ltxtra

An R package for calculating non-standard life table variables

Installation

You can install the development version of ltxtra from GitHub with:

# install.packages("pak")
pak::pak("sdyrting/ltxtra")

Example 1: Adding life disparity to a life table

This is a basic example which shows you how to add a life disparity column to a life table:

library(ltxtra)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(ggplot2)

head(aus_2021_2023 %>% select(!c(mx,ax)))
#> # A tibble: 6 × 10
#> # Groups:   State, Sex [1]
#>   State Sex      Age      qx      lx     dx    Lx      Tx    ex OpenInterval
#>   <chr> <chr>  <dbl>   <dbl>   <dbl>  <dbl> <dbl>   <dbl> <dbl> <lgl>       
#> 1 NSW   Female     0 0.00251 100000  251    99772 8521447  85.2 FALSE       
#> 2 NSW   Female     1 0.00019  99749   19.0  99739 8421675  84.4 FALSE       
#> 3 NSW   Female     2 0.00011  99730.  11.0  99724 8321936  83.4 FALSE       
#> 4 NSW   Female     3 0.00009  99719.   8.97 99715 8222212  82.5 FALSE       
#> 5 NSW   Female     4 0.00008  99710.   7.98 99706 8122497  81.5 FALSE       
#> 6 NSW   Female     5 0.00007  99702.   6.98 99698 8022791  80.5 FALSE

daus_lt <- aus_2021_2023 %>% group_by(State,Sex) %>% lifedisp()

head(daus_lt %>% select(!c(mx,ax)))
#> # A tibble: 6 × 11
#> # Groups:   State, Sex [1]
#>   State Sex      Age      qx     lx     dx    Lx     Tx    ex OpenInterval    vx
#>   <chr> <chr>  <dbl>   <dbl>  <dbl>  <dbl> <dbl>  <dbl> <dbl> <lgl>        <dbl>
#> 1 NSW   Female     0 0.00251 1   e5 251    99772 8.52e6  85.2 FALSE         8.86
#> 2 NSW   Female     1 0.00019 9.97e4  19.0  99739 8.42e6  84.4 FALSE         8.67
#> 3 NSW   Female     2 0.00011 9.97e4  11.0  99724 8.32e6  83.4 FALSE         8.65
#> 4 NSW   Female     3 0.00009 9.97e4   8.97 99715 8.22e6  82.5 FALSE         8.64
#> 5 NSW   Female     4 0.00008 9.97e4   7.98 99706 8.12e6  81.5 FALSE         8.64
#> 6 NSW   Female     5 0.00007 9.97e4   6.98 99698 8.02e6  80.5 FALSE         8.63

daus_lt %>% ggplot() +
  geom_line(aes(x=Age,y=vx,group=State,colour=State)) +
  labs(y='Life Disparity (Years)')+
  facet_wrap(vars(Sex))

Example 2: HMD and subnational databases

ltxtra assumes life tables are in same form as those produced by the HMDHFDplus package. This makes it easy to import and decorate life tables from the Human Mortality Database and associated subnational databases.

library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ forcats   1.0.0     ✔ stringr   1.5.2
#> ✔ lubridate 1.9.3     ✔ tibble    3.2.1
#> ✔ purrr     1.0.2     ✔ tidyr     1.3.1
#> ✔ readr     2.1.5     
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(HMDHFDplus)
library(ltxtra)
library(ggplot2)

prefID <- '00' #All Japan

jpn_lt <- readJMDweb(prefID,"fltper_1x1") %>% 
  mutate(Sex = 'Female') %>%
  bind_rows(readJMDweb(prefID,"mltper_1x1") %>% 
  mutate(Sex = 'Male')) %>%
  group_by(Year,Sex) %>% lifedisp()

adult_age <- 15 

jpn_lt %>% mutate(Country='Japan') %>% 
  filter(Age == adult_age) %>% 
  pivot_longer(cols=c(ex,vx)) %>%
  mutate(name=ifelse(name == 'ex', 'Adult Life Expectancy','Adult Life Disparity'),
         name=factor(name,levels=c('Adult Life Expectancy','Adult Life Disparity'))) %>%
  ggplot() + 
  geom_line(aes(x=Year,y=value, group=Sex,colour=Sex)) +
  facet_wrap(facets=vars(name),ncol=2,scales='free') +
  labs(y='Years')

Japanese Adult Life Expectancy and Disparity by Year and Sex