Plot a (set of) epidemic curve(s)

plot_epicurve is a simplifying wrapper around ggplot to produce curves of cumulative cases versus time. The input data frame should contain at least:

plot_epicurve(
  df,
  filter_expression,
  date_column = "date",
  case_column = "count",
  ...,
  log = TRUE
)

Arguments

df

a data frame with columns that include at least a date column and an integer count column

filter_expression

an expression that is passed directly to dplyr::filter(). This parameter is a convenience feature since the filtering could also be done easily outside this function.

date_column

character(1) the column name of the date type column

case_column

character(1) the column name of the count of cases column

...

passed to ggplot2::aes_string(), useful providing colors or line types to separate out datasets.

log

logical(1) TRUE for log10 based y-scale, FALSE for linear

Value

a ggplot2 object

Details

• a date column (or any data type that has a natural time order); this will become the x-axis

• a cumulative count column; this will become the y-axis

An additional common use case is to provide a grouping variable in the data.frame; specifying color=... will draw group-specific curves on the same plot. See examples.

See also

Other plotting: align_to_baseline()

Other case-tracking: align_to_baseline(), beoutbreakprepared_data(), bulk_estimate_Rt(), combined_us_cases_data(), coronadatascraper_data(), covidtracker_data(), ecdc_data(), estimate_Rt(), jhu_data(), nytimes_county_data(), owid_data(), test_and_trace_data(), usa_facts_data(), who_cases()

Examples

library(dplyr)


jhu = jhu_data() %>% 
    filter(CountryRegion=='China' & subset=='confirmed') %>% 
    group_by(CountryRegion,date) %>% summarize(count=sum(count))
#> `summarise()` has grouped output by 'CountryRegion'. You can override using the
#> `.groups` argument.

head(jhu)
#> # A tibble: 6 × 3
#> # Groups:   CountryRegion [1]
#>   CountryRegion date       count
#>   <chr>         <date>     <dbl>
#> 1 China         2020-01-22   548
#> 2 China         2020-01-23   643
#> 3 China         2020-01-24   920
#> 4 China         2020-01-25  1406
#> 5 China         2020-01-26  2075
#> 6 China         2020-01-27  2877

jhu %>% plot_epicurve(log=FALSE)


# add a title
library(ggplot2)
jhu %>% plot_epicurve() + ggtitle('Cumulative cases for China')


# Work with testing data
cc = covidtracker_data() %>%
    dplyr::mutate(total_tests = positive+negative) %>%
    dplyr::filter(total_tests>0)
#> Warning: One or more parsing issues, see `problems()` for details
head(cc)
#> # A tibble: 6 × 17
#>   date       fips  state positive negative death pending hospitalized
#>   <date>     <chr> <chr>    <int>    <int> <int>   <int>        <int>
#> 1 2021-03-07 00001 AL      499819  1931711 10148      NA        45976
#> 2 2021-03-07 00005 AR      324818  2480716  5319      NA        14926
#> 3 2021-03-07 00060 AS           0     2140     0      NA           NA
#> 4 2021-03-07 00004 AZ      826454  3073010 16328      NA        57907
#> 5 2021-03-07 00008 CO      436602  2199458  5989      NA        23904
#> 6 2021-03-07 00010 DE       88354   545070  1473      NA           NA
#> # … with 9 more variables: hospitalizedCurrently <int>, recovered <int>,
#> #   inIcuCumulative <int>, inIcuCurrently <int>, onVentilatorCurrently <int>,
#> #   onVentilatorCumulative <int>, dateChecked <dttm>, dataQualityGrade <lgl>,
#> #   total_tests <int>

plot_epicurve(cc, case_column='total_tests', color='state', log=FALSE) +
    ggtitle('Total tests by state') +
    ggplot2::theme(legend.position='bottom')


# get tests per 100k population
# use the tidycensus package to get
# population data
if(require(tidycensus)) {
    pop = tidycensus::get_estimates(geography='state',product='population') %>%
        dplyr::filter(variable=='POP')
    head(pop)
    # fix GEOID column to be 5-digit fips
    pop$GEOID=integer_to_fips(as.integer(pop$GEOID))
    cc_pop = merge(cc,pop, by.x='fips', by.y='GEOID', all.x=FALSE, all.y=FALSE)
    cc_pop = cc_pop %>% mutate(tests_per_100k = total_tests/value * 100000)
    plot_epicurve(cc_pop, case_column='tests_per_100k', color='state', log=FALSE) +
        ggtitle('Total tests per 100,000 people') +
        ggplot2::theme(legend.position='bottom')
}
#> Loading required package: tidycensus
#> Error in tidycensus::get_estimates(geography = "state", product = "population"): A Census API key is required.  Obtain one at http://api.census.gov/data/key_signup.html, and then supply the key to the `census_api_key()` function to use it throughout your tidycensus session.