vital

Tidy Analysis Tools for Mortality, Fertility, Migration and Population Data

https://github.com/robjhyndman/vital

Repository

Tidy Analysis Tools for Mortality, Fertility, Migration and Population Data

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

# Okabe-Ito colours for discrete scales
options(
  ggplot2.discrete.colour = c("#D55E00", "#0072B2", "#009E73", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"),
  ggplot2.discrete.fill = c("#D55E00", "#0072B2", "#009E73", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442")
)
# Fira Sans font for graphics
ggplot2::theme_set(
  ggplot2::theme_get() +
    ggplot2::theme(text = ggplot2::element_text(family = "Fira Sans"))
)
```

# vital  


[![R-CMD-check](https://github.com/robjhyndman/vital/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/robjhyndman/vital/actions/workflows/R-CMD-check.yaml)


The goal of vital is to allow analysis of demographic data using tidy tools.

## Installation

You can install the **stable** version from
[CRAN](https://cran.r-project.org/package=vital):

```r
pak::pak("vital")
```

You can install the **development** version from
[Github](https://github.com/robjhyndman/vital):

``` r
pak::pak("robjhyndman/vital")
```

## Examples

First load the necessary packages.

```{r packages, message=FALSE}
library(vital)
library(tsibble)
library(dplyr)
library(ggplot2)
```

### vital objects

The basic data object is a `vital`, which is time-indexed tibble that contains vital statistics such as births, deaths, population counts, and mortality and fertility rates.

Here is an example of a `vital` object containing mortality data for Norway.

```{r aus}
norway_mortality <- norway_mortality |>
  collapse_ages(max_age = 100)
```

We can use functions to see which variables are index, key or vital:

```{r vars}
index_var(norway_mortality)
key_vars(norway_mortality)
vital_vars(norway_mortality)
```

### Plots

```{r autoplot, warning = FALSE}
norway_mortality |>
  filter(Sex != "Total", Year < 1980, Age < 90) |>
  autoplot(Mortality) + scale_y_log10()
```

### Life tables and life expectancy

```{r lifetable}
# Life table for Norwegian males in 2000
norway_mortality |>
  filter(Sex == "Male", Year == 2000) |>
  life_table()
```

```{r e0}
# Life expectancy
norway_mortality |>
  filter(Sex != "Total") |>
  life_expectancy() |>
  ggplot(aes(x = Year, y = ex, color = Sex)) +
  geom_line()
```

### Smoothing

Several smoothing functions are provided: `smooth_spline()`, `smooth_mortality()`, `smooth_fertility()`, and `smooth_loess()`, each smoothing across the age variable for each year.

```{r smoothing}
# Smoothed data
norway_mortality |>
  filter(Sex != "Total", Year == 1967) |>
  smooth_mortality(Mortality) |>
  autoplot(Mortality) +
  geom_line(aes(y = .smooth), col = "#0072B2") +
  ylab("Mortality rate") +
  scale_y_log10()
```

### Mortality models

Several mortality models are available including variations on Lee-Carter models (Lee & Carter, JASA, 1992), and functional data models (Hyndman & Ullah, CSDA, 2007).

```{r lc}
fit <- norway_mortality |>
  filter(Sex != "Total") |>
  model(
    lee_carter = LC(log(Mortality)),
    fdm = FDM(log(Mortality))
  )
fit
```

Models are fitted for all combinations of key variables excluding age.

```{r lc2}
fit |>
  select(lee_carter) |>
  filter(Sex == "Female") |>
  report()
```


```{r lc3}
fit |>
  select(lee_carter) |>
  autoplot()
```

```{r}
fit |>
  select(lee_carter) |>
  age_components()
fit |>
  select(lee_carter) |>
  time_components()
```

```{r lc5}
fit |> forecast(h = 20)
```

The forecasts are returned as a distribution column (here transformed normal because of the log transformation used in the model). The `.mean` column gives the point forecasts equal to the mean of the distribution column.

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