Where and when (not) to eat in France?

Results of sanitary controls in France can be found on data.gouv.fr however, only the running year is available… Thanks to @cquest@amicale.net we can access the archives since 2017.

Config and data cleaning

Code

library(tidyverse)
library(janitor)
library(fs)
library(sf)
library(glue)
library(ggspatial)
library(rnaturalearth)
library(patchwork)

# smoothing
source(glue("fonctions_lissage.R"), encoding = "UTF-8")

# move DROM
source(glue("translation.R"), encoding = "UTF-8")

# params ------------------------------------------------------------------

rayon <- 30000 # smoothing radius (m)
pixel <- 2000  # pixel resolution output for smoothing (m)
proj_liss <- "EPSG:3035" # an equal area projection

Code

# 2017-2018 : yearly archive ; extract the last doy export
# 
# http://data.cquest.org/alim_confiance/exports_alim_confiance_2017.7z
#  -> export_alimconfiance_2017-12-31.csv
# http://data.cquest.org/alim_confiance/exports_alim_confiance_2018.7z
#  -> export_alimconfiance_2018-12-31.csv
alim_dcq <- dir_ls("data/data.cquest.org", regexp = "\\.csv") |> 
  map_dfr(read_csv2) |> 
  clean_names()  

# 2019-2023
# archives at the end of each year + last export
# 
# http://files.opendatarchives.fr/dgal.opendatasoft.com/archives/export_alimconfiance/20191231T053939Z%20export_alimconfiance.csv.gz
# http://files.opendatarchives.fr/dgal.opendatasoft.com/archives/export_alimconfiance/20201231T083455Z%20export_alimconfiance.csv.gz
# http://files.opendatarchives.fr/dgal.opendatasoft.com/archives/export_alimconfiance/20211231T083316Z%20export_alimconfiance.csv.gz
# http://files.opendatarchives.fr/dgal.opendatasoft.com/archives/export_alimconfiance/20221231T150844Z%20export_alimconfiance.csv.gz
# http://files.opendatarchives.fr/dgal.opendatasoft.com/export_alimconfiance.csv.gz
alim_oda <- dir_ls("data/files.opendatarchives.fr", regexp = "\\.csv") |> 
  map_dfr(read_csv2) |> 
  clean_names() |> 
  distinct() |> 
  filter(date_inspection < "2023-03-01")

# https://datanova.laposte.fr/explore/dataset/laposte_hexasmal/download?format=shp
cp <- read_sf("~/data/laposte/laposte_hexasmal.shp")

# Built from IGN Adminexpress. Get the data :
# https://r.iresmi.net/posts/2021/simplifying_polygons_layers/results/adminexpress_simpl_2022.zip
dep <- read_sf("~/data/adminexpress/adminexpress_cog_simpl_000_2022.gpkg", 
               layer = "departement") |> 
  translater_drom(ajout_zoom_pc = TRUE)

reg_fx <- read_sf("~/data/adminexpress/adminexpress_cog_simpl_000_2022.gpkg", 
               layer = "region") |> 
  filter(insee_reg > "06")

fx <- read_sf("~/data/adminexpress/adminexpress_cog_simpl_000_2022.gpkg", 
              layer = "region") |> 
  filter(insee_reg > "06") |> 
  summarise() |> 
  st_transform(proj_liss)

# projection names
nom_proj_liss <- str_extract(st_crs(fx)$wkt, '(?<=PROJCRS\\[").*(?=",)')
nom_proj_legale <- str_extract(st_crs(dep)$wkt, '(?<=PROJCRS\\[").*(?=",)')

# basemap
countries <- ne_countries(50, returnclass = "sf") |> 
  filter(admin != "France")

Code

alim_raw <- bind_rows(alim_dcq,
                      alim_oda) |> 
  mutate(insee_dep = case_when(str_sub(code_postal, 1, 2) >= "97" ~ str_sub(code_postal, 1, 3), 
                         str_sub(code_postal, 1, 3) %in% c("200", "201") ~ "2A",
                         str_sub(code_postal, 1, 3) %in% c("202", "206") ~ "2B",
                         TRUE ~ str_sub(code_postal, 1, 2)))


