README.md
In avallecam/covid19viz: Vizualize the WHO COVID-19 sitreps per Country
covid19viz
The goal of covid19viz is to access and summarize WHO sitreps for
covid-19 in simple graphics.
This package works using two data repositories:
-
digitalized WHO sitreps by Fabienne
Krauer available in
fkrauer/COVID-19.
-
Johns Hopkins University (JHU CSSE) available in
CSSEGISandData/COVID-19.
Additionally, it imports external data sources like:
-
Google Mobility Reports.
View.
-
ACAPS data on #COVID19 Government Measures Dataset.
View.
-
UNESCO COVID-19 Impact on Education.
View.
Installation
You can install the development version of covid19viz using:
if(!require("remotes")) install.packages("remotes")
remotes::install_github("avallecam/covid19viz")
Quick Examples
Import and visualize JHU collection
library(covid19viz)
library(tidyverse)
#import all data at once
jhu_sitrep_peru <- jhu_sitrep_all_sources(country_region="Peru")
jhu_sitrep_peru
#> # A tibble: 3 x 4
#> country_region source data_filter sum_data
#> <chr> <chr> <list> <dbl>
#> 1 Peru confirmed <tibble [242 x 7]> 756412
#> 2 Peru deaths <tibble [242 x 7]> 31283
#> 3 Peru recovered <tibble [242 x 7]> 594513
#transform to tidy format
jhu_sitrep_peru %>%
jhu_sitrep_all_sources_tidy() %>%
arrange(desc(confirmed_cumulative)) %>%
glimpse()
#> Rows: 242
#> Columns: 8
#> $ country_region <chr> "Peru", "Peru", "Peru", "Peru", "Peru", "...
#> $ date <date> 2020-09-19, 2020-09-18, 2020-09-17, 2020...
#> $ confirmed_cumulative <dbl> 756412, 750098, 744400, 738020, 733860, 7...
#> $ deaths_cumulative <dbl> 31283, 31146, 31051, 30927, 30812, 30710,...
#> $ recovered_cumulative <dbl> 594513, 594513, 594513, 580753, 573364, 5...
#> $ confirmed_incidence <dbl> 6314, 5698, 6380, 4160, 4241, 6787, 6162,...
#> $ deaths_incidence <dbl> 137, 95, 124, 115, 102, 184, 56, 126, 108...
#> $ recovered_incidence <dbl> 0, 0, 13760, 7389, 6568, 7475, 0, 14576, ...
jhu_sitrep_country_report(country_region = "Peru")
Import more external data sources
- Google Mobility Reports.
View.
- ACAPS data on #COVID19 Government Measures Dataset.
View.
- UNESCO COVID-19 Impact on Education.
View.
library(covid19viz)
library(tidyverse)
# google mobility reports
# global
read_google_global() %>% glimpse()
#> Rows: 2,441,517
#> Columns: 14
#> $ country_region_code <chr> "AE", "AE", "AE", ...
#> $ country_region <chr> "United Arab Emira...
#> $ sub_region_1 <chr> NA, NA, NA, NA, NA...
#> $ sub_region_2 <lgl> NA, NA, NA, NA, NA...
#> $ metro_area <lgl> NA, NA, NA, NA, NA...
#> $ iso_3166_2_code <chr> NA, NA, NA, NA, NA...
#> $ census_fips_code <lgl> NA, NA, NA, NA, NA...
#> $ date <date> 2020-02-15, 2020-...
#> $ retail_and_recreation_percent_change_from_baseline <dbl> 0, 1, -1, -2, -2, ...
#> $ grocery_and_pharmacy_percent_change_from_baseline <dbl> 4, 4, 1, 1, 0, 1, ...
#> $ parks_percent_change_from_baseline <dbl> 5, 4, 5, 5, 4, 6, ...
#> $ transit_stations_percent_change_from_baseline <dbl> 0, 1, 1, 0, -1, 1,...
#> $ workplaces_percent_change_from_baseline <dbl> 2, 2, 2, 2, 2, 1, ...
#> $ residential_percent_change_from_baseline <dbl> 1, 1, 1, 1, 1, 1, ...
