In SimonCoulombe/covidtwitterbot: What the Package Does (One Line, Title Case)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  warning = FALSE,
  error = FALSE,
  message = FALSE
)

covidtwitterbot

The goal of covidtwitterbot is to generate the figures and maps that are posted on my twitter bot, @covid_coulsim.

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("SimonCoulombe/covidtwitterbot")

Pour les utilisateurs linux, vous aurez probablement besoin d'installer des librairies tels que

geos-devel
sqlite3-devel
proj-devel
gdal-devel
libopenssl-devel

Example

Tables

Here are the tables you can fetch and the graphics you can generate.. They are generated from the most up-to-date data.

library(ggplot2)
library(patchwork)
library(covidtwitterbot)
library(knitr)

get_inspq_covid19_hist()

The get_inspq_covid19_hist() function fetches historical data for Quebec by age, health region and gender. This includes the numbers of cases ("cas_"), of deaths ("dec_") of hospitalizations ("hos_") and persons tested ("psi_").

dictionnaire (partiel)des données:

cas_cum_tot_n = nombre de cas confirmés (cumulatifs) selon date de déclaration
cas_cum_lab_n = nombre de cas confirmés en laboratoire (cumulatif) selon date de déclaration
cas_cum_epi_n = nombre de cas confirmés par lien épidémiologique (cumulatif) selon date de déclaration
cas_quo_tot_n = nombre de cas confirmés (quotidien) selon date de déclaration
cas_quo_lab_n = nombre de cas confirmés en laboratoire (quotidien) selon date de déclaration
cas_quo_epi_n = nombre de cas confirmés par lien épidémiologique (quotidien) selon date de déclaration

act_cum_tot_n = nombre de cas actifs (aujourd'hui)

ret_cum_tot_n = nombre de cas rétablis (cumulatifs
ret_quo_tot_n = nombre de cas rétablis (quotidien)

dec_cum_tot_n = total des décès (cumulatif)
dec_cum_chs_n = total des décès en CHSLD (cumulatif)
dec_cum_rpa_n = total des décès en RPA (cumulatif)
dec_cum_dom_n = total des décès à domicile et inconnu (cumulatif)
dec_cum_aut_n = total des décès en RI et autre (cumulatif)
dec_quo_tot_n = total des décès (quotidien)
dec_quo_chs_n = total des décès en CHSLD (quotidien)
dec_quo_rpa_n = total des décès en RPA (quotidien)
dec_quo_dom_n = total des décès à domicile et inconnu (quotidien)
dec_quo_aut_n = total des décès en RI et autre (quotidien)

ATTENTION: EN DATE DU 18 novembre 2020 LES HOSPITALISATIONS NE SONT PAS AUGMENTÉES ENTRE LE DERNIER JOUR ET L'AVANT DERNIER JOUR Date Nom hos_cum_reg_n hos_cum_si_n hos_cum_tot_n hos_cum_tot_t hos_quo_tot_t hos_quo_reg_n hos_quo_si_n hos_quo_tot_n hos_quo_tot_m 1 2020-11-17 Ensemble du Québec 8846 1847 10693 125. 0.37 30 2 32 NA 2 2020-11-18 Ensemble du Québec 8846 1847 10693 125. 0 0 0 0 NA

hos_quo_tot_n = nouvelles hospitalisations (régulières + soins intensifs)
hos_quo_reg_n = nouvelles hospitalisations hors soins intensifs
hos_quo_si_n = nouvelles hospitalisations aux soins intensifs

ATENTION : EN DATE DU 18 novembre 2020 les PSI quotidiens sont à 0 pour la dernière journée .. sur le site ils utilisent le cumulatif du 18 novembre 2079892 et le quotidien du 17 novembre 28121

