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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,
  .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.

.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, area_code (numeric FAOSTAT), item_prod_code, item_cbs_code, live_anim_code, unit, value. Names can be recovered via add_area_name(), add_item_prod_name(), etc. 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 × 8
#>     year area_code item_prod_code item_cbs_code live_anim_code unit        value
#>    <dbl>     <dbl> <chr>                  <dbl> <chr>          <chr>       <dbl>
#>  1  1912       165 772                      772 NA             tonnes    3.25e+2
#>  2  2012       112 982                     2848 976            t_head    2.68e-2
#>  3  1943        41 515                     2617 NA             t_ha      6   e-1
#>  4  1979        45 977                     2732 976            tonnes    3.3 e+1
#>  5  1910       141 1098                    2736 1096           t_LU      1.86e-3
#>  6  1867        90 976                      976 NA             heads     1.12e+5
#>  7  1939        15 157                     2537 NA             ha        4.59e+4
#>  8  1935       211 270                     2558 NA             ha        4.02e+3
#>  9  1937         9 772                      772 NA             ha        7.86e+5
#> 10  2000         9 571                     2625 NA             ha        2.36e+2
#> # ℹ 1 more variable: source <chr>