poputils

Aims

poputils contains tools for carrying out common tasks when working with demographic data. Some distinctive features:

  • poputils tries to into tidyverse workflows. For instance, poputils functions use data frames for inputs and outputs, use tidyselect methods to specify variables, and follow tidyverse conventions for variable names.
  • poputils allows for uncertainty through the use of rvecs. An rvec is an object holding multiple draws from a distribution that behaves similarly to an ordinary R vector.
  • Allow users to work directly with age and time labels, based on common set of methods.

Some functions in poputils are designed for data analysts working on demographic datasets. Others are designed for programmers creating functions to be used at 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 age group 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,

library(poputils)
library(dplyr, warn.conflicts = FALSE)
tibble(original = c("5 to 9", "5_9", "05-09"),
       reformated = reformat_age(original))
#> # A tibble: 3 × 2
#>   original reformated
#>   <chr>    <fct>     
#> 1 5 to 9   5-9       
#> 2 5_9      5-9       
#> 3 05-09    5-9

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

df <- data.frame(age = c("5-9", "0-4", "15-19", "10-14"),
                 population = c(3, 7, 2, 4))
df
#>     age population
#> 1   5-9          3
#> 2   0-4          7
#> 3 15-19          2
#> 4 10-14          4
df |>
  arrange(age_lower(age))
#>     age population
#> 1   0-4          7
#> 2   5-9          3
#> 3 10-14          4
#> 4 15-19          2

and plotting

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,

tibble(age = age_labels("lt", max = 30),
       age_5 = combine_age(age, to = "five"),
       age_25plus = set_age_open(age, lower = 20))
#> # A tibble: 8 × 3
#>   age   age_5 age_25plus
#>   <chr> <chr> <chr>     
#> 1 0     0-4   0         
#> 2 1-4   0-4   1-4       
#> 3 5-9   5-9   5-9       
#> 4 10-14 10-14 10-14     
#> 5 15-19 15-19 15-19     
#> 6 20-24 20-24 20+       
#> 7 25-29 25-29 20+       
#> 8 30+   30+   20+

The aim is that users should be able to with age group labels throughout the analysis.

Sex/gender

Function reformat_sex() converts sex/gender categories to "Female", "Male", and any additional categories specified through the except argument,

reformat_sex(c("M", "F", "Diverse", "Fem"), except = "Diverse")
#> [1] Male    Female  Diverse Female 
#> Levels: Female Male Diverse

Life tables and life expectancy

A life table a way of summarizing mortality conditions. It consists of quantities calculated from age-specific mortality rates. The most widely-used life table quantity is life expectancy at birth.

Basic functionality

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

nzmort |>
  filter(year == 2022,
         gender == "Female") |>
  lifetab(mx = mx)  
#> # A tibble: 21 × 10
#>     year gender age         deaths   popn       qx      lx    dx      Lx    ex
#>    <int> <chr>  <fct>        <int>  <int>    <dbl>   <dbl> <dbl>   <dbl> <dbl>
#>  1  2022 Female Infant          84  29680 0.00283  100000  283.   99859.  83.4
#>  2  2022 Female 1-4 years       18 118420 0.000608  99717.  60.6 398748.  82.6
#>  3  2022 Female 5-9 years       12 156820 0.000383  99657.  38.1 498189.  78.7
#>  4  2022 Female 10-14 years     15 164830 0.000455  99619.  45.3 497980.  73.7
#>  5  2022 Female 15-19 years     42 154150 0.00136   99573. 136.  497528.  68.8
#>  6  2022 Female 20-24 years     63 156860 0.00201   99438. 199.  496690.  63.8
#>  7  2022 Female 25-29 years     72 172770 0.00208   99238. 207.  495675.  59.0
#>  8  2022 Female 30-34 years     78 194570 0.00200   99032. 198.  494663.  54.1
#>  9  2022 Female 35-39 years    111 175050 0.00317   98833. 313.  493385.  49.2
#> 10  2022 Female 40-44 years    147 160070 0.00458   98521. 451.  491474.  44.3
#> # ℹ 11 more rows

lifetab() and lifeexp() both have a by argument. Separate results are calculated for each combination of the by variables,

nzmort |>
  lifeexp(mx = mx,
          by = c(gender, year))  
#> # A tibble: 4 × 3
#>   gender  year    ex
#>   <chr>  <int> <dbl>
#> 1 Female  2021  84.0
#> 2 Male    2021  80.5
#> 3 Female  2022  83.4
#> 4 Male    2022  80.0

The same effect can be obtained using dplyr::group_by(),

nzmort |>
  group_by(gender, year) |>
  lifeexp(mx = mx)
#> # A tibble: 4 × 3
#>   gender  year    ex
#>   <chr>  <int> <dbl>
#> 1 Female  2021  84.0
#> 2 Male    2021  80.5
#> 3 Female  2022  83.4
#> 4 Male    2022  80.0

