Dataset: Covid19

This vignette details why the covid19 dataset was created, how to load it and an example of how to use the stringr package to search for countries and territories.

Using the data

Using the stringr package to find parts of a word we’ll look for countries and territories which have the word “and” in them

covid19 |>
  dplyr::filter(stringr::str_detect(countries_and_territories, "and")) |>
  dplyr::distinct(countries_and_territories) |>
  head(5)
#> # A tibble: 5 × 1
#>   countries_and_territories        
#>   <fct>                            
#> 1 Antigua_and_Barbuda              
#> 2 Bonaire, Saint Eustatius and Saba
#> 3 Bosnia_and_Herzegovina           
#> 4 British_Virgin_Islands           
#> 5 Cayman_Islands

Luckily, in this top 5 we can see that “and” appears with underscores either side, with spaces either side and also appears as part of the name, for example Islands.

Knowing that “and” can be surrounded by spaces or underscores we could search for those two formats

covid19 |>
  # rather than having two separate filters which is "AND" and returns nothing the code uses | for "OR"
  dplyr::filter(stringr::str_detect(countries_and_territories, "_and_") |
    stringr::str_detect(countries_and_territories, " and ")) |>
  dplyr::distinct(countries_and_territories)
#> # A tibble: 9 × 1
#>   countries_and_territories        
#>   <fct>                            
#> 1 Antigua_and_Barbuda              
#> 2 Bonaire, Saint Eustatius and Saba
#> 3 Bosnia_and_Herzegovina           
#> 4 Saint_Kitts_and_Nevis            
#> 5 Saint_Vincent_and_the_Grenadines 
#> 6 Sao_Tome_and_Principe            
#> 7 Trinidad_and_Tobago              
#> 8 Turks_and_Caicos_islands         
#> 9 Wallis_and_Futuna

Just to check that “and” never occurs as “And” which R would see as distinct to “and” we could look for that but we can also make all text lowercase before searching

covid19 |>
  dplyr::mutate(countries_and_territories = tolower(countries_and_territories)) |>
  dplyr::filter(stringr::str_detect(countries_and_territories, "_and_") |
    stringr::str_detect(countries_and_territories, " and ")) |>
  dplyr::distinct(countries_and_territories)
#> # A tibble: 9 × 1
#>   countries_and_territories        
#>   <chr>                            
#> 1 antigua_and_barbuda              
#> 2 bonaire, saint eustatius and saba
#> 3 bosnia_and_herzegovina           
#> 4 saint_kitts_and_nevis            
#> 5 saint_vincent_and_the_grenadines 
#> 6 sao_tome_and_principe            
#> 7 trinidad_and_tobago              
#> 8 turks_and_caicos_islands         
#> 9 wallis_and_futuna

That brings back the same number of countries and territories but given that the text is likely to be printed and read we don’t want to keep the text as lower case but are more likely to want to remove the underscore

covid19 |>
  dplyr::filter(stringr::str_detect(countries_and_territories, "_and_") |
    stringr::str_detect(countries_and_territories, " and ")) |>
  dplyr::distinct(countries_and_territories) |>
  dplyr::mutate(countries_and_territories = stringr::str_replace(countries_and_territories, "_", ""))
#> # A tibble: 9 × 1
#>   countries_and_territories        
#>   <chr>                            
#> 1 Antiguaand_Barbuda               
#> 2 Bonaire, Saint Eustatius and Saba
#> 3 Bosniaand_Herzegovina            
#> 4 SaintKitts_and_Nevis             
#> 5 SaintVincent_and_the_Grenadines  
#> 6 SaoTome_and_Principe             
#> 7 Trinidadand_Tobago               
#> 8 Turksand_Caicos_islands          
#> 9 Wallisand_Futuna

str_replace removes the first underscore in the text because it replaces only the first match. To replace all instances of _ which is what we’d want in this case we need to use str_replace_all

covid19 |>
  dplyr::filter(stringr::str_detect(countries_and_territories, "_and_") |
    stringr::str_detect(countries_and_territories, " and ")) |>
  dplyr::distinct(countries_and_territories) |>
  dplyr::mutate(countries_and_territories = stringr::str_replace_all(countries_and_territories, "_", " "))
#> # A tibble: 9 × 1
#>   countries_and_territories        
#>   <chr>                            
#> 1 Antigua and Barbuda              
#> 2 Bonaire, Saint Eustatius and Saba
#> 3 Bosnia and Herzegovina           
#> 4 Saint Kitts and Nevis            
#> 5 Saint Vincent and the Grenadines 
#> 6 Sao Tome and Principe            
#> 7 Trinidad and Tobago              
#> 8 Turks and Caicos islands         
#> 9 Wallis and Futuna

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Dataset: Covid19

Covid19 data

Using the data

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