README.md
In SimonCoulombe/covidtwitterbot: What the Package Does (One Line, Title Case)
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()
| Date | Regroupement | Croisement | Nom | cas_cum_lab_n | cas_cum_epi_n | cas_cum_tot_n | cas_cum_tot_t | cas_quo_tot_t | cas_quo_lab_n | cas_quo_epi_n | cas_quo_tot_n | act_cum_tot_n | act_cum_tot_t | cas_quo_tot_m | cas_quo_tot_tm | ret_cum_tot_n | ret_quo_tot_n | dec_cum_tot_n | dec_cum_tot_t | dec_quo_tot_t | dec_cum_chs_n | dec_cum_rpa_n | dec_cum_dom_n | dec_cum_aut_n | dec_quo_tot_n | dec_quo_chs_n | dec_quo_rpa_n | dec_quo_dom_n | dec_quo_aut_n | dec_quo_tot_m | dec_quo_tot_tm | 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 | psi_cum_tes_n | psi_cum_pos_n | psi_cum_inf_n | psi_cum_adm_n | psi_quo_pos_n | psi_quo_inf_n | psi_quo_tes_n | psi_quo_pos_t | date | type | groupe | cas_totaux_cumul | cas_totaux_quotidien | deces_totaux_quotidien | pop |
|:-----------|:-----------------|:-----------|:---------------------|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|------------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|------------------:|-----------------:|----------------:|-----------------:|-----------------:|-----------------:|-----------------:|----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|:-----------|:-----------------|:---------------------|-------------------:|-----------------------:|-------------------------:|--------:|
| 2021-03-28 | Groupe de région | REG02 | Ceinture de Montréal | 50932 | 1949 | 52881 | 3572.59 | 4.44 | 66 | 0 | 66 | 1023 | 68.81 | NA | NA | 50017 | 98 | 1529 | 103.30 | 0.00 | 753 | 349 | 332 | 95 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 579227 | 50521 | 528706 | 1287262 | NA | NA | NA | NA | 2021-03-28 | region_montreal | Ceinture de Montréal | 52881 | 66 | 0 | NA |
| 2021-03-28 | Groupe de région | REG03 | Autres régions | 110532 | 3279 | 113811 | 2491.18 | 6.20 | 278 | 6 | 284 | 2990 | 65.28 | NA | NA | 106351 | 240 | 3545 | 77.60 | 0.02 | 1452 | 1043 | 856 | 194 | 1 | 0 | 1 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1599203 | 109751 | 1489452 | 3329728 | NA | NA | NA | NA | 2021-03-28 | region_montreal | Autres régions | 113811 | 284 | 1 | NA |
| 2021-03-28 | Groupe de région | REG00 | Inconnu | 4 | 0 | 4 | NA | NA | 0 | 0 | 0 | 0 | NA | NA | NA | 4 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 10737 | 358 | 10379 | 14363 | NA | NA | NA | NA | 2021-03-28 | region_montreal | Inconnue | 4 | 0 | 0 | NA |
| 2021-03-28 | Groupe de région | REG98 | Hors Québec | 276 | 5 | 281 | NA | NA | 0 | 0 | 0 | 10 | NA | NA | NA | 267 | 1 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 13953 | 436 | 13517 | 19794 | NA | NA | NA | NA | 2021-03-28 | region_montreal | Hors Québec | 281 | 0 | 0 | NA |
| 2021-03-28 | Groupe d’âge | 0_9 | 0-9 ans | 22250 | 1977 | 24227 | 2713.39 | 7.40 | 63 | 3 | 66 | 874 | 98.01 | NA | NA | 23132 | 85 | 0 | 0.00 | 0.00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 359348 | 19142 | 340206 | 589150 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 0 à 9 ans | 24227 | 66 | 0 | 893977 |
| 2021-03-28 | Groupe d’âge | 10_19 | 10-19 ans | 34621 | 1478 | 36099 | 4078.30 | 11.44 | 100 | 2 | 102 | 1179 | 132.18 | NA | NA | 34689 | 119 | 1 | 0.11 | 0.00 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 349659 | 30396 | 319263 | 534252 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 10 à 19 ans | 36099 | 102 | 0 | 878347 |
| 2021-03-28 | Groupe d’âge | 20_29 | 20-29 ans | 45769 | 1475 | 47244 | 4504.25 | 8.07 | 84 | 0 | 84 | 1077 | 103.41 | NA | NA | 45825 | 113 | 7 | 0.67 | 0.00 | 0 | 0 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 407874 | 41066 | 366808 | 848617 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 20 à 29 ans | 47244 | 84 | 0 | 1056242 |
| 2021-03-28 | Groupe d’âge | 30_39 | 30-39 ans | 42666 | 1251 | 43917 | 3930.75 | 6.44 | 72 | 0 | 72 | 1085 | 96.99 | NA | NA | 42514 | 108 | 14 | 1.25 | 0.00 | 3 | 0 | 8 | 3 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 443729 | 38063 | 405666 | 959075 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 30 à 39 ans | 43917 | 72 | 0 | 1115906 |
| 2021-03-28 | Groupe d’âge | 40_49 | 40-49 ans | 44924 | 1214 | 46138 | 4163.78 | 8.07 | 90 | 0 | 90 | 1172 | 105.08 | NA | NA | 44631 | 136 | 39 | 3.52 | 0.00 | 11 | 2 | 25 | 1 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 402124 | 40682 | 361442 | 917876 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 40 à 49 ans | 46138 | 90 | 0 | 1100826 |
| 2021-03-28 | Groupe d’âge | 50_59 | 50-59 ans | 39516 | 1025 | 40541 | 3469.14 | 5.28 | 60 | 1 | 61 | 1021 | 88.38 | NA | NA | 39068 | 102 | 182 | 15.57 | 0.00 | 71 | 2 | 98 | 11 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 352267 | 36077 | 316190 | 836906 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 50 à 59 ans | 40541 | 61 | 0 | 1182044 |
| 2021-03-28 | Groupe d’âge | 60_69 | 60-69 ans | 24597 | 546 | 25143 | 2170.75 | 2.99 | 35 | 0 | 35 | 696 | 59.55 | NA | NA | 23691 | 55 | 628 | 54.22 | 0.09 | 278 | 39 | 276 | 35 | 1 | 0 | 0 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 293320 | 22912 | 270408 | 614959 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 60 à 69 ans | 25143 | 35 | 1 | 1147664 |
| 2021-03-28 | Groupe d’âge | 70_79 | 70-79 ans | 16479 | 241 | 16720 | 2175.91 | 2.04 | 16 | 0 | 16 | 382 | 48.78 | NA | NA | 14202 | 26 | 2010 | 261.58 | 0.00 | 960 | 273 | 671 | 106 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 196183 | 14897 | 181286 | 450721 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 70 à 79 ans | 16720 | 16 | 0 | 753700 |
| 2021-03-28 | Groupe d’âge | 80_89 | 80-89 ans | 17900 | 280 | 18180 | 5458.30 | 2.66 | 9 | 0 | 9 | 368 | 108.73 | NA | NA | 13442 | 11 | 4188 | 1257.39 | 0.00 | 2240 | 1008 | 729 | 211 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 123337 | 16018 | 107319 | 410989 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 80 à 89 ans | 18180 | 9 | 0 | 327675 |
| 2021-03-28 | Groupe d’âge | 90_ | 90 ans et plus | 10730 | 219 | 10949 | 12902.20 | 2.30 | 2 | 0 | 2 | 132 | 151.63 | NA | NA | 7360 | 4 | 3486 | 4107.87 | 1.15 | 2078 | 923 | 303 | 182 | 1 | 0 | 1 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 51319 | 10722 | 40597 | 227249 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 90 ans et + | 10949 | 2 | 1 | 82670 |
