Catégories
R

Opening a spatial subset with {sf}

Intersecting an area of interest with a layer at opening time

Days 3 and 4 of 30DayMapChallenge : « polygons » and « green » (previously).

The CORINE Landcover dataset is distributed as a geopackage weighting more than 8 Go. To limit the memory used when we only work on a subset, we can clip it at opening time. Here we will map the Cyprus Island :

library(dplyr)
library(ggplot2)
library(stringr)
library(sf)
library(rnaturalearth)
library(glue)

# Using the contour of Cyprus (enlarged) from naturalearth to clip
bb <- ne_countries(scale = "medium", country = "cyprus", returnclass = "sf") %>% 
  st_transform("EPSG:3035") %>% 
  st_buffer(90000) %>% 
  pull(geometry) %>% 
  st_as_text()

# Corine Land Cover 
# download from https://land.copernicus.eu/pan-european/corine-land-cover/clc2018
# (registration required)
# passing the bounding area
cyprus_clc <- read_sf("data/U2018_CLC2018_V2020_20u1.gpkg", query = glue("
  SELECT * 
  FROM U2018_CLC2018_V2020_20u1
  WHERE st_intersects(Shape, st_polygonfromtext('{bb}'))"))

legend_colors <- list("TRUE"  = "mediumaquamarine",
                      "FALSE" = "grey90")

legend_labs <- list("TRUE"  = "forest",
                    "FALSE" = "other")

# exclude sea (code 523)
# and classify on forest (codes 3xx)
cyprus_clc %>% 
  filter(Code_18 != "523") %>% 
  ggplot() +
  geom_sf(aes(fill = str_detect(Code_18, "^3"),
              color = str_detect(Code_18, "^3"))) +
  scale_fill_manual(name= "type",
                    values = legend_colors,
                    labels = legend_labs) +
  scale_color_manual(name = "type",
                     values = legend_colors,
                     labels = legend_labs) +
  labs(title = "Cyprus",
       subtitle = "Landcover",
       caption = glue("data: Copernicus CLC 2018
                      projection LAEA
                      r.iresmi.net {Sys.Date()}")) +
  theme_minimal() +
  theme(legend.position = "bottom",
               plot.caption = element_text(size = 7))

ggsave("cyprus.png", width = 20, height = 12.36, units = "cm", scale = 1.1, type = "cairo")
Cyprus landcover
Catégories
R

My air travel carbon footprint

I shouldn’t have

library(tidyverse)
library(sf)
library(glue)
library(rnaturalearth)
library(units)

# grams of carbon dioxide-equivalents per passenger kilometer
# https://en.wikipedia.org/wiki/Fuel_economy_in_aircraft
co2_eq <- set_units(88, g/km)

# countries map from Naturalearth
countries <- ne_countries(scale = "small", returnclass = "sf")

# airport code and coordinates to geolocate itineraries
airport <- read_csv("https://raw.githubusercontent.com/jpatokal/openflights/master/data/airports.dat",
                    col_names = c("airport",
                                  "name",
                                  "city",
                                  "country",
                                  "iata",
                                  "icao",
                                  "latitude",
                                  "longitude",
                                  "altitude",
                                  "timezone",
                                  "dst",
                                  "tz",
                                  "type",
                                  "source")) %>% 
  # Add Kai Tak, missig from the airport data
  add_row(iata = "HKGX",
          name = "Kai Tak", 
          city = "Hong Kong",
          latitude = 22.328611,
          longitude = 114.194167)

# itineraries
flight <- read_delim("from-to
LYS-LHR
LHR-LYS
LYS-BOD
LYS-BOD
LYS-BOD
LYS-BOD
BOD-LYS
BOD-LYS
BOD-LYS
LYS-BOD
BOD-LYS
BOD-LGW
LHR-JNB
CPT-JNB
JNB-LHR
LHR-ORY
BOD-ORY
CDG-HKGX
HKGX-PER
SYD-HKGX
HKGX-CDG
ORY-CAY
CAY-BEL
BEL-BSB
BSB-MAO
MAO-VVI
VVI-LPB
LPB-MAO
MAO-BEL
BEL-CAY
CAY-XAU
XAU-CAY
CAY-XAU
XAU-CAY
CAY-XAU
XAU-CAY
CAY-ORY
NCE-MXP
MXP-NCE
CDG-CAY
CAY-MPY
MPY-CAY
CAY-CDG
CDG-HKG
HKG-SYD
SYD-HKG
HKG-SYD
TLN-ORY
CDG-CPH
CPH-ORY
ORY-TLN
CDG-YYZ
YYZ-SFO
SFO-YYZ
YYZ-CDG
ORY-TLN
TLN-ORY
LYS-AMS
AMS-SHJ
SHJ-KTM
KTM-SHJ
SHJ-AMS
AMS-LYS
CDG-AUH
AUH-MCT
MCT-KTM
KTM-PKR
PKR-KTM
KTM-MCT
MCT-AUH
AUH-CDG
GVA-FCO
FCO-GVA
CDG-RUN
RUN-CDG
GVA-KEF
KEF-GVA
CDG-ARN
ARN-KRN
KRN-ARN
ARN-CDG
CDG-RUN
RUN-CDG", delim = "-")

# geolocate
flight_geo <- flight %>% 
  left_join(airport, by = c("from" = "iata")) %>% 
  left_join(airport, by = c("to" = "iata"), suffix = c("_from", "_to"))

# create lines
flight_lines <- flight_geo %>% 
  mutate(line = glue("LINESTRING ({longitude_from} {latitude_from}, {longitude_to} {latitude_to})")) %>% 
  st_as_sf(wkt = "line", crs = "EPSG:4326")

# create great circles and compute costs
flight_geo_gc <- flight_lines %>% 
  st_segmentize(set_units(100, km)) %>% 
  mutate(distance = set_units(st_length(line), km),
         co2 = set_units(distance * co2_eq, t))

# totals
total_flight <- flight_geo_gc %>% 
  st_drop_geometry() %>% 
  summarise(total_distance = sum(distance, na.rm = TRUE),
            total_co2 = sum(co2, na.rm = TRUE))

# map
ggplot() +
  geom_sf(data = countries, fill = "lightgrey", color = "lightgrey") +
  geom_sf(data = flight_geo_gc, color = "red") + 
  # geom_sf(data = flight_lines, color = "blue") + 
  coord_sf(crs = "+proj=eqearth") +
  # coord_sf(crs = "+proj=robin") +
  # coord_sf(crs = "+proj=fouc") +
  # coord_sf(crs = "+proj=eck1") +
  # coord_sf(crs = "+proj=moll") +
  # coord_sf(crs = "+proj=bonne +lat_1=10") +
  # coord_sf(crs = "+proj=laea") +
  labs(title = "My air travel carbon footprint 1993-2020",
       subtitle = glue("{round(total_flight$total_distance, -2)} km - {round(total_flight$total_co2, 1)} teqCO₂")) +
  theme_minimal()
map of flights
Flygskam (equal-earth projection)

Catégories
R

Using the geofacet package to spatially arrange plots

The {geofacet} package allows to « arrange a sequence of plots of data for different geographical entities into a grid that strives to preserve some of the original geographical orientation of the entities« .

Like the previous post, it’s interesting if you view each entity as a unit and don’t care for its real size or weight, and don’t want to spend too much time manually finding the best grid.

We will again use the same COVID-19 dataset. We manually add the overseas départements once we have found the right grid (by trying different seeds) and adjust Corsica position.

