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Runs a GET request of reports data from the COVID-19 tracker API, and returns parsed data. Via the split argument, data may be "overall" (all provinces/territories combined), or by "province". Alternatively, provide one or more two-letter codes (e.g. "AB") to province to return reports for specific provinces, or one or more numeric region codes (e.g. "1204") to return specific health regions.

Usage

get_reports(
  split = c("overall", "province"),
  province = NULL,
  region = NULL,
  fill_dates = NULL,
  stat = NULL,
  date = NULL,
  after = NULL,
  before = NULL
)

Arguments

split

One of "overall", or "province" to specify how the data is split. An "overall" report gives cumulative numbers across Canada. Splitting by "province" returns all reports for all provinces/territories.

province

One or more province/territory codes ("AB", "BC", "MB", "NB", "NL", "NS", "NT", "NU", "ON", "PE", "QC", "SK", "YT") to get reports. Upper, lower and mixed case strings are accepted.

region

One or more health region IDs to get reports. Numeric and character values are accepted.

fill_dates

When TRUE, the response fills in any missing dates with blank entries.

stat

Returns only the specified statistics, e.g. "cases".

date

Returns reports from only the specified date, in YYYY-MM-DD format.

after

Returns reports from only on or after the specified date, in YYYY-MM-DD format.

before

Returns reports from only on or before the specified date, in YYYY-MM-DD format.

Value

A data frame containing the reports data, one row per day. Includes a province variable if data is split by province, and a hr_uid variable if data is split by health region.

Examples


get_reports()
#> # A tibble: 938 × 24
#>    last_updated        date       change_cases change_…¹ chang…² chang…³ chang…⁴
#>    <dttm>              <date>            <int>     <int>   <int>   <int>   <int>
#>  1 2022-08-22 04:23:04 2020-01-25            1         0       2       0       0
#>  2 2022-08-22 04:23:04 2020-01-26            1         0       4       0       0
#>  3 2022-08-22 04:23:04 2020-01-27            0         0      20       0       0
#>  4 2022-08-22 04:23:04 2020-01-28            1         0      10       0       0
#>  5 2022-08-22 04:23:04 2020-01-29            0         0       3       0       0
#>  6 2022-08-22 04:23:04 2020-01-30            0         0      26       0       0
#>  7 2022-08-22 04:23:04 2020-01-31            1         0      33       0       0
#>  8 2022-08-22 04:23:04 2020-02-01            0         0      23       0       0
#>  9 2022-08-22 04:23:04 2020-02-02            0         0      24       0       0
#> 10 2022-08-22 04:23:04 2020-02-03            0         0      16       0       0
#> # … with 928 more rows, 17 more variables: change_recoveries <int>,
#> #   change_vaccinations <int>, change_vaccinated <int>,
#> #   change_boosters_1 <int>, change_boosters_2 <int>,
#> #   change_vaccines_distributed <int>, total_cases <int>,
#> #   total_fatalities <int>, total_tests <int>, total_hospitalizations <int>,
#> #   total_criticals <int>, total_recoveries <int>, total_vaccinations <int>,
#> #   total_vaccinated <int>, total_boosters_1 <int>, total_boosters_2 <int>, …
get_reports(province = c("AB", "SK"))
#> # A tibble: 1,874 × 25
#>    province last_updated        date       change_cases change…¹ chang…² chang…³
#>    <chr>    <dttm>              <date>            <int>    <int>   <int>   <int>
#>  1 AB       2022-08-21 20:15:30 2020-01-25            0        0       0       0
#>  2 AB       2022-08-21 20:15:30 2020-01-26            0        0       0       0
#>  3 AB       2022-08-21 20:15:30 2020-01-27            0        0       0       0
#>  4 AB       2022-08-21 20:15:30 2020-01-28            0        0       0       0
#>  5 AB       2022-08-21 20:15:30 2020-01-29            0        0       0       0
#>  6 AB       2022-08-21 20:15:30 2020-01-30            0        0       0       0
#>  7 AB       2022-08-21 20:15:30 2020-01-31            0        0       0       0
#>  8 AB       2022-08-21 20:15:30 2020-02-01            0        0       0       0
#>  9 AB       2022-08-21 20:15:30 2020-02-02            0        0       0       0
#> 10 AB       2022-08-21 20:15:30 2020-02-03            0        0       0       0
#> # … with 1,864 more rows, 18 more variables: change_criticals <int>,
#> #   change_recoveries <int>, change_vaccinations <int>,
#> #   change_vaccinated <int>, change_boosters_1 <int>, change_boosters_2 <int>,
#> #   change_vaccines_distributed <int>, total_cases <int>,
#> #   total_fatalities <int>, total_tests <int>, total_hospitalizations <int>,
#> #   total_criticals <int>, total_recoveries <int>, total_vaccinations <int>,
#> #   total_vaccinated <int>, total_boosters_1 <int>, total_boosters_2 <int>, …
get_reports(region = 1204)
#> # A tibble: 856 × 9
#>    hr_uid last_updated        date       chang…¹ chang…² total…³ total…⁴ chang…⁵
#>     <int> <dttm>              <date>       <int>   <int>   <int>   <int>   <int>
#>  1   1204 2022-08-22 04:23:04 2020-01-15       0       0       0       0      NA
#>  2   1204 2022-08-22 04:23:04 2020-01-16       0       0       0       0      NA
#>  3   1204 2022-08-22 04:23:04 2020-01-17       0       0       0       0      NA
#>  4   1204 2022-08-22 04:23:04 2020-01-18       0       0       0       0      NA
#>  5   1204 2022-08-22 04:23:04 2020-01-19       0       0       0       0      NA
#>  6   1204 2022-08-22 04:23:04 2020-01-20       0       0       0       0      NA
#>  7   1204 2022-08-22 04:23:04 2020-01-21       0       0       0       0      NA
#>  8   1204 2022-08-22 04:23:04 2020-01-22       0       0       0       0      NA
#>  9   1204 2022-08-22 04:23:04 2020-01-23       0       0       0       0      NA
#> 10   1204 2022-08-22 04:23:04 2020-01-24       0       0       0       0      NA
#> # … with 846 more rows, 1 more variable: total_recoveries <int>, and
#> #   abbreviated variable names ¹​change_cases, ²​change_fatalities, ³​total_cases,
#> #   ⁴​total_fatalities, ⁵​change_recoveries
get_reports(region = c("472", 1204), stat = "cases")
#> # A tibble: 1,710 × 5
#>    hr_uid last_updated        date       change_cases total_cases
#>     <int> <dttm>              <date>            <int>       <int>
#>  1    472 2022-08-22 04:23:04 2020-01-15            0           0
#>  2    472 2022-08-22 04:23:04 2020-01-16            0           0
#>  3    472 2022-08-22 04:23:04 2020-01-17            0           0
#>  4    472 2022-08-22 04:23:04 2020-01-18            0           0
#>  5    472 2022-08-22 04:23:04 2020-01-19            0           0
#>  6    472 2022-08-22 04:23:04 2020-01-20            0           0
#>  7    472 2022-08-22 04:23:04 2020-01-21            0           0
#>  8    472 2022-08-22 04:23:04 2020-01-22            0           0
#>  9    472 2022-08-22 04:23:04 2020-01-23            0           0
#> 10    472 2022-08-22 04:23:04 2020-01-24            0           0
#> # … with 1,700 more rows