---
title: "poputils"
output:
  bookdown::html_document2:
    base_format: rmarkdown::html_vignette
    fig_caption: yes
    toc: true
    toc_depth: 2
    number_sections: true
pkgdown:
  as_is: true
vignette: >
  %\VignetteIndexEntry{poputils}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
bibliography: references.bib
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# Aims

`poputils` provides tools for common tasks with demographic data. It has some distinctive features:

- *Tidyverse compliance*. `poputils` tries to into [tidyverse](https://tidyverse.org/) workflows. For instance, `poputils` functions use data frames for inputs and outputs, use [tidyselect](https://tidyselect.r-lib.org/reference/language.html) methods to specify variables, and follow tidyverse conventions for variable names.
- *Uncertainty*. `poputils` handles uncertainty through the use of [rvecs](https://bayesiandemography.github.io/rvec/reference/rvec.html). An rvec is an object holding multiple draws from a distribution that behaves similarly to an ordinary R vector.
- *Age and time labels*. `poputils` allows users to work directly with age and time labels, based on common set of methods.

Some functions in `poputils` are designed for data analysts. Others are designed for programmers creating functions to be used by data analysts.


# Tools for data analysts

## Labels

### Age

Producers of demographic data follow a wide variety of styles for labeling age groups. `poputils` contains tools for parsing and manipulating these labels. 

Age label functions in `poputils` require that age labels belong to one of three types:

- `"single"`. Single years of age, possibly including an open age group, eg `"0"`, `"81"`, `"17"`, `"100+"`. 
- `"five"`. Five-year age groups, possibly including an open age group, eg `"0-4"`, `"80-84"`, `"15-19"`, `"100+"`.
- `"lt"`. Life table age groups. Like `"five"`, but with the `"0-4"` age group split into `"0"` and `"1-4"`.

Age labels created by `poputils` functions such as `age_labels()` follow a standard set of rules. Many age labels created using other rules can, however, be parsed by `poputils` functions,

```{r}
library(poputils)
library(dplyr, warn.conflicts = FALSE)
tibble(original = c("5 to 9", "5_9", "05-09"),
       reformated = reformat_age(original))
```

Functions `age_lower()`, `age_upper()`, and  `age_mid()` extract information about lower limits, upper limits, and centers of age groups. This can be useful for ordering data
```{r}
df <- data.frame(age = c("5-9", "0-4", "15-19", "10-14"),
                 population = c(3, 7, 2, 4))
df
df |>
  arrange(age_lower(age))
```

and plotting

```{r}
library(ggplot2)
ggplot(df, aes(x = age_mid(age), y = population)) +
  geom_point()
```

among other things.

Functions `combine_age()` and `set_age_open()` can be used to collapse age groups,
```{r}
tibble(age = age_labels("lt", max = 30),
       age_5 = combine_age(age, to = "five"),
       age_25plus = set_age_open(age, lower = 20))
```


### Sex/gender

Function `reformat_sex()` converts sex/gender categories to `"Female"`, `"Male"`, and any additional categories specified through the `except` argument,
```{r}
reformat_sex(c("M", "F", "Diverse", "Fem"), except = "Diverse")
```


## Life tables and life expectancy

A life table a way of describing mortality. The best known life table quantity is life expectancy at birth.

### Basic functionality

Life tables can be calculated from age-specific mortality rates using function `lifetab()`. 

```{r}
nzl_mort |>
  filter(year == 2022,
         gender == "Female") |>
  lifetab(mx = mx)  
```

`lifetab()` and `lifeexp()` both have a `by` argument. Separate results are calculated for each combination of the `by` variables,
```{r}
nzl_mort |>
  lifeexp(mx = mx,
          by = c(gender, year))  
```

The same effect can be obtained using `dplyr::group_by()`,
```{r}
nzl_mort |>
  group_by(gender, year) |>
  lifeexp(mx = mx)
```

The input data for life tables and life expectancies can be probabilities of dying (`qx`), rather than mortality rates (`mx`)
```{r}
west_lifetab |>
  group_by(level, sex) |>
  lifeexp(qx = qx)
```

By default, `lifeexp()` calculates life expectancy at age zero. It can, however, be used to calculate life expectancy at other ages.
```{r}
nzl_mort |>
  lifeexp(mx = mx,
          at = c(0, 65),
          by = c(gender, year))  
```