# trying to add coordinates to unknown locations using postal codes
# only 5800 on 7500
georef <- cp |> 
  group_by(code_postal) |> 
  summarise(geometry = first(geometry)) |> 
  inner_join(alim_raw |> 
              filter(is.na(geores),
                     !is.na(code_postal)),
             by = "code_postal")

# build sf object
alim <- alim_raw |> 
  filter(!is.na(geores)) |> 
  separate(geores, c("y", "x"), sep = ",") |> 
  st_as_sf(coords = c("x", "y"), crs = "EPSG:4326") |> 
  bind_rows(georef) 

alim |> 
  write_sf(glue("results/alim.gpkg"))

Exploring data

First a global view of the dataset:

Code

alim |> 
  st_drop_geometry() |> 
  count(annee = year(date_inspection), 
        semaine = isoweek(date_inspection)) |> 
  ggplot(aes(semaine, n, group = annee, color = as_factor(annee))) +
  geom_point() +
  geom_smooth(span = 0.4) +
  scale_y_continuous(labels = scales::label_number(big.mark = " ")) +
  labs(title = "Contrôles officiels sanitaires",
       subtitle = "France - 2017-2023",
       color = "année") +
  guides(color = guide_legend(reverse=TRUE))

A scatter plot with smoothed lines showing the number of controls by week and year — Figure 1: Overview by week

About 800 controls per week, except during the lock-down in 2020, and a slightly lower control pressure in 2021 and 2022.

Code

alim |> 
  st_drop_geometry() |> 
  group_by(annee = year(date_inspection)) |> 
  summarise(pcent_neg = sum(synthese_eval_sanit %in% c("A améliorer", "A corriger de manière urgente")) / n()) |> 
  ggplot(aes(annee, pcent_neg)) +
  geom_col() +
  scale_y_continuous(breaks = scales::breaks_pretty(), 
                     labels = scales::label_percent(decimal.mark = ",", suffix = " %")) +
  labs(title = "Résultats des contrôles officiels sanitaires",
       subtitle = "France - 2017-2023",
       x = "année",
       y = "% mauvais",
       caption = glue("https://r.iresmi.net/
                     données : Alim'confiance via opendatarchives.fr
                     mauvais = 'À améliorer' ou 
                       'À corriger de manière urgente'")) +
  theme(panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.ticks.x = element_blank(),
        plot.caption = element_text(size = 7))

A bar plot of bad controls by year — Figure 2: Yearly

Poor results (To be improved or To be corrected urgently) are stable at around 6 %, except a recent spike in 2023?

Code

alim |> 
  st_drop_geometry() |> 
  group_by(mois = ymd(paste0("2020", str_pad(month(date_inspection), 2, "left", "0"), "01"))) |> 
  summarise(pcent_neg = sum(synthese_eval_sanit %in% c("A améliorer", "A corriger de manière urgente")) / n()) |> 
  ggplot(aes(mois, pcent_neg)) +
  geom_col() +
  scale_x_date(date_labels = "%b", date_breaks = "2 months", expand = c(0.01, 0.01)) +
  scale_y_continuous(breaks = scales::breaks_pretty(), 
                     labels = scales::percent_format(decimal.mark = ",", suffix = " %")) +
  labs(title = "Résultats des contrôles officiels sanitaires",
       subtitle = "France - 2017-2023",
       y = "% mauvais",
       caption = glue("https://r.iresmi.net/
                     données : Alim'confiance via opendatarchives.fr
                     mauvais = 'À améliorer' ou 
                       'À corriger de manière urgente'")) +
  theme(panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.ticks.x = element_blank(),
        plot.caption = element_text(size = 7))

A bar plot of bad controls by month — Figure 3: Monthly

It seems that poor results increase during the year, from June to November.