| 2021-03-28 | Groupe d’âge | INC | Inconnu | 18 | 24 | 42 | NA | NA | 0 | 0 | 0 | 1 | NA | NA | NA | 39 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 317012 | 29484 | 287528 | 684468 | NA | NA | NA | NA | 2021-03-28 | groupe_age | Âge inconnu | 42 | 0 | 0 | NA |
| 2021-03-28 | Groupe d’âge | TOT | Total | 299470 | 9730 | 309200 | 3609.84 | 6.25 | 531 | 6 | 537 | 7987 | 92.96 | NA | NA | 288593 | 759 | 10555 | 123.23 | 0.02 | 5641 | 2247 | 2117 | 550 | 2 | 0 | 1 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 3296172 | 299459 | 2996713 | 7074262 | NA | NA | NA | NA | 2021-03-28 | groupe_age | Total | 309200 | 537 | 2 | 8390499 |
| 2021-03-28 | Sexe | MASC | Masculin | 139423 | 4847 | 144270 | 3369.77 | 6.15 | 262 | 2 | 264 | 4034 | 93.91 | NA | NA | 134298 | 385 | 4973 | 116.16 | 0.05 | 2436 | 1020 | 1261 | 256 | 2 | 0 | 1 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1506973 | 138943 | 1368030 | 2838213 | NA | NA | NA | NA | 2021-03-28 | sexe | Masculin | 144270 | 264 | 2 | NA |
| 2021-03-28 | Sexe | FEM | Féminin | 159922 | 4882 | 164804 | 3846.82 | 6.33 | 268 | 4 | 272 | 3952 | 91.98 | NA | NA | 154172 | 374 | 5581 | 130.27 | 0.00 | 3205 | 1226 | 856 | 294 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1780768 | 159981 | 1620787 | 4224217 | NA | NA | NA | NA | 2021-03-28 | sexe | Féminin | 164804 | 272 | 0 | NA |
| 2021-03-28 | Sexe | INC | Inconnu | 125 | 1 | 126 | NA | NA | 1 | 0 | 1 | 1 | NA | NA | NA | 123 | 0 | 1 | NA | NA | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 8431 | 535 | 7896 | 11832 | NA | NA | NA | NA | 2021-03-28 | sexe | Sexe inconnu | 126 | 1 | 0 | NA |
| 2021-03-28 | Sexe | TOT | Total | 299470 | 9730 | 309200 | 3609.84 | 6.25 | 531 | 6 | 537 | 7987 | 92.96 | NA | NA | 288593 | 759 | 10555 | 123.23 | 0.02 | 5641 | 2247 | 2117 | 550 | 2 | 0 | 1 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 3296172 | 299459 | 2996713 | 7074262 | NA | NA | NA | NA | 2021-03-28 | sexe | Total | 309200 | 537 | 2 | NA |
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()
| date | hospits | hospits_ancien | si | volumetrie |
|:-----------|--------:|----------------:|----:|-----------:|
| 2021-03-09 | 469 | NA | 112 | 29655 |
| 2021-03-10 | 452 | NA | 111 | 29175 |
| 2021-03-11 | 444 | NA | 106 | 29507 |
| 2021-03-12 | 445 | NA | 106 | 28466 |
| 2021-03-13 | 447 | NA | 100 | 23159 |
| 2021-03-14 | 457 | NA | 96 | 18232 |
| 2021-03-15 | 442 | NA | 91 | 23432 |
| 2021-03-16 | 425 | NA | 107 | 31945 |
| 2021-03-17 | 418 | NA | 101 | 33670 |
| 2021-03-18 | 405 | NA | 99 | 32943 |
| 2021-03-19 | 406 | NA | 99 | 31995 |
| 2021-03-20 | 399 | NA | 102 | 24609 |
| 2021-03-21 | 399 | NA | 114 | 21447 |
| 2021-03-22 | 406 | NA | 113 | 25675 |
| 2021-03-23 | 390 | NA | 118 | 35129 |
| 2021-03-24 | 379 | NA | 117 | 39663 |
| 2021-03-25 | 366 | NA | 115 | 35960 |
| 2021-03-26 | 373 | NA | 108 | 33671 |
| 2021-03-27 | 366 | NA | 114 | 26993 |
| 2021-03-28 | 357 | NA | 120 | NA |
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()
| cas | deces | hospit | soins | gueris | analyses | type |
|-------:|------:|-------:|------:|-------:|---------:|:----------|
| 309202 | 10651 | 477 | 120 | 290564 | 26993 | cumulatif |
| 891 | 4 | -3 | 6 | 737 | NA | quotidien |
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()
| RSS | RLS | date_report | cumulative_cases | source | fix_cummin | cases | shortname_rls | Population | cases_per_100k | cases_last_7_days | previous_cases_last_7_days | cases_last_7_days_per_100k | RLS_code | cases_per_1M | last_cases_per_1M | previous_cases_per_1M | color_per_pop | pop | RLS_petit_nom |
|:-----------------------------------|:------------------------------------------------|:-------------|------------------:|:-------------|------------:|------:|:----------------------------------|-----------:|-----------------:|---------------------:|-------------------------------:|--------------------------------:|:----------|---------------:|---------------------:|-------------------------:|:--------------------------------|-------:|:--------------------------------------|
| 14 - Lanaudière | 1411 - RLS de Lanaudière-Nord | 2021-03-29 | 8415 | cronjob_new | 8415 | 12 | Lanaudière-Nord | 223099 | 5.378778 | 70 | 111 | 31.37621 | 1411 | 44.82315 | 44.82315 | 71.076710 | entre 20 et 60 cas par million | 223099 | 1411 - Lanaudière-Nord |
| 14 - Lanaudière | 1412 - RLS de Lanaudière-Sud | 2021-03-29 | 12778 | cronjob_new | 12778 | 28 | Lanaudière-Sud | 296993 | 9.427832 | 169 | 202 | 56.90370 | 1412 | 81.29100 | 81.29100 | 97.164387 | entre 60 et 100 cas par million | 296993 | 1412 - Lanaudière-Sud |
| 15 - Laurentides | 1511 - RLS d’Antoine-Labelle | 2021-03-29 | 836 | cronjob_new | 836 | 4 | d’Antoine-Labelle | 35580 | 11.242271 | 14 | 16 | 39.34795 | 1511 | 56.21135 | 56.21135 | 64.241548 | entre 20 et 60 cas par million | 35580 | 1511 - d’Antoine-Labelle |
| 15 - Laurentides | 1512 - RLS des Laurentides | 2021-03-29 | 922 | cronjob_new | 922 | 4 | des Laurentides | 48016 | 8.330557 | 29 | 11 | 60.39653 | 1512 | 86.28076 | 86.28076 | 32.727186 | entre 60 et 100 cas par million | 48016 | 1512 - Laurentides |
| 15 - Laurentides | 1513 - RLS des Pays-d’en-Haut | 2021-03-29 | 916 | cronjob_new | 916 | 2 | des Pays-d’en-Haut | 44362 | 4.508363 | 12 | 8 | 27.05018 | 1513 | 38.64311 | 38.64311 | 25.762074 | entre 20 et 60 cas par million | 44362 | 1513 - Pays-d’en-Haut |
| 15 - Laurentides | 1514 - RLS d’Argenteuil | 2021-03-29 | 676 | cronjob_new | 676 | 1 | d’Argenteuil | 33422 | 2.992041 | 19 | 25 | 56.84878 | 1514 | 81.21255 | 81.21255 | 106.858613 | entre 60 et 100 cas par million | 33422 | 1514 - d’Argenteuil |
| 15 - Laurentides | 1515 - RLS de Deux-Montagnes - Mirabel-Sud | 2021-03-29 | 3661 | cronjob_new | 3661 | 8 | Deux-Montagnes - Mirabel-Sud | 124641 | 6.418434 | 65 | 75 | 52.14977 | 1515 | 74.49968 | 74.49968 | 85.961166 | entre 60 et 100 cas par million | 124641 | 1515 - Deux-Montagnes - Mirabel-Sud |
| 15 - Laurentides | 1516 - RLS de la Rivière-du-Nord - Mirabel-Nord | 2021-03-29 | 5020 | cronjob_new | 5020 | 14 | la Rivière-du-Nord - Mirabel-Nord | 177517 | 7.886569 | 57 | 64 | 32.10960 | 1516 | 45.87086 | 45.87086 | 51.504121 | entre 20 et 60 cas par million | 177517 | 1516 - Rivière-du-Nord - Mirabel-Nord |
| 15 - Laurentides | 1517 - RLS de Thérèse-De Blainville | 2021-03-29 | 5714 | cronjob_new | 5714 | 27 | Thérèse-De Blainville | 163430 | 16.520835 | 146 | 95 | 89.33488 | 1517 | 127.62126 | 127.62126 | 83.041232 | plus de 100 cas par million | 163430 | 1517 - Thérèse-De Blainville |
