covid19R

The goal of covid19R is to provide a single package that allows users to access all of the tidy covid-19 datasets collected by data packages that implement the covid19R tidy data standard. It provides access to multiple data sets that meet a tidy data standard.

To learn more abou the Covid19R project, check our extensive documentation about data standards, how to get your data added to this list, and more.

Installation

You can install the development version from github with:

remotes::install_github("covid19r/covid19r")

Getting the Data Information

To see what datasets are available, use get_covid19_data_info()

library(covid19R)

data_info <- get_covid19_data_info()

head(data_info) %>% knitr::kable()

Accessing data

Once you have figured out what dataset you want, you can access it with get_covid19_dataset()

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

nytimes_states <- get_covid19_dataset("covid19nytimes_states")
#> Parsed with column specification:
#> cols(
#>   date = col_date(format = ""),
#>   location = col_character(),
#>   location_type = col_character(),
#>   location_code = col_character(),
#>   location_code_type = col_character(),
#>   data_type = col_character(),
#>   value = col_double()
#> )

nytimes_states %>%
  filter(date == max(date)) %>%
  filter(data_type == "cases_total") %>%
  arrange(desc(value)) %>%
  head()
#> # A tibble: 6 x 7
#>   date       location location_type location_code location_code_t… data_type
#>   <date>     <chr>    <chr>         <chr>         <chr>            <chr>    
#> 1 2020-05-03 New York state         36            fips_code        cases_to…
#> 2 2020-05-03 New Jer… state         34            fips_code        cases_to…
#> 3 2020-05-03 Massach… state         25            fips_code        cases_to…
#> 4 2020-05-03 Illinois state         17            fips_code        cases_to…
#> 5 2020-05-03 Califor… state         06            fips_code        cases_to…
#> 6 2020-05-03 Pennsyl… state         42            fips_code        cases_to…
#> # … with 1 more variable: value <dbl>

The covid19R Data Standard

While many data sets have their own unique additional columns (e.g., Latitude, Longitude, population, etc.), all datasets have the following columns and are arranged in a long format:

• date - The date in YYYY-MM-DD form
• location - The name of the location as provided by the data source. The counties dataset provides county and state. They are combined and separated by a ,, and can be split by tidyr::separate(), if you wish.
• location_type - The type of location using the covid19R controlled vocabulary. Nested locations are indicated by multiple location types being combined with a `_
• location_code - A standardized location code using a national or international standard. In this case, FIPS state or county codes. See https://en.wikipedia.org/wiki/Federal_Information_Processing_Standard_state_code and https://en.wikipedia.org/wiki/FIPS_county_code for more
• location_code_type The type of standardized location code being used according to the covid19R controlled vocabulary. Here we use fips_code
• data_type - the type of data in that given row. Includes total_cases and total_deaths, cumulative measures of both.
• value - number of cases of each data type

Vocabularies

The location_type, location_code_type, and data_type from datasets and spatial_extent from the data info table all have their own controlled vocabularies. Others might be introduced as the collection of packages matures. To see the possible values of a standardized vocabulary, use get_covid19_controlled_vocab()

get_covid19_controlled_vocab("location_type") %>%
  knitr::kable()