This surprising periodic phenomenon is also visible by day:

Code

alim |> 
  st_drop_geometry() |> 
  group_by(date_inspection = as.Date(date_inspection)) |> 
  summarise(pcent_neg = sum(synthese_eval_sanit %in% c("A améliorer", "A corriger de manière urgente")) / n()) |> 
  ggplot(aes(date_inspection, pcent_neg)) +
  geom_point() +
  geom_smooth(method = "gam", formula = y ~ s(x, bs = "cs", k = 30)) +
  scale_x_date(labels = ~ format(.x, "%b\n%Y"), date_breaks = "6 months") +
  scale_y_continuous(breaks = scales::breaks_pretty(), 
                     labels = scales::percent_format(decimal.mark = ",", suffix = " %")) +
  labs(title = "Résultats des contrôles officiels sanitaires",
       subtitle = "France - 2017-2023",
       x = "jour",
       y = "% mauvais",
       caption = glue("https://r.iresmi.net/
                     données : Alim'confiance via opendatarchives.fr
                     mauvais = 'À améliorer' ou 
                       'À corriger de manière urgente'")) +
  theme(plot.caption = element_text(size = 7))

A bar plot of bad controls by day with a smooth line — Figure 4: Daily

So for the « when », it is: not in summer or autumn.

Maps

What about the « where »? It seems you also could be careful in some départements…

Code

bilan_dep <- alim |> 
  st_drop_geometry() |> 
  group_by(insee_dep) |> 
  summarise(pcent_neg = sum(synthese_eval_sanit %in% c("A améliorer", "A corriger de manière urgente")) / n() * 100) |> 
  arrange(desc(pcent_neg))

dep |> 
  left_join(bilan_dep, by = "insee_dep") |> 
  ggplot() +
  geom_sf(aes(fill = pcent_neg), color = "white", linewidth = 0.05) +
  geom_sf(data = reg_fx, fill = NA, color = "white", linewidth = .1) +
  scale_fill_viridis_b("% mauvais", breaks = c(2, 5, 10, 15), na.value = NA) +
  labs(title = "Résultats des contrôles officiels sanitaires",
       subtitle = "France - 2017-2023",
       caption = glue("https://r.iresmi.net/
                     données : Alim'confiance via opendatarchives.fr
                     mauvais = 'À améliorer' ou 
                       'À corriger de manière urgente'
                     proj. métropole {nom_proj_legale}
                     fond carto. : d'après IGN adminexpress")) +
  theme_void() +
  theme(plot.caption = element_text(size = 7))

A map of bad controls by département of France — Figure 5: All controls (by dép.)

Not good in Guadeloupe, Guyane, Réunion and the southern lower Seine valley, west of Paris.

Can we see more in details? Using a 30 km kernel smoothing:

Code

lissage_alim <- function(annee = NULL) {
  if (is.null(annee)) {
    alim <- alim |> 
      filter(insee_dep < "97") |> 
      mutate(poids = 1)
    
    annee_titre <- glue("{year(min(alim$date_inspection))}-{year(max(alim$date_inspection))}")
  } else {
    alim <- alim |> 
      filter(insee_dep < "97",
             year(date_inspection) == annee) |> 
      mutate(poids = 1)
    
    annee_titre <- annee
  }
  
  smooth_total <- alim |> 
    lissage(poids, rayon, pixel, zone = fx) 
  
  smooth_mauvais <- alim |> 
    filter(synthese_eval_sanit %in% c("A améliorer", "A corriger de manière urgente")) |> 
    lissage(poids, bandwidth = rayon, resolution = pixel, zone = fx) 
  
  pcent_mauvais <- smooth_mauvais / smooth_total * 100

  raster::writeRaster(pcent_mauvais,
                    glue("results/alimconfiance_pcent_mauvais_{annee_titre}_fx.tif"), 
                    overwrite = TRUE)
  
  p <- ggplot() +
    geom_sf(data = countries, color = "grey", fill = "white") +
    layer_spatial(as(pcent_mauvais, "SpatRaster")) +
    geom_sf(data = dep, fill = NA, color = "white", linewidth = .05) +
    geom_sf(data = reg_fx, fill = NA, color = "white", linewidth = .1) +
    scale_fill_viridis_b("% mauvais", breaks = c(2, 5, 10, 15), na.value = NA) +
    coord_sf(xlim = st_bbox(pcent_mauvais)[c(1, 3)], 
             ylim = st_bbox(pcent_mauvais)[c(2, 4)],
             crs = proj_liss) +
    theme_void() +
    theme(plot.caption = element_text(size = 7))
  