### Calculation methods

Alternative methods for calculating life tables differ mainly in their assumptions about how mortality varies within age groups [@preston2001demography; @keyfitz2005applied]. It turns out that, for the purposes of constructing life tables, all the relevant information about the way that mortality varies within age groups can be captured by a single number: the average length of time lived in an interval by people who die in that interval [@preston2001demography, p.43]. This number is denoted $_na_x$, where $x$ is exact age at the start of the internal, and $n$ is the length of the interval. The quantity $_5a_{20}$, for instance, refers to the average number of years lived after their 20th birthday by people who die between their 20th and 25th birthdays. When $n=1$, the $n$ subscript is typically omitted.

Functions `lifetab()` and `lifeexp()` have four arguments for specifying calculation methods: 

- `infant`, which specifies how $a_0$ is calculated,
- `child`, which specifies how $_4a_1$ is calculated,
- `closed`, which specifies how $_na_x$ for all other closed intervals are calculated, and 
- `open`, which specifies how the final interval, $_{\infty}a_x$ is calculated.

Different choices of method are available for each argument. In some cases, different formulas are used for females and males. The formulas can also differ depending on whether the input data is of mortality rates or probabilities of dying.

| `argument` | `sex`    | `method`       | input | formula                                                                                                                                                            |
| :-------   | :------- | :------------- |:-----:|:-----------------------------------------------------------:|
| `infant`   | \<any\>  | `"constant"`   | `mx`  | $$a_0 = \frac{1 - (m_0 + 1) e^{-m_0}}{m_0 (1 - e^{-m_0})}$$                                                                                                        |
| `infant`   | \<any\>  | `"constant"`   | `qx`  | $$a_0 = \frac{(1 - \log(1 - q_0) (1 - q_0)) - 1}{\log(1 - q_0) q_0}$$                                                                                               |
| `infant`   | \<any\>  | `"linear"`     | `mx`  | $$a_0 = 0.5$$                                                                                                                                                      |
| `infant`   | \<any\>  | `"linear"`     | `qx`  | $$a_0 = 0.5$$                                                                                                                                                      |
| `infant`   | Female   | `"CD"`         | `mx`  | $$a_0 = \begin{cases} 0.053 + 2.8 m_0 & 0 \le m_0 < 0.107 \\ 0.35 &  m_0 \ge 0.107 \end{cases}$$                                                                   |
| `infant`   | Female   | `"CD"`         | `qx`  | $$a_0 = \begin{cases} 0.05 + 3 q_0 & 0 \le m_0 < 0.1 \\ 0.35 &  q_0 \ge 0.1 \end{cases}$$                                                                          |
| `infant`   | Male     | `"CD"`         | `mx`  | $$a_0 = \begin{cases} 0.045 + 2.684 m_0 & 0 \le m_0 < 0.107 \\ b0.33 &  m_0 \ge 0.107 \end{cases}$$                                                                |
| `infant`   | Male     | `"CD"`         | `qx`  | $$a_0 = \begin{cases} 0.0425 + 2.875 q_0 & 0 \le q_0 < 0.1 \\ 0.33 &     q_0 \ge 0.1 \end{cases}$$                                                                  |
| `infant`   | Female   | `"AK"`         | `mx`  | $$a_0 = \begin{cases} 0.14903 - 2.05527 m_0 & 0 \le m_0 < 0.01724 \\ 0.04667 + 3.88089 m_0 & 0.01724 \le m_0 < 0.06891 \\ 0.31411 &  m_0 \ge 0.06891 \end{cases}$$ |
| `infant`   | Female   | `"AK"`         | `qx`  | $$a_0 = \begin{cases} 0.149 - 2.0867 q_0 & 0 \le q_0 < 0.017 \\ 0.0438 + 4.1075 q_0 & 0.017 \le q_0 < 0.0658 \\ 0.3141 &  q_0 \ge 0.0658 \end{cases}$$              |
| `infant`   | Male     | `"AK"`         | `mx`  | $$a_0 = \begin{cases} 0.14929 - 1.99545 m_0 & 0 \le m_0 <  0.023 \\ 0.02832 + 3.26021 m_0 &  0.023 \le m_0 < 0.08307 \\ 0.29915 &  m_0 \ge 0.08307 \end{cases}$$   |