The input data for life tables and life expectancies can be probabilities of dying (qx), rather than mortality rates (mx)

west_lifetab |>
  group_by(level, sex) |>
  lifeexp(qx = qx)
#> # A tibble: 50 × 3
#>    level sex       ex
#>    <int> <chr>  <dbl>
#>  1     1 Female  20.1
#>  2     1 Male    18.1
#>  3     2 Female  22.5
#>  4     2 Male    20.5
#>  5     3 Female  25.0
#>  6     3 Male    22.9
#>  7     4 Female  27.5
#>  8     4 Male    25.3
#>  9     5 Female  30.0
#> 10     5 Male    27.7
#> # ℹ 40 more rows

By default, lifeexp() calculates life expectancy at age zero. It can, however, be used to calculate life expectancy at other ages.

nzmort |>
  lifeexp(mx = mx,
          at = 65,
          by = c(gender, year))  
#> # A tibble: 4 × 3
#>   gender  year    ex
#>   <chr>  <int> <dbl>
#> 1 Female  2021  22.0
#> 2 Male    2021  19.8
#> 3 Female  2022  21.4
#> 4 Male    2022  19.3

Calculation methods

Alternative methods for calculating life tables differ mainly in their assumptions variation within age groups (Preston, Heuveline, and Guillot 2001; Keyfitz and Caswell 2005). It turns out that, for the purposes of constructing life tables, all the relevant information about the way that mortality varies by age within each age group can be captured by a single number: the average length of time lived in an interval by people who die in that interval (Preston, Heuveline, and Guillot 2001, 43). This number is denoted nax, where x is exact age at the start of the internal, and n is the length of the interval. The quantity 5a20, 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 a0 is calculated,
  • child, which specifies how 4a1 is calculated,
  • closed, which specifies how nax for all other closed intervals are calculated, and
  • open, which specifies how the final interval, ∞ax 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 a0 = 0.5
infant <any> "linear" qx a0 = 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 4a1 = 2
child <any> "linear" qx 4a1 = 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 nax = 0.5n
closed <any> "linear" qx nax = 0.5n
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, the values for "CD" are from Coale, Demeny, and Vaughan (1983), p20, and Preston, Heuveline, and Guillot (2001), p48; the values for "AK" are from Andreev and Kingkade (2015), p376, and Wilmoth et al. (2021), p37; and the values for "constant" are expected values for an exponential distribution that has been right-truncated at n.

When the inputs data are nqx, the value of nax 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 nqx is always 1 in the last age group, and therefore provides no information about mortality conditions in that age group.

Once the nax 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 ∞qω = 1. Quantity lx is the number of people surviving to exact age x. In lifetab(), by default, l0 = 100, 000. Remaining values are calculated using

lx + n = (1 − nqx) × lx. Quantity ndx is the number of people who die between exact ages x and x + n,

ndx = lx − lx + n.

Quantity nLx 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,

nLx = lx + n × n + ndx × nax. Finally, ex, the number of years of life remaining to a person aged exactly x, is ex = nLx + nLx + n + ⋯ + ∞Lω.

Although the results for lifetab() and lifeexp() do vary with difference choices for infant, child, or closed, the differences are often small,

lin <- nzmort |>
  lifeexp(mx = mx,
          by = c(gender, year),
          infant = "linear",
          suffix = "lin")
ak <- nzmort |>
  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)
#> # A tibble: 4 × 5
#>   gender  year ex.lin ex.ak     diff
#>   <chr>  <int>  <dbl> <dbl>    <dbl>
#> 1 Female  2021   84.0  84.0 0.000906
#> 2 Male    2021   80.5  80.5 0.00110 
#> 3 Female  2022   83.4  83.4 0.000771
#> 4 Male    2022   80.0  80.0 0.000965

Uncertainty

The examples of life tables and life expectancy so far have all been based on a deterministic input, mx column of data frame nzmort,

nzmort
#> # A tibble: 84 × 6
#>     year gender age         deaths   popn        mx
#>    <int> <chr>  <fct>        <int>  <int>     <dbl>
#>  1  2021 Female Infant         108  29570 0.00365  
#>  2  2021 Female 1-4 years       30 118950 0.000252 
#>  3  2021 Female 5-9 years       12 158350 0.0000758
#>  4  2021 Female 10-14 years     21 163800 0.000128 
#>  5  2021 Female 15-19 years     51 152960 0.000333 
#>  6  2021 Female 20-24 years     54 160250 0.000337 
#>  7  2021 Female 25-29 years     60 180290 0.000333 
#>  8  2021 Female 30-34 years     63 192070 0.000328 
#>  9  2021 Female 35-39 years    105 171970 0.000611 
#> 10  2021 Female 40-44 years    153 157740 0.000970 
#> # ℹ 74 more rows

