plot_epicurve: Plot a (set of) epidemic curve(s)

In seandavi/sars2pack: COVID-19 data resources and analysis tools

Plot a (set of) epidemic curve(s)

Description

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

Usage

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

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.

Value

a ggplot2 object

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))

head(jhu)

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)
head(cc)

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')
}

seandavi/sars2pack documentation built on May 13, 2022, 3:41 p.m.