COVID-19 deceased in hospital, by département, for 100 000 inhab.
# packages ----------------------------------------------------------------
library(tidyverse)
library(httr)
library(fs)
library(sf)
library(readxl)
library(janitor)
library(glue)
library(geofacet)
# also install ragg

# sources -----------------------------------------------------------------

# https://www.data.gouv.fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/
fichier_covid <- "donnees/covid.csv"
url_donnees_covid <- "https://www.data.gouv.fr/fr/datasets/r/63352e38-d353-4b54-bfd1-f1b3ee1cabd7"

# https://www.insee.fr/fr/statistiques/2012713#tableau-TCRD_004_tab1_departements
fichier_pop <- "donnees/pop.xls"
url_donnees_pop <- "https://www.insee.fr/fr/statistiques/fichier/2012713/TCRD_004.xls"

# Adminexpress : à télécharger manuellement
# https://geoservices.ign.fr/documentation/diffusion/telechargement-donnees-libres.html#admin-express
aex <- path_expand("~/Downloads/ADMIN-EXPRESS_2-2__SHP__FRA_2020-02-24/ADMIN-EXPRESS/1_DONNEES_LIVRAISON_2020-02-24")


# config ------------------------------------------------------------------

options(scipen = 999)

force_download <- FALSE # retélécharger même si le fichier existe et a été téléchargé aujourd'hui ?


# téléchargement -------------------------------------------------

if (!dir_exists("donnees")) dir_create("donnees")
if (!dir_exists("resultats")) dir_create("resultats")
if (!dir_exists("resultats/animation_spf")) dir_create("resultats/animation_spf")

if (!file_exists(fichier_covid) |
    file_info(fichier_covid)$modification_time < Sys.Date() |
    force_download) {
  GET(url_donnees_covid,
      progress(),
      write_disk(fichier_covid, overwrite = TRUE)) %>%
    stop_for_status()
}

if (!file_exists(fichier_pop)) {
  GET(url_donnees_pop,
      progress(),
      write_disk(fichier_pop)) %>%
    stop_for_status()
}

covid <- read_csv2(fichier_covid)

pop <- read_xls(fichier_pop, skip = 2) %>%
  clean_names()

# adminexpress prétéléchargé
dep <- read_sf(path(aex, "ADE_2-2_SHP_LAMB93_FR/DEPARTEMENT.shp")) %>%
  clean_names() %>%
  st_set_crs(2154)


# construction de la grille ----------------------------------------

grid_fr <- dep %>%
  select(insee_dep, nom_dep) %>%
  grid_auto(names = "nom_dep", codes = "insee_dep", seed = 4) %>%
  add_row(row = 8,
          col = 1,
          name_nom_dep = "Guadeloupe",
          code_insee_dep = "971") %>%
  add_row(row = 9,
          col = 1,
          name_nom_dep = "Martinique",
          code_insee_dep = "972") %>%
  add_row(row = 10,
          col = 1,
          name_nom_dep = "Guyane",
          code_insee_dep = "973") %>%
  add_row(row = 7,
          col = 13,
          name_nom_dep = "Mayotte",
          code_insee_dep = "976") %>%
  add_row(row = 8,
          col = 13,
          name_nom_dep = "La Réunion",
          code_insee_dep = "974")

grid_fr[grid_fr$code_insee_dep %in% c("2A", "2B"), "col"] <- 13
grid_fr[grid_fr$code_insee_dep %in% c("2A", "2B"), "row"] <- grid_fr[grid_fr$code_insee_dep %in% c("2A", "2B"), "row"] - 1


# graphique -----------------------------------------------------

df <- covid %>%
  filter(sexe == 0) %>%
  rename(deces = dc,
         reanim = rea,
         hospit = hosp) %>%
  left_join(pop,
            by = c("dep" = "x1")) %>%
  mutate(incidence = deces / x2020_p * 100000) %>%
  rename(insee_dep = dep) %>%
  left_join(grid_fr %>%
              select(nom_dep = name_nom_dep,
                     insee_dep = code_insee_dep)) %>%
  drop_na(insee_dep) %>%
  ggplot(aes(jour, incidence)) +
    geom_area() +
    facet_geo(~ nom_dep, grid = grid_fr) +
    labs(title = "Mortalité",
       subtitle = "COVID-19 - France",
       x = "date",
       y = "décès pour\n100 000 hab.",
       caption = glue("http://r.iresmi.net/\ndonnées SPF {Sys.Date()}")) +
    theme_minimal() +
    theme(strip.text = element_text(hjust = 0, size = 7))

ggsave(glue("resultats/covid_fr_mortalite_geofacette_{Sys.Date()}.png"),
       width = 25, height = 20, units = "cm", scaling = .8, res = 300, device = ragg::agg_png)

Catégories
R

Polygons to hexagons

Hexagon tessellation using the great {geogrid} package.

The départements are the second level of administrative government in France. They neither have the same area nor the same population and this heterogeneity provides a few challenges for a fair and accurate map representation (see the post on smoothing).

However if we are just interested in the départements as units, we can use a regular grid for visualization. Since France is often called the hexagon, we could even use an hexagon tiling (a fractal map !)…

Creating the grid and conserving minimal topological relations and the general shape can be time consuming, but thanks to Geogrid it’s quite easy. The geogrid dev page provides nice examples. We will reuse our code of the COVID19 animation. The resulting GIS file is provided below.

# Carto décès COVID 19 hexagones
# France métro. + DOM
# Animation
# DONNEES SPF


# packages ----------------------------------------------------------------
library(tidyverse)
library(httr)
library(fs)
library(sf)
library(readxl)
library(janitor)
library(glue)
library(tmap)
library(grid)
library(classInt)
library(magick)
library(geogrid)


# sources -----------------------------------------------------------------

# https://www.data.gouv.fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/
fichier_covid <- "donnees/covid.csv"
url_donnees_covid <- "https://www.data.gouv.fr/fr/datasets/r/63352e38-d353-4b54-bfd1-f1b3ee1cabd7"

# https://www.insee.fr/fr/statistiques/2012713#tableau-TCRD_004_tab1_departements
fichier_pop <- "donnees/pop.xls"
url_donnees_pop <- "https://www.insee.fr/fr/statistiques/fichier/2012713/TCRD_004.xls"

# Adminexpress : à télécharger manuellement
# https://geoservices.ign.fr/documentation/diffusion/telechargement-donnees-libres.html#admin-express
aex <- path_expand("~/Downloads/ADMIN-EXPRESS_2-2__SHP__FRA_2020-02-24/ADMIN-EXPRESS/1_DONNEES_LIVRAISON_2020-02-24")


# config ------------------------------------------------------------------

force_download <- FALSE # retélécharger même si le fichier existe et a été téléchargé aujourd'hui ?


# téléchargement ------------------------------------------------------

if (!dir_exists("donnees")) dir_create("donnees")
if (!dir_exists("resultats")) dir_create("resultats")
if (!dir_exists("resultats/animation_spf_hex")) dir_create("resultats/animation_spf_hex")

if (!file_exists(fichier_covid) |
    file_info(fichier_covid)$modification_time < Sys.Date() |
    force_download) {
  GET(url_donnees_covid,
      progress(),
      write_disk(fichier_covid, overwrite = TRUE)) %>%
    stop_for_status()
}

if (!file_exists(fichier_pop)) {
  GET(url_donnees_pop,
      progress(),
      write_disk(fichier_pop)) %>%
    stop_for_status()
}


# données -----------------------------------------------------------------

covid <- read_csv2(fichier_covid)

# adminexpress prétéléchargé
dep <- read_sf(path(aex, "ADE_2-2_SHP_LAMB93_FR/DEPARTEMENT.shp")) %>%
  clean_names() %>%
  mutate(surf_ha = st_area(geometry) * 10000) %>%
  st_set_crs(2154)

# grille hexagonale
dep_cells_hex <- calculate_grid(shape = dep, grid_type = "hexagonal", seed = 3)
dep_hex <- assign_polygons(dep, dep_cells_hex) %>%
  st_set_crs(2154)

