The dimension reduction module — dimension

The module combines multiple variables into a new variable. The new variable can be a linear combination of the original variables, aggregate_linear(), or a geometric mean of the original variables, aggregate_geometry(), or created from an user formula input, aggregate_manual().

Usage

dimension_reduction(data, ...)

aggregate_linear(formula, weight)

aggregate_geometrical(formula)

aggregate_manual(formula)

Arguments

data: used in dimension_reduction(), an idx_tbl object, see [tidyindex::init()]
...: used in dimension_reduction(), a dimension reduction object of dim_red class, currently one of aggregate_linear(), aggregate_geometrical(), or aggregate_manual().
formula: the formula to evaluate
weight: used in aggregate_linear(), the column of the linear weights from the roles element in an index table object. See [tidyindex::add_paras()]

Value

an index table object

Examples

dt <- gggi |>
  dplyr::select(country, sex_ratio_at_birth:healthy_life_expectancy) |>
  init()

dt |>
  dimension_reduction(health = aggregate_manual(
    ~sex_ratio_at_birth * 0.693 + healthy_life_expectancy * 0.307))
#> Index pipeline: 
#> 
#> Steps: 
#> dimension_reduction: `aggregate_manual()` -> health
#> 
#> Data: 
#> # A tibble: 146 × 4
#>    country                      sex_ratio_at_birth healthy_life_expecta…¹ health
#>    <chr>                                     <dbl>                  <dbl>  <dbl>
#>  1 Afghanistan                               0.944                  0.971  0.952
#>  2 Chad                                      0.944                  1.03   0.970
#>  3 Algeria                                   0.944                  0.99   0.958
#>  4 Iran (Islamic Republic of)                0.944                  1.01   0.964
#>  5 Pakistan                                  0.944                  0.999  0.961
#>  6 Mali                                      0.944                  0.993  0.959
#>  7 Congo, Democratic Republic …              0.944                  1.05   0.977
#>  8 Oman                                      0.944                  1      0.961
#>  9 Benin                                     0.944                  1.04   0.973
#> 10 Guinea                                    0.944                  1.02   0.966
#> # ℹ 136 more rows
#> # ℹ abbreviated name: ¹healthy_life_expectancy
dt |>
  add_paras(gggi_weights, by = variable) |>
  dimension_reduction(health = aggregate_linear(
    ~sex_ratio_at_birth:healthy_life_expectancy, weight = var_weight))
#> Index pipeline: 
#> 
#> Steps: 
#> dimension_reduction: `aggregate_linear()` -> health
#> 
#> Data: 
#> # A tibble: 146 × 4
#>    country                      sex_ratio_at_birth healthy_life_expecta…¹ health
#>    <chr>                                     <dbl>                  <dbl>  <dbl>
#>  1 Afghanistan                               0.944                  0.971  0.952
#>  2 Chad                                      0.944                  1.03   0.970
#>  3 Algeria                                   0.944                  0.99   0.958
#>  4 Iran (Islamic Republic of)                0.944                  1.01   0.964
#>  5 Pakistan                                  0.944                  0.999  0.961
#>  6 Mali                                      0.944                  0.993  0.959
#>  7 Congo, Democratic Republic …              0.944                  1.05   0.977
#>  8 Oman                                      0.944                  1      0.961
#>  9 Benin                                     0.944                  1.04   0.973
#> 10 Guinea                                    0.944                  1.02   0.966
#> # ℹ 136 more rows
#> # ℹ abbreviated name: ¹healthy_life_expectancy
dt |>
  dimension_reduction(health = aggregate_geometrical(
    ~sex_ratio_at_birth:healthy_life_expectancy)
  )
#> Index pipeline: 
#> 
#> Steps: 
#> dimension_reduction: `aggregate_geometrical()` -> health
#> 
#> Data: 
#> # A tibble: 146 × 4
#>    country                      sex_ratio_at_birth healthy_life_expecta…¹ health
#>    <chr>                                     <dbl>                  <dbl>  <dbl>
#>  1 Afghanistan                               0.944                  0.971  0.957
#>  2 Chad                                      0.944                  1.03   0.986
#>  3 Algeria                                   0.944                  0.99   0.967
#>  4 Iran (Islamic Republic of)                0.944                  1.01   0.975
#>  5 Pakistan                                  0.944                  0.999  0.971
#>  6 Mali                                      0.944                  0.993  0.968
#>  7 Congo, Democratic Republic …              0.944                  1.05   0.996
#>  8 Oman                                      0.944                  1      0.972
#>  9 Benin                                     0.944                  1.04   0.990
#> 10 Guinea                                    0.944                  1.02   0.979
#> # ℹ 136 more rows
#> # ℹ abbreviated name: ¹healthy_life_expectancy