A tibble: 2 x 9 Date Nom psi_cum_tes_n psi_cum_pos_n psi_cum_inf_n psi_quo_pos_n psi_quo_inf_n psi_quo_tes_n psi_quo_pos_t 1 2020-11-17 Ensemble du Québec 2074847 119933 1954914 1246 26875 28121 4.44 2 2020-11-18 Ensemble du Québec 2079892 120548 1959344 0 0 0 NA

psi_cum_test_pos = cas confirmés cumulatif
psi_cum_test_inf = personnes infirmées (test négatifs) cumulatif
psi_cum_test_n = cumul de personnes testées cumulatif
psi_quo_test_pos = cas confirmés quotidien ## ATTENTION cet colonne est vide pour la dernière journée
psi_quo_test_inf = personnes infirmées (test négatifs) quotidien ## ATTENTION cet colonne est vide pour la dernière journée
psi_quo_test_n = cumul de personnes testées quotidien ## ATTENTION cet colonne est vide pour la dernière journée

get_inspq_covid19_hist() %>% tail(20)  %>%  knitr::kable()

get_inspq_manual_data_hospits()

The get_inspq_manual_data_hospits() function returns the historical number of hospitalisation (hospits_ancien and hospits), intensive care (si) and number of tests (volumetrie) for the province

dictionnaire des données
hospits = hospitalisation hors intensif en cours
hospits_ancien = hospitalisation hors intensif en cours (ancienne mesure, remplacée au printemps 2020 par l'actuelle)
si = soins intensifs en cours
volumetrie = nombre de tests (cette colonne est vide pour la dernière journée)

get_inspq_manual_data_hospits() %>% tail(20)   %>%  knitr::kable()

get_inspq_manual_data_tableau_accueil()

get_inspq_manual_data_tableau_accueil() retourne les chiffres utilisés dans le haut de la page de données covid19 de l'inspq. ça balance pas toujours avec l'output des 2 fonctions précédentes.

get_inspq_manual_data_tableau_accueil()   %>%  knitr::kable()

get_rls_data()

The get_rls_data() function returns the historical number of cases at the RLS (région locale de service). There are no historical files at the RLS level on the INSPQ website. This function depends on a bunch of historical data that I collected, plus a daily archive repository maintained by Jean-Paul R Soucy.

This function takes a while because it has to download a few hundred of CSVs before aggregating them.

rls_data <-suppressMessages( get_rls_data() )

rls_data %>% tail(20)   %>%  knitr::kable()

We estimate number of cases at the school board (centre de service scolaire) level based on the more fine-grained RLS data. It is not perfect (sometime a RLS stradles 2 CSS so we have to split the cases proportionally to the population in the intersections), but it's a good start. The get_css_last_week() functions runs faster if you provide it with the rls_data object we created when we downloaded the RLS data. If this is not provided then the RLS data has to be downloaded again.

get_css_last_week()

css_last_week  <- get_css_last_week(rls_data)   
css_last_week %>% tail(20) %>% sf::st_drop_geometry() %>% knitr::kable()

Montreal data

mtl_data <-suppressMessages( get_mtl_data() )

mtl_data %>% tail(20)   %>%  knitr::kable()

Figures and maps

graph_deces_hospit_tests()

graph_quebec_cas_par_region()

graph_quebec_cas_par_age()

graph_quebec_cas_par_age_heatmap()

graph_quebec_cas_par_rls_heatmap() generates the heatmap showing the history of cases per capita. It runs faster if you provide it with an already-downloaded "rls_data" dataframe provided by get_rls_data()

graph_quebec_cas_par_rls_heatmap(rls_data)

carte_rls() crée une carte montrant le nombre moyen de cas par jour par million d'habitant durant les 7 derniers jours

carte_rls(rls_data)

carte_rls_zoom_montreal(rls_data)

We can also generate some charts from the estimated cases by CSS for the previous week:

carte_css(css_last_week)

graph_css_bars(css_last_week)

graph_quebec_cas_par_mtl_heatmap(mtl_data)

Shapefiles

Some shapefiles are included with this package. They are created using script located in the data-raw folder in the repo.

