replacement argument in str_replace has differen length than string

[kmera]

Hi, I want to generate a code to automatically replace/clean names from one tibble (clean_db) based on the correct names from another tibble (Provincia). The code is the following:

str_replace(clean_db$provincia, paste(str_sub(clean_db$provincia, 1, 2),
".+",
str_sub(clean_db$provincia, -2),
sep = ""),
as.character(as_tibble(str_extract(Provincia$descripcion,
paste(str_sub(clean_db$provincia, 1, 2),
".+",
str_sub(clean_db$provincia, -2),
sep = ""))) %>% drop_na()))

The clean_db tibble has 532 obs. The thing is, the replacement argument picks all the strings in Provincia tibble, but what I need is to check the string in Provincia tibble (which is a 25 obs.) and just pick one string and then go to the next line of the clean_db tibble and do the same thing, so in this way the replacement just pick one string from Provincia tibble and then keep doing it for the entire clean_db tibble.

Thanks a lot,
KM

[andresrcs]

Hi!

To help us help you, could you please prepare a reproducible example (reprex) illustrating your issue? Please have a look at this guide, to see how to create one:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[kmera]

Hi, so sorry, I include the minimal datasets (clean_db and Provincia):

clean_db <- tibble(provincia = c("AZUY", "BOLI$BAR", "CAN_AR", "GUY$AS", "PICHI.CHA",
"COTPAXI", "MORON/A SANTIAGO"),
ciudad = c("QUITO", "CUENCA", "GUAYAQUIL", "MANTA", "PORTOVIEJO",
"AZOGUES", "SALINAS"))

Provincia <- tibble(codigo = c(1:17),
descripcion = c("AZUAY",
"BOLIVAR",
"CAÑAR",
"CARCHI",
"CHIMBORAZO",
"COTOPAXI",
"EL ORO",
"ESMERALDAS",
"GALAPAGOS",
"GUAYAS",
"IMBABURA",
"LOJA",
"LOS RIOS",
"MANABI",
"MORONA SANTIAGO",
"NAPO",
"SANTO DOMINGO DE LOS TSACHILAS"))

the code is

str_replace(clean_db$provincia, paste(str_sub(clean_db$provincia, 1, 2),
".+",
str_sub(clean_db$provincia, -2),
sep = ""),
as.character(as_tibble(str_extract(Provincia$descripcion,
paste(str_sub(clean_db$provincia, 1, 2),
".+",
str_sub(clean_db$provincia, -2),
sep = ""))) %>% drop_na()))

Regards,
KM

[andresrcs]

Trying to replace words based on the first two and last two characters doesn't seem like a reliable method, I think you should consider using string distance metrics like in this example:

library(tidyverse)
library(fuzzyjoin)

clean_db <- tibble(provincia = c("AZUY", "BOLI$BAR", "CAN_AR", "GUY$AS", "PICHI.CHA",
                                 "COTPAXI", "MORON/A SANTIAGO"),
                   ciudad = c("QUITO", "CUENCA", "GUAYAQUIL", "MANTA", "PORTOVIEJO",
                              "AZOGUES", "SALINAS"))

Provincia <- tibble(codigo = c(1:17),
                    descripcion = c("AZUAY",
                                    "BOLIVAR",
                                    "CAÑAR",
                                    "CARCHI",
                                    "CHIMBORAZO",
                                    "COTOPAXI",
                                    "EL ORO",
                                    "ESMERALDAS",
                                    "GALAPAGOS",
                                    "GUAYAS",
                                    "IMBABURA",
                                    "LOJA",
                                    "LOS RIOS",
                                    "MANABI",
                                    "MORONA SANTIAGO",
                                    "NAPO",
                                    "SANTO DOMINGO DE LOS TSACHILAS"))

clean_db %>% 
    stringdist_left_join(Provincia %>% select(descripcion),
                         by = c(provincia = "descripcion"),
                         method = "osa") %>% 
    mutate(provincia = coalesce(descripcion, provincia)) %>% 
    select(-descripcion)
#> # A tibble: 7 × 2
#>   provincia       ciudad    
#>   <chr>           <chr>     
#> 1 AZUAY           QUITO     
#> 2 BOLIVAR         CUENCA    
#> 3 CAÑAR           GUAYAQUIL 
#> 4 GUAYAS          MANTA     
#> 5 PICHI.CHA       PORTOVIEJO
#> 6 COTOPAXI        AZOGUES   
#> 7 MORONA SANTIAGO SALINAS

^{Created on 2022-03-30 by the reprex package (v2.0.1)}

Or, if possible, manually define a vector with equivalences e.g. c('misspelling' = 'correct'), which would have the most accurate results.

Note: Next time please provide a proper REPRoducible EXample (reprex) illustrating your issue.