# Pour les DOM on duplique et déplace un département existant
d971 <- dep_hex[dep_hex$insee_dep == "29", ]
d971$geometry[[1]] <- d971$geometry[[1]] + st_point(c(0, -150000))
d971$insee_dep <- "971"

d972 <- dep_hex[dep_hex$insee_dep == "29", ]
d972$geometry[[1]] <- d972$geometry[[1]] + st_point(c(0, -250000))
d972$insee_dep <- "972"

d973 <- dep_hex[dep_hex$insee_dep == "29", ]
d973$geometry[[1]] <- d973$geometry[[1]] + st_point(c(0, -350000))
d973$insee_dep <- "973"

d974 <- dep_hex[dep_hex$insee_dep == "2A", ]
d974$geometry[[1]] <- d974$geometry[[1]] + st_point(c(0, 250000))
d974$insee_dep <- "974"

d976 <- dep_hex[dep_hex$insee_dep == "2A", ]
d976$geometry[[1]] <- d976$geometry[[1]] + st_point(c(0, 350000))
d976$insee_dep <- "976"

dep_hex <- rbind(dep_hex, d971, d972, d973, d974, d976)

# population
pop <- read_xls(fichier_pop, skip = 2) %>%
  clean_names()

# lignes de séparation DOM / métropole
encarts <- st_multilinestring(
  list(st_linestring(matrix(c(1100000, 6500000,
                              1100000, 6257000,
                              1240000, 6257000), byrow = TRUE, nrow = 3)),
       st_linestring(matrix(c(230000, 6692000,
                              230000, 6391000), byrow = TRUE, nrow = 2)))) %>%
  st_sfc() %>%
  st_sf(id = 1, geometry = .) %>%
  st_set_crs(2154)

# traitement --------------------------------------------------------------

# jointures des données
creer_df <- function(territoire, date = NULL) {
  territoire %>%
    left_join(pop, by = c("insee_dep" = "x1")) %>%
    left_join(
      covid %>%
        filter(jour == if_else(is.null(date), max(jour), date),
               sexe == 0) %>%
        rename(deces = dc,
               reanim = rea,
               hospit = hosp),
      by = c("insee_dep" = "dep")) %>%
    mutate(incidence = deces / x2020_p * 100000)
}

incidence <- creer_df(dep_hex)

set.seed(1234)
classes <- classIntervals(incidence$incidence, n = 6, style = "kmeans", dataPrecision = 0)$brks

# carto -------------------------------------------------------------------
# décès cate du dernier jour dispo

carte <- tm_layout(title = glue("COVID-19\nFrance\n{max(covid$jour)}"),
                         legend.position = c("left", "bottom"),
                         frame = FALSE) +
  tm_shape(incidence) +
  tm_polygons(col = "incidence", title = "décés\ncumulés pour\n100 000 hab.",
              breaks = classes,
              palette = "viridis",
              legend.reverse = TRUE,
              legend.format = list(text.separator = "à moins de",
                                   digits = 0)) +
  tm_text("insee_dep", size = .8) +
  tm_shape(encarts) +
  tm_lines(lty = 3) +
  tm_credits(glue("http://r.iresmi.net/
                    classif. kmeans
                    données départementales Santé Publique France,
                    INSEE RP 2020, d'après IGN Adminexpress 2020"),
             position = c(.6, 0),
             size = .5)

fichier_carto <- glue("resultats/covid_hex_fr_{max(covid$jour)}.png")

tmap_save(carte, fichier_carto, width = 900, height = 900, scale = .4)


# animation ---------------------------------------------------------------

image_animation <- function(date) {
  message(glue("\n\n{date}\n==========\n"))

  m <- creer_df(dep_hex, date) %>%
    tm_shape() +
    tm_polygons(col = "incidence", title = "décés\ncumulés pour\n100 000 hab.",
                breaks = classes,
                palette = "viridis",
                legend.reverse = TRUE,
                legend.format = list(text.separator = "à moins de",
                                     digits = 0)) +
    tm_text("insee_dep", size = .8) +
    tm_shape(encarts) +
    tm_lines(lty = 3) +
    tm_layout(title = glue("COVID-19\nFrance\n{date}"),
              legend.position = c("left", "bottom"),
              frame = FALSE) +
    tm_credits(glue("http://r.iresmi.net/
                    classif. kmeans
                    données départementales Santé Publique France,
                    INSEE RP 2020, d'après IGN Adminexpress 2020"),
               position = c(.6, 0),
               size = .5)

  tmap_save(m, glue("resultats/animation_spf_hex/covid_fr_{date}.png"),
            width = 800, height = 800, scale = .4,)
}

unique(covid$jour) %>%
  walk(image_animation)

animation <- glue("resultats/deces_covid19_fr_hex_spf_{max(covid$jour)}.gif")

dir_ls("resultats/animation_spf_hex") %>%
  map(image_read) %>%
  image_join() %>%
  image_animate(fps = 2, optimize = TRUE) %>%
  image_write(animation)

COVID decease

The global shape and relations are well rendered. Deformations are quite important for the small départements around Paris, but the map remains legible.

Shift
Catégories
R

Europe COVID-19 death map

COVID-19 deaths in Europe
# Europe COVID-19 deaths animated map
# http://r.iresmi.net/
# data European Centre for Disease Prevention and Control


# packages ----------------------------------------------------------------
library(tidyverse)
library(httr)
library(fs)
library(sf)
library(readxl)
library(janitor)
library(glue)
library(tmap)
library(grid)
library(classInt)
library(magick)
# + btb, raster, fasterize, plyr


# sources -----------------------------------------------------------------

# https://data.europa.eu/euodp/en/data/dataset/covid-19-coronavirus-data
covid_file <- "covid_eu.csv"
covid_url <- "https://opendata.ecdc.europa.eu/covid19/casedistribution/csv"

countries_file <- "ne_50m_admin_0_countries.shp"
countries_url <- "https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/cultural/ne_50m_admin_0_countries.zip"


# config ------------------------------------------------------------------

radius <- 600000 # smoothing radius (m)
pixel <- 100000 # grid resolution (m)

force_download <- FALSE # download even if already downloaded today ?

#' Kernel weighted smoothing with arbitrary bounding area
#'
#' @param df sf object (points)
#' @param field weight field in the df
#' @param bandwidth kernel bandwidth (map units)
#' @param resolution output grid resolution (map units)
#' @param zone sf study zone (polygon)
#' @param out_crs EPSG (should be an equal-area projection)
#'
#' @return a raster object
#' @import btb, raster, fasterize, dplyr, plyr, sf
lissage <- function(df, field, bandwidth, resolution, zone, out_crs = 3035) {
  if (st_crs(zone)$epsg != out_crs) {
    message("reprojecting data...")
    zone <- st_transform(zone, out_crs)
  }

  if (st_crs(df)$epsg != out_crs) {
    message("reprojecting study zone...")
    df <- st_transform(df, out_crs)
  }

  zone_bbox <- st_bbox(zone)

  # grid generation
  message("generating reference grid...")
  zone_xy <- zone %>%
    dplyr::select(geometry) %>%
    st_make_grid(
      cellsize = resolution,
      offset = c(plyr::round_any(zone_bbox[1] - bandwidth, resolution, f = floor),
                 plyr::round_any(zone_bbox[2] - bandwidth, resolution, f = floor)),
      what = "centers") %>%
    st_sf() %>%
    st_join(zone, join = st_intersects, left = FALSE) %>%
    st_coordinates() %>%
    as_tibble() %>%
    dplyr::select(x = X, y = Y)