shp_water is a (simplified) shapefile showing all coastal water in a single multipolygon. It is used when I want to remove water from other shapefiles (such as RLS or CSS). It is derived from statistics canada's "Coastal waters (polygons)" for Census 2011 at https://www12.statcan.gc.ca/census-recensement/2011/geo/bound-limit/bound-limit-2011-eng.cfm . I tried the 2016 version, but some polygons are broken.

ggplot() + 
  geom_sf(data = shp_water, fill = "#56B4E950")+
  labs(title = "shp_water: Coastal water polyhon shapefile")

The shapefile shp_rls shows all the RLS in Quebec after removing water. It is derived from "Limites territoriales des réseaux locaux de santé (RLS) en 2020" (https://www.donneesquebec.ca/recherche/fr/dataset/limites-territoriales/resource/a73c9996-010d-41ac-a4ba-4def322d55bf),from which I removed the coastal waters.

ggplot() +
  geom_sf(data= shp_rls, fill ="#E69F00B0")+
  labs(title = "shp_rls: Réseaux locaux de services shapefile")

The shapefile shp_css shows all the CSS (centres de services scolaires) in Quebec after removing water. It is derived from "Centres de services scolaires francophones (SDA)" shapefile https://www.donneesquebec.ca/recherche/dataset/territoires-des-commissions-scolaires-du-quebec/resource/4cd70cc8-2663-4f10-bfce-bb46f62a77da,from which I removed the coastal waters.

ggplot() + 
  geom_sf(data= shp_css, fill = "#E69F00B0") +
  labs(title = "shp_css: Centres de services francophones shapefile ")

coordinate reference systems (CRS)

I added references to 2 useful CRS : quebec_lambertand statistics_canada_lambert

Quebec Lambert is defined here on spatialreference.org the string is '+proj=lcc +lat_1=60 +lat_2=46 +lat_0=44 +lon_0=-68.5 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs' and was found here.

ggplot() + 
  geom_sf(data= shp_rls, fill = "#E69F00B0") + 
  coord_sf(crs= quebec_lambert) + 
  labs(title = "RLS with Quebec Lambert CRS")

Statistics Canada Lambert is useful when mapping Canada as a whole. I found a reference to it here and the string '+proj=lcc +lat_1=49 +lat_2=77 +lat_0=63.390675 +lon_0=-91.86666666666666 +x_0=6200000 +y_0=3000000 +ellps=GRS80 +datum=NAD83 +units=m +no_defs' was here

ggplot() + 
  geom_sf(data = shp_water, fill = "#56B4E950")+
  coord_sf(crs=statistics_canada_lambert)+
  labs(title = "shp_water: Coastal water polygon shapefile projected with statistics canada lambert")

Theme

theme_simon is my default theme for figures. I start with the default cowplottheme and change the color of the axis lines and axis labels to grey It comes with grids, with can be removed with the nogrid() function.

base_plot <- get_inspq_covid19_hist() %>% 
  dplyr::filter(Nom == "Ensemble du Québec") %>% 
  ggplot()+
  geom_line( aes(x=date, y= hos_quo_tot_n))+
  #expand_limits(y = 0)
  scale_y_continuous(expand = c(0, 0))

p1 <- base_plot +
  labs(title = "no theme") 

p2 <-base_plot +
  cowplot::theme_cowplot()  + 
  labs(title = "theme_cowplot(") 


p3 <- base_plot +
  theme_simon() + 
  labs(title = "theme_simon()")

p4 <- base_plot +
  theme_simon() + nogrid() + 
  labs(title = "theme_simon() + nogrid()")

(p1 + p2) / (p3 + p4 )

Palettes

palette_OkabeIto is a color palette that was built by higher beings to be visible to color-blind people. I copied the code from Claus Wilke's colorblindr package because I don't know how to import packages from github.

palette_OkabeIto

SimonCoulombe/covidtwitterbot documentation built on May 1, 2021, 6:50 a.m.