The data frame nzmort_rvec instead uses a rvec to represent mortality rates,

library(rvec)
#> 
#> Attaching package: 'rvec'
#> The following objects are masked from 'package:stats':
#> 
#>     sd, var
#> The following object is masked from 'package:base':
#> 
#>     rank
nzmort_rvec
#> # A tibble: 84 × 4
#>     year gender age                                 mx
#>    <int> <chr>  <fct>                     <rdbl<1000>>
#>  1  2021 Female Infant         0.0032 (0.0028, 0.0037)
#>  2  2021 Female 1-4 years   0.00018 (0.00014, 0.00022)
#>  3  2021 Female 5-9 years   9.2e-05 (7.3e-05, 0.00012)
#>  4  2021 Female 10-14 years 0.00012 (9.9e-05, 0.00014)
#>  5  2021 Female 15-19 years 0.00027 (0.00023, 0.00031)
#>  6  2021 Female 20-24 years 0.00032 (0.00028, 0.00036)
#>  7  2021 Female 25-29 years 0.00034 (0.00031, 0.00038)
#>  8  2021 Female 30-34 years 0.00042 (0.00038, 0.00046)
#>  9  2021 Female 35-39 years   6e-04 (0.00054, 0.00065)
#> 10  2021 Female 40-44 years 0.00091 (0.00084, 0.00098)
#> # ℹ 74 more rows

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() or lifeexp() is an rvec, then the output will be too. Uncertainty about mortality rates is propagated through to quantities derived from these rates.

library(rvec)
nzmort_rvec |>
  filter(year == 2022,
         gender == "Female") |>
  lifetab(mx = mx) |>
  select(age, qx, lx)
#> # A tibble: 21 × 3
#>    age                                 qx                   lx
#>    <fct>                     <rdbl<1000>>         <rdbl<1000>>
#>  1 Infant         0.0034 (0.0029, 0.0039) 1e+05 (1e+05, 1e+05)
#>  2 1-4 years     0.00075 (6e-04, 0.00095) 99661 (99613, 99708)
#>  3 5-9 years     5e-04 (0.00039, 0.00063) 99587 (99534, 99638)
#>  4 10-14 years 0.00064 (0.00053, 0.00076) 99538 (99479, 99594)
#>  5 15-19 years    0.0014 (0.0012, 0.0016) 99474 (99411, 99532)
#>  6 20-24 years    0.0017 (0.0015, 0.0019) 99332 (99265, 99393)
#>  7 25-29 years    0.0018 (0.0017, 0.0021) 99163 (99093, 99231)
#>  8 30-34 years    0.0023 (0.0021, 0.0025) 98979 (98901, 99053)
#>  9 35-39 years    0.0032 (0.0029, 0.0035) 98753 (98666, 98837)
#> 10 40-44 years    0.0048 (0.0044, 0.0052) 98438 (98339, 98530)
#> # ℹ 11 more rows

Tools for developers

poputils provides some 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 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.

Future developments

Definite

  • Stable populations. Given mortality and fertility profiles, generate the associated stable population.
  • Time labels Functions for dealing with time labels analogous to the existing ones dealing with age labels. The functions need to allow for one-month and one-quarter periods, and for ‘exact times’, ie dates.
  • Multiple decrement life tables Extend lifetab() and lifeexp() to allow for multiple decrements.
  • Projection accounting Functions to turn projected demographic rates, and an initial population, into a projected demographic account. Needs flexibility over dimensions included, and needs deterministic and probabilistic versions.
  • TFR Function to calculation total fertility rates from age-specific fertility rates
  • Age, period, cohort labels Functions to allocate events to age groups, periods, or cohorts, based on data on dates of event and dates of birth.

Possible

  • Aggregation function Using dplyr::count(), dplyr::summarise(), or stats::aggregate() to aggregate counts or rates in a data frame is awkward. Given that this is such a common operation, it might be worthwhile to do a replacement.

References

Andreev, Evgeny M, and W Ward Kingkade. 2015. “Average Age at Death in Infancy and Infant Mortality Level: Reconsidering the Coale-Demeny Formulas at Current Levels of Low Mortality.” Demographic Research 33: 363–90.
Coale, Ansley J, Paul Demeny, and Barbara Vaughan. 1983. Regional Model Life Tables and Stable Populations: Studies in Population. Academic Press.
Keyfitz, Nathan, and Hal Caswell. 2005. Applied Mathematical Demography. Springer.
Preston, Samuel H., Patrick Heuveline, and Michel Guillot. 2001. Demography: Measuring and Modeling Population Processes. Blackwell.
Wilmoth, John R, Kirill Andreev, Dmitri Jdanov, Dana A Glei, and Tim Riffe. 2021. “Methods Protocol for the Human Mortality Database. Version 6.”