Check Cases Within Radius

Based on the postcodes4_afstanden data set, this function determines the specified minimum number of cases within a certain radius.

cases_within_radius(
  data,
  radius_km = 10,
  minimum_cases = 10,
  column_count = NULL,
  ...
)

Arguments

data

data set containing a column 'postcode'

radius_km

radius in kilometres from each zip code. The search diameter is twice this number (since zip codes e.g. to the west and to the east are searched).

minimum_cases

minimum number of cases to search for

column_count

column name in data with the number of case counts, defaults to the first column with numeric values

...

ignored, allows for future extensions

Value

This function adds two columns ("cases_within_radius"

<dbl> and "minimum_met"

<lgl>) to the input data.

Examples

library(dplyr, warn.conflicts = FALSE)

postcodes_friesland <- geo_postcodes4 |> 
  filter_geolocation(provincie == "Friesland") |> 
  pull(postcode)

# example with Norovirus cases:
noro <- data.frame(postcode = postcodes_friesland,
                   n = floor(runif(length(postcodes_friesland),
                                   min = 0, max = 3)))
head(noro)
#>   postcode n
#> 1     8388 0
#> 2     8389 2
#> 3     8391 1
#> 4     8392 0
#> 5     8393 0
#> 6     8394 1

radial_check <- cases_within_radius(noro, radius_km = 10, minimum_cases = 10)
#> Using column 'n' for cases_within_radius()
head(radial_check)
#>   postcode n cases_within_radius minimum_met
#> 1     8388 0                  37        TRUE
#> 2     8389 2                  44        TRUE
#> 3     8391 1                  44        TRUE
#> 4     8392 0                  30        TRUE
#> 5     8393 0                  39        TRUE
#> 6     8394 1                  49        TRUE


# dplyr group support:
mdro <- data.frame(type = rep(c("ESBL", "MRSA", "VRE"), 20),
                   pc4 = postcodes_friesland[1:20],
                   n = floor(runif(60, min = 0, max = 3)))
mdro |> 
  group_by(type) |> 
  cases_within_radius()
#> Using column 'n' for cases_within_radius()
#> Analysing cases for group 1 out of 3 (n = 20)... 
#> OK.
#> Analysing cases for group 2 out of 3 (n = 20)... 
#> OK.
#> Analysing cases for group 3 out of 3 (n = 20)... 
#> OK.
#> # A tibble: 60 × 5
#> # Groups:   type [3]
#>    type    pc4     n cases_within_radius minimum_met
#>    <chr> <dbl> <dbl>               <dbl> <lgl>      
#>  1 ESBL   8388     0                  12 TRUE       
#>  2 MRSA   8389     2                  19 TRUE       
#>  3 VRE    8391     2                  10 TRUE       
#>  4 ESBL   8392     0                  12 TRUE       
#>  5 MRSA   8393     1                  15 TRUE       
#>  6 VRE    8394     0                  10 TRUE       
#>  7 ESBL   8395     2                  12 TRUE       
#>  8 MRSA   8396     2                  13 TRUE       
#>  9 VRE    8397     0                   4 FALSE      
#> 10 ESBL   8398     2                   8 FALSE      
#> # ℹ 50 more rows
  
  
# plotting support:
if (require("certeplot2")) {

  radial_check |>
    add_map() |>
    filter_geolocation(provincie == "Friesland") |>
    plot2(category = cases_within_radius,
          category.title = "Cases",
          datalabels = FALSE,
          colour_fill = "viridis")

}
#> Joining, by postcode