---
title: "Population Map"
output:
  prettydoc::html_pretty:
    toc: true
    theme: architect
    highlight: github
    includes:
      # in_header: header.html
vignette: >
  %\VignetteIndexEntry{Population Map} 
  %\VignetteEngine{knitr::rmarkdown} 
  %\VignetteEncoding{UTF-8}
  %\VignetteDepends{kableExtra,magrittr,htmltools}
---

```{r setup,echo=FALSE, include=FALSE}
# setup chunk
NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")),"true")
knitr::opts_chunk$set(purl = NOT_CRAN)
library(insee)

embed_png <- function(path, dpi = NULL) {
  meta <- attr(png::readPNG(path, native = TRUE, info = TRUE), "info")
  if (!is.null(dpi)) meta$dpi <- rep(dpi, 2)
  knitr::asis_output(paste0(
    "<img src='", path, "'",
    " width=", round(meta$dim[1] / (meta$dpi[1] / 96)),
    " height=", round(meta$dim[2] / (meta$dpi[2] / 96)),
    " />"
  ))}
```

```{r message=FALSE, warning=FALSE, include=FALSE}
library(kableExtra)
library(htmltools)
library(prettydoc)
```

```{r, echo = FALSE}
embed_png("pop_map.png")
```

```{r message=FALSE, warning=FALSE,eval=FALSE}
library(insee)
library(ggplot2)
library(dplyr)
library(magrittr)
library(stringr)

library(raster)
library(rgdal)
library(geosphere)
library(broom)
library(viridis)
library(mapproj)

dataset_list = get_dataset_list()

list_idbank = 
  get_idbank_list("TCRED-ESTIMATIONS-POPULATION") %>%
  filter(AGE == "00-") %>% #all ages
  filter(SEXE == 0) %>% #men and women
  filter(str_detect(REF_AREA, "^D")) %>% #select only departements
  add_insee_title()

list_idbank_selected = list_idbank %>% pull(idbank)

# get population data by departement
pop = get_insee_idbank(list_idbank_selected) 

#get departements' geographical limits
FranceMap <- raster::getData(name = "GADM", country = "FRA", level = 2)

# extract the population by departement in 2020
pop_plot = pop %>%
  group_by(TITLE_EN) %>%
  filter(DATE == "2020-01-01") %>%
  mutate(dptm = gsub("D", "", REF_AREA)) %>%
  filter(dptm %in% FranceMap@data$CC_2) %>%
  mutate(dptm = factor(dptm, levels = FranceMap@data$CC_2)) %>%
  arrange(dptm) %>%
  mutate(id = dptm)

vec_pop = pop_plot %>% pull(OBS_VALUE)

# add population data to the departement object map
FranceMap@data$pop = vec_pop

get_area = function(long, lat){
  area = areaPolygon(data.frame(long = long, lat = lat)) / 1000000
  return(data.frame(area = area))
}

# extract the departements' limits from the spatial object and compute the surface
FranceMap_tidy_area <- 
  broom::tidy(FranceMap) %>% 
  group_by(id) %>%
  group_modify(~get_area(long = .x$long, lat = .x$lat))

FranceMap_tidy <- 
  broom::tidy(FranceMap) %>% 
  left_join(FranceMap_tidy_area)

# mapping table
dptm_df = data.frame(dptm = FranceMap@data$CC_2,
                     dptm_name = FranceMap@data$NAME_2,
                     pop = FranceMap@data$pop,
                     id = rownames(FranceMap@data))

FranceMap_tidy_final_all =
  FranceMap_tidy %>%
  left_join(dptm_df, by = "id") %>%
  mutate(pop_density = pop/area) %>% 
  mutate(density_range = case_when(pop_density < 40 ~ "< 40",
                                   pop_density >= 40 & pop_density < 50 ~ "[40, 50]",
                                   pop_density >= 50 & pop_density < 70 ~ "[50, 70]",
                                   pop_density >= 70 & pop_density < 100 ~ "[70, 100]",
                                   pop_density >= 100 & pop_density < 120 ~ "[100, 120]",
                                   pop_density >= 120 & pop_density < 160 ~ "[120, 160]",
                                   pop_density >= 160 & pop_density < 200 ~ "[160, 200]",
                                   pop_density >= 200 & pop_density < 240 ~ "[200, 240]",
                                   pop_density >= 240 & pop_density < 260 ~ "[240, 260]",
                                   pop_density >= 260 & pop_density < 410 ~ "[260, 410]",
                                   pop_density >= 410 & pop_density < 600 ~ "[410, 600]",
                                   pop_density >= 600 & pop_density < 1000 ~ "[600, 1000]",
                                   pop_density >= 1000 & pop_density < 5000 ~ "[1000, 5000]",
                                   pop_density >= 5000 & pop_density < 10000 ~ "[5000, 10000]",
                                   pop_density >= 20000 ~ ">= 20000"
  )) %>% 
  mutate(`people per square kilometer` = factor(density_range,
                                                levels = c("< 40","[40, 50]", "[50, 70]","[70, 100]",
                                                           "[100, 120]", "[120, 160]", "[160, 200]",
                                                           "[200, 240]", "[240, 260]", "[260, 410]",
                                                           "[410, 600]",  "[600, 1000]", "[1000, 5000]",
                                                           "[5000, 10000]", ">= 20000")))

ggplot(data = FranceMap_tidy_final_all,
       aes(fill = `people per square kilometer`, x = long, y = lat, group = group) ,
       size = 0, alpha = 0.9) +
  geom_polygon() +
  geom_path(colour = "white") +
  coord_map() +
  theme_void() +
  scale_fill_viridis(discrete = T) + 
  ggtitle("Distribution of the population within French territory in 2020") +
  labs(subtitle = "the density displayed here is an approximation, it should not be considered as an official statistics")
  
  
```