Prepare Data from the Human Fertility Database

Process age-specific fertility data from the the Human Fertility Database so that it is ready to create a scaled SVD.

Usage

data_ssvd_hfd(
  data,
  age_min_max = 15,
  age_max_min = 50,
  n_comp = 5,
  eps = 1e-05
)

Arguments

data

A data frame containing HFD data.

age_min_max, age_max_min

Every age classification must at least span the range [age_min_max, age_max_min). Defaults are 15 and 50.

n_comp

Number of SVD components to include in result. Default is 5.

eps

Floor for rates. Default is 0.00001.

Value

A tibble

Usage

Step 1: Download data

Register or log in at the Human Fertility Database, and go to page ZippedDataFiles. Go to the "By statistic" table, and download the file from the "Age-specific fertility rate" row.

Step 2: Extract asfrRR.txt

Extract the file asfrRR.txt from the downloaded data, and read in the contents,

asfr <- readr::read_table("asfrRR.txt", skip = 2)

Step 3: Call function data_ssvd_hfd()

hfd_data <- data_ssvd_hfd(asfr)

Lowest and highest ages

The original HFD data contains age groups "12-" (ie 12 and younger) and "55+" (ie 55 and older). We treat these as closed intervals "12" and "55".

If an age classification in the return value covers a narrower range than the data itself, then the youngest age group in the return value includes ASFRs from younger age groups in the data, and the oldest age group in the return value includes ASFRs from older age groups in the data. For instance, if the classification in the return value starts at age 15, but the data starts at age 12, the ASFR for age 15 includes ASFRs from ages 12, 13, and 14. This shifting of ASFRs is common in analyses of fertility.

Truncation and transformation

Rates below eps are shifted to eps, and all rates are log-transformed, before the SVD is applied.

See also

• coef_hfd() Obtain time series of SVD coefficients from Human Fertility Database data

• tidy_hfd() Format data from the Human Fertility Database into a tidy data frame

Examples

data <- data_ssvd_hfd(asfr_subset)
#> Tidying data...
#> Carrying out SVD for 'total'...
data
#> # A tibble: 34 × 5
#>    type  labels_age labels_sexgender matrix         offset    
#>    <chr> <list>     <list>           <list>         <list>    
#>  1 total <chr [38]> <NULL>           <dgCMatrx[,5]> <dbl [38]>
#>  2 total <chr [39]> <NULL>           <dgCMatrx[,5]> <dbl [39]>
#>  3 total <chr [40]> <NULL>           <dgCMatrx[,5]> <dbl [40]>
#>  4 total <chr [41]> <NULL>           <dgCMatrx[,5]> <dbl [41]>
#>  5 total <chr [42]> <NULL>           <dgCMatrx[,5]> <dbl [42]>
#>  6 total <chr [43]> <NULL>           <dgCMatrx[,5]> <dbl [43]>
#>  7 total <chr [44]> <NULL>           <dgCMatrx[,5]> <dbl [44]>
#>  8 total <chr [37]> <NULL>           <dgCMatrx[,5]> <dbl [37]>
#>  9 total <chr [38]> <NULL>           <dgCMatrx[,5]> <dbl [38]>
#> 10 total <chr [39]> <NULL>           <dgCMatrx[,5]> <dbl [39]>
#> # ℹ 24 more rows