This repository aims to simplify the visualisation of the COVID-19 datasets. Data and geometries are provided in the same file and with different formats to immediately plot the data in R or other software for geospatial data.
The R folder provides functions to directly download the data in R, for Italy, and USA.
For more details on the functions and packages used look at the references section.
| functions | description |
|---|---|
getDataCovid_IT() |
retrives data from this repository for the italian cases |
getDataCovid_US() |
retrives data from this repository for the United States of America cases |
Data can be downloaded first loaded the R function getDataCovid_IT() available in the R folder of this repo:
library(devtools)
source_url("https://raw.githubusercontent.com/dataallaround/mapCOVID19/master/R/getDataCovid_IT.R")getDataCovid_IT() requires the date in the format yyyy-mm-dd, and the geographical level, "regioni" or "province":
dt = getDataCovid_IT(date = "2020-04-17", level = "regioni")
names(dt)## [1] "date" "ripartizione" "regione"
## [4] "hospitalized" "ventilation" "hospitalized_total"
## [7] "confinement" "current_positive" "variation_current_positive"
## [10] "new_positive_total" "recovered" "death"
## [13] "positive" "tests" "geometry"
for "regions", and for "province":
dt = getDataCovid_IT(date = "2020-04-17", level = "province")
names(dt)## [1] "regione" "provincia" "date" "ripartizione" "positive" "geometry"
Multiple date can be loaded and aggregate in a single file:
dt = getDataCovid_IT(date = c("2020-04-17","2020-04-16"), level = "regioni")
names(dt)## [1] "date" "ripartizione" "regione"
## [4] "hospitalized" "ventilation" "hospitalized_total"
## [7] "confinement" "current_positive" "variation_current_positive"
## [10] "new_positive_total" "recovered" "death"
## [13] "positive" "tests" "geometry"
Data can be also downloaded using "download_dir" in getDataCovid_IT() function, indicating the destination directory.
Data can be downloaded first loaded the R function getDataCovid_US() available in the R folder of this repo:
library(devtools)
source_url("https://raw.githubusercontent.com/dataallaround/mapCOVID19/master/R/getDataCovid_US.R")getDataCovid_US() requires the date in the format yyyy-mm-dd, for only the states of US:
dtUS = getDataCovid_US(date = "2020-04-17")
names(dtUS)## [1] "abbr" "state" "date" "Confirmed"
## [5] "Deaths" "Recovered" "Active" "FIPS"
## [9] "Incident_Rate" "People_Tested" "People_Hospitalized" "Mortality_Rate"
## [13] "UID" "ISO3" "Testing_Rate" "Hospitalization_Rate"
## [17] "geometry"
Multiple date can be loaded and aggregate in a single file:
dtUS = getDataCovid_US(date = c("2020-04-17","2020-04-16"))
names(dtUS)## [1] "abbr" "state" "date" "Confirmed"
## [5] "Deaths" "Recovered" "Active" "FIPS"
## [9] "Incident_Rate" "People_Tested" "People_Hospitalized" "Mortality_Rate"
## [13] "UID" "ISO3" "Testing_Rate" "Hospitalization_Rate"
## [17] "geometry"
Data can be also downloaded using "download_dir" in getDataCovid_US() function, indicating the destination directory.
Data can be downloaded first loaded the R function getDataCovid_WORLD() available in the R folder of this repo:
library(devtools)
source_url("https://raw.githubusercontent.com/dataallaround/mapCOVID19/master/R/getDataCovid_WORLD.R")getDataCovid_WORLD() requires the date in the format yyyy-mm-dd, for only the states of US:
dtWD = getDataCovid_WORLD(date = "2020-04-17")
names(dtWD)## [1] "date" "country" "country_id" "iso3"
## [5] "region" "deaths" "cumulative_deaths" "confirmed"
## [9] "cumulative_confirmed" "geometry"
Multiple date can be loaded and aggregate in a single file:
dtWD = getDataCovid_WORLD(date = c("2020-04-17","2020-04-16"))
names(dtWD)## [1] "date" "country" "country_id" "iso3"
## [5] "region" "deaths" "cumulative_deaths" "confirmed"
## [9] "cumulative_confirmed" "geometry"
Specific countries can be download used country for the country name, country_id for iso2 country id, and country_iso3 for iso3 country id.
dtWD = getDataCovid_WORLD(date = "2020-04-19", country = "Italy")
names(dtWD)## [1] "date" "country" "country_id" "iso3"
## [5] "region" "deaths" "cumulative_deaths" "confirmed"
## [9] "cumulative_confirmed" "geometry"
dtWD = getDataCovid_WORLD(date = c("2020-04-18","2020-04-19"), country_iso3 = c("ITA", "ESP"))
names(dtWD)## [1] "date" "country" "country_id" "iso3"
## [5] "region" "deaths" "cumulative_deaths" "confirmed"
## [9] "cumulative_confirmed" "geometry"
Data can be also downloaded using "download_dir" in getDataCovid_US() function, indicating the destination directory.