| `infant`   | Male     | `"AK"`         | `qx`  | $$a_0 = \begin{cases} 0.1493 - 2.0367 q_0 & 0 \le q_0 <  0.0226 \\ 0.0244 + 3.4994 q_0 &  0.0226 \le q_0 < 0.0785 \\ 0.2991 &  q_0 \ge 0.0785 \end{cases}$$        |
| `child`    | \<any\>  | `"constant"`   | `mx`  | $$_4a_1 = \frac{1 - (4 \times {_4}m_1 + 1) e^{-4 \times {_4}m_1}}{_4m_1 (1 - e^{-4 \times {_4}m_1})}$$                                                             |
| `child`    | \<any\>  | `"constant"`   | `qx`  | $$_4a_1 = \frac{4((1 - \log(1-{_4}q_1)) (1 - {_4}m_1) - 1)}{\log(1 - {_4q_1}) {_4}q_1}$$                                                                      |
| `child`    | \<any\>  | `"linear"`     | `mx`  | $$_4a_1 = 2$$                                                                                                                                                      |
| `child`    | \<any\>  | `"linear"`     | `qx`  | $$_4a_1 = 2$$                                                                                                                                                      |
| `child`    | Female   | `"CD"`         | `mx`  | $$_4a_1 = \begin{cases} 1.522 - 1.518 m_0 & 0 \le m_0 < 0.107 \\ 1.361 &  m_0 \ge 0.107 \end{cases}$$                                                              |
| `child`    | Female   | `"CD"`         | `qx`  | $$_4a_1 = \begin{cases} 1.542 - 1.625 q_0 & 0 \le q_0 < 0.1 \\ 1.361 &  q_0 \ge 0.1 \end{cases}$$                                                                  |
| `child`    | Male     | `"CD"`         | `mx`  | $$_4a_1 = \begin{cases} 1.651 - 2.816 m_0 & 0 \le m_0 < 0.107 \\ 1.352 &  m_0 \ge 0.107 \end{cases}$$                                                              |
| `child`    | Male     | `"CD"`         | `qx`  | $$_4a_1 = \begin{cases} 1.653 - 3.013 q_0 & 0 \le q_0 < 0.1 \\ 1.352 &  q_0 \ge 0.1 \end{cases}$$                                                           |
| `closed`   | \<any\>  | `"constant"`   | `mx`  | $$_na_x = \frac{1 - (n \times {_n}m_x + 1) e^{-n \times {_n}m_x}}{_nm_x (1 - e^{-n \times {_n}m_x})}$$                                                             |
| `closed`   | \<any\>  | `"constant"`   | `qx`  | $$_na_x = \frac{n((1 - \log(1 - {_n}q_x))(1 - {_nq_x}) - 1)}{\log(1 - {_nq_x}) {_n}q_x}$$                                      |
| `closed`   | \<any\>  | `"linear"`     | `mx`  | $$_na_x = 0.5 n$$                                                                                                                                                  |
| `closed`   | \<any\>  | `"linear"`     | `qx`  | $$_na_x = 0.5 n$$                                                                                                                                                  |
| `open`     | \<any\>  | `"constant"`   | `mx`  | $$_{\infty}a_{\omega} = \frac{1}{_{\infty}m_{\omega}}$$                                                                                                            |
| `open`     | \<any\>  | `"constant"`   | `qx`  | $$_{\infty}a_{\omega} = \frac{1}{_{n}m_{\omega-n}}$$                                                                                                               |

In the table above,

- values for `"CD"` are from @coale1983regional, p20, and @preston2001demography, p48;
- values for `"AK"` are from @andreev2015average, p376, and @wilmoth2021methods, p37; and
- values for `"constant"` are expected values for an exponential distribution that has been right-truncated at $n$.

When the inputs data are $_nq_x$, the value of $_na_x$ for the last age group is based in mortality rates in the second-to-last age group. This is an expedient to deal with the fact that $_nq_x$ is always 1 in the last age group, and therefore provides no information about mortality in that age group.

Once the $_na_x$ have been determined, the life table is fully specified, and the required calculations can be carried out with no further input from the user.