| 16 - Montérégie | 1611 - RLS de Champlain | 2021-03-29 | 7821 | cronjob_new | 7821 | 15 | Champlain | 225442 | 6.653596 | 131 | 102 | 58.10807 | 1611 | 83.01153 | 83.01153 | 64.634933 | entre 60 et 100 cas par million | 225442 | 1611 - Champlain |
| 16 - Montérégie | 1612 - RLS du Haut-Richelieu - Rouville | 2021-03-29 | 5000 | cronjob_new | 5000 | 14 | du Haut-Richelieu - Rouville | 195297 | 7.168569 | 99 | 66 | 50.69202 | 1612 | 72.41718 | 72.41718 | 48.278117 | entre 60 et 100 cas par million | 195297 | 1612 - Haut-Richelieu - Rouville |
| 16 - Montérégie | 1621 - RLS Pierre-Boucher | 2021-03-29 | 9028 | cronjob_new | 9028 | 21 | Pierre-Boucher | 262440 | 8.001829 | 137 | 94 | 52.20241 | 1621 | 74.57487 | 74.57487 | 51.168158 | entre 60 et 100 cas par million | 262440 | 1621 - Pierre-Boucher |
| 16 - Montérégie | 1622 - RLS de Richelieu-Yamaska | 2021-03-29 | 7340 | cronjob_new | 7340 | 18 | Richelieu-Yamaska | 220957 | 8.146381 | 140 | 76 | 63.36074 | 1622 | 90.51535 | 90.51535 | 49.136904 | entre 60 et 100 cas par million | 220957 | 1622 - Richelieu-Yamaska |
| 16 - Montérégie | 1623 - RLS Pierre-De Saurel | 2021-03-29 | 1203 | cronjob_new | 1203 | 0 | Pierre-De Saurel | 51371 | 0.000000 | 6 | 3 | 11.67974 | 1623 | 16.68535 | 16.68535 | 8.342673 | moins de 20 cas par million | 51371 | 1623 - Pierre-De Saurel |
| 16 - Montérégie | 1631 - RLS de Vaudreuil-Soulanges | 2021-03-29 | 4032 | cronjob_new | 4032 | 12 | Vaudreuil-Soulanges | 160002 | 7.499906 | 71 | 45 | 44.37445 | 1631 | 63.39206 | 63.39206 | 40.178069 | entre 60 et 100 cas par million | 160002 | 1631 - Vaudreuil-Soulanges |
| 16 - Montérégie | 1632 - RLS du Suroît | 2021-03-29 | 1938 | cronjob_new | 1938 | 3 | du Suroît | 60523 | 4.956793 | 22 | 16 | 36.34982 | 1632 | 51.92831 | 51.92831 | 37.766044 | entre 20 et 60 cas par million | 60523 | 1632 - Suroît |
| 16 - Montérégie | 1633 - RLS du Haut-Saint-Laurent | 2021-03-29 | 603 | cronjob_new | 603 | 0 | du Haut-Saint-Laurent | 24346 | 0.000000 | 3 | 6 | 12.32235 | 1633 | 17.60336 | 17.60336 | 35.206722 | moins de 20 cas par million | 24346 | 1633 - Haut-Saint-Laurent |
| 16 - Montérégie | 1634 - RLS de Jardins-Roussillon | 2021-03-29 | 7043 | cronjob_new | 7043 | 20 | Jardins-Roussillon | 232373 | 8.606852 | 119 | 75 | 51.21077 | 1634 | 73.15824 | 73.15824 | 46.108135 | entre 60 et 100 cas par million | 232373 | 1634 - Jardins-Roussillon |
| 17 - Nunavik | 1701 - Nunavik | 2021-03-29 | 46 | cronjob_new | 46 | 0 | NA | 14260 | 0.000000 | 0 | 0 | 0.00000 | 1701 | 0.00000 | 0.00000 | 0.000000 | moins de 20 cas par million | 14260 | 1701 - Nunavik |
| 18 - Terres-Cries-de-la-Baie-James | 1801 - Terres-Cries-de-la-Baie-James | 2021-03-29 | 119 | cronjob_new | 119 | 0 | NA | 18385 | 0.000000 | 0 | 0 | 0.00000 | 1801 | 0.00000 | 0.00000 | 0.000000 | moins de 20 cas par million | 18385 | 1801 - Terres-Cries-de-la-Baie-James |
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()
| CD_CS | NOM_CS | SITE_WEB | SUPRF_KM2 | PERMT_KM | NOM_VERSN | dummy | dummy2 | date_report | cases_last_7_days | previous_cases_last_7_days | Population | dailycases_per_1M_avg_7_days | previous_dailycases_per_1M_avg_7_days | color_per_pop | cases_last_7_days_per_100k | cases_per_1M | NOM_CS_petit_nom |
|:-------|:----------------------------------|:-----------------------------|------------:|-----------:|:-----------|------:|-------:|:-------------|---------------------:|-------------------------------:|-----------:|----------------------------------:|--------------------------------------------:|:--------------------------------|--------------------------------:|---------------:|:---------------------------|
| 731000 | CSS de Charlevoix | www.cscharlevoix.qc.ca | 7066.5687 | 403.05820 | V2020-07 | 1 | 1 | 2021-03-29 | 12 | 13.9355987 | 28295 | 60.3 | 70.4 | entre 60 et 100 cas par million | 42.2 | 60.3 | Charlevoix |
| 821000 | CSS de la Côte-du-Sud | http://www.cscotesud.qc.ca | 5959.9203 | 418.42028 | V2020-07 | 1 | 1 | 2021-03-29 | 27 | 7.8543048 | 70237 | 54.8 | 16.0 | entre 20 et 60 cas par million | 38.3 | 54.8 | Côte-du-Sud |
| 852000 | CSS de la Rivière-du-Nord | http://www2.csrdn.qc.ca/ | 2049.6010 | 322.44185 | V2020-07 | 1 | 1 | 2021-03-29 | 75 | 87.9243392 | 190425 | 56.3 | 66.0 | entre 20 et 60 cas par million | 39.4 | 56.3 | Rivière-du-Nord |
| 772000 | CSS des Portages-de-l’Outaouais | www.cspo.qc.ca | 1382.6493 | 195.52992 | V2020-07 | 1 | 1 | 2021-03-29 | 220 | 130.6675249 | 153423 | 204.6 | 121.7 | plus de 100 cas par million | 143.2 | 204.6 | Portages-de-l’Outaouais |
| 724000 | CSS De La Jonquière | www.csjonquiere.qc.ca | 1001.8302 | 169.10289 | V2020-07 | 1 | 1 | 2021-03-29 | 6 | 6.0001742 | 66718 | 12.8 | 12.8 | moins de 20 cas par million | 9.0 | 12.8 | De La Jonquière |
| 773000 | CSS au Coeur-des-Vallées | www.cscv.qc.ca | 3458.9423 | 322.35632 | V2020-07 | 1 | 1 | 2021-03-29 | 25 | 9.8282775 | 53658 | 65.4 | 26.2 | entre 60 et 100 cas par million | 45.8 | 65.4 | au Coeur-des-Vallées |
| 774000 | CSS des Hauts-Bois-de-l’Outaouais | www.cshbo.qc.ca | 28080.7957 | 1363.04393 | V2020-07 | 1 | 1 | 2021-03-29 | 17 | 9.1360234 | 33868 | 71.8 | 38.5 | entre 60 et 100 cas par million | 50.3 | 71.8 | Hauts-Bois-de-l’Outaouais |
| 791000 | CSS de l’Estuaire | www.csestuaire.qc.ca | 57965.0122 | 1241.96584 | V2020-07 | 1 | 1 | 2021-03-29 | 0 | 3.0005544 | 41543 | 0.0 | 10.3 | moins de 20 cas par million | 0.0 | 0.0 | l’Estuaire |
| 782000 | CSS de Rouyn-Noranda | www.csrn.qc.ca | 6073.5950 | 416.44383 | V2020-07 | 1 | 1 | 2021-03-29 | 4 | 0.9868319 | 41784 | 13.5 | 3.4 | moins de 20 cas par million | 9.4 | 13.5 | Rouyn-Noranda |
| 801000 | CSS de la Baie-James | http://csbj.qc.ca/ | 343574.3467 | 5631.60890 | V2020-07 | 1 | 1 | 2021-03-29 | 0 | 0.0000636 | 16151 | 0.0 | 0.0 | moins de 20 cas par million | 0.0 | 0.0 | Baie-James |
| 742000 | CSS de l’Énergie | www.csenergie.qc.ca | 37903.5713 | 1189.64144 | V2020-07 | 1 | 1 | 2021-03-29 | 19 | 5.4940374 | 97133 | 27.5 | 8.1 | entre 20 et 60 cas par million | 19.2 | 27.5 | l’Énergie |
| 762000 | CSS de Montréal | www.csdm.qc.ca | 163.9897 | 112.21365 | V2020-07 | 1 | 1 | 2021-03-29 | 1072 | 1025.4063524 | 1009727 | 151.7 | 145.1 | plus de 100 cas par million | 106.2 | 151.7 | Montréal |