  # kernel
  message("computing kernel...")
  kernel <- df %>%
    cbind(., st_coordinates(.)) %>%
    st_set_geometry(NULL) %>%
    dplyr::select(x = X, y = Y, field) %>%
    btb::kernelSmoothing(
      dfObservations = .,
      sEPSG = out_crs,
      iCellSize = resolution,
      iBandwidth = bandwidth,
      vQuantiles = NULL,
      dfCentroids = zone_xy
    )

  # rasterization
  message("\nrasterizing...")
  raster::raster(
    xmn = plyr::round_any(zone_bbox[1] - bandwidth, resolution, f = floor),
    ymn = plyr::round_any(zone_bbox[2] - bandwidth, resolution, f = floor),
    xmx = plyr::round_any(zone_bbox[3] + bandwidth, resolution, f = ceiling),
    ymx = plyr::round_any(zone_bbox[4] + bandwidth, resolution, f = ceiling),
    resolution = resolution
  ) %>%
    fasterize::fasterize(kernel, ., field = field)
}


# download data ------------------------------------------------------------

if (!dir_exists("data")) dir_create("data")
if (!dir_exists("results")) dir_create("results")
if (!dir_exists("results/animation_eu")) dir_create("results/animation_eu")

if (!file_exists(path("data", covid_file)) |
    file_info(path("data", covid_file))$modification_time < Sys.Date() |
    force_download) {
  GET(covid_url,
      progress(),
      write_disk(path("data", covid_file), overwrite = TRUE)) %>%
    stop_for_status()
}

if (!file_exists(path("data", countries_file))) {
  dl <- file_temp()

  GET(countries_url,
      progress(),
      write_disk(dl)) %>%
    stop_for_status()

  unzip(dl, exdir = "data")
}


# data --------------------------------------------------------------------

# some countries doesn't have data for the first or latest days ; we fill with latest
# data
covid <- read_csv(path("data", covid_file),
                  col_types = cols(dateRep = col_date(format = "%d/%m/%Y")),
                  na = c("N/A", "")) %>%
  clean_names() %>%
  complete(geo_id, date_rep) %>%
  replace_na(list(deaths = 0)) %>%
  group_by(geo_id) %>%
  arrange(date_rep) %>%
  mutate(deaths_cum = cumsum(deaths)) %>%
  fill(countryterritory_code, countries_and_territories, pop_data2018, continent_exp, .direction = "up") %>%
  ungroup() %>%
  select(-c(day, month, year, cases))

# keep only european countries minus Russia and adding TUR and CYP
# remove overseas territories, reproject in LAEA
countries <- read_sf(path("data", countries_file)) %>%
  clean_names() %>%
  filter(continent == "Europe" & iso_a3_eh != "RUS" | iso_a3_eh %in% c("TUR", "CYP")) %>%
  st_cast("POLYGON") %>%
  st_set_crs(4326) %>%
  st_join(c(xmin = -20, xmax = 35, ymin = 35, ymax = 70) %>%
            st_bbox() %>%
            st_as_sfc() %>%
            st_as_sf() %>%
            st_set_crs(4326),
          left = FALSE) %>%
  group_by(iso_a3_eh) %>%
  summarise(geometry = st_combine(geometry)) %>%
  st_transform(3035)

# pretreatment -----------------------------------------------------------


# mask to generate grid : union all countries
unioned_countries_file <- "data/eu.rds"

if (!file_exists(unioned_countries_file)) {
  unioned_countries <- countries %>%
    st_union() %>%
    st_sf() %>%
    write_rds(unioned_countries_file)
} else {
  unioned_countries <- read_rds(unioned_countries_file)
}

# join countries/data for a specific date
create_df <- function(territory, date = NULL) {
  covid %>%
    filter(date_rep == if_else(is.null(date), max(date_rep), date)) %>%
    right_join(countries,
              by = c("countryterritory_code" = "iso_a3_eh")) %>%
    st_as_sf() %>%
    st_point_on_surface() %>% 
    drop_na(deaths_cum) %>% 
    st_as_sf()
}

covid_geo <- create_df(countries)


# smoothing for last date ---------------------------------------------------

# deaths
d <- covid_geo %>%
  lissage("deaths_cum", radius, pixel, unioned_countries)

# population 
p <- covid_geo %>%
  lissage("pop_data2018", radius, pixel, unioned_countries)

# grid per 100000 inhab
death_pop <- d * 100000 / p


# carto -------------------------------------------------------------------

# classification for last date to be reused in animation
set.seed(1234)
classes <- classIntervals(raster::values(death_pop), n = 6, style = "kmeans", dataPrecision = 0)$brks


# animation ---------------------------------------------------------------

image_animation <- function(date) {
  message(glue("\n\n{date}\n==========\n"))

  m <- create_df(countries, date) %>%
    lissage("deaths_cum", radius, pixel, unioned_countries) %>%
    magrittr::divide_by(p) %>%
    magrittr::multiply_by(100000) %>%
    tm_shape() +
    tm_raster(title = glue("deaths
                         per 100 000 inhab."),
              style = "fixed",
              breaks = classes,
              palette = "viridis",
              legend.format = list(text.separator = "to less than",
                                   digits = 0),
              legend.reverse = TRUE) +
    tm_layout(title = glue("COVID-19 - Europe\ncumulative as of {date}"),
              legend.position = c("right", "top"),
              frame = FALSE) +
    #tm_shape(countries, bbox = death_pop) +
    #tm_borders() +
    tm_credits(glue("http://r.iresmi.net/
                  bisquare kernel smoothing {radius / 1000} km on {pixel / 1000} km grid
                  classif. kmeans, LAEA Europe projection
                  data European Centre for Disease Prevention and Control / map Naturalearth"),
               size = .5,
               position = c(.5, .025))
  
  message("saving map...")
  tmap_save(m, glue("results/animation_eu/covid_eu_{date}.png"),
            width = 800, height = 800, scale = .4,)
}

covid %>% 
  filter(date_rep >= "2020-03-15") %>% 
  pull(date_rep) %>% 
  unique() %>%
  walk(image_animation)

animation <- glue("results/deaths_covid19_eu_{max(covid$date_rep)}.gif")

dir_ls("results/animation_eu") %>%
  map(image_read) %>%
  image_join() %>%
  #image_scale("500x500") %>%
  image_morph(frames = 1) %>%
  image_animate(fps = 2, optimize = TRUE) %>%
  image_write(animation)
Catégories
R

COVID-19 decease animation map

Coronavirus decease in France
# Animation carto décès COVID 19 France
# avec lissage

# packages -----------------------------------------------------------------
library(tidyverse)
library(httr)
library(fs)
library(sf)
library(readxl)
library(janitor)
library(glue)
library(tmap)
library(grid)
library(classInt)
library(magick)
# + btb, raster, fasterize, plyr

# sources -----------------------------------------------------------------

# https://www.data.gouv.fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/
fichier_covid <- "donnees/covid.csv"
url_donnees_covid <- "https://www.data.gouv.fr/fr/datasets/r/63352e38-d353-4b54-bfd1-f1b3ee1cabd7"

# https://www.insee.fr/fr/statistiques/2012713#tableau-TCRD_004_tab1_departements
fichier_pop <- "donnees/pop.xls"
url_donnees_pop <- "https://www.insee.fr/fr/statistiques/fichier/2012713/TCRD_004.xls"

# Adminexpress : à télécharger manuellement
# https://geoservices.ign.fr/documentation/diffusion/telechargement-donnees-libres.html#admin-express
aex <- path_expand("~/Downloads/ADMIN-EXPRESS_2-2__SHP__FRA_2020-02-24/ADMIN-EXPRESS/1_DONNEES_LIVRAISON_2020-02-24")

# config ------------------------------------------------------------------

rayon <- 100000 # distance de lissage (m)
pixel <- 10000 # résolution grille (m)

force_download <- FALSE # retélécharger même si le fichier existe et a été téléchargé aujourd'hui ?