The probability of dying within each interval is

$$_nq_x = \frac{n \times {_n}m_x}{1 + (n -  {_n}a_x) \times {_nm_x}},$$
with $_{\infty}q_{\omega} = 1$. Quantity $l_x$ is the number of people surviving to exact age $x$. In `lifetab()`, by default, $l_0 = 100,000$. Remaining values are calculated using 

$$l_{x+n} = (1 - {_nq_x}) \times l_x.$$
Quantity $_nd_x$ is the number of people who die between exact ages $x$ and $x+n$,

$$_nd_x = l_x - l_{x+n}.$$

Quantity $_nL_x$ is the number of person-years lived between exact ages $x$ and $x+n$. It consists of person-years lived by people who survive the interval, plus person-years lived by people who die within the interval,

$$_nL_x = l_{x+n} \times n + {_nd_x} \times {_na_x}.$$
Finally, $e_x$, the number of years of life remaining to a person aged exactly $x$, is 
$$e_x = {_nL_x} + {_nL_{x+n}} + \cdots + {_{\infty}L_{\omega}}$$.

Although the results for `lifetab()` and `lifeexp()` do vary with the values for `infant`, `child`, or `closed`, the differences are typically small, 
```{r}
lin <- nzl_mort |>
  lifeexp(mx = mx,
          by = c(gender, year),
          infant = "linear",
          suffix = "lin")
ak <- nzl_mort |>
  lifeexp(mx = mx,
          sex = gender,
          by = year,
          infant = "AK", 
          suffix = "ak")
inner_join(lin, ak, by = c("year", "gender")) |>
  mutate(diff = ex.lin - ex.ak)
```

### Deriving life tables from life expectancies

When working with very limited data, or when constructing simulations, it is sometimes helpful to be able to derive a full life table that is consistent with a given life expectancy. This is what function `e0_to_lifetab_logit()` does:

```{r}
target_e0 <- data.frame(sex = "Female", e0 = 51.4)

west10 <- west_lifetab |>
  filter(level == 10) |>
  select(age, sex, lx)

e0_to_lifetab_logit(target = target_e0,
                    standard = west10)
```


## Fertility

Function `tfr()` calculates total fertility rates from age-specific fertility rates. The interface is similar to that of `lifeexp()`,

```{r}
irn_fert |>
  tfr(asfr = rate,
      by = c(area, time),
      denominator = 1000)      
```

Function `tfr_to_asfr_scale()` is the fertility equivalent of `e0_to_lifetab_logit()`.


## Uncertainty

The examples so far have all used deterministic inputs. The data frame `nzl_mort_rvec`, in constrast, uses a rvec to represent mortality rates,

```{r}
library(rvec)
nzl_mort_rvec
```

The `mx` rvec holds 1000 draws from the posterior distribution from a Bayesian model of mortality. The posterior distribution for infant mortality for females in 2021, for instance, has a posterior median of 0.0032, and a 95% credible interval of (0.0028, 0.0037).

If the input to `lifetab()`, `lifeexp()`, or `tfr()` is an rvec, then the output will be too. Uncertainty in the input is propagated through to the output.

```{r}
library(rvec)
nzl_mort_rvec |>
  filter(year == 2022,
         gender == "Female") |>
  lifetab(mx = mx) |>
  select(age, qx, lx)
```


# Tools for developers

`poputils` provides functions that developers creating packages to be used by demographers may find useful. 

## Labels

`check_age()` and `age_group_type()` can be useful in functions that involve age group labels. `check_age()` performs some basic validity checks, while `age_group_type()` assesses whether a set of labels belongs to type `"single"`, `"five"`, or `"lt"`.

It is often possible to guess the nature of a demographic variable, or of categories within a demographic variable, based on names and labels. Functions `find_var_age()`, `find_var_sexgender()`, `find_var_time()`, `find_label_female()`, and `find_label_male()` help with these sorts of inferences.

## Data manipulation

Function `groups_colnums()` is helpful when implementing [tidyselect](https://tidyselect.r-lib.org/reference/language.html) methods when the data are held in a grouped data frame.

`matrix_to_list_of_cols()` and `matrix_to_list_of_rows()` convert from matrices to lists of vectors. 

`to_matrix()` converts a data frame to a matrix. The data frame potentially has more than two classification variables, and the rows and/or columns of the matrix can be formed from combinations of these variables.



# References
