README.md

covid19us

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MIT

This is an R wrapper around the COVID Tracking Project API. It provides updates on the spread of the virus in the US with a few simple functions.

Installation

install.packages("covid19us")

Or the dev version:

devtools::install_github("aedobbyn/covid19us")

Examples

library(covid19us)

Get the most recent COVID-19 top-line data for the country:

get_us_current()
#> # A tibble: 1 x 18
#>   positive negative pending hospitalized_cu… hospitalized_cu… in_icu_currently
#>      <int>    <int>   <int>            <int>            <int>            <int>
#> 1  1520778 10713209    2944            41174           159634             9829
#> # … with 12 more variables: in_icu_cumulative <int>,
#> #   on_ventilator_currently <int>, on_ventilator_cumulative <int>,
#> #   recovered <int>, hash <chr>, last_modified <chr>, death <int>,
#> #   hospitalized <int>, total <int>, total_test_results <int>, notes <chr>,
#> #   request_datetime <dttm>

Or the same by state:

get_states_current()
#> # A tibble: 56 x 30
#>    state positive positive_score negative_score negative_regula…
#>    <chr>    <int>          <int>          <int>            <int>
#>  1 AK         399              1              1                1
#>  2 AL       12376              1              1                0
#>  3 AR        4923              1              1                1
#>  4 AZ       14566              1              1                0
#>  5 CA       81795              1              1                0
#>  6 CO       22202              1              1                1
#>  7 CT       38430              1              1                1
#>  8 DC        7434              1              1                1
#>  9 DE        8037              1              1                1
#> 10 FL       46944              1              1                1
#> # … with 46 more rows, and 25 more variables: commercial_score <int>,
#> #   grade <chr>, score <int>, notes <chr>, data_quality_grade <chr>,
#> #   negative <int>, pending <int>, hospitalized_currently <int>,
#> #   hospitalized_cumulative <int>, in_icu_currently <int>,
#> #   in_icu_cumulative <int>, on_ventilator_currently <int>,
#> #   on_ventilator_cumulative <int>, recovered <int>, last_update <dttm>,
#> #   check_time <dttm>, death <int>, hospitalized <int>, total <int>,
#> #   total_test_results <int>, fips <chr>, date_modified <dttm>,
#> #   date_checked <dttm>, hash <chr>, request_datetime <dttm>

Daily state counts can be filtered by state and/or date:

get_states_daily(
  state = "NY", 
  date = "2020-03-17"
)
#> # A tibble: 1 x 27
#>   date       state positive negative pending hospitalized_cu… hospitalized_cu…
#>   <date>     <chr>    <int>    <int>   <int>            <int>            <int>
#> 1 2020-03-17 NY        1700     5506      NA              325               NA
#> # … with 20 more variables: in_icu_currently <int>, in_icu_cumulative <int>,
#> #   on_ventilator_currently <int>, on_ventilator_cumulative <int>,
#> #   recovered <int>, data_quality_grade <chr>, last_update <dttm>, hash <chr>,
#> #   date_checked <dttm>, death <int>, hospitalized <int>, total <int>,
#> #   total_test_results <int>, fips <chr>, death_increase <int>,
#> #   hospitalized_increase <int>, negative_increase <int>,
#> #   positive_increase <int>, total_test_results_increase <int>,
#> #   request_datetime <dttm>

For data in long format:

(dat <- refresh_covid19us())
#> # A tibble: 80,123 x 7
#>    date       location location_type location_code location_code_t… data_type
#>    <date>     <chr>    <chr>         <chr>         <chr>            <chr>    
#>  1 2020-05-19 AK       state         02            fips_code        positive 
#>  2 2020-05-19 AK       state         02            fips_code        negative 
#>  3 2020-05-19 AK       state         02            fips_code        pending  
#>  4 2020-05-19 AK       state         02            fips_code        hospital…
#>  5 2020-05-19 AK       state         02            fips_code        hospital…
#>  6 2020-05-19 AK       state         02            fips_code        in_icu_c…
#>  7 2020-05-19 AK       state         02            fips_code        in_icu_c…
#>  8 2020-05-19 AK       state         02            fips_code        on_venti…
#>  9 2020-05-19 AK       state         02            fips_code        on_venti…
#> 10 2020-05-19 AK       state         02            fips_code        recovered
#> # … with 80,113 more rows, and 1 more variable: value <int>

Which can be easier to plot

library(dplyr)
library(ggplot2)

dat %>% 
  filter(
    location == "NY" &
      data_type %in% 
      c(
        "positive_increase",
        "total_test_results_increase",
        "death_increase",
        "hospitalized_increase"
      )
  ) %>% 
  mutate(
    Type = data_type %>% 
      stringr::str_replace_all("_", " ") %>% 
      stringr::str_to_title()
  ) %>% 
  arrange(date) %>% 
  ggplot(aes(date, value, color = Type)) +
  geom_smooth(se = FALSE) + 
  scale_x_date(date_breaks = "2 weeks") +
  labs(title = "COVID in NY") +
  xlab("Date") +
  ylab("Value") +
  theme_minimal(base_family = "Source Sans Pro")

To get information about the data:

get_info_covid19us()
#> # A tibble: 1 x 10
#>   data_set_name package_name function_to_get… data_details data_url license_url
#>   <chr>         <chr>        <chr>            <chr>        <chr>    <chr>      
#> 1 covid19us     covid19us    refresh_covid19… Open Source… https:/… https://gi…
#> # … with 4 more variables: data_types <chr>, location_types <chr>,
#> #   spatial_extent <chr>, has_geospatial_info <lgl>

All Functions

get_counties_info
get_info_covid19us
get_states_current
get_states_daily
get_states_info
get_tracker_urls
get_us_current
get_us_daily
refresh_covid19us

Other Details

PRs and bug reports / feature requests welcome. Stay safe!



aedobbyn/covid19us documentation built on Sept. 4, 2020, 1:21 p.m.