# regional
# first: select country ISO
read_google_region_list()
#> # A tibble: 135 x 4
#> country_iso Name Length Date
#> <chr> <chr> <dbl> <dttm>
#> 1 AE 2020_AE_Region_Mobility_Report.csv 121354 2020-09-13 15:27:00
#> 2 AF 2020_AF_Region_Mobility_Report.csv 20553 2020-09-13 15:27:00
#> 3 AG 2020_AG_Region_Mobility_Report.csv 57484 2020-09-13 15:27:00
#> 4 AO 2020_AO_Region_Mobility_Report.csv 58954 2020-09-13 15:27:00
#> 5 AR 2020_AR_Region_Mobility_Report.csv 6932241 2020-09-13 15:27:00
#> 6 AT 2020_AT_Region_Mobility_Report.csv 1402047 2020-09-13 15:27:00
#> 7 AU 2020_AU_Region_Mobility_Report.csv 4329094 2020-09-13 15:27:00
#> 8 AW 2020_AW_Region_Mobility_Report.csv 9967 2020-09-13 15:27:00
# second: read specific country data
peru <- read_google_region_country(country_iso = "PE")
peru %>% glimpse()
#> Rows: 28,194
#> Columns: 14
#> $ country_region_code <chr> "PE", "PE", "PE", ...
#> $ country_region <chr> "Peru", "Peru", "P...
#> $ sub_region_1 <chr> NA, NA, NA, NA, NA...
#> $ sub_region_2 <chr> NA, NA, NA, NA, NA...
#> $ metro_area <lgl> NA, NA, NA, NA, NA...
#> $ iso_3166_2_code <chr> NA, NA, NA, NA, NA...
#> $ census_fips_code <lgl> NA, NA, NA, NA, NA...
#> $ date <date> 2020-02-15, 2020-...
#> $ retail_and_recreation_percent_change_from_baseline <dbl> 4, 1, 0, 0, 0, 0, ...
#> $ grocery_and_pharmacy_percent_change_from_baseline <dbl> 1, 0, 1, 0, -1, -1...
#> $ parks_percent_change_from_baseline <dbl> 3, -2, 1, 0, 0, -1...
#> $ transit_stations_percent_change_from_baseline <dbl> 3, 2, 2, 1, 0, 3, ...
#> $ workplaces_percent_change_from_baseline <dbl> 0, 0, 1, 1, 1, 0, ...
#> $ residential_percent_change_from_baseline <dbl> -1, 0, 0, 0, 1, 0,...
peru %>% count(sub_region_1)
#> # A tibble: 27 x 2
#> sub_region_1 n
#> <chr> <int>
#> 1 Amazonas 1050
#> 2 Ancash 1871
#> 3 Apurimac 816
#> 4 Arequipa 1470
#> 5 Ayacucho 1050
#> 6 Cajamarca 1850
#> 7 Callao Region 210
#> 8 Cusco 1668
#> 9 Huancavelica 630
#> 10 Huanuco 630
#> # ... with 17 more rows
# acaps data
read_acaps_governments() %>% glimpse()
#> Rows: 193
#> Columns: 8
#> $ Country_name <chr> "Afghanistan", "Albania", "Algeria", "Angola",...
#> $ ISO3 <chr> "AFG", "ALB", "DZA", "AGO", "ATG", "ARG", "ARM...
#> $ REGION <chr> "Asia", "Europe", "Africa", "Africa", "America...
#> $ Measure_taxonomy <chr> "Schools closure ", "Border checks ", "Border ...
#> $ Measure_Category <chr> "Social distancing", "Movement restrictions", ...
#> $ SOURCE_TYPE <chr> "Government", "Media", "UN", "Other organisati...
#> $ Enforcement_taxonomy <chr> "Fines", "Arrest/Detention", "Deportation", "R...
#> $ Log_type <chr> "Introduction / extension of measures", "Phase...
# unesco education data
read_unesco_education() %>% glimpse()
#> Rows: 41,714
#> Columns: 5
#> $ Date <chr> "17/02/2020", "17/02/2020", "18/02/2020", "...
#> $ ISO <chr> "CHN", "MNG", "CHN", "MNG", "CHN", "MNG", "...
#> $ Country <chr> "China", "Mongolia", "China", "Mongolia", "...
#> $ Status <chr> "Partially open", "Closed due to COVID-19",...
#> $ Note <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
Import initial stages of the WHO sitreps
- Created and maintained by Fabienne
Krauer
library(covid19viz)
# paste last update available at
# https://github.com/fkrauer/COVID-19
update <- "2020-03-10"
# apply
who_sitrep_country_report(
update = update,
country_region = "Brazil")
avallecam/covid19viz documentation built on Sept. 24, 2020, 3:43 p.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.
covid19viz
The goal of covid19viz is to access and summarize WHO sitreps for
covid-19 in simple graphics.