#' Kernel weighted smoothing with arbitrary bounding area
#'
#' @param df sf object (points)
#' @param field weight field in the df
#' @param bandwidth kernel bandwidth (map units)
#' @param resolution output grid resolution (map units)
#' @param zone sf study zone (polygon)
#' @param out_crs EPSG (should be an equal-area projection)
#'
#' @return a raster object
#' @import btb, raster, fasterize, dplyr, plyr, sf
lissage <- function(df, field, bandwidth, resolution, zone, out_crs = 3035) {
  if (st_crs(zone)$epsg != out_crs) {
    message("reprojecting data...")
    zone <- st_transform(zone, out_crs)
  }
  
  if (st_crs(df)$epsg != out_crs) {
    message("reprojecting study zone...")
    df <- st_transform(df, out_crs)
  }
  
  zone_bbox <- st_bbox(zone)
  
  # grid generation
  message("generating reference grid...")
  zone_xy <- zone %>%
    dplyr::select(geometry) %>%
    st_make_grid(
      cellsize = resolution,
      offset = c(
        plyr::round_any(zone_bbox[1] - bandwidth, resolution, f = floor),
        plyr::round_any(zone_bbox[2] - bandwidth, resolution, f = floor)
      ),
      what = "centers"
    ) %>%
    st_sf() %>%
    st_join(zone, join = st_intersects, left = FALSE) %>%
    st_coordinates() %>%
    as_tibble() %>%
    dplyr::select(x = X, y = Y)
  
  # kernel
  message("computing kernel...")
  kernel <- df %>%
    cbind(., st_coordinates(.)) %>%
    st_set_geometry(NULL) %>%
    dplyr::select(x = X, y = Y, field) %>%
    btb::kernelSmoothing(
      dfObservations = .,
      sEPSG = out_crs,
      iCellSize = resolution,
      iBandwidth = bandwidth,
      vQuantiles = NULL,
      dfCentroids = zone_xy
    )
  
  # rasterization
  message("\nrasterizing...")
  raster::raster(
    xmn = plyr::round_any(zone_bbox[1] - bandwidth, resolution, f = floor),
    ymn = plyr::round_any(zone_bbox[2] - bandwidth, resolution, f = floor),
    xmx = plyr::round_any(zone_bbox[3] + bandwidth, resolution, f = ceiling),
    ymx = plyr::round_any(zone_bbox[4] + bandwidth, resolution, f = ceiling),
    resolution = resolution
  ) %>%
    fasterize::fasterize(kernel, ., field = field)
}


# téléchargement--------------------------------------------------------------

if (!dir_exists("donnees")) dir_create("donnees")
if (!dir_exists("resultats")) dir_create("resultats")
if (!dir_exists("resultats/animation")) dir_create("resultats/animation")

if (!file_exists(fichier_covid) |
    file_info(fichier_covid)$modification_time < Sys.Date() |
    force_download) {
  GET(url_donnees_covid,
      progress(),
      write_disk(fichier_covid, overwrite = TRUE))
}

if (!file_exists(fichier_pop)) {
  GET(url_donnees_pop,
      progress(),
      write_disk(fichier_pop))
}


# données -----------------------------------------------------------------

covid <- read_csv2(fichier_covid)

# adminexpress prétéléchargé
dep <- read_sf(path(aex, "ADE_2-2_SHP_LAMB93_FR/DEPARTEMENT.shp")) %>%
  clean_names() %>%
  st_set_crs(2154)

pop <- read_xls(fichier_pop, skip = 2) %>%
  clean_names()


# prétraitement -----------------------------------------------------------

# contour métropole pour grille de référence
fichier_fr <- "donnees/fr.rds"

if (!file_exists(fichier_fr)) {
  fr <- dep %>%
    st_union() %>%
    st_sf() %>%
    write_rds(fichier_fr)
} else {
  fr <- read_rds(fichier_fr)
}

# jointures des données
creer_df <- function(territoire, date = NULL) {
  territoire %>%
    left_join(pop, by = c("insee_dep" = "x1")) %>%
    left_join(
      covid %>%
        filter(jour == if_else(is.null(date), max(jour), date),
               sexe == 0) %>%
               rename(deces = dc,
                      reanim = rea,
                      hospit = hosp),
      by = c("insee_dep" = "dep")) %>%
    st_point_on_surface()
}

covid_geo_pop <- creer_df(dep)


# lissage -----------------------------------------------------------------
# génération de la dernière grille mortalité
# et création des grilles pour 100000 habitants

# décès métropole 
d <- covid_geo_pop %>%
  lissage("deces", rayon, pixel, fr)


# population métropole et DOM
p <- covid_geo_pop %>%
  lissage("x2020_p", rayon, pixel, fr)

# grilles pour 100000 hab
d100k <- d * 100000 / p


# classification à réutiliser pour les autres cartes
set.seed(1234)
classes <- classIntervals(raster::values(d100k), n = 6, style = "kmeans", dataPrecision = 0)$brks


# animation ---------------------------------------------------------------

image_animation <- function(date) {
  m <- creer_df(dep, date) %>%
    lissage("deces", rayon, pixel, fr) %>%
    magrittr::divide_by(p) %>%
    magrittr::multiply_by(100000) %>%
    tm_shape() +
    tm_raster(title = glue("décès à l'hôpital
                         pour 100 000 hab."),
              style = "fixed",
              breaks = classes,
              palette = "viridis",
              legend.format = list(text.separator = "à moins de",
                                   digits = 0),
              legend.reverse = TRUE) +
    tm_shape(dep) +
    tm_borders() +
    tm_layout(title = glue("COVID-19 - France métropolitaine - cumul au {date}"),
              legend.position = c("left", "bottom"),
              frame = FALSE) +
    tm_credits(glue("http://r.iresmi.net/
                  lissage noyau bisquare {rayon / 1000} km sur grille {pixel / 1000} km
                  classif. kmeans
                  projection LAEA Europe
                  données départementales Santé publique France,
                  INSEE RP 2020, IGN Adminexpress 2020"),
               size = .5,
               position = c(.5, .025))
  
  tmap_save(m, glue("resultats/animation/covid_fr_{date}.png"),
            width = 800, height = 800, scale = .4,)
}

unique(covid$jour) %>%
  walk(image_animation)

animation <- glue("resultats/deces_covid19_fr_{max(covid$jour)}.gif")

dir_ls("resultats/animation") %>%
  map(image_read) %>%
  image_join() %>%
  #image_scale("500x500") %>%
  image_morph(frames = 1) %>%
  image_animate(fps = 2, optimize = TRUE) %>%
  image_write(animation)


Catégories
R

Coronavirus : spatially smoothed decease in France

Coronavirus decease in France

See also the animated map.

From the official data by Santé Publique France, we spatially smooth the decease (produced by SPF at the département scale) and normalize by a similarly smoothed population grid. For that we use the {btb} package.

# Carto décès COVID 19 France
# avec lissage


# sources -----------------------------------------------------------------

# https://www.data.gouv.fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/
fichier_covid <- "donnees/covid.csv"
url_donnees_covid <- "https://www.data.gouv.fr/fr/datasets/r/63352e38-d353-4b54-bfd1-f1b3ee1cabd7"

# https://www.insee.fr/fr/statistiques/2012713#tableau-TCRD_004_tab1_departements
fichier_pop <- "donnees/pop.xls"
url_donnees_pop <- "https://www.insee.fr/fr/statistiques/fichier/2012713/TCRD_004.xls"

# Adminexpress : à télécharger manuellement
# https://geoservices.ign.fr/documentation/diffusion/telechargement-donnees-libres.html#admin-express
#aex <- "donnees/1_DONNEES_LIVRAISON_2019-03-14/"
aex <- path_expand("~/Downloads/ADMIN-EXPRESS_2-2__SHP__FRA_2020-02-24/ADMIN-EXPRESS/1_DONNEES_LIVRAISON_2020-02-24")

# config ------------------------------------------------------------------
library(tidyverse)
library(httr)
library(fs)
library(sf)
library(readxl)
library(janitor)
library(glue)
library(tmap)
library(grid)
library(classInt)
# + btb, raster, fasterize, plyr

rayon <- 100000 # distance de lissage (m)
pixel <- 10000 # résolution grille (m)

force_download <- TRUE # retélécharger même si le fichier existe et a été téléchargé aujourd'hui ?