| 763000 | CSS Marguerite-Bourgeoys | www.csmb.qc.ca | 356.2161 | 133.05741 | V2020-07 | 1 | 1 | 2021-03-29 | 660 | 626.4738529 | 611365 | 154.3 | 146.4 | plus de 100 cas par million | 108.0 | 154.3 | Marguerite-Bourgeoys |
| 869000 | CSS des Trois-Lacs | www.cstrois-lacs.qc.ca | 1023.8689 | 154.98997 | V2020-07 | 1 | 1 | 2021-03-29 | 71 | 45.0033481 | 147799 | 68.6 | 43.5 | entre 60 et 100 cas par million | 48.0 | 68.6 | Trois-Lacs |
| 823000 | CSS de la Beauce-Etchemin | www.csbe.qc.ca | 6202.0129 | 542.92776 | V2020-07 | 1 | 1 | 2021-03-29 | 158 | 52.1075145 | 123821 | 182.5 | 60.1 | plus de 100 cas par million | 127.8 | 182.5 | Beauce-Etchemin |
| 868000 | CSS de la Vallée-des-Tisserands | www.csvt.qc.ca | 1847.3375 | 219.29223 | V2020-07 | 1 | 1 | 2021-03-29 | 29 | 24.4664251 | 86081 | 48.0 | 40.6 | entre 20 et 60 cas par million | 33.6 | 48.0 | Vallée-des-Tisserands |
| 854000 | CSS Pierre-Neveu | www.cspn.qc.ca | 16291.9939 | 859.45936 | V2020-07 | 1 | 1 | 2021-03-29 | 14 | 15.9993964 | 35249 | 56.7 | 64.8 | entre 20 et 60 cas par million | 39.7 | 56.7 | Pierre-Neveu |
| 714000 | CSS de Kamouraska-Rivière-du-Loup | www.cskamloup.qc.ca | 5054.5614 | 413.10309 | V2020-07 | 1 | 1 | 2021-03-29 | 194 | 36.2003193 | 55614 | 497.3 | 93.0 | plus de 100 cas par million | 348.1 | 497.3 | Kamouraska-Rivière-du-Loup |
| 853000 | CSS des Laurentides | www.cslaurentides.qc.ca | 3970.8926 | 362.47283 | V2020-07 | 1 | 1 | 2021-03-29 | 42 | 21.1322007 | 92025 | 66.0 | 32.8 | entre 60 et 100 cas par million | 46.2 | 66.0 | Laurentides |
| 831000 | CSS de Laval | www.cslaval.qc.ca | 266.9471 | 83.47677 | V2020-07 | 1 | 1 | 2021-03-29 | 628 | 524.0000000 | 418557 | 214.3 | 178.8 | plus de 100 cas par million | 150.0 | 214.3 | Laval |
Montreal data
mtl_data <-suppressMessages( get_mtl_data() )
mtl_data %>% tail(20) %>% knitr::kable()
| arrondissement | date_report | cumulative_cases | source | fix_cummin | cases | arrond_ville_liee | Population | cases_per_100k | cases_last_7_days | previous_cases_last_7_days | cases_last_7_days_per_100k | cases_per_1M | last_cases_per_1M | previous_cases_per_1M | color_per_pop | pop |
|:-----------------------------------------|:-------------|------------------:|:-------|------------:|------:|:--------------------|-----------:|-----------------:|---------------------:|-------------------------------:|--------------------------------:|---------------:|---------------------:|-------------------------:|:--------------------------------|--------:|
| Montréal Est | 2021-03-29 | 241 | github | 241 | 1 | Ville | 4012 | 24.925224 | 6 | 7 | 149.55135 | 213.64478 | 213.64478 | 249.25224 | plus de 100 cas par million | 4012 |
| Montréal-Nord | 2021-03-29 | 8256 | github | 8256 | 17 | Arrond. | 87928 | 19.334000 | 111 | 132 | 126.23965 | 180.34236 | 180.34236 | 214.46118 | plus de 100 cas par million | 87928 |
| Montréal-Ouest | 2021-03-29 | 145 | github | 145 | 1 | Ville | 5287 | 18.914318 | 11 | 6 | 208.05750 | 297.22500 | 297.22500 | 162.12273 | plus de 100 cas par million | 5287 |
| Outremont | 2021-03-29 | 1149 | github | 1149 | 3 | Arrond. | 25826 | 11.616201 | 17 | 32 | 65.82514 | 94.03591 | 94.03591 | 177.00877 | entre 60 et 100 cas par million | 25826 |
| Pierrefonds–Roxboro | 2021-03-29 | 3554 | github | 3554 | 12 | Arrond. | 73230 | 16.386727 | 76 | 85 | 103.78260 | 148.26086 | 148.26086 | 165.81807 | plus de 100 cas par million | 73230 |
| Plateau Mont-Royal | 2021-03-29 | 3963 | github | 3963 | 8 | Arrond. | 108102 | 7.400418 | 54 | 74 | 49.95282 | 71.36117 | 71.36117 | 97.79124 | entre 60 et 100 cas par million | 108102 |
| Pointe-Claire | 2021-03-29 | 924 | github | 924 | 3 | Ville | 33382 | 8.986879 | 21 | 15 | 62.90815 | 89.86879 | 89.86879 | 64.19199 | entre 60 et 100 cas par million | 33382 |
| Rivière-des-Prairies–Pointe-aux-Trembles | 2021-03-29 | 8045 | github | 8045 | 16 | Arrond. | 114732 | 13.945543 | 106 | 117 | 92.38922 | 131.98460 | 131.98460 | 145.68112 | plus de 100 cas par million | 114732 |
| Rosemont–La Petite Patrie | 2021-03-29 | 6257 | github | 6257 | 17 | Arrond. | 147624 | 11.515743 | 112 | 74 | 75.86842 | 108.38346 | 108.38346 | 71.61050 | plus de 100 cas par million | 147624 |
| Saint-Laurent | 2021-03-29 | 6845 | github | 6845 | 26 | Arrond. | 105248 | 24.703557 | 184 | 151 | 174.82517 | 249.75025 | 249.75025 | 204.95809 | plus de 100 cas par million | 105248 |
| Saint-Léonard | 2021-03-29 | 6880 | github | 6880 | 19 | Arrond. | 82841 | 22.935503 | 121 | 108 | 146.06294 | 208.66134 | 208.66134 | 186.24318 | plus de 100 cas par million | 82841 |
| Sainte-Anne-de-Bellevue | 2021-03-29 | 122 | github | 122 | 0 | Ville | 5038 | 0.000000 | 2 | 2 | 39.69829 | 56.71185 | 56.71185 | 56.71185 | entre 20 et 60 cas par million | 5038 |
| Senneville | 2021-03-29 | 22 | github | 22 | 0 | Ville | 981 | 0.000000 | 0 | 0 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | moins de 20 cas par million | 981 |
| Sud-Ouest | 2021-03-29 | 3334 | github | 3334 | 8 | Arrond. | 84299 | 9.490030 | 48 | 44 | 56.94018 | 81.34311 | 81.34311 | 74.56452 | entre 60 et 100 cas par million | 84299 |
| Territoire à confirmer | 2021-03-29 | 1745 | github | 1745 | 1 | NA | NA | NA | 21 | 10 | NA | NA | 0.00000 | 0.00000 | moins de 20 cas par million | NA |
| Total à Montréal | 2021-03-29 | 115154 | github | 115154 | 306 | NA | 2050053 | 14.926443 | 2168 | 2036 | 105.75336 | 151.07623 | 151.07623 | 141.87787 | plus de 100 cas par million | 2050053 |
| Verdun | 2021-03-29 | 2823 | github | 2823 | 9 | Arrond. | 72419 | 12.427678 | 72 | 71 | 99.42142 | 142.03060 | 142.03060 | 140.05796 | plus de 100 cas par million | 72419 |
| Ville-Marie | 2021-03-29 | 4138 | github | 4138 | 8 | Arrond. | 95231 | 8.400626 | 69 | 57 | 72.45540 | 103.50771 | 103.50771 | 85.50637 | plus de 100 cas par million | 95231 |
| Villeray–Saint-Michel–Parc-Extension | 2021-03-29 | 9576 | github | 9576 | 27 | Arrond. | 148202 | 18.218378 | 189 | 127 | 127.52864 | 182.18378 | 182.18378 | 122.41979 | plus de 100 cas par million | 148202 |
| Westmount | 2021-03-29 | 655 | github | 655 | 4 | Ville | 20974 | 19.071231 | 23 | 11 | 109.65958 | 156.65654 | 156.65654 | 74.92269 | plus de 100 cas par million | 20974 |
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
#> orange skyblue bluishgreen yellow blue
#> "#E69F00" "#56B4E9" "#009E73" "#F0E442" "#0072B2"
#> vermillion reddishpurple gray
#> "#D55E00" "#CC79A7" "#999999"