Some examples to map the data in R software.
library(sf)
library(tmap)
library(cartography)
suppressMessages(tmap_mode("plot"))
dt = getDataCovid_IT(date = "2020-04-17", level = "regioni")
dtUS = getDataCovid_US(date = "2020-04-17")
dtWD = getDataCovid_WORLD(date = "2020-04-19")tm_shape(dt) + tm_borders() + tm_fill("positive") + tm_layout(frame = FALSE)
library("grid")
alaska <- tm_shape(dtUS[dtUS$state=="Alaska",], projection = 3338) + tm_borders() + tm_layout("Alaska", legend.show = FALSE, bg.color = NA, title.size = 0.8, frame = FALSE) + tm_fill("Confirmed", n = 10)
hawaii <- tm_shape(dtUS[dtUS$state=="Hawaii",], projection = 3759) + tm_borders() + tm_layout("Hawaii",legend.show = FALSE, bg.color=NA, title.position = c("LEFT", "BOTTOM"), title.size = 0.8, frame=FALSE) + tm_fill("Confirmed", n = 10)
alk <- viewport(x = 0.15, y = 0.15, width = 0.3, height = 0.3)
haw <- viewport(x = 0.4, y = 0.1, width = 0.2, height = 0.1)
tm_shape(dtUS[!(dtUS$state %in% c("Alaska", "Hawaii")),], projection=2163) + tm_borders() + tm_fill("Confirmed", n = 10) + tm_layout(legend.position = NULL, frame = FALSE, inner.margins = c(0.1, 0.1, 0.05, 0.05), legend.show = FALSE)
print(alaska, vp = alk)
print(hawaii, vp = haw)
tm_shape(dtWD) + tm_borders() + tm_fill("deaths", n = 10) + tm_layout(frame = FALSE, legend.show = TRUE)
tm_shape(dt) + tm_borders() + tm_fill("positive", n = 10) + tm_facets("regione")
For different date we can plot as follows:
dt = getDataCovid_IT(date = c("2020-04-17","2020-04-16","2020-04-15"), level = "regioni")
tm_shape(dt) + tm_borders() + tm_fill("positive") + tm_facets("date")
dtWD = getDataCovid_WORLD(date = c("2020-04-01","2020-04-19"), country_iso3 = c("ITA", "ESP"))
tm_shape(dtWD) + tm_borders() + tm_fill("deaths") + tm_facets(c("date","country"))
Starting from nuts3 (regions), we can aggregate, for example, by nuts1 (ripartizione)
dt = getDataCovid_IT(date = c("2020-04-17"), level = "province")
dt_aggr <- aggregate(dt[,"positive"], by = list( group = dt[,"ripartizione", drop = TRUE]), FUN = sum)
tm_shape(dt_aggr) + tm_borders() + tm_fill("positive")
tm_shape(dt_aggr) + tm_borders() + tm_fill("positive") + tm_facets("group")
library(tmap)
library(mapview)
suppressMessages(tmap_mode("view"))
dt = getDataCovid_IT(date = "2020-04-17", level = "regioni")
dtUS = getDataCovid_US(date = "2020-04-17")tm_shape(dt) + tm_borders() + tm_fill("positive")tm_shape(dtUS) + tm_borders() + tm_fill("Confirmed")mapview(dt, zcol = "positive")mapview(dtUS, zcol = "Confirmed")Dipartimento della Protezione Civile ItalianaISTATUnited States Census BureauCenter For Systems Science and Engineering at JHUWorld Health OrganizationEurostat Country GISCOApple Mobility Trends Reports
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- Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal, 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009
- https://r-spatial.github.io/sf/index.html
-
- Tim Appelhans, Florian Detsch, Christoph Reudenbach and Stefan Woellauer (2019). mapview: Interactive Viewing of Spatial Data in R. R package version 2.7.0. https://CRAN.R-project.org/package=mapview
- https://r-spatial.github.io/mapview/
-
- Tennekes M (2018). “tmap: Thematic Maps in R.” Journal of Statistical Software, 84(6), 1-39. doi: 10.18637/jss.v084.i06 (URL: https://doi.org/10.18637/jss.v084.i06).
- https://cran.r-project.org/web/packages/tmap/vignettes/tmap-getstarted.html
Serafini A. (2020). dataallaround/mapCOVID19: mapCOVID19 (Version v1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3763033
@dataset{alessio_2020_3763033,
author = {Alessio},
title = {dataallaround/mapCOVID19: mapCOVID19},
month = apr,
year = 2020,
publisher = {Zenodo},
version = {v1.0},
doi = {10.5281/zenodo.3763033},
url = {https://doi.org/10.5281/zenodo.3763033}
}