#' Kernel weighted smoothing with arbitrary bounding area
#'
#' @param df sf object (points)
#' @param field weight field in the df
#' @param bandwidth kernel bandwidth (map units)
#' @param resolution output grid resolution (map units)
#' @param zone sf study zone (polygon)
#' @param out_crs EPSG (should be an equal-area projection)
#'
#' @return a raster object
#' @import btb, raster, fasterize, dplyr, plyr, sf
lissage <- function(df, field, bandwidth, resolution, zone, out_crs = 3035) {
    if (st_crs(zone)$epsg != out_crs) {
      message("reprojecting data...")
      zone <- st_transform(zone, out_crs)
    }

    if (st_crs(df)$epsg != out_crs) {
      message("reprojecting study zone...")
      df <- st_transform(df, out_crs)
    }

    zone_bbox <- st_bbox(zone)

    # grid generation
    message("generating reference grid...")
    zone_xy <- zone %>%
      dplyr::select(geometry) %>%
      st_make_grid(
        cellsize = resolution,
        offset = c(
          plyr::round_any(zone_bbox[1] - bandwidth, resolution, f = floor),
          plyr::round_any(zone_bbox[2] - bandwidth, resolution, f = floor)
        ),
        what = "centers"
      ) %>%
      st_sf() %>%
      st_join(zone, join = st_intersects, left = FALSE) %>%
      st_coordinates() %>%
      as_tibble() %>%
      dplyr::select(x = X, y = Y)

    # kernel
    message("computing kernel...")
    kernel <- df %>%
      cbind(., st_coordinates(.)) %>%
      st_set_geometry(NULL) %>%
      dplyr::select(x = X, y = Y, field) %>%
      btb::kernelSmoothing(
        dfObservations = .,
        sEPSG = out_crs,
        iCellSize = resolution,
        iBandwidth = bandwidth,
        vQuantiles = NULL,
        dfCentroids = zone_xy
      )

    # rasterization
    message("\nrasterizing...")
    raster::raster(
      xmn = plyr::round_any(zone_bbox[1] - bandwidth, resolution, f = floor),
      ymn = plyr::round_any(zone_bbox[2] - bandwidth, resolution, f = floor),
      xmx = plyr::round_any(zone_bbox[3] + bandwidth, resolution, f = ceiling),
      ymx = plyr::round_any(zone_bbox[4] + bandwidth, resolution, f = ceiling),
      resolution = resolution
    ) %>%
      fasterize::fasterize(kernel, ., field = field)
  }


# téléchargement--------------------------------------------------------------
if (!file_exists(fichier_covid) |
    file_info(fichier_covid)$modification_time < Sys.Date() |
    force_download) {
  GET(url_donnees_covid,
      progress(),
      write_disk(fichier_covid, overwrite = TRUE))
}

if (!file_exists(fichier_pop)) {
  GET(url_donnees_pop,
      progress(),
      write_disk(fichier_pop))
}


# données -----------------------------------------------------------------

covid <- read_csv2(fichier_covid)

# adminexpress prétéléchargé
dep <- read_sf(path(aex, "ADE_2-2_SHP_LAMB93_FR/DEPARTEMENT.shp")) %>%
  clean_names() %>%
  st_set_crs(2154)

dep_971 <- read_sf(path(aex, "ADE_2-2_SHP_RGAF09UTM20_D971/DEPARTEMENT.shp")) %>%
  clean_names() %>%
  st_set_crs(5490)

dep_972 <- read_sf(path(aex, "ADE_2-2_SHP_RGAF09UTM20_D972/DEPARTEMENT.shp")) %>%
  clean_names() %>%
  st_set_crs(5490)

dep_973 <- read_sf(path(aex, "ADE_2-2_SHP_UTM22RGFG95_D973/DEPARTEMENT.shp")) %>%
  clean_names() %>%
  st_set_crs(2972)

dep_974 <- read_sf(path(aex, "ADE_2-2_SHP_RGR92UTM40S_D974/DEPARTEMENT.shp")) %>%
  clean_names() %>%
  st_set_crs(2975)

dep_976 <- read_sf(path(aex, "ADE_2-2_SHP_RGM04UTM38S_D976/DEPARTEMENT.shp")) %>%
  clean_names() %>%
  st_set_crs(4471)

pop <- read_xls(fichier_pop, skip = 2) %>%
  clean_names()


# prétraitement -----------------------------------------------------------

# contour métropole
fr <- dep %>%
  st_union() %>%
  st_sf()

# jointures des données
creer_df <- function(territoire) {
  territoire %>%
    left_join(pop, by = c("insee_dep" = "x1")) %>%
    left_join(
      covid %>%
        filter(jour == max(jour),
               sexe == 0) %>%
        group_by(dep) %>%
        summarise(deces = sum(dc, na.rm = TRUE),
                  reanim = sum(rea, na.rm = TRUE),
                  hospit = sum(hosp, na.rm = TRUE)),
      by = c("insee_dep" = "dep")) %>%
    st_point_on_surface()
}

covid_geo_pop     <- creer_df(dep)
covid_geo_pop_971 <- creer_df(dep_971)
covid_geo_pop_972 <- creer_df(dep_972)
covid_geo_pop_973 <- creer_df(dep_973)
covid_geo_pop_974 <- creer_df(dep_974)
covid_geo_pop_976 <- creer_df(dep_976)

# lissage -----------------------------------------------------------------
# génération des grilles mortalité, hospitalisation et réanimation et population
# et création des grilles pour 100000 habitants

# décès métropole et DOM
d <- covid_geo_pop %>%
  lissage("deces", rayon, pixel, fr)

d_971 <- covid_geo_pop_971 %>%
  lissage("deces", rayon, pixel, dep_971, 5490)

d_972 <- covid_geo_pop_972 %>%
  lissage("deces", rayon, pixel, dep_972, 5490)

d_973 <- covid_geo_pop_973 %>%
  lissage("deces", rayon, pixel, dep_973, 2972)

d_974 <- covid_geo_pop_974 %>%
  lissage("deces", rayon, pixel, dep_974, 2975)

d_976 <- covid_geo_pop_976 %>%
  lissage("deces", rayon, pixel, dep_976, 4471)

# population métropole et DOM
p <- covid_geo_pop %>%
  lissage("x2020_p", rayon, pixel, fr)

p_971 <- covid_geo_pop_971 %>%
  lissage("x2020_p", rayon, pixel, dep_971, 5490)

p_972 <- covid_geo_pop_972 %>%
  lissage("x2020_p", rayon, pixel, dep_972, 5490)

p_973 <- covid_geo_pop_973 %>%
  lissage("x2020_p", rayon, pixel, dep_973, 2972)

p_974 <- covid_geo_pop_974 %>%
  lissage("x2020_p", rayon, pixel, dep_974, 2975)

p_976 <- covid_geo_pop_976 %>%
  lissage("x2020_p", rayon, pixel, dep_976, 4471)

