automapper:

#########################################################################
### Syria Mapping Example (Facetted by Governorate, Iterated by Item) ###
#########################################################################

# Sometimes a whole country map is not the best way to visualize data in a country
# where there is a lot of empty space. Facetting allows you to compare regions 
# side-by-side in same image. In this case we will create a background map of the
# entire country, and then make a list of every governorate where there is data.
# Finally, we will create a mini-map of each governorate with data, and put
# those side by side in same image for easier comparison.

# This script will print out a map of a Syria as a background layer, and the
# and then for every column in the imported .csv file (excepting the id column used 
# to join the dataset to the spatial data), will create a map layering this column's 
# numeric data on top of the country foranalysis, then outputting the 
# resulting map to a subfolder.

# Labeling each region with data is optional, but the output is too cluttered so
# not done in this example.

# As the current HDX Syria Spatial data does shows communities, this is a good 
# example of mapping data by communities as points, instead of by polygons 
# (as would be appropriate if we were mapping subdistrict medians). 

# DEFINE NEEDED INPUTS HERE
output_folder_path <- paste("Output/Syria/Overview Maps/Facet") # Define output directory
label_variable <- "NAME_EN" # (Typically the english-language name of the spatial unit
                  # you merged the dataframe with the spatial data.)
facet_variable <- "ADM1_EN" # Define region variable (on data_layer) to facet over

#INPUTS ALREADY DEFINED (if ran the 01- Load Enviroment and 02 - Join Data scripts) 

#df <- imported csv dataframe containing data to be mapped
#df_key <- vartiable in csv used as key to join spaital layer with csv
#spatial_layer <- spatial layer (must have already been joined with csv data)
#spatial_key <- vartiable in spatial layer used as key to join spaital layer with csv

# Below this Line should not need to touch #
############################################

# Define output directory
dir.create(output_folder_path, recursive = TRUE)

# Set list of variables to map (all but key in original dataframe)
var_list <- names(df)[names(df) != df_key]

# Define Background Map
background_map_syria_country <- function(){
  admin1 <- tm_shape(shp = syr_admin1, is.master = TRUE) + tm_borders(lwd = 2)
  admin3 <- tm_shape(shp = syr_admin3) + tm_borders(lwd = .5)
  admin4 <- tm_shape(shp = syr_pplp_adm4) + tm_dots(size = .01)
  background_map <- admin1 + admin3 + admin4
  background_map
}

#Create Background Map (Whole Country)
background_map <- background_map_syria_country()

#################################################################
# Create Output Map (Facetted by Governorate), Iterate per item #
#################################################################

# For every entry in var_list (referred to as 'i' below), this function will create
# a map layer visualizing the data, layer this on top of the background map, facet 
# by governorate and then save the output.
lapply(var_list, function(i){
  
  # fill_map_country creates a country-level map, data displayed as points.
  data_layer <- try(points_map_country(spatial_dataframe = spatial_layer,
                                       # Selects which variable to map
                                       mapping_variable = i))
  
  # Layer data_layer on top of background
  output_map <- try(background_map + data_layer + 
                      # Facet by govenorate
                      tm_facets(facet_variable))
  
  # Save Map to output file, at specified dimensions
  try(save_tmap(tm = output_map, 
                # File name 
                filename = paste(output_folder_path,"/",i,"_facet.jpg", sep = ""),
                # Specify dimensions of image here
                width = 1720, height = 1020, units = "px"))
})