inst/examples/examples_from_README.R

In sjbeckett/subregionalcovid19: Processing and mapping COVID-19 case data at subnational scales

library("localcovid19now") #if not already installed

## Functionality is provided to load in data for a particular country, for example:

US <- LoadData("LoadUS")

Malaysia <- LoadData("LoadMalaysia")

US_and_Malaysia <- LoadData(c("LoadUS","LoadMalaysia"))

#Note that to load data for the Philippines the googledrive package is used, requiring users google credentials.
#See more here: https://googledrive.tidyverse.org/reference/drive_auth.html
#credentials can be set before running the function:
googledrive::drive_auth(email = TRUE)

Philippines <- LoadData("LoadPhilippines")

#A list of loading options is given by:
countrylist


## or to load all available datasets as:

GLOBALMAP <- LoadData() #note, by default, this applies tidying to data such that countries whose data is older than 30 days is dropped.

#can additionally see the full scope of potential datasets as:
GLOBALMAP2 <- LoadData(dropNACountry = FALSE) 


## Once loaded, the per capita active case data can be mapped with built in helper functions: 

#show per capita active cases per 100,000 people in leaflet for GLOBALMAP
PerCapitaMap_leaflet(GLOBALMAP,100000)

#show per capita active cases per 100,000 people in tmap for GLOBALMAP2
PerCapitaMap_tmap(GLOBALMAP2,100000)

#Can save this type of object using tmap options.
GMAP = PerCapitaMap_tmap(GLOBALMAP2,100000)
tmap::tmap_save(GMAP,"\inst\examples\Global_pc_tmap.png")

## Additionally, with a provided assumed ascertainment bias (ratio between true infections and recorded cases), the risk that one or more people in a group of a particular size can be estimated and mapped:

#assume an ascertainment bias of 4 infections per recorded case.
US$AB <- 4 

#show risk that one or more people are infectious in a group of 50 using leaflet
EventMap_leaflet(US,50,US$AB)

#show risk that one or more people are infectious in a group of 100 using tmap
EventMap_tmap(US,100,US$AB)

#maps can be saved using tmap commands. Here we also use a projection more suited to the US:
MAP = EventMap_tmap(US,100,US$AB,projection=5070)
tmap::tmap_save(MAP,"\inst\examples\US_RiskMap.png")

#Some resources for choosing projections can be found here: https://proj.org/usage/index.html and here: https://epsg.io/ and 

#we can also create a table of event risk by location as:
create_c19r_data(df_in = US, risk_output = "\inst\examples\USrisk.csv", asc_bias_list = cbind(AB4 = US$AB))

## We caution that the case ascertainment bias may differ both across different regions and across time (due to differences in testing strategies).