This package works using two data repositories:
-
digitalized WHO sitreps by Fabienne Krauer available in fkrauer/COVID-19.
-
Johns Hopkins University (JHU CSSE) available in CSSEGISandData/COVID-19.
Additionally, it imports external data sources like:
-
Google Mobility Reports. View.
-
ACAPS data on #COVID19 Government Measures Dataset. View.
-
UNESCO COVID-19 Impact on Education. View.
Installation
You can install the development version of covid19viz using:
if(!require("remotes")) install.packages("remotes")
remotes::install_github("avallecam/covid19viz")
Quick Examples
Import and visualize JHU collection
library(covid19viz)
library(tidyverse)
#import all data at once
jhu_sitrep_peru <- jhu_sitrep_all_sources(country_region="Peru")
jhu_sitrep_peru
#> # A tibble: 3 x 4
#> country_region source data_filter sum_data
#> <chr> <chr> <list> <dbl>
#> 1 Peru confirmed <tibble [242 x 7]> 756412
#> 2 Peru deaths <tibble [242 x 7]> 31283
#> 3 Peru recovered <tibble [242 x 7]> 594513
#transform to tidy format
jhu_sitrep_peru %>%
jhu_sitrep_all_sources_tidy() %>%
arrange(desc(confirmed_cumulative)) %>%
glimpse()
#> Rows: 242
#> Columns: 8
#> $ country_region <chr> "Peru", "Peru", "Peru", "Peru", "Peru", "...
#> $ date <date> 2020-09-19, 2020-09-18, 2020-09-17, 2020...
#> $ confirmed_cumulative <dbl> 756412, 750098, 744400, 738020, 733860, 7...
#> $ deaths_cumulative <dbl> 31283, 31146, 31051, 30927, 30812, 30710,...
#> $ recovered_cumulative <dbl> 594513, 594513, 594513, 580753, 573364, 5...
#> $ confirmed_incidence <dbl> 6314, 5698, 6380, 4160, 4241, 6787, 6162,...
#> $ deaths_incidence <dbl> 137, 95, 124, 115, 102, 184, 56, 126, 108...
#> $ recovered_incidence <dbl> 0, 0, 13760, 7389, 6568, 7475, 0, 14576, ...
jhu_sitrep_country_report(country_region = "Peru")
Import more external data sources
- Google Mobility Reports. View.
- ACAPS data on #COVID19 Government Measures Dataset. View.
- UNESCO COVID-19 Impact on Education. View.
library(covid19viz)
library(tidyverse)
# google mobility reports
# global
read_google_global() %>% glimpse()
#> Rows: 2,441,517
#> Columns: 14
#> $ country_region_code <chr> "AE", "AE", "AE", ...
#> $ country_region <chr> "United Arab Emira...
#> $ sub_region_1 <chr> NA, NA, NA, NA, NA...
#> $ sub_region_2 <lgl> NA, NA, NA, NA, NA...
#> $ metro_area <lgl> NA, NA, NA, NA, NA...
#> $ iso_3166_2_code <chr> NA, NA, NA, NA, NA...
#> $ census_fips_code <lgl> NA, NA, NA, NA, NA...
#> $ date <date> 2020-02-15, 2020-...
#> $ retail_and_recreation_percent_change_from_baseline <dbl> 0, 1, -1, -2, -2, ...
#> $ grocery_and_pharmacy_percent_change_from_baseline <dbl> 4, 4, 1, 1, 0, 1, ...
#> $ parks_percent_change_from_baseline <dbl> 5, 4, 5, 5, 4, 6, ...
#> $ transit_stations_percent_change_from_baseline <dbl> 0, 1, 1, 0, -1, 1,...
#> $ workplaces_percent_change_from_baseline <dbl> 2, 2, 2, 2, 2, 1, ...
#> $ residential_percent_change_from_baseline <dbl> 1, 1, 1, 1, 1, 1, ...