SimonCoulombe/covidtwitterbot documentation built on May 1, 2021, 6:50 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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()
| Date | Regroupement | Croisement | Nom | cas_cum_lab_n | cas_cum_epi_n | cas_cum_tot_n | cas_cum_tot_t | cas_quo_tot_t | cas_quo_lab_n | cas_quo_epi_n | cas_quo_tot_n | act_cum_tot_n | act_cum_tot_t | cas_quo_tot_m | cas_quo_tot_tm | ret_cum_tot_n | ret_quo_tot_n | dec_cum_tot_n | dec_cum_tot_t | dec_quo_tot_t | dec_cum_chs_n | dec_cum_rpa_n | dec_cum_dom_n | dec_cum_aut_n | dec_quo_tot_n | dec_quo_chs_n | dec_quo_rpa_n | dec_quo_dom_n | dec_quo_aut_n | dec_quo_tot_m | dec_quo_tot_tm | 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 | psi_cum_tes_n | psi_cum_pos_n | psi_cum_inf_n | psi_cum_adm_n | psi_quo_pos_n | psi_quo_inf_n | psi_quo_tes_n | psi_quo_pos_t | date | type | groupe | cas_totaux_cumul | cas_totaux_quotidien | deces_totaux_quotidien | pop | |:-----------|:-----------------|:-----------|:---------------------|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|------------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|------------------:|-----------------:|----------------:|-----------------:|-----------------:|-----------------:|-----------------:|----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|:-----------|:-----------------|:---------------------|-------------------:|-----------------------:|-------------------------:|--------:| | 2021-03-28 | Groupe de région | REG02 | Ceinture de Montréal | 50932 | 1949 | 52881 | 3572.59 | 4.44 | 66 | 0 | 66 | 1023 | 68.81 | NA | NA | 50017 | 98 | 1529 | 103.30 | 0.00 | 753 | 349 | 332 | 95 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 579227 | 50521 | 528706 | 1287262 | NA | NA | NA | NA | 2021-03-28 | region_montreal | Ceinture de Montréal | 52881 | 66 | 0 | NA | | 2021-03-28 | Groupe de région | REG03 | Autres régions | 110532 | 3279 | 113811 | 2491.18 | 6.20 | 278 | 6 | 284 | 2990 | 65.28 | NA | NA | 106351 | 240 | 3545 | 77.60 | 0.02 | 1452 | 1043 | 856 | 194 | 1 | 0 | 1 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1599203 | 109751 | 1489452 | 3329728 | NA | NA | NA | NA | 2021-03-28 | region_montreal | Autres régions | 113811 | 284 | 1 | NA | | 2021-03-28 | Groupe de région | REG00 | Inconnu | 4 | 0 | 4 | NA | NA | 0 | 0 | 0 | 0 | NA | NA | NA | 4 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 10737 | 358 | 10379 | 14363 | NA | NA | NA | NA | 2021-03-28 | region_montreal | Inconnue | 4 | 0 | 0 | NA | | 2021-03-28 | Groupe de région | REG98 | Hors Québec | 276 | 5 | 281 | NA | NA | 0 | 0 | 0 | 10 | NA | NA | NA | 267 | 1 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 13953 | 436 | 13517 | 19794 | NA | NA | NA | NA | 2021-03-28 | region_montreal | Hors Québec | 281 | 0 | 0 | NA | | 2021-03-28 | Groupe d’âge | 0_9 | 0-9 ans | 22250 | 1977 | 24227 | 2713.39 | 7.40 | 63 | 3 | 66 | 874 | 98.01 | NA | NA | 23132 | 85 | 0 | 0.00 | 0.00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 359348 | 19142 | 340206 | 589150 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 0 à 9 ans | 24227 | 66 | 0 | 893977 | | 2021-03-28 | Groupe d’âge | 10_19 | 10-19 ans | 34621 | 1478 | 36099 | 4078.30 | 11.44 | 100 | 2 | 102 | 1179 | 132.18 | NA | NA | 34689 | 119 | 1 | 0.11 | 0.00 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 349659 | 30396 | 319263 | 534252 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 10 à 19 ans | 36099 | 102 | 0 | 878347 | | 2021-03-28 | Groupe d’âge | 20_29 | 20-29 ans | 45769 | 1475 | 47244 | 4504.25 | 8.07 | 84 | 0 | 84 | 1077 | 103.41 | NA | NA | 45825 | 113 | 7 | 0.67 | 0.00 | 0 | 0 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 407874 | 41066 | 366808 | 848617 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 20 à 29 ans | 47244 | 84 | 0 | 1056242 | | 2021-03-28 | Groupe d’âge | 30_39 | 30-39 ans | 42666 | 1251 | 43917 | 3930.75 | 6.44 | 72 | 0 | 72 | 1085 | 96.99 | NA | NA | 42514 | 108 | 14 | 1.25 | 0.00 | 3 | 0 | 8 | 3 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 443729 | 38063 | 405666 | 959075 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 30 à 39 ans | 43917 | 72 | 0 | 1115906 | | 2021-03-28 | Groupe d’âge | 40_49 | 40-49 ans | 44924 | 1214 | 46138 | 4163.78 | 8.07 | 90 | 0 | 90 | 1172 | 105.08 | NA | NA | 44631 | 136 | 39 | 3.52 | 0.00 | 11 | 2 | 25 | 1 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 402124 | 40682 | 361442 | 917876 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 40 à 49 ans | 46138 | 90 | 0 | 1100826 | | 2021-03-28 | Groupe d’âge | 50_59 | 50-59 ans | 39516 | 1025 | 40541 | 3469.14 | 5.28 | 60 | 1 | 61 | 1021 | 88.38 | NA | NA | 39068 | 102 | 182 | 15.57 | 0.00 | 71 | 2 | 98 | 11 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 352267 | 36077 | 316190 | 836906 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 50 à 59 ans | 40541 | 61 | 0 | 1182044 | | 2021-03-28 | Groupe d’âge | 60_69 | 60-69 ans | 24597 | 546 | 25143 | 2170.75 | 2.99 | 35 | 0 | 35 | 696 | 59.55 | NA | NA | 23691 | 55 | 628 | 54.22 | 0.09 | 278 | 39 | 276 | 35 | 1 | 0 | 0 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 293320 | 22912 | 270408 | 614959 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 60 à 69 ans | 25143 | 35 | 1 | 1147664 | | 2021-03-28 | Groupe d’âge | 70_79 | 70-79 ans | 16479 | 241 | 16720 | 2175.91 | 2.04 | 16 | 0 | 16 | 382 | 48.78 | NA | NA | 14202 | 26 | 2010 | 261.58 | 0.00 | 960 | 273 | 671 | 106 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 196183 | 14897 | 181286 | 450721 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 70 à 79 ans | 16720 | 16 | 0 | 753700 | | 2021-03-28 | Groupe d’âge | 80_89 | 80-89 ans | 17900 | 280 | 18180 | 5458.30 | 2.66 | 9 | 0 | 9 | 368 | 108.73 | NA | NA | 13442 | 11 | 4188 | 1257.39 | 0.00 | 2240 | 1008 | 729 | 211 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 123337 | 16018 | 107319 | 410989 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 80 à 89 ans | 18180 | 9 | 0 | 327675 | | 2021-03-28 | Groupe d’âge | 90_ | 90 ans et plus | 10730 | 219 | 10949 | 12902.20 | 2.30 | 2 | 0 | 2 | 132 | 151.63 | NA | NA | 7360 | 4 | 3486 | 4107.87 | 1.15 | 2078 | 923 | 303 | 182 | 1 | 0 | 1 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 51319 | 10722 | 40597 | 227249 | NA | NA | NA | NA | 2021-03-28 | groupe_age | 90 ans et + | 10949 | 2 | 1 | 82670 | | 2021-03-28 | Groupe d’âge | INC | Inconnu | 18 | 24 | 