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Bind Cleaned Indicator Tibbles

Source: R/bind_indicators.R
bind_indicators.Rd

Combines two or more tibbles produced by gho_clean() or sdg_clean() into a single tibble. Because both cleaners output the same 15-column schema, the result is a uniform table that can be filtered, joined, or visualised without source-specific code paths; use the source column to tell GHO rows apart from SDG rows.

Usage

bind_indicators(...)

Arguments

...

Two or more tibbles returned by gho_clean() or sdg_clean() (or any data frame with the same column set). NULL arguments are dropped. Calling with no inputs — or only NULL inputs — returns the empty 15-column tibble.

Value

A single tibble with the unified cleaned- indicator schema (15 columns). Row order is c(input_1, input_2, ...), preserving within-input order.

Details

Inputs do not need to be in any particular order. NULL inputs are silently dropped, which makes it ergonomic to write code like bind_indicators(maybe_gho, maybe_sdg) where some sources may not have been fetched.

See also

gho_clean(), sdg_clean().

Examples

# \donttest{
gho <- gho_data("NCDMORT3070", area = wpro_cty) |> gho_clean()
#> Assuming `spatial_type` = "country" since `area` was given.
#> ℹ Pass `spatial_type` explicitly to silence this message.
#> Fetching:
#> <https://ghoapi.azureedge.net/api/NCDMORT3070?$filter=SpatialDimType%20eq%20%27COUNTRY%27%20and%20SpatialDim%20in%20%28%27AUS%27%2C%27BRN%27%2C%27CHN%27%2C%27COK%27%2C%27FJI%27%2C%27FSM%27%2C%27IDN%27%2C%27JPN%27%2C%27KHM%27%2C%27KIR%27%2C%27KOR%27%2C%27LAO%27%2C%27MHL%27%2C%27MNG%27%2C%27MYS%27%2C%27NIU%27%2C%27NRU%27%2C%27NZL%27%2C%27PHL%27%2C%27PLW%27%2C%27PNG%27%2C%27SGP%27%2C%27SLB%27%2C%27TON%27%2C%27TUV%27%2C%27VNM%27%2C%27VUT%27%2C%27WSM%27%29>
#> Fetching: <https://ghoapi.azureedge.net/api/Indicator>
sdg <- sdg_data("3.4.1",        area = wpro_cty) |> sdg_clean()
#> Fetching:
#> <https://unstats.un.org/sdgs/UNSDGAPIV5/v1/sdg/Indicator/Data?indicator=3.4.1&pageSize=1000&areaCode=036&areaCode=096&areaCode=156&areaCode=184&areaCode=242&areaCode=583&areaCode=360&areaCode=392&areaCode=116&areaCode=296&areaCode=410&areaCode=418&areaCode=584&areaCode=496&areaCode=458&areaCode=570&areaCode=520&areaCode=554&areaCode=608&areaCode=585&areaCode=598&areaCode=702&areaCode=090&areaCode=776&areaCode=798&areaCode=704&areaCode=548&areaCode=882&page=1>
bind_indicators(gho, sdg)
#> # A tibble: 1,914 × 15
#>    source id        indicator location iso3  location_name  year value value_num
#>    <chr>  <chr>     <chr>     <chr>    <chr> <chr>         <int> <chr>     <dbl>
#>  1 gho    NCDMORT3… Probabil… AUS      AUS   Australia      2000 13.0…      13  
#>  2 gho    NCDMORT3… Probabil… AUS      AUS   Australia      2000 16.0…      16  
#>  3 gho    NCDMORT3… Probabil… AUS      AUS   Australia      2000 9.8 …       9.8
#>  4 gho    NCDMORT3… Probabil… AUS      AUS   Australia      2001 9.6 …       9.6
#>  5 gho    NCDMORT3… Probabil… AUS      AUS   Australia      2001 15.6…      15.6
#>  6 gho    NCDMORT3… Probabil… AUS      AUS   Australia      2001 12.6…      12.6
#>  7 gho    NCDMORT3… Probabil… AUS      AUS   Australia      2002 15.0…      15  
#>  8 gho    NCDMORT3… Probabil… AUS      AUS   Australia      2002 9.6 …       9.6
#>  9 gho    NCDMORT3… Probabil… AUS      AUS   Australia      2002 12.3…      12.3
#> 10 gho    NCDMORT3… Probabil… AUS      AUS   Australia      2003 9.1 …       9.1
#> # ℹ 1,904 more rows
#> # ℹ 6 more variables: low <dbl>, high <dbl>, series <chr>, dim1 <chr>,
#> #   dim2 <chr>, dim3 <chr>
# }