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Build primary production dataset

Source: R/build_production.R
build_primary_production.Rd

Construct the full primary production dataset from raw FAOSTAT inputs. This is a convenience wrapper that chains the three pipeline steps:

  1. .read_production() — read & reformat FAOSTAT data.

  2. .fix_production() — apply Global-ported corrections.

  3. .qc_production() — flag data-quality anomalies.

Usage

build_primary_production(
  start_year = 1850,
  end_year = 2023,
  smooth_carry_forward = FALSE,
  example = FALSE,
  show_duplicates = FALSE,
  historical_data = NULL,
  .raw_data = NULL
)

Arguments

start_year

Integer. First year to include. Default 1850.

end_year

Integer. Last year to include. Default 2023.

smooth_carry_forward

Logical. If TRUE, carry-forward tails are replaced with a linear trend. Default FALSE.

example

Logical. If TRUE, return a small hardcoded example tibble instead of reading remote data. Default FALSE.

show_duplicates

Logical. If TRUE, return only the rows that have competing sources in wide format (one column per source) for diagnostic comparison. Default FALSE.

historical_data

Optional harmonized historical production rows to add before the LUH2 historical extension. May be a data frame or a path to a parquet/csv file. Required semantic columns are year, item_prod_code, unit, value, and one of area_code or polity_area_code. Names such as item_prod_name, item_cbs_name, and source are used when present; WHEP item and area tables fill canonical names where possible. Observed historical rows are retained, and LUH2 proxy filling can use them as anchors. Default NULL.

.raw_data

Optional tibble with the same structure as the output of the internal .read_production() step. When supplied, the remote-data read is skipped entirely and the pipeline starts from .fix_production(). Columns required: year, area, area_code, item_prod, item_prod_code, item_cbs, item_cbs_code, live_anim, live_anim_code, unit, value, source. Default NULL.

Value

A tibble with the same columns as get_primary_production(): year, legacy numeric area_code, numeric polity_area_code, reporting_polity_code, reporting_polity_name, reporting_polity_has_geometry, item_prod_code, item_cbs_code, live_anim_code, unit, value, and source. Item names can be recovered via add_item_prod_name() and related helpers. When show_duplicates = TRUE, returns a wide tibble with one column per source showing the competing values.

Examples

build_primary_production(example = TRUE)
#> # A tibble: 10 × 12
#>     year area_code polity_area_code reporting_polity_code reporting_polity_name
#>    <dbl>     <dbl>            <int> <chr>                 <chr>                
#>  1  1912       165              165 PAK-1949-1971         Pakistan (1949-1971) 
#>  2  2012       112              112 JOR-1946-2025         Jordan               
#>  3  1943        41               41 CHN-1950-2025         China (PRC)          
#>  4  1979        45               45 COM-1975-2025         Comoros              
#>  5  1910       141              141 MNG-1921-2025         Mongolia             
#>  6  1867        90               90 GIN-1958-2025         Guinea               
#>  7  1939        15               15 BLX-1850-1999         Belgium-Luxembourg   
#>  8  1935       211              211 CHE-1800-2025         Switzerland          
#>  9  1937         9                9 ARG-1800-2025         Argentina            
#> 10  2000         9                9 ARG-1800-2025         Argentina            
#> # ℹ 7 more variables: reporting_polity_has_geometry <lgl>,
#> #   item_prod_code <chr>, item_cbs_code <dbl>, live_anim_code <chr>,
#> #   unit <chr>, value <dbl>, source <chr>