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Prepare Data from Human Mortality Database

data_ssvd_hmd.Rd

Process data on age-specific mortality rates from a zipped file downloaded from the Human Mortality Database so that it is ready to created a scaled SVD.

Usage

data_ssvd_hmd(
  zipfile,
  n_comp = 5,
  date = c("2025-09-25", "2024-02-26"),
  year_min = NULL,
  eps = 1e-05
)

Arguments

zipfile

The name of a zipped file downloaded from the Human Mortality Database. A path name that is handled by utils::unzip().

n_comp

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

date

Date on which data produced. Choices are "2025-09-25", "2024-02-26". (Class is character, not Date.)

year_min

Only include data for year_min onwards. Ignored if year_min is NULL (the default).

eps

Floor for rates. Default is 0.00001.

Value

A tibble

Usage

Step 1: Download data

Register or log in at the Human Mortality Database, and go to page Downloading the HMD in zipped data files. Go to the "Previous Versions" table at the bottom of the page, and download a file from the "Statistics" column, eg file

https://www.mortality.org/File/Download/hmd.v6/zip/all_hmd/hmd_statistics_20240226.zip

Step 2: Call function data_ssvd_hmd()

Put the file into the working directory, and supply the name of the file to function data_ssvd_hmd(), eg

hmd_data <- data_ssvd_hmd("hmd_statistcs_20240226")

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 for Human Mortality Database data

  • tidy_hmd() Format data from the Human Mortality Database into a tidy data frame

Examples

zipfile <- system.file("extdata", "hmd_statistics_20250925_subset.zip",
                       package = "bssvd")
data <- data_ssvd_hmd(zipfile)
#> Unzipping file...
#> Tidying data...
#> Creating five-year age groups...
#> Assembling datasets for alternative open age groups...
#> Carrying out SVD for 'total'...
#> Carrying out SVD for 'indep'...
#> Carrying out SVD for 'joint'...
#> Combining results...
data
#> # A tibble: 99 × 5
#>    type  labels_age  labels_sexgender matrix         offset     
#>    <chr> <list>      <list>           <list>         <list>     
#>  1 total <chr [61]>  <NULL>           <dgCMatrx[,5]> <dbl [61]> 
#>  2 total <chr [66]>  <NULL>           <dgCMatrx[,5]> <dbl [66]> 
#>  3 total <chr [71]>  <NULL>           <dgCMatrx[,5]> <dbl [71]> 
#>  4 total <chr [76]>  <NULL>           <dgCMatrx[,5]> <dbl [76]> 
#>  5 total <chr [81]>  <NULL>           <dgCMatrx[,5]> <dbl [81]> 
#>  6 total <chr [86]>  <NULL>           <dgCMatrx[,5]> <dbl [86]> 
#>  7 total <chr [91]>  <NULL>           <dgCMatrx[,5]> <dbl [91]> 
#>  8 total <chr [96]>  <NULL>           <dgCMatrx[,5]> <dbl [96]> 
#>  9 total <chr [101]> <NULL>           <dgCMatrx[,5]> <dbl [101]>
#> 10 total <chr [106]> <NULL>           <dgCMatrx[,5]> <dbl [106]>
#> # ℹ 89 more rows