# grilles pour 100000 hab
d100k <- d * 100000 / p
d100k_971 <- d_971 * 100000 / p_971
d100k_972 <- d_972 * 100000 / p_972
d100k_973 <- d_973 * 100000 / p_973
d100k_974 <- d_974 * 100000 / p_974
d100k_976 <- d_976 * 100000 / p_976

# réanimation et hospitalisation métropole uniquement
r <- covid_geo_pop %>%
  lissage("reanim", rayon, pixel, fr)
r100k <- r * 100000 / p

h <- covid_geo_pop %>%
  lissage("hospit", rayon, pixel, fr)
h100k <- h * 100000 / p


# carto -------------------------------------------------------------------

# décès métropole et DOM

# classification à réutiliser pour les 6 cartes
set.seed(1234)
classes <- classIntervals(raster::values(d100k), n = 5, style = "kmeans", dataPrecision = 0)$brks

# métro et DOM
(carte_d <- tm_layout(title = paste("COVID-19 - France - cumul au", max(covid$jour)),
                     legend.position = c("left", "bottom"),
                     frame = FALSE) +
  tm_shape(d100k) +
  tm_raster(title = glue("décès à l'hôpital
                         pour 100 000 hab."),
            style = "fixed",
            breaks = classes,
            palette = "viridis",
            legend.format = list(text.separator = "à moins de",
                                 digits = 0),
            legend.reverse = TRUE) +
  tm_shape(dep) +
  tm_borders() +
  tm_credits(glue("http://r.iresmi.net/
                  lissage noyau bisquare {rayon / 1000} km sur grille {pixel / 1000} km
                  classif. kmeans
                  projections LAEA Europe (métropole) et locales (DOM)
                  données départementales Santé publique France,
                  INSEE RP 2020, IGN Adminexpress 2020"),
             size = .5,
             position = c(.5, .025))
)

tm_971 <- tm_shape(d100k_971, ext = 0.7) +
  tm_raster(style = "fixed",
            breaks = classes,
            palette = "viridis",
            legend.show = FALSE) +
  tm_shape(dep_971) +
  tm_borders() +
  tm_layout(frame = FALSE,
            bg.color = NA)

tm_972 <- tm_shape(d100k_972, ext = 0.7) +
  tm_raster(style = "fixed",
            breaks = classes,
            palette = "viridis",
            legend.show = FALSE) +
  tm_shape(dep_972) +
  tm_borders() +
  tm_layout(frame = FALSE,
            bg.color = NA)

tm_973 <- tm_shape(d100k_973) +
  tm_raster(style = "fixed",
            breaks = classes,
            palette = "viridis",
            legend.show = FALSE) +
  tm_shape(dep_973) +
  tm_borders() +
  tm_layout(frame = FALSE,
            bg.color = NA)

tm_974 <- tm_shape(d100k_974, ext = 0.75) +
  tm_raster(style = "fixed",
            breaks = classes,
            palette = "viridis",
            legend.show = FALSE) +
  tm_shape(dep_974) +
  tm_borders()+
  tm_layout(frame = FALSE,
            bg.color = NA)

tm_976 <- tm_shape(d100k_976, ext = 0.6) +
  tm_raster(style = "fixed",
            breaks = classes,
            palette = "viridis",
            legend.show = FALSE) +
  tm_shape(dep_976) +
  tm_borders()+
  tm_layout(frame = FALSE,
            bg.color = NA)

# assemblage
fichier_carto <- glue("resultats/covid_fr_{max(covid$jour)}.png")

tmap_save(carte_d, fichier_carto, width = 900, height = 900, scale = .4,
          insets_tm = list(tm_971, tm_972, tm_973, tm_974, tm_976),
          insets_vp = list(viewport(x = .1, y = .65, width = .15, height = .15),
                           viewport(x = .1, y = .58, width = .15, height = .15),
                           viewport(x = .15, y = .4, width = .35, height = .45),
                           viewport(x = .9, y = .4, width = .15, height = .15),
                           viewport(x = .9, y = .5, width = .15, height = .15)))

Catégories
R

Mauna Loa CO₂ polar plot

After a classic plot of the Keeling curve (see our former post) used on Wikipedia, we can explore another data visualization. The CO₂ atmospheric concentration, the main cause of the climate warming, is following a seasonal cycle so it could be interesting (or ironic ?) to use a polar plot.

Config and data

We only keep two translations for brevity here…

# Required packages
library(tidyverse)
library(scales)
library(lubridate)

# Translations ------------------------------------------------------------

language <- list(
  en_US = list(
    locale_lc_time = "en_US.UTF-8",
    title = bquote("Monthly mean"~CO[2]~"concentration"),
    caption = paste("Data : P. Tans, NOAA/ESRL (www.esrl.noaa.gov/gmd/ccgg/trends/)\nand R. Keeling, Scripps Institution of Oceanography (scrippsco2.ucsd.edu/). Accessed", Sys.Date()),
    x = "Year",
    y = bquote(CO[2]~"fraction in dry air ("*mu*"mol/mol)"),
    x2 = "Month",
    y2 = bquote(atop(CO[2]~"fraction in dry air ("*mu*"mol/mol)", "Departure from yearly average")),
    title2 = "Seasonal variation"
  ),
  fr_FR = list(
    locale_lc_time = "fr_FR.UTF-8",
    title = bquote("Moyenne mensuelle de la concentration de"~CO[2]),
    caption = paste("données : P. Tans, NOAA/ESRL (www.esrl.noaa.gov/gmd/ccgg/trends/)\net R. Keeling, Scripps Institution of Oceanography (scrippsco2.ucsd.edu/). Accédé le", Sys.Date()),
    x = "année",
    y = bquote("fraction de"~CO[2]~"dans l'air sec ("*mu*"mol/mol)"),
    x2 = "mois",
    y2 = bquote(atop("fraction de"~CO[2]~"dans l'air sec ("*mu*"mol/mol)", "en écart à la moyenne annuelle")),
    title2 = "Variation saisonnière"
  ))

# Data --------------------------------------------------------------------

# https://www.esrl.noaa.gov/gmd/ccgg/trends/
co2ml <- read_delim("ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt",
                    delim = " ",
                    locale = locale(decimal_mark = "."),
                    na = c("-99.99", "-1"),
                    col_types = "iiddddi",
                    col_names = c("year", "month", "decimal", "co2", "co2_interpol", "co2_trend", "days"),
                    comment = "#",
                    trim_ws = TRUE) %>% 
  group_by(year) %>% 
  mutate(year_mean = mean(co2_interpol, na.rm = TRUE),
         delta = co2_interpol - year_mean,
         vdate = ymd(paste0("2015-", month, "-01"))) %>% 
  ungroup()

Create the plot for each language and save

We use a virtual date to keep the data in the same January-December interval and we add a partial dataframe to smooth the Dec./Jan. transition and build the spiral.

# Polar plot
for (l in names(language)) {
  message(l)
  current <- language[[l]]
  
  # format the date in local names
  Sys.setlocale("LC_TIME", current$locale_lc_time)
  
  p3 <- co2ml %>% 
    filter(vdate == "2015-01-01") %>% 
    mutate(vdate = ymd("2015-12-31"),
           year = year -1) %>% 
    bind_rows(co2ml) %>% 
    ggplot(aes(vdate, co2_interpol, group = year, color = year)) +
      geom_line(size = 1.2) +
      scale_x_date(breaks = pretty_breaks(12), labels = date_format("%b")) +
      scale_color_viridis_c() +
      labs(subtitle = current$title,
           x = "",
           y = current$y,
           color = current$x,
           title = paste("Mauna Loa", min(co2ml$year), "-", max(co2ml$year)),
           caption = current$caption) +
      coord_polar() +
      theme_bw() +
      theme(axis.title.y = element_text(hjust = .85),
            panel.grid.major.y  = element_blank(),
            panel.grid.minor.x = element_blank(),
            panel.border = element_blank(),
            plot.caption = element_text(size = 7))
  
  ggsave(p3, file = paste("co2_mauna_loa_polar", l, Sys.Date(), "wp.svg", sep = "_"), width = 20, height = 20, units = "cm", device = svg)
}
60 years of CO₂ increase

Does this new year look good ? Will the spiral cross its path soon ?

