Package 'poputils' reference manual

Title:	Demographic Analysis and Data Manipulation
Description:	Perform tasks commonly encountered when preparing and analysing demographic data. Some functions are intended for end users, and others for developers. Includes functions for working with life tables.
Authors:	John Bryant [aut, cre], Bayesian Demography Limited [cph]
Maintainer:	John Bryant <[email protected]>
License:	MIT + file LICENSE
Version:	0.6.1
Built:	2026-06-01 05:23:03 UTC
Source:	https://github.com/bayesiandemography/poputils

Calculate Intrinsic Growth Rate

Description

Calculate the intrinsic growth rate implied by fertility and mortality schedules. The intrinsic growth rate is the rate at which all age groups in a population would eventually grow if the population was subject to the fertility and mortality schedules indefinitely. The fertility and mortality schedules apply to a single sex, typically females.

Usage

.intrinsic_growth_rate(mx, Lx, age_mid)
.intrinsic_growth_rate(mx, Lx, age_mid)

Arguments

mx

Age specific fertility rates, for a single sex (typically daughters). An ordinary numeric vector or an rvec().

Lx

Life table mortality measure, for a single sex (typically females). An ordinary numeric vector or an rvec().

age_mid

The midpoint of each age group. Numeric vector.

Details

The fertility scehdule mx and mortality schedule Lx refer only to the reproductive ages. The mortality schedule Lx is the number of years that a newborn baby would be expected to spend in each age group under prevailing mortality rates. It is the life table quantity nLx where the radix l0 has been set to 1.

Value

A numeric vector of length 1.

Examples

mx <- c(0.072, 0.148, 0.156, 0.140, 0.108, 0.054, 0.013)
Lx <- c(3.49, 3.37, 3.23, 3.07, 2.91, 2.74, 2.56)
age_mid <- c(17.5, 22.5, 27.5, 32.5, 37.5, 42.5, 47.5)
.intrinsic_growth_rate(mx = mx, Lx = Lx, age_mid = age_mid)
mx <- c(0.072, 0.148, 0.156, 0.140, 0.108, 0.054, 0.013)
Lx <- c(3.49, 3.37, 3.23, 3.07, 2.91, 2.74, 2.56)
age_mid <- c(17.5, 22.5, 27.5, 32.5, 37.5, 42.5, 47.5)
.intrinsic_growth_rate(mx = mx, Lx = Lx, age_mid = age_mid)

Infer Age Label Type

Description

