This R-package fetches and organises the human mortality data from the
Human Mortality Database in a tidy fashion. For a more
richer and complete package, you should have a look at the demography
package.
The mortality package is a modern re-imagination and extension of the
functions hmd.mx, hmd.e0, and hmd.pop in the demography package.
Specifically:
- import of
RCurlis replaced bycurlwhere the latter is a modern version of the former with zero import, - the fetched data is a special type of
tibble, calledhumble(think human life is short so stay humble – okay, I’ll see my way out 😅), instead of a list of classdemogdata, - it grabs data that are more than just period 1x1,
- it provides the data in the long or wide format for sex,
- it provides a helper function,
as_demogdata, to convert the Mx and exposure table tohmd.mxoutput (i.e.demogdata), and it provides a helper function to convert(not implemented yet).demogdatato ahumbletable
You can install the development version of mortality as below:
install.packages("remotes")
remotes::install_github("emitanaka/mortality")library(mortality)You first need to register as a user (if you haven’t already) at the Human Mortality Database. To now set a session with your username and password, fill in your details below and run the code.
hmd_session(username = <YOUR USERNAME>,
password = <YOUR PASSWORD>)Alternatively, you can store your username and password in the
.Renviron file containing below:
HMD_USERNAME=<YOUR USERNAME>
HMD_PASSWORD=<YOUR PASSWORD>
then simply just run the command before just once before getting the data:
hmd_session()All data are obtained via hmd_data().
hmd_data("AUS", stats = "death")
#> # A humble: 10,989 x 6
#> # Countries: AUS
#> # Year: 1921-2019
#> # Age: 0-110
#> year age death_female death_male death_total country
#> <int> <int> <dbl> <dbl> <dbl> <chr>
#> 1 1921 0 3842. 5125. 8967. AUS
#> 2 1921 1 719. 890. 1610. AUS
#> 3 1921 2 330. 359. 689. AUS
#> 4 1921 3 166. 250. 416. AUS
#> 5 1921 4 190. 197. 387. AUS
#> 6 1921 5 149. 153. 302. AUS
#> 7 1921 6 150. 137. 287. AUS
#> 8 1921 7 109. 118. 227. AUS
#> 9 1921 8 81.0 114. 195. AUS
#> 10 1921 9 78.0 82.2 160. AUS
#> # … with 10,979 more rowsThe function offers support for multiple countries:
hmd_data(c("AUS", "JPN"), stats = "death")
#> # A humble: 19,203 x 6
#> # Countries: AUS, JPN
#> # Year: 1921-2020
#> # Age: 0-110
#> year age death_female death_male death_total country
#> <int> <int> <dbl> <dbl> <dbl> <chr>
#> 1 1921 0 3842. 5125. 8967. AUS
#> 2 1921 1 719. 890. 1610. AUS
#> 3 1921 2 330. 359. 689. AUS
#> 4 1921 3 166. 250. 416. AUS
#> 5 1921 4 190. 197. 387. AUS
#> 6 1921 5 149. 153. 302. AUS
#> 7 1921 6 150. 137. 287. AUS
#> 8 1921 7 109. 118. 227. AUS
#> 9 1921 8 81.0 114. 195. AUS
#> 10 1921 9 78.0 82.2 160. AUS
#> # … with 19,193 more rowsor multiple statistics:
hmd_data(c("AUS", "JPN"), stats = c("death", "death_rate"))
#> # A humble: 19,203 x 9
#> # Countries: AUS, JPN
#> # Year: 1921-2020
#> # Age: 0-110
#> year age country death_female death_male death_total deathrate_female
