convert

Try to convert columns according to types sended by the API.

Usage

convert(data)

Arguments

data

a dataframe returned by get_data()

Value

a tibble with converted integer and number columns

Details

CAUTION For private life reason, data returns by DiDo are secretize (the value is replaced by the string "secret") so readr can't determine data type.

All secret values will be transform to NA

You can find column's type with columns()

Examples

datafiles() %>%
  dido_search("6c79805c-def9-4695-9d9f-7d86599c4d8a") %>%
  get_data() %>%
  convert()
#> ℹ downloading data and caching to : /tmp/RtmpF5CYw6/6c79805c-def9-4695-9d9f-7d86599c4d8a-2022-10.csv
#> 
Downloading: 340 B     
Downloading: 340 B     
Downloading: 1.3 kB     
Downloading: 1.3 kB     
Downloading: 1.3 kB     
Downloading: 1.3 kB     
#> # A tibble: 55 × 10
#>    DEPARTEMENT_CODE DEPARTEMENT_LIBELLE REGION_CODE REGION_LIBELLE ANNEE
#>    <chr>            <chr>               <chr>       <chr>          <chr>
#>  1 971              Guadeloupe          01          Guadeloupe     2021 
#>  2 972              Martinique          02          Martinique     2021 
#>  3 973              Guyane              03          Guyane         2021 
#>  4 974              La Réunion          04          La Réunion     2021 
#>  5 976              Mayotte             06          Mayotte        2021 
#>  6 971              Guadeloupe          01          Guadeloupe     2020 
#>  7 972              Martinique          02          Martinique     2020 
#>  8 973              Guyane              03          Guyane         2020 
#>  9 974              La Réunion          04          La Réunion     2020 
#> 10 976              Mayotte             06          Mayotte        2020 
#> # ℹ 45 more rows
#> # ℹ 5 more variables: ESSENCE_M3 <dbl>, GAZOLE_M3 <dbl>, FIOUL_M3 <dbl>,
#> #   GPL_M3 <dbl>, CARBUREACTEUR_M3 <dbl>