dataallaround / mapcovid19

COVID-19 Datasets with Coordinates

COVID-19 Datasets with Coordinates

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.

Table of contents

• Download in R
  • Italy
  • USA
  • WORLD
• Static map examples in R
• Dynamic map examples in R
• Sources
• References
• Citation

Download in R

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

---

Italy

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.

USA

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.

WORLD

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.

Static map examples in R

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")

Dynamic map examples in R

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")

Sources

• Dipartimento della Protezione Civile Italiana
• ISTAT
• United States Census Bureau
• Center For Systems Science and Engineering at JHU
• World Health Organization
• Eurostat Country GISCO
• Apple Mobility Trends Reports

References

• sf

  • 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

• mapview

  • 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/

• tmap

  • 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

Citation

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}
}