  if (!is.null(annee)) {
    p +
      labs(title = glue("{annee_titre} - {format(nrow(alim), big.mark = ' ')} inspections"))
  } else {
    p +
      annotation_scale(height = unit(0.1, "cm"),
                       bar_cols = c("grey", "white"),
                       text_col = "grey",
                       line_col = "grey") +
      labs(title = "Résultats des contrôles officiels sanitaires",
           subtitle = glue("France métropolitaine - {annee_titre}"),
           caption = glue("https://r.iresmi.net/
                       données : Alim'confiance via opendatarchives.fr
                       mauvais = 'À améliorer' ou 
                         'À corriger de manière urgente'
                       lissage à {rayon / 1000} km
                       proj. {nom_proj_liss}
                       fond carto. : d'après IGN adminexpress")) 
  }
}

lissage_alim()

A map of bad controls in France (with kernel smoothing) — Figure 6: All controls (smoothed)

It confirms some hot-spots west of Paris, in Alsace, in Indre, Cher, Alpes-Maritimes and between Gironde and Landes. You are safer in Paris and Bretagne…

Has it changed?

Code

2018:(year(max(alim$date_inspection)) - 1) |> 
  map(lissage_alim) |>
  reduce(`+`) +
  plot_layout(ncol = 3,
              guides = "collect") +
  plot_annotation(
    title = "Résultats des contrôles officiels sanitaires - France métropolitaine",
    caption = glue("https://r.iresmi.net/
                       données : Alim'confiance via opendatarchives.fr
                       mauvais = 'À améliorer' ou 
                         'À corriger de manière urgente'
                       lissage à {rayon / 1000} km
                       proj. {nom_proj_liss}
                       fond carto. : d'après IGN adminexpress")) &
  theme(plot.caption = element_text(size = 7), 
        plot.title =  element_text(size = 10))

A small multiple map of bad controls in France (with kernel smoothing) by year — Figure 7: Evolution 2018-2022

