topomatch
Helper function for matching toponyms from different sources, that can be written in slightly different ways. Allows to inspect the matching and act accordingly.
countries1 <- spData::world$name_long
countries2 <- unique(maps::world.cities$country.etc)
(country_matches <- topomatch(countries1, countries2))
#> 156 names matched exactly: Fiji, Tanzania, Western Sahara, ...
#>
#> 15 matches based on similarity:
#> 1. United States: United Arab Emirates
#> 2. Democratic Republic of the Congo: Congo Democratic Republic
#> 3. Russian Federation: Russia
#> 4. French Southern and Antarctic Lands: Northern Mariana Islands
#> 5. Timor-Leste: East Timor
#> 6. Côte d'Ivoire: Cape Verde
#> 7. The Gambia: Gambia
#> 8. United Kingdom: United Arab Emirates
#> 9. Brunei Darussalam: Brunei
#> 10. Antarctica: Vatican City
#> 11. Northern Cyprus: Northern Mariana Islands
#> 12. Somaliland: Swaziland
#> 13. Serbia: Serbia and Montenegro
#> 14. Montenegro: Serbia and Montenegro
#> 15. South Sudan: South Africa
#>
#> 6 unresolved matches:
#> 1. Republic of the Congo: Czech Republic, Dominican Republic, ...
#> 2. eSwatini: Palestine, Estonia
#> 3. Lao PDR: San Marino, ...
#> 4. Dem. Rep. Korea: Korea South, Korea North
#> 5. Republic of Korea: Czech Republic, Dominican Republic, ...
#> 6. Kosovo: Comoros, Solomon IslandsThere are some manual fixes needed for those toponyms that weren’t
correctly matched. Just write the fixes in a named vector. If there is
no correct match for one toponym, give it an NA.
## Inspect the competing candidates for the unmatched countries
(bm <- best_matches(country_matches)[unmatched(country_matches)])
#> $`Republic of the Congo`
#> [1] "Czech Republic" "Dominican Republic"
#> [3] "Congo Democratic Republic" "Central African Republic"
#>
#> $eSwatini
#> [1] "Palestine" "Estonia"
#>
#> $`Lao PDR`
#> [1] "San Marino" "Central African Republic"
#> [3] "Sao Tome and Principe"
#>
#> $`Dem. Rep. Korea`
#> [1] "Korea South" "Korea North"
#>
#> $`Republic of Korea`
#> [1] "Czech Republic" "Dominican Republic"
#> [3] "Congo Democratic Republic" "Central African Republic"
#>
#> $Kosovo
#> [1] "Comoros" "Solomon Islands"
cnames_fixes <- setNames(
c("Congo Democratic Republic", NA, "Laos", "Korea North",
"Korea South", NA),
names(bm)
)
## Fix the incorrectly matches from similarity as well
cnames_fixes <- c(
cnames_fixes,
"United States" = "USA",
"French Southern and Antarctic Lands" = "France",
"Côte d'Ivoire" = "Ivory Coast",
"United Kingdom" = "UK",
"Antarctica" = NA,
"Northern Cyprus" = "Cyprus",
"Somaliland" = "Somalia",
"South Sudan" = "Sudan"
)Now you can transcribe the original toponyms to the matched terms.
translate <- transcribe(country_matches, fixes = cnames_fixes)
translate(c("United Kingdom", "Kosovo"))
#> [1] "UK" NA
## "Translate" all of the original toponyms
countries1_trans <- translate(countries1)
## Only those "fixed" as NA are not found in the second list
countries1[!countries1_trans %in% countries2]
#> [1] "eSwatini" "Antarctica" "Kosovo"Method
Wraps local-global alignment algorithm borrwed from bioConductor package
Biostrings. Works better than global alignment and requires less
fine-tuning (although is considerably slower too)
https://ro-che.info/articles/2016-12-11-local-alignment.
Installation
remotes::install_gitlab("umr-astre/topomatch", host = "forgemia.inra.fr")