# regional
# first: select country ISO
read_google_region_list()
#> # A tibble: 135 x 4
#> country_iso Name Length Date
#> <chr> <chr> <dbl> <dttm>
#> 1 AE 2020_AE_Region_Mobility_Report.csv 121354 2020-09-13 15:27:00
#> 2 AF 2020_AF_Region_Mobility_Report.csv 20553 2020-09-13 15:27:00
#> 3 AG 2020_AG_Region_Mobility_Report.csv 57484 2020-09-13 15:27:00
#> 4 AO 2020_AO_Region_Mobility_Report.csv 58954 2020-09-13 15:27:00
#> 5 AR 2020_AR_Region_Mobility_Report.csv 6932241 2020-09-13 15:27:00
#> 6 AT 2020_AT_Region_Mobility_Report.csv 1402047 2020-09-13 15:27:00
#> 7 AU 2020_AU_Region_Mobility_Report.csv 4329094 2020-09-13 15:27:00
#> 8 AW 2020_AW_Region_Mobility_Report.csv 9967 2020-09-13 15:27:00
# second: read specific country data
peru <- read_google_region_country(country_iso = "PE")
peru %>% glimpse()
#> Rows: 28,194
#> Columns: 14
#> $ country_region_code <chr> "PE", "PE", "PE", ...
#> $ country_region <chr> "Peru", "Peru", "P...
#> $ sub_region_1 <chr> NA, NA, NA, NA, NA...
#> $ sub_region_2 <chr> NA, NA, NA, NA, NA...
#> $ metro_area <lgl> NA, NA, NA, NA, NA...
#> $ iso_3166_2_code <chr> NA, NA, NA, NA, NA...
#> $ census_fips_code <lgl> NA, NA, NA, NA, NA...
#> $ date <date> 2020-02-15, 2020-...
#> $ retail_and_recreation_percent_change_from_baseline <dbl> 4, 1, 0, 0, 0, 0, ...
#> $ grocery_and_pharmacy_percent_change_from_baseline <dbl> 1, 0, 1, 0, -1, -1...
#> $ parks_percent_change_from_baseline <dbl> 3, -2, 1, 0, 0, -1...
#> $ transit_stations_percent_change_from_baseline <dbl> 3, 2, 2, 1, 0, 3, ...
#> $ workplaces_percent_change_from_baseline <dbl> 0, 0, 1, 1, 1, 0, ...
#> $ residential_percent_change_from_baseline <dbl> -1, 0, 0, 0, 1, 0,...
peru %>% count(sub_region_1)
#> # A tibble: 27 x 2
#> sub_region_1 n
#> <chr> <int>
#> 1 Amazonas 1050
#> 2 Ancash 1871
#> 3 Apurimac 816
#> 4 Arequipa 1470
#> 5 Ayacucho 1050
#> 6 Cajamarca 1850
#> 7 Callao Region 210
#> 8 Cusco 1668
#> 9 Huancavelica 630
#> 10 Huanuco 630
#> # ... with 17 more rows
# acaps data
read_acaps_governments() %>% glimpse()
#> Rows: 193
#> Columns: 8
#> $ Country_name <chr> "Afghanistan", "Albania", "Algeria", "Angola",...
#> $ ISO3 <chr> "AFG", "ALB", "DZA", "AGO", "ATG", "ARG", "ARM...
#> $ REGION <chr> "Asia", "Europe", "Africa", "Africa", "America...
#> $ Measure_taxonomy <chr> "Schools closure ", "Border checks ", "Border ...
#> $ Measure_Category <chr> "Social distancing", "Movement restrictions", ...
#> $ SOURCE_TYPE <chr> "Government", "Media", "UN", "Other organisati...
#> $ Enforcement_taxonomy <chr> "Fines", "Arrest/Detention", "Deportation", "R...
#> $ Log_type <chr> "Introduction / extension of measures", "Phase...
# unesco education data
read_unesco_education() %>% glimpse()
#> Rows: 41,714
#> Columns: 5
#> $ Date <chr> "17/02/2020", "17/02/2020", "18/02/2020", "...
#> $ ISO <chr> "CHN", "MNG", "CHN", "MNG", "CHN", "MNG", "...
#> $ Country <chr> "China", "Mongolia", "China", "Mongolia", "...
#> $ Status <chr> "Partially open", "Closed due to COVID-19",...
#> $ Note <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
Import initial stages of the WHO sitreps
- Created and maintained by Fabienne Krauer
library(covid19viz)
# paste last update available at
# https://github.com/fkrauer/COVID-19
update <- "2020-03-10"
# apply
who_sitrep_country_report(
update = update,
country_region = "Brazil")
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.
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