#> <int> <int> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1921 0 AUS 3842. 5125. 8967. 0.0600
#> 2 1921 1 AUS 719. 890. 1610. 0.0121
#> 3 1921 10 AUS 69.0 85.2 154. 0.00125
#> 4 1921 100 AUS 2 6.02 8.02 0.170
#> 5 1921 101 AUS 5 4.01 9.01 0.560
#> 6 1921 102 AUS 2 1 3 0.453
#> 7 1921 103 AUS 0 1 1 0
#> 8 1921 104 AUS 1 1 2 1.04
#> 9 1921 105 AUS 0 0 0 NA
#> 10 1921 106 AUS 0 0 0 NA
#> # … with 19,193 more rows, and 2 more variables: deathrate_male <dbl>,
#> # deathrate_total <dbl>or relabeling of the countries like below:
hmd_data(c("Australia" = "AUS", "Japan" = "JPN"), stats = "population")
#> # A humble: 19,536 x 6
#> # Countries: Australia, Japan
#> # Year: 1921-2021
#> # Age: 0-110
#> year age pop_female pop_male pop_total country
#> <chr> <int> <dbl> <dbl> <dbl> <chr>
#> 1 1921 0 62848. 65851. 128699. Australia
#> 2 1921 1 57777. 60217. 117994. Australia
#> 3 1921 2 56941. 59047. 115988. Australia
#> 4 1921 3 58272. 60218. 118490. Australia
#> 5 1921 4 58719. 60773. 119492. Australia
#> 6 1921 5 59888. 61687. 121574. Australia
#> 7 1921 6 61034. 62333. 123366. Australia
#> 8 1921 7 59455. 60776. 120231. Australia
#> 9 1921 8 57736. 58806. 116542. Australia
#> 10 1921 9 56181. 57140. 113321. Australia
#> # … with 19,526 more rowsYou can also get a different age or year range:
hmd_data(c("Australia" = "AUS", "Japan" = "JPN"),
stats = "exposure_to_risk", year_range = 5, age_range = 5)
#> # A humble: 840 x 6
#> # Countries: Australia, Japan
#> year_range_5 age_range_5 exprisk_female exprisk_male exprisk_total country
#> <itvl> <itvl> <dbl> <dbl> <dbl> <chr>
#> 1 1921-1924 0 256600. 267063. 523663. Australia
#> 2 1921-1924 1-4 957177. 994721. 1951898. Australia
#> 3 1921-1924 5-9 1180057. 1213273. 2393330. Australia
#> 4 1921-1924 10-14 1096076. 1123037. 2219113. Australia
#> 5 1921-1924 15-19 968522. 997946. 1966469. Australia
#> 6 1921-1924 20-24 925053. 914989. 1840042. Australia
#> 7 1921-1924 25-29 941637. 894549. 1836186. Australia
#> 8 1921-1924 30-34 915723. 926174. 1841897. Australia
#> 9 1921-1924 35-39 806054. 842748. 1648803. Australia
#> 10 1921-1924 40-44 681851. 719764. 1401615. Australia
#> # … with 830 more rowsNow also supports getting the data with a long format with sex as a variable:
hmd_data(c("AUS", "JPN"), sex_format = "long")
#> # A humble: 57,609 x 5
#> # Countries: AUS, JPN
#> # Year: 1921-2020
#> # Age: 0-110
#> year age country deathrate sex
#> <int> <int> <chr> <dbl> <chr>
#> 1 1921 0 AUS 0.0600 female
#> 2 1921 1 AUS 0.0121 female
#> 3 1921 2 AUS 0.00578 female
#> 4 1921 3 AUS 0.00289 female
#> 5 1921 4 AUS 0.00325 female
#> 6 1921 5 AUS 0.00252 female
#> 7 1921 6 AUS 0.00248 female
#> 8 1921 7 AUS 0.00181 female
#> 9 1921 8 AUS 0.00138 female
#> 10 1921 9 AUS 0.00137 female
#> # … with 57,599 more rowsdemographyHMDHFDplusMortalityLawstidylife(note: developmental)raw- List of R packages for Acturial
Science:
actuarActuDistns(note: archived on CRAN)CompLognormallifecontingenciesChainLadderELTDCLMRMR(note: archived on CRAN)lossDev(note: archived on CRAN)