  s[1] <- NULL   # The first column contains no data; we drop it
  assign(paste0(sheet_names_alt[n],'_alt'), s)
}

# District labels
district_labels <- variable.names(TabPrimSchoolOutOfSchool9to11_base)
district_labels <- district_labels[3:length(district_labels)-1]

f_shape$DISTRICT <- district_labels

tmap_options_reset()
bgc     <- "white"                          # Background color for plot maps
                                            # Suggestions: lightblue, grey85, white

credit  <- "World Bank, DYNAMIS-POP v2.0"    # Text for credit
p_cred  <- "left"                            # Position of credit
titl_sz <- 0.8                               # Title size
savemap <- "Y"                               # Maps to be saved as images? Y/N
sav_loc <- "Figures/"                       # Location of saved maps (leave blank if = default folder)
sav_fmt <- "jpg"                             # File format for saved maps. Options: pdf, eps, svg,
                                             # wmf (Windows only), png, jpg, bmp, or tiff

####################################################################################################
# Read Simulation Output Files and generate data objects
####################################################################################################

g_data_base <- read.csv("../../Model/OutputScenarios/Output_ABC_2000_base.csv")
g_data_alt  <- read.csv("../../Model/OutputScenarios/Output_ABC_2000_alt.csv")
g_data_base$g_scenario <- 'base'
g_data_alt$g_scenario  <- 'alt'
g_data                 <- rbind(g_data_base,g_data_alt)

g_data$m_agegr5 <- as.integer((g_data$TIME - g_data$BIRTH)/5) * 5

####################################################################################################
# Generate a 5y age pyramid by education data frame
####################################################################################################


g_pyramid   <- as.data.frame(xtabs(g_data$WEIGHT ~ g_data$MALE +
                        g_data$m_agegr5 + g_data$EDUCATION + g_data$TIME +
                        g_data$DISTRICT + g_data$g_scenario))

g_pyramid   <- g_pyramid[!(g_pyramid$g_data.m_agegr5==100),]

g_pyramid$gender[g_pyramid$g_data.MALE==0]    <- 'female'
g_pyramid$gender[g_pyramid$g_data.MALE==1]    <- 'male'

g_pyramid$educ[g_pyramid$g_data.EDUCATION==0] <- '1-non '
g_pyramid$educ[g_pyramid$g_data.EDUCATION==1] <- '2-incomplete'
g_pyramid$educ[g_pyramid$g_data.EDUCATION==2] <- '3-completed'

g_pyramid$age[g_pyramid$g_data.m_agegr5==0]  <- ' 0-4'
g_pyramid$age[g_pyramid$g_data.m_agegr5==5]  <- ' 5-9'
g_pyramid$age[g_pyramid$g_data.m_agegr5==10] <- '10-14'
g_pyramid$age[g_pyramid$g_data.m_agegr5==15] <- '15-19'
g_pyramid$age[g_pyramid$g_data.m_agegr5==20] <- '20-24'
g_pyramid$age[g_pyramid$g_data.m_agegr5==25] <- '25-29'
g_pyramid$age[g_pyramid$g_data.m_agegr5==30] <- '30-34'
g_pyramid$age[g_pyramid$g_data.m_agegr5==35] <- '35-39'
g_pyramid$age[g_pyramid$g_data.m_agegr5==40] <- '40-44'
g_pyramid$age[g_pyramid$g_data.m_agegr5==45] <- '45-49'
g_pyramid$age[g_pyramid$g_data.m_agegr5==50] <- '50-54'
g_pyramid$age[g_pyramid$g_data.m_agegr5==55] <- '55-59'
g_pyramid$age[g_pyramid$g_data.m_agegr5==60] <- '60-64'
g_pyramid$age[g_pyramid$g_data.m_agegr5==65] <- '65-69'
g_pyramid$age[g_pyramid$g_data.m_agegr5==70] <- '70-74'
g_pyramid$age[g_pyramid$g_data.m_agegr5==75] <- '75-79'
g_pyramid$age[g_pyramid$g_data.m_agegr5==80] <- '80-84'
g_pyramid$age[g_pyramid$g_data.m_agegr5==85] <- '85-89'
g_pyramid$age[g_pyramid$g_data.m_agegr5==90] <- '90-94'
g_pyramid$age[g_pyramid$g_data.m_agegr5==95] <- '95+'


for (nY in 1:length(district_labels))
{
  g_pyramid$sDistrict[g_pyramid$g_data.DISTRICT==nY-1]  <- district_labels[nY]
}


####################################################################################################
# Save global objects
####################################################################################################

save.image(file="globals_for_output_analysis.RData")


```