No real trend…

--- title: "Where and when (not) to eat in France?" description: "To eat or not to eat..." author: "Michaël" date: "2023-04-16" date-modified: last-modified categories: - R - datavisualization - spatial draft: false freeze: true code-fold: true code-tools: true editor_options: chunk_output_type: console --- Results of sanitary controls in France can be found on [data.gouv.fr](https://www.data.gouv.fr/fr/datasets/resultats-des-controles-officiels-sanitaires-dispositif-dinformation-alimconfiance/) however, only the running year is available... Thanks to [\@cquest\@amicale.net](https://amicale.net/@cquest) we can access the [archives](https://www.opendatarchives.fr/) since 2017. ## Config and data cleaning ```{r} #| label: config library(tidyverse) library(janitor) library(fs) library(sf) library(glue) library(ggspatial) library(rnaturalearth) library(patchwork) # smoothing source(glue("fonctions_lissage.R"), encoding = "UTF-8") # move DROM source(glue("translation.R"), encoding = "UTF-8") # params ------------------------------------------------------------------ rayon <- 30000 # smoothing radius (m) pixel <- 2000 # pixel resolution output for smoothing (m) proj_liss <- "EPSG:3035" # an equal area projection ``` ```{r} #| label: data # 2017-2018 : yearly archive ; extract the last doy export # # http://data.cquest.org/alim_confiance/exports_alim_confiance_2017.7z # -> export_alimconfiance_2017-12-31.csv # http://data.cquest.org/alim_confiance/exports_alim_confiance_2018.7z # -> export_alimconfiance_2018-12-31.csv alim_dcq <- dir_ls("data/data.cquest.org", regexp = "\\.csv") |> map_dfr(read_csv2) |> clean_names() # 2019-2023 # archives at the end of each year + last export # # http://files.opendatarchives.fr/dgal.opendatasoft.com/archives/export_alimconfiance/20191231T053939Z%20export_alimconfiance.csv.gz # http://files.opendatarchives.fr/dgal.opendatasoft.com/archives/export_alimconfiance/20201231T083455Z%20export_alimconfiance.csv.gz # http://files.opendatarchives.fr/dgal.opendatasoft.com/archives/export_alimconfiance/20211231T083316Z%20export_alimconfiance.csv.gz # http://files.opendatarchives.fr/dgal.opendatasoft.com/archives/export_alimconfiance/20221231T150844Z%20export_alimconfiance.csv.gz # http://files.opendatarchives.fr/dgal.opendatasoft.com/export_alimconfiance.csv.gz alim_oda <- dir_ls("data/files.opendatarchives.fr", regexp = "\\.csv") |> map_dfr(read_csv2) |> clean_names() |> distinct() |> filter(date_inspection < "2023-03-01") # https://datanova.laposte.fr/explore/dataset/laposte_hexasmal/download?format=shp cp <- read_sf("~/data/laposte/laposte_hexasmal.shp") # Built from IGN Adminexpress. Get the data : # https://r.iresmi.net/posts/2021/simplifying_polygons_layers/results/adminexpress_simpl_2022.zip dep <- read_sf("~/data/adminexpress/adminexpress_cog_simpl_000_2022.gpkg", layer = "departement") |> translater_drom(ajout_zoom_pc = TRUE) reg_fx <- read_sf("~/data/adminexpress/adminexpress_cog_simpl_000_2022.gpkg", layer = "region") |> filter(insee_reg > "06") fx <- read_sf("~/data/adminexpress/adminexpress_cog_simpl_000_2022.gpkg", layer = "region") |> filter(insee_reg > "06") |> summarise() |> st_transform(proj_liss) # projection names nom_proj_liss <- str_extract(st_crs(fx)$wkt, '(?<=PROJCRS\\[").*(?=",)') nom_proj_legale <- str_extract(st_crs(dep)$wkt, '(?<=PROJCRS\\[").*(?=",)') # basemap countries <- ne_countries(50, returnclass = "sf") |> filter(admin != "France") ``` ```{r} #| label: preprocessing alim_raw <- bind_rows(alim_dcq, alim_oda) |> mutate(insee_dep = case_when(str_sub(code_postal, 1, 2) >= "97" ~ str_sub(code_postal, 1, 3), str_sub(code_postal, 1, 3) %in% c("200", "201") ~ "2A", str_sub(code_postal, 1, 3) %in% c("202", "206") ~ "2B", TRUE ~ str_sub(code_postal, 1, 2))) # trying to add coordinates to unknown locations using postal codes # only 5800 on 7500 georef <- cp |> group_by(code_postal) |> summarise(geometry = first(geometry)) |> inner_join(alim_raw |> filter(is.na(geores), !is.na(code_postal)), by = "code_postal") # build sf object alim <- alim_raw |> filter(!is.na(geores)) |> separate(geores, c("y", "x"), sep = ",") |> st_as_sf(coords = c("x", "y"), crs = "EPSG:4326") |> bind_rows(georef) alim |> write_sf(glue("results/alim.gpkg")) ``` ## Exploring data First a global view of the dataset: ```{r} #| label: fig-week #| fig-cap: Overview by week #| fig-alt: A scatter plot with smoothed lines showing the number of controls by week and year alim |> st_drop_geometry() |> count(annee = year(date_inspection), semaine = isoweek(date_inspection)) |> ggplot(aes(semaine, n, group = annee, color = as_factor(annee))) + geom_point() + geom_smooth(span = 0.4) + scale_y_continuous(labels = scales::label_number(big.mark = " ")) + labs(title = "Contrôles officiels sanitaires", subtitle = "France - 2017-2023", color = "année") + guides(color = guide_legend(reverse=TRUE)) ``` About 800 controls per week, except during the lock-down in 2020, and a slightly lower control pressure in 2021 and 2022. ```{r} #| label: fig-year #| fig-cap: Yearly #| fig-alt: A bar plot of bad controls by year alim |> st_drop_geometry() |> group_by(annee = year(date_inspection)) |> summarise(pcent_neg = sum(synthese_eval_sanit %in% c("A améliorer", "A corriger de manière urgente")) / n()) |> ggplot(aes(annee, pcent_neg)) + geom_col() + scale_y_continuous(breaks = scales::breaks_pretty(), labels = scales::label_percent(decimal.mark = ",", suffix = " %")) + labs(title = "Résultats des contrôles officiels sanitaires", subtitle = "France - 2017-2023", x = "année", y = "% mauvais", caption = glue("https://r.iresmi.net/ données : Alim'confiance via opendatarchives.fr mauvais = 'À améliorer' ou 'À corriger de manière urgente'")) + theme(panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank(), axis.ticks.x = element_blank(), plot.caption = element_text(size = 7)) ``` Poor results (*To be improved* or *To be corrected urgently*) are stable at around 6 %, except a recent spike in 2023? ```{r} #| label: fig-month #| fig-cap: Monthly #| fig-alt: A bar plot of bad controls by month alim |> st_drop_geometry() |> group_by(mois = ymd(paste0("2020", str_pad(month(date_inspection), 2, "left", "0"), "01"))) |> summarise(pcent_neg = sum(synthese_eval_sanit %in% c("A améliorer", "A corriger de manière urgente")) / n()) |> ggplot(aes(mois, pcent_neg)) + geom_col() + scale_x_date(date_labels = "%b", date_breaks = "2 months", expand = c(0.01, 0.01)) + scale_y_continuous(breaks = scales::breaks_pretty(), labels = scales::percent_format(decimal.mark = ",", suffix = " %")) + labs(title = "Résultats des contrôles officiels sanitaires", subtitle = "France - 2017-2023", y = "% mauvais", caption = glue("https://r.iresmi.net/ données : Alim'confiance via opendatarchives.fr mauvais = 'À améliorer' ou 'À corriger de manière urgente'")) + theme(panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank(), axis.ticks.x = element_blank(), plot.caption = element_text(size = 7)) ``` It seems that poor results increase during the year, from June to November. This surprising periodic phenomenon is also visible by day: ```{r} #| label: fig-day #| fig-cap: Daily #| fig-alt: A bar plot of bad controls by day with a smooth line alim |> st_drop_geometry() |> group_by(date_inspection = as.Date(date_inspection)) |> summarise(pcent_neg = sum(synthese_eval_sanit %in% c("A améliorer", "A corriger de manière urgente")) / n()) |> ggplot(aes(date_inspection, pcent_neg)) + geom_point() + geom_smooth(method = "gam", formula = y ~ s(x, bs = "cs", k = 30)) + scale_x_date(labels = ~ format(.x, "%b\n%Y"), date_breaks = "6 months") + scale_y_continuous(breaks = scales::breaks_pretty(), labels = scales::percent_format(decimal.mark = ",", suffix = " %")) + labs(title = "Résultats des contrôles officiels sanitaires", subtitle = "France - 2017-2023", x = "jour", y = "% mauvais", caption = glue("https://r.iresmi.net/ données : Alim'confiance via opendatarchives.fr mauvais = 'À améliorer' ou 'À corriger de manière urgente'")) + theme(plot.caption = element_text(size = 7)) ``` So for the « when », it is: not in summer or autumn. ## Maps What about the « where »? It seems you also could be careful in some