42 | NA | NA | 0 | 0 | 0 | 1 | NA | NA | NA | 39 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 317012 | 29484 | 287528 | 684468 | NA | NA | NA | NA | 2021-03-28 | groupe_age | Âge inconnu | 42 | 0 | 0 | NA | | 2021-03-28 | Groupe d’âge | TOT | Total | 299470 | 9730 | 309200 | 3609.84 | 6.25 | 531 | 6 | 537 | 7987 | 92.96 | NA | NA | 288593 | 759 | 10555 | 123.23 | 0.02 | 5641 | 2247 | 2117 | 550 | 2 | 0 | 1 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 3296172 | 299459 | 2996713 | 7074262 | NA | NA | NA | NA | 2021-03-28 | groupe_age | Total | 309200 | 537 | 2 | 8390499 | | 2021-03-28 | Sexe | MASC | Masculin | 139423 | 4847 | 144270 | 3369.77 | 6.15 | 262 | 2 | 264 | 4034 | 93.91 | NA | NA | 134298 | 385 | 4973 | 116.16 | 0.05 | 2436 | 1020 | 1261 | 256 | 2 | 0 | 1 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1506973 | 138943 | 1368030 | 2838213 | NA | NA | NA | NA | 2021-03-28 | sexe | Masculin | 144270 | 264 | 2 | NA | | 2021-03-28 | Sexe | FEM | Féminin | 159922 | 4882 | 164804 | 3846.82 | 6.33 | 268 | 4 | 272 | 3952 | 91.98 | NA | NA | 154172 | 374 | 5581 | 130.27 | 0.00 | 3205 | 1226 | 856 | 294 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1780768 | 159981 | 1620787 | 4224217 | NA | NA | NA | NA | 2021-03-28 | sexe | Féminin | 164804 | 272 | 0 | NA | | 2021-03-28 | Sexe | INC | Inconnu | 125 | 1 | 126 | NA | NA | 1 | 0 | 1 | 1 | NA | NA | NA | 123 | 0 | 1 | NA | NA | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 8431 | 535 | 7896 | 11832 | NA | NA | NA | NA | 2021-03-28 | sexe | Sexe inconnu | 126 | 1 | 0 | NA | | 2021-03-28 | Sexe | TOT | Total | 299470 | 9730 | 309200 | 3609.84 | 6.25 | 531 | 6 | 537 | 7987 | 92.96 | NA | NA | 288593 | 759 | 10555 | 123.23 | 0.02 | 5641 | 2247 | 2117 | 550 | 2 | 0 | 1 | 1 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 3296172 | 299459 | 2996713 | 7074262 | NA | NA | NA | NA | 2021-03-28 | sexe | Total | 309200 | 537 | 2 | NA |
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()
| date | hospits | hospits_ancien | si | volumetrie | |:-----------|--------:|----------------:|----:|-----------:| | 2021-03-09 | 469 | NA | 112 | 29655 | | 2021-03-10 | 452 | NA | 111 | 29175 | | 2021-03-11 | 444 | NA | 106 | 29507 | | 2021-03-12 | 445 | NA | 106 | 28466 | | 2021-03-13 | 447 | NA | 100 | 23159 | | 2021-03-14 | 457 | NA | 96 | 18232 | | 2021-03-15 | 442 | NA | 91 | 23432 | | 2021-03-16 | 425 | NA | 107 | 31945 | | 2021-03-17 | 418 | NA | 101 | 33670 | | 2021-03-18 | 405 | NA | 99 | 32943 | | 2021-03-19 | 406 | NA | 99 | 31995 | | 2021-03-20 | 399 | NA | 102 | 24609 | | 2021-03-21 | 399 | NA | 114 | 21447 | | 2021-03-22 | 406 | NA | 113 | 25675 | | 2021-03-23 | 390 | NA | 118 | 35129 | | 2021-03-24 | 379 | NA | 117 | 39663 | | 2021-03-25 | 366 | NA | 115 | 35960 | | 2021-03-26 | 373 | NA | 108 | 33671 | | 2021-03-27 | 366 | NA | 114 | 26993 | | 2021-03-28 | 357 | NA | 120 | NA |
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()
| cas | deces | hospit | soins | gueris | analyses | type | |-------:|------:|-------:|------:|-------:|---------:|:----------| | 309202 | 10651 | 477 | 120 | 290564 | 26993 | cumulatif | | 891 | 4 | -3 | 6 | 737 | NA | quotidien |
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()
| RSS | RLS | date_report | cumulative_cases | source | fix_cummin | cases | shortname_rls | Population | cases_per_100k | cases_last_7_days | previous_cases_last_7_days | cases_last_7_days_per_100k | RLS_code | cases_per_1M | last_cases_per_1M | previous_cases_per_1M | color_per_pop | pop | RLS_petit_nom | |:-----------------------------------|:------------------------------------------------|:-------------|------------------:|:-------------|------------:|------:|:----------------------------------|-----------:|-----------------:|---------------------:|-------------------------------:|--------------------------------:|:----------|---------------:|---------------------:|-------------------------:|:--------------------------------|-------:|:--------------------------------------| | 14 - Lanaudière | 1411 - RLS de Lanaudière-Nord | 2021-03-29 | 8415 | cronjob_new | 8415 | 12 | Lanaudière-Nord | 223099 | 5.378778 | 70 | 111 | 31.37621 | 1411 | 44.82315 | 44.82315 | 71.076710 | entre 20 et 60 cas par million | 223099 | 1411 - Lanaudière-Nord | | 14 - Lanaudière | 1412 - RLS de Lanaudière-Sud | 2021-03-29 | 12778 | cronjob_new | 12778 | 28 | Lanaudière-Sud | 296993 | 9.427832 | 169 | 202 | 56.90370 | 1412 | 81.29100 | 81.29100 | 97.164387 | entre 60 et 100 cas par million | 296993 | 1412 - Lanaudière-Sud | | 15 - Laurentides | 1511 - RLS d’Antoine-Labelle | 2021-03-29 | 836 | cronjob_new | 836 | 4 | d’Antoine-Labelle | 35580 | 11.242271 | 14 | 16 | 39.34795 | 1511 | 56.21135 | 56.21135 | 64.241548 | entre 20 et 60 cas par million | 35580 | 1511 - d’Antoine-Labelle | | 15 - Laurentides | 1512 - RLS des Laurentides | 2021-03-29 | 922 | cronjob_new | 922 | 4 | des Laurentides | 48016 | 8.330557 | 29 | 11 | 60.39653 | 1512 | 86.28076 | 86.28076 | 32.727186 | entre 60 et 100 cas par million | 48016 | 1512 - Laurentides | | 15 - Laurentides | 1513 - RLS des Pays-d’en-Haut | 2021-03-29 | 916 | cronjob_new | 916 | 2 | des Pays-d’en-Haut | 44362 | 4.508363 | 12 | 8 | 27.05018 | 1513 | 38.64311 | 38.64311 | 25.762074 | entre 20 et 60 cas par million | 44362 | 1513 - Pays-d’en-Haut | | 15 - Laurentides | 1514 - RLS d’Argenteuil | 2021-03-29 | 676 | cronjob_new | 676 | 1 | d’Argenteuil | 33422 | 2.992041 | 19 | 25 | 56.84878 | 1514 | 81.21255 | 81.21255 | 106.858613 | entre 60 et 100 cas par million | 33422 | 1514 - d’Argenteuil | | 15 - Laurentides | 1515 - RLS de Deux-Montagnes - Mirabel-Sud | 2021-03-29 | 3661 | cronjob_new | 3661 | 8 | Deux-Montagnes - Mirabel-Sud | 124641 | 6.418434 | 65 | 75 | 52.14977 | 1515 | 74.49968 | 74.49968 | 85.961166 | entre 60 et 100 cas par million | 124641 | 1515 - Deux-Montagnes - Mirabel-Sud | | 15 - Laurentides | 1516 - RLS de la Rivière-du-Nord - Mirabel-Nord | 2021-03-29 | 5020 | cronjob_new | 5020 | 14 | la Rivière-du-Nord - Mirabel-Nord | 177517 | 7.886569 | 57 | 64 | 32.10960 | 1516 | 45.87086 | 45.87086 | 51.504121 | entre 20 et 60 cas par million | 177517 | 1516 - Rivière-du-Nord - Mirabel-Nord | | 15 - Laurentides | 1517 - RLS de Thérèse-De Blainville | 2021-03-29 | 5714 | cronjob_new | 5714 | 27 | Thérèse-De Blainville | 163430 | 16.520835 | 146 | 95 | 89.33488 | 1517 | 