inst/doc/v6_pop_map-vignettes.R

In insee: Tools to Easily Download Data from INSEE BDM Database

## ----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)
library(tidyverse)

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)),
    " />"
  ))}

## ----message=FALSE, warning=FALSE, include=FALSE------------------------------
library(kableExtra)
library(magrittr)
library(htmltools)
library(prettydoc)

## ---- echo = FALSE------------------------------------------------------------
embed_png("pop_map.png")

## ----message=FALSE, warning=FALSE,eval=FALSE----------------------------------
#  library(insee)
#  library(tidyverse)
#  
#  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")
#  
#