départements... ```{r} #| label: fig-full-dep #| fig-cap: All controls (by dép.) #| fig-alt: A map of bad controls by département of France bilan_dep <- alim |> st_drop_geometry() |> group_by(insee_dep) |> summarise(pcent_neg = sum(synthese_eval_sanit %in% c("A améliorer", "A corriger de manière urgente")) / n() * 100) |> arrange(desc(pcent_neg)) dep |> left_join(bilan_dep, by = "insee_dep") |> ggplot() + geom_sf(aes(fill = pcent_neg), color = "white", linewidth = 0.05) + geom_sf(data = reg_fx, fill = NA, color = "white", linewidth = .1) + scale_fill_viridis_b("% mauvais", breaks = c(2, 5, 10, 15), na.value = NA) + labs(title = "Résultats des contrôles officiels sanitaires", subtitle = "France - 2017-2023", caption = glue("https://r.iresmi.net/ données : Alim'confiance via opendatarchives.fr mauvais = 'À améliorer' ou 'À corriger de manière urgente' proj. métropole {nom_proj_legale} fond carto. : d'après IGN adminexpress")) + theme_void() + theme(plot.caption = element_text(size = 7)) ``` Not good in Guadeloupe, Guyane, Réunion and the southern lower Seine valley, west of Paris. Can we see more in details? Using a 30 km kernel smoothing: ```{r} #| label: fig-full-smooth #| fig-cap: All controls (smoothed) #| fig-alt: A map of bad controls in France (with kernel smoothing) lissage_alim <- function(annee = NULL) { if (is.null(annee)) { alim <- alim |> filter(insee_dep < "97") |> mutate(poids = 1) annee_titre <- glue("{year(min(alim$date_inspection))}-{year(max(alim$date_inspection))}") } else { alim <- alim |> filter(insee_dep < "97", year(date_inspection) == annee) |> mutate(poids = 1) annee_titre <- annee } smooth_total <- alim |> lissage(poids, rayon, pixel, zone = fx) smooth_mauvais <- alim |> filter(synthese_eval_sanit %in% c("A améliorer", "A corriger de manière urgente")) |> lissage(poids, bandwidth = rayon, resolution = pixel, zone = fx) pcent_mauvais <- smooth_mauvais / smooth_total * 100 raster::writeRaster(pcent_mauvais, glue("results/alimconfiance_pcent_mauvais_{annee_titre}_fx.tif"), overwrite = TRUE) p <- ggplot() + geom_sf(data = countries, color = "grey", fill = "white") + layer_spatial(as(pcent_mauvais, "SpatRaster")) + geom_sf(data = dep, fill = NA, color = "white", linewidth = .05) + geom_sf(data = reg_fx, fill = NA, color = "white", linewidth = .1) + scale_fill_viridis_b("% mauvais", breaks = c(2, 5, 10, 15), na.value = NA) + coord_sf(xlim = st_bbox(pcent_mauvais)[c(1, 3)], ylim = st_bbox(pcent_mauvais)[c(2, 4)], crs = proj_liss) + theme_void() + theme(plot.caption = element_text(size = 7)) if (!is.null(annee)) { p + labs(title = glue("{annee_titre} - {format(nrow(alim), big.mark = ' ')} inspections")) } else { p + annotation_scale(height = unit(0.1, "cm"), bar_cols = c("grey", "white"), text_col = "grey", line_col = "grey") + labs(title = "Résultats des contrôles officiels sanitaires", subtitle = glue("France métropolitaine - {annee_titre}"), caption = glue("https://r.iresmi.net/ données : Alim'confiance via opendatarchives.fr mauvais = 'À améliorer' ou 'À corriger de manière urgente' lissage à {rayon / 1000} km proj. {nom_proj_liss} fond carto. : d'après IGN adminexpress")) } } lissage_alim() ``` It confirms some hot-spots west of Paris, in Alsace, in Indre, Cher, Alpes-Maritimes and between Gironde and Landes. You are safer in Paris and Bretagne... Has it changed? ```{r} #| label: fig-periode #| fig-cap: !expr 'glue("Evolution 2018-{year(max(alim$date_inspection)) - 1}")' #| fig-alt: A small multiple map of bad controls in France (with kernel smoothing) by year 2018:(year(max(alim$date_inspection)) - 1) |> map(lissage_alim) |> reduce(`+`) + plot_layout(ncol = 3, guides = "collect") + plot_annotation( title = "Résultats des contrôles officiels sanitaires - France métropolitaine", caption = glue("https://r.iresmi.net/ données : Alim'confiance via opendatarchives.fr mauvais = 'À améliorer' ou 'À corriger de manière urgente' lissage à {rayon / 1000} km proj. {nom_proj_liss} fond carto. : d'après IGN adminexpress")) & theme(plot.caption = element_text(size = 7), plot.title = element_text(size = 10)) ``` No real trend...