Last version of the code on Gitlab.

Catégories
R

Generate multiple language version plots

The use case is to create the same plot in different languages. I used this technique for Wikipedia plots.

We are going to build a list containing all translations, we will then loop over each language, generating and saving the plot.

# Mauna Loa atmospheric CO2 change
# multi language plot for Wikipedia

# Required packages
library(tidyverse)
library(gridExtra)
library(scales)
library(lubridate)
library(Hmisc)

# Translations ------------------------------------------------------------

language <- list(
  en_US = list(
    locale_lc_time = "en_US.UTF-8",
    title = expression(paste("Monthly mean ", CO[2], " concentration ")),
    caption = paste("Data : R. F. Keeling, S. J. Walker, S. C. Piper and A. F. Bollenbacher\nScripps CO2 Program (http://scrippsco2.ucsd.edu). Accessed ", Sys.Date()),
    x = "Year",
    y = expression(paste(CO[2], " fraction in dry air (", mu, "mol/mol)")),
    x2 = "Month",
    y2 = expression(atop(paste(CO[2], " fraction in dry air (", mu, "mol/mol)"), "Departure from yearly average")),
    title2 = "Seasonal variation"
  ),
  fr_FR = list(
    locale_lc_time = "fr_FR.UTF-8",
    title = expression(paste("Moyenne mensuelle de la concentration de ", CO[2])),
    caption = paste("données : R. F. Keeling, S. J. Walker, S. C. Piper et A. F. Bollenbacher\nScripps CO2 Program (http://scrippsco2.ucsd.edu). Accédé le", Sys.Date()),
    x = "année",
    y = expression(paste("fraction de ", CO[2], " dans l'air sec (", mu, "mol/mol)")),
    x2 = "mois",
    y2 = expression(atop(paste("fraction de ", CO[2], " dans l'air sec (", mu, "mol/mol)"), "en écart à la moyenne annuelle")),
    title2 = "Variation saisonnière"
  ),
  de_DE = list(
    locale_lc_time = "de_DE.UTF-8",
    title = expression(paste("Monatliche durchschnittliche ", CO[2], "-Konzentration")),
    caption = paste("Datei : R. F. Keeling, S. J. Walker, S. C. Piper und A. F. Bollenbacher\nScripps CO2 Program (http://scrippsco2.ucsd.edu). Zugänglich am", Sys.Date()),
    x = "Jahr",
    y = expression(paste(CO[2], "-Anteil in trockener Luft (", mu, "mol/mol)")),
    x2 = "Monate",
    y2 = expression(atop(paste(CO[2], "-Anteil in trockener Luft (", mu, "mol/mol)"), "Abweichung vom Jahresmittel")),
    title2 = "Monatliche Variation"
  ),
  es_ES = list(
    locale_lc_time = "es_ES.UTF-8",
    title = expression(paste("Media mensual de la concentración de ", CO[2])),
    caption = paste("dato : R. F. Keeling, S. J. Walker, S. C. Piper y A. F. Bollenbacher\nScripps CO2 Program (http://scrippsco2.ucsd.edu). Visitada", Sys.Date()),
    x = "Año",
    y = expression(paste("Fraccion de ", CO[2],  " en aire secco (", mu, "mol/mol)")),
    x2 = "Mes",
    y2 = expression(atop(paste("Fraccion de ", CO[2],  " en aire secco (", mu, "mol/mol)"), "Desviación de la media anual")),
    title2 = "Variación mensual"
  ),
  cs_CZ = list(
    locale_lc_time = "cs_CZ.UTF-8",
    title = expression(paste("Průměrné měsíční koncentrace oxidu uhličitého")),
    caption = paste("data : R. F. Keeling, S. J. Walker, S. C. Piper a A. F. Bollenbacher\nScripps CO2 Program (http://scrippsco2.ucsd.edu). Přístupné", Sys.Date()),
    x = "rok",
    y = expression(paste("koncentrace ", CO[2], " v suchém vzduchu (", mu, "mol/mol)")),
    x2 = "měsíc",
    y2 = expression(atop(paste("koncentrace ", CO[2], " v suchém vzduchu (", mu, "mol/mol)"), "odchylka od ročního průměru")),
    title2 = "Měsíční změna (průměrná roční odchylka)"
  ),
  nn_NO = list(
    locale_lc_time = "nn_NO.UTF-8",
    title = expression(paste("Gjennomsnittlig månedlig ", CO[2], "-konsentrasjon")),
    caption = paste("data : R. F. Keeling, S. J. Walker, S. C. Piper og A. F. Bollenbacher\nScripps CO2 Program (http://scrippsco2.ucsd.edu). Vist", Sys.Date()),
    x = "År",
    y = expression(paste(CO[2],"-andel i tørr luft (", mu, "mol/mol)")),
    x2 = "Måned",
    y2 = expression(atop(paste(CO[2],"-andel i tørr luft (", mu, "mol/mol)"),
                         "Avvik fra årlig gjennomsnitt")),
    title2 = "Årlig variasjon"
  )
)


# Data --------------------------------------------------------------------

# http://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record
# used during US gov shutdown
co2ml <- read_csv("http://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv",
                  col_names = c("year", "month", "xls_date", "decimal",
                                "co2", "co2_seas_adj", "fit", "fit_seas_adj",
                                "co2_filled", "co2_filled_seas_adj"),
                  col_types = "iiiddddddd",
                  skip = 57,
                  na = "-99.99",
                  comment = "\"") %>% 
  group_by(year) %>% 
  mutate(year_mean = mean(co2_filled, na.rm = TRUE),
         delta = co2_filled - year_mean,
         vdate = ymd(paste0("2015-", month, "-01")))

# Generate the plot for each language -------------------------------------

for (l in names(language)) {
  message(l)
  current <- language[[l]]
  
  # format the date in local names
  Sys.setlocale("LC_TIME", current$locale_lc_time)
  
  # main plot
  p1 <- ggplot(co2ml, aes(decimal, co2_filled)) + 	
    geom_line(color = "pink") +
    geom_point(color = "red", size = 0.6) +
    stat_smooth(span = 0.1) +
    scale_x_continuous(breaks = pretty_breaks()) +
    scale_y_continuous(breaks = pretty_breaks(4), minor_breaks = pretty_breaks(8)) +
    labs(
      x = current$x,
      y = current$y,
      title = current$title,
      subtitle = paste("Mauna Loa", min(co2ml$year), "-", max(co2ml$year)),
      caption = current$caption) +
    theme_bw() +
    theme(plot.caption = element_text(size = 7))
  
  # inset plot
  p2 <- ggplot(co2ml, aes(vdate, delta)) +
    geom_hline(yintercept = 0) +
    stat_smooth(span = 0.4, se = FALSE) +
    stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 0.3) + 
    scale_x_date(breaks = pretty_breaks(4), minor_breaks = pretty_breaks(12), labels = date_format("%b")) +
    labs(
      x = current$x2,
      y = current$y2,
      title = current$title2) +
    theme_bw()
  
  # merge the plots and export in SVG
  p1 + annotation_custom(grob = ggplotGrob(p2), xmin = 1957, xmax = 1991, ymin = 361, ymax = 412)
  ggsave(file = paste("co2_mauna_loa", l, Sys.Date(), "wp.svg", sep = "_"), width = 20, height = 20, units = "cm", device = svg)
  
}

Our plots as a nice gallery :

Last version of the code on Gitlab.