127.62126 | 127.62126 | 83.041232 | plus de 100 cas par million | 163430 | 1517 - Thérèse-De Blainville | | 16 - Montérégie | 1611 - RLS de Champlain | 2021-03-29 | 7821 | cronjob_new | 7821 | 15 | Champlain | 225442 | 6.653596 | 131 | 102 | 58.10807 | 1611 | 83.01153 | 83.01153 | 64.634933 | entre 60 et 100 cas par million | 225442 | 1611 - Champlain | | 16 - Montérégie | 1612 - RLS du Haut-Richelieu - Rouville | 2021-03-29 | 5000 | cronjob_new | 5000 | 14 | du Haut-Richelieu - Rouville | 195297 | 7.168569 | 99 | 66 | 50.69202 | 1612 | 72.41718 | 72.41718 | 48.278117 | entre 60 et 100 cas par million | 195297 | 1612 - Haut-Richelieu - Rouville | | 16 - Montérégie | 1621 - RLS Pierre-Boucher | 2021-03-29 | 9028 | cronjob_new | 9028 | 21 | Pierre-Boucher | 262440 | 8.001829 | 137 | 94 | 52.20241 | 1621 | 74.57487 | 74.57487 | 51.168158 | entre 60 et 100 cas par million | 262440 | 1621 - Pierre-Boucher | | 16 - Montérégie | 1622 - RLS de Richelieu-Yamaska | 2021-03-29 | 7340 | cronjob_new | 7340 | 18 | Richelieu-Yamaska | 220957 | 8.146381 | 140 | 76 | 63.36074 | 1622 | 90.51535 | 90.51535 | 49.136904 | entre 60 et 100 cas par million | 220957 | 1622 - Richelieu-Yamaska | | 16 - Montérégie | 1623 - RLS Pierre-De Saurel | 2021-03-29 | 1203 | cronjob_new | 1203 | 0 | Pierre-De Saurel | 51371 | 0.000000 | 6 | 3 | 11.67974 | 1623 | 16.68535 | 16.68535 | 8.342673 | moins de 20 cas par million | 51371 | 1623 - Pierre-De Saurel | | 16 - Montérégie | 1631 - RLS de Vaudreuil-Soulanges | 2021-03-29 | 4032 | cronjob_new | 4032 | 12 | Vaudreuil-Soulanges | 160002 | 7.499906 | 71 | 45 | 44.37445 | 1631 | 63.39206 | 63.39206 | 40.178069 | entre 60 et 100 cas par million | 160002 | 1631 - Vaudreuil-Soulanges | | 16 - Montérégie | 1632 - RLS du Suroît | 2021-03-29 | 1938 | cronjob_new | 1938 | 3 | du Suroît | 60523 | 4.956793 | 22 | 16 | 36.34982 | 1632 | 51.92831 | 51.92831 | 37.766044 | entre 20 et 60 cas par million | 60523 | 1632 - Suroît | | 16 - Montérégie | 1633 - RLS du Haut-Saint-Laurent | 2021-03-29 | 603 | cronjob_new | 603 | 0 | du Haut-Saint-Laurent | 24346 | 0.000000 | 3 | 6 | 12.32235 | 1633 | 17.60336 | 17.60336 | 35.206722 | moins de 20 cas par million | 24346 | 1633 - Haut-Saint-Laurent | | 16 - Montérégie | 1634 - RLS de Jardins-Roussillon | 2021-03-29 | 7043 | cronjob_new | 7043 | 20 | Jardins-Roussillon | 232373 | 8.606852 | 119 | 75 | 51.21077 | 1634 | 73.15824 | 73.15824 | 46.108135 | entre 60 et 100 cas par million | 232373 | 1634 - Jardins-Roussillon | | 17 - Nunavik | 1701 - Nunavik | 2021-03-29 | 46 | cronjob_new | 46 | 0 | NA | 14260 | 0.000000 | 0 | 0 | 0.00000 | 1701 | 0.00000 | 0.00000 | 0.000000 | moins de 20 cas par million | 14260 | 1701 - Nunavik | | 18 - Terres-Cries-de-la-Baie-James | 1801 - Terres-Cries-de-la-Baie-James | 2021-03-29 | 119 | cronjob_new | 119 | 0 | NA | 18385 | 0.000000 | 0 | 0 | 0.00000 | 1801 | 0.00000 | 0.00000 | 0.000000 | moins de 20 cas par million | 18385 | 1801 - Terres-Cries-de-la-Baie-James |
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()
| CD_CS | NOM_CS | SITE_WEB | SUPRF_KM2 | PERMT_KM | NOM_VERSN | dummy | dummy2 | date_report | cases_last_7_days | previous_cases_last_7_days | Population | dailycases_per_1M_avg_7_days | previous_dailycases_per_1M_avg_7_days | color_per_pop | cases_last_7_days_per_100k | cases_per_1M | NOM_CS_petit_nom | |:-------|:----------------------------------|:-----------------------------|------------:|-----------:|:-----------|------:|-------:|:-------------|---------------------:|-------------------------------:|-----------:|----------------------------------:|--------------------------------------------:|:--------------------------------|--------------------------------:|---------------:|:---------------------------| | 731000 | CSS de Charlevoix | www.cscharlevoix.qc.ca | 7066.5687 | 403.05820 | V2020-07 | 1 | 1 | 2021-03-29 | 12 | 13.9355987 | 28295 | 60.3 | 70.4 | entre 60 et 100 cas par million | 42.2 | 60.3 | Charlevoix | | 821000 | CSS de la Côte-du-Sud | http://www.cscotesud.qc.ca | 5959.9203 | 418.42028 | V2020-07 | 1 | 1 | 2021-03-29 | 27 | 7.8543048 | 70237 | 54.8 | 16.0 | entre 20 et 60 cas par million | 38.3 | 54.8 | Côte-du-Sud | | 852000 | CSS de la Rivière-du-Nord | http://www2.csrdn.qc.ca/ | 2049.6010 | 322.44185 | V2020-07 | 1 | 1 | 2021-03-29 | 75 | 87.9243392 | 190425 | 56.3 | 66.0 | entre 20 et 60 cas par million | 39.4 | 56.3 | Rivière-du-Nord | | 772000 | CSS des Portages-de-l’Outaouais | www.cspo.qc.ca | 1382.6493 | 195.52992 | V2020-07 | 1 | 1 | 2021-03-29 | 220 | 130.6675249 | 153423 | 204.6 | 121.7 | plus de 100 cas par million | 143.2 | 204.6 | Portages-de-l’Outaouais | | 724000 | CSS De La Jonquière | www.csjonquiere.qc.ca | 1001.8302 | 169.10289 | V2020-07 | 1 | 1 | 2021-03-29 | 6 | 6.0001742 | 66718 | 12.8 | 12.8 | moins de 20 cas par million | 9.0 | 12.8 | De La Jonquière | | 773000 | CSS au Coeur-des-Vallées | www.cscv.qc.ca | 3458.9423 | 322.35632 | V2020-07 | 1 | 1 | 2021-03-29 | 25 | 9.8282775 | 53658 | 65.4 | 26.2 | entre 60 et 100 cas par million | 45.8 | 65.4 | au Coeur-des-Vallées | | 774000 | CSS des Hauts-Bois-de-l’Outaouais | www.cshbo.qc.ca | 28080.7957 | 1363.04393 | V2020-07 | 1 | 1 | 2021-03-29 | 17 | 9.1360234 | 33868 | 71.8 | 38.5 | entre 60 et 100 cas par million | 50.3 | 71.8 | Hauts-Bois-de-l’Outaouais | | 791000 | CSS de l’Estuaire | www.csestuaire.qc.ca | 57965.0122 | 1241.96584 | V2020-07 | 1 | 1 | 2021-03-29 | 0 | 3.0005544 | 41543 | 0.0 | 10.3 | moins de 20 cas par million | 0.0 | 0.0 | l’Estuaire | | 782000 | CSS de Rouyn-Noranda | www.csrn.qc.ca | 6073.5950 | 416.44383 | V2020-07 | 1 | 1 | 2021-03-29 | 4 | 0.9868319 | 41784 | 13.5 | 3.4 | moins de 20 cas par million | 9.4 | 13.5 | Rouyn-Noranda | | 801000 | CSS de la Baie-James | http://csbj.qc.ca/ | 343574.3467 | 5631.60890 | V2020-07 | 1 | 1 | 2021-03-29 | 0 | 0.0000636 | 16151 | 0.0 | 0.0 | moins de 20 cas par million | 0.0 | 0.0 | Baie-James | | 742000 | CSS de l’Énergie | www.csenergie.qc.ca | 37903.5713 | 1189.64144 | V2020-07 | 1 | 1 | 2021-03-29 | 19 | 5.4940374 | 97133 | 27.5 | 8.1 | entre 20 et 60 cas par million | 19.2 | 27.5 | l’Énergie | | 762000 | CSS de Montréal | www.csdm.qc.ca | 163.9897 | 112.21365 | V2020-07 | 1 | 1 | 2021-03-29 | 1072 | 1025.4063524 | 1009727 | 151.7 | 145.1 | plus de 100 cas par million | 106.2 | 151.7 | Montréal | | 763000 | CSS Marguerite-Bourgeoys | www.csmb.qc.ca | 356.2161 | 133.05741 | V2020-07 | 1 | 1 | 2021-03-29 | 660 | 626.4738529 | 611365 | 154.3 | 146.4 | plus de 100 cas par million | 108.0 | 154.3 | Marguerite-Bourgeoys | | 869000 | CSS des Trois-Lacs | www.cstrois-lacs.qc.ca | 1023.8689 | 154.98997 | V2020-07 | 1 | 1 | 2021-03-29 | 71 | 45.0033481 | 147799 | 68.6 | 43.5 | entre 60 et 100 cas par million | 48.0 | 68.6 | Trois-Lacs | | 823000 | CSS de la Beauce-Etchemin | www.csbe.qc.ca | 6202.0129 | 542.92776 | V2020-07 | 1 | 1 | 2021-03-29 | 158 | 52.1075145 | 123821 | 182.5 | 60.1 | plus de 100 cas par million | 127.8 | 182.5 | Beauce-Etchemin | | 868000 | CSS de la Vallée-des-Tisserands | www.csvt.qc.ca | 1847.3375 | 219.29223 | V2020-07 | 1 | 1 | 2021-03-29 | 29 | 24.4664251 | 86081 | 48.0 | 40.6 | entre 20 et 60 cas par million | 33.6 | 48.0 | Vallée-des-Tisserands | | 854000 | CSS Pierre-Neveu | www.cspn.qc.ca | 16291.9939 | 859.45936 | V2020-07 | 1 | 1 | 2021-03-29 | 14 | 15.9993964 | 35249 | 56.7 | 64.8 | entre 20 et 60 cas par million | 39.7 | 56.7 | Pierre-Neveu | | 714000 | CSS de Kamouraska-Rivière-du-Loup | www.cskamloup.qc.ca | 5054.5614 | 413.10309 | V2020-07 | 1 | 1 | 2021-03-29 | 194 | 36.2003193 | 55614 | 497.3 | 93.0 | plus de 100 cas par million | 348.1 | 497.3 | Kamouraska-Rivière-du-Loup | | 853000 | CSS des Laurentides | www.cslaurentides.qc.ca | 3970.8926 | 362.47283 | V2020-07 | 1 | 1 | 2021-03-29 | 42 | 21.1322007 | 92025 | 66.0 | 32.8 | entre 60 et 100 cas par million | 46.2 | 66.0 | Laurentides | | 831000 | CSS de Laval | www.cslaval.qc.ca | 266.9471 | 83.47677 | V2020-07 | 1 | 1 | 2021-03-29 | 628 | 524.0000000 | 418557 | 214.3 | 178.8 | plus de 100 cas par million | 150.0 | 214.3 | Laval |
Montreal data
mtl_data <-suppressMessages( get_mtl_data() )
mtl_data %>% tail(20) %>% knitr::kable()
| arrondissement | date_report | cumulative_cases | source | fix_cummin | cases | arrond_ville_liee | Population | cases_per_100k | cases_last_7_days | previous_cases_last_7_days | cases_last_7_days_per_100k | cases_per_1M | last_cases_per_1M | previous_cases_per_1M | color_per_pop | pop | |:-----------------------------------------|:-------------|------------------:|:-------|------------:|------:|:--------------------|-----------:|-----------------:|---------------------:|-------------------------------:|--------------------------------:|---------------:|---------------------:|-------------------------:|:--------------------------------|--------:| | Montréal Est | 2021-03-29 | 241 | github | 241 | 1 | Ville | 4012 | 24.925224 | 6 | 7 | 149.55135 | 213.64478 | 213.64478 | 249.25224 | plus de 100 cas par million | 4012 | | Montréal-Nord | 2021-03-29 | 8256 | github | 8256 | 17 | Arrond. | 87928 | 19.334000 | 111 | 132 | 126.23965 | 180.34236 | 180.34236 | 214.46118 | plus de 100 cas par million | 87928 | | Montréal-Ouest | 2021-03-29 | 145 | github | 145 | 1 | Ville | 5287 | 18.914318 | 11 | 6 | 208.05750 | 297.22500 | 297.22500 | 162.12273 | plus de 100 cas par million | 5287 | | Outremont | 2021-03-29 | 1149 | github | 1149 | 3 | Arrond. | 25826 | 11.616201 | 17 | 32 | 65.82514 | 94.03591 | 94.03591 | 177.00877 | entre 60 et 100 cas par million | 25826 | | Pierrefonds–Roxboro | 2021-03-29 | 3554 | github | 3554 | 12 | Arrond. | 73230 | 16.386727 | 76 | 85 | 103.78260 | 148.26086 | 148.26086 | 165.81807 | plus de 100 cas par million | 73230 | | Plateau Mont-Royal | 2021-03-29 | 3963 | github | 3963 | 8 | Arrond. | 108102 | 7.400418 | 54 | 74 | 49.95282 | 71.36117 | 71.36117 | 97.79124 | entre 60 et 100 cas par million | 108102 | | Pointe-Claire | 2021-03-29 | 924 | github | 924 | 3 | Ville | 33382 | 8.986879 | 21 | 15 | 62.90815 | 89.86879 | 89.86879 | 64.19199 | entre 60 et 100 cas par million | 33382 | | Rivière-des-Prairies–Pointe-aux-Trembles | 2021-03-29 | 8045 | github | 8045 | 16 | Arrond. | 114732 | 13.945543 | 106 | 117 | 92.38922 | 131.98460 | 131.98460 | 145.68112 | plus de 100 cas par million | 114732 | | Rosemont–La Petite Patrie | 2021-03-29 | 6257 | github | 6257 | 17 | Arrond. | 147624 | 11.515743 | 112 | 74 | 75.86842 | 108.38346 | 108.38346 | 71.61050 | plus de 100 cas par million | 147624 | | Saint-Laurent | 2021-03-29 | 6845 | github | 6845 | 26 | Arrond. | 105248 | 24.703557 | 184 | 151 | 174.82517 | 249.75025 | 249.75025 | 204.95809 | plus de 100 cas par million | 105248 | | Saint-Léonard | 2021-03-29 | 6880 | github | 6880 | 19 | Arrond. | 82841 | 22.935503 | 121 | 108 | 146.06294 | 208.66134 | 208.66134 | 186.24318 | plus de 100 cas par million | 82841 | | Sainte-Anne-de-Bellevue | 2021-03-29 | 122 | github | 122 | 0 | Ville | 5038 | 0.000000 | 2 | 2 | 39.69829 | 56.71185 | 56.71185 | 56.71185 | entre 20 et 60 cas par million | 5038 | | Senneville | 2021-03-29 | 22 | github | 22 | 0 | Ville | 981 | 0.000000 | 0 | 0 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | moins de 20 cas par million | 981 | | Sud-Ouest | 2021-03-29 | 3334 | github | 3334 | 8 | Arrond. | 84299 | 9.490030 | 48 | 44 | 56.94018 | 81.34311 | 81.34311 | 74.56452 | entre 60 et 100 cas par million | 84299 | | Territoire à confirmer | 2021-03-29 | 1745 | github | 1745 | 1 | NA | NA | NA | 21 | 10 | NA | NA | 0.00000 | 0.00000 | moins de 20 cas par million | NA | | Total à Montréal | 2021-03-29 | 115154 | github | 115154 | 306 | NA | 2050053 | 14.926443 | 2168 | 2036 | 105.75336 | 151.07623 | 151.07623 | 141.87787 | plus de 100 cas par million | 2050053 | | Verdun | 2021-03-29 | 2823 | github | 2823 | 9 | Arrond. | 72419 | 12.427678 | 72 | 71 | 99.42142 | 142.03060 | 142.03060 | 140.05796 | plus de 100 cas par million | 72419 | | Ville-Marie | 2021-03-29 | 4138 | github | 4138 | 8 | Arrond. | 95231 | 8.400626 | 69 | 57 | 72.45540 | 103.50771 | 103.50771 | 85.50637 | plus de 100 cas par million | 95231 | | Villeray–Saint-Michel–Parc-Extension | 2021-03-29 | 9576 | github | 9576 | 27 | Arrond. | 148202 | 18.218378 | 189 | 127 | 127.52864 | 182.18378 | 182.18378 | 122.41979 | plus de 100 cas par million | 148202 | | Westmount | 2021-03-29 | 655 | github | 655 | 4 | Ville | 20974 | 19.071231 | 23 | 11 | 109.65958 | 156.65654 | 156.65654 | 74.92269 | plus de 100 cas par million | 20974 |
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
#> orange skyblue bluishgreen yellow blue
#> "#E69F00" "#56B4E9" "#009E73" "#F0E442" "#0072B2"
#> vermillion reddishpurple gray
#> "#D55E00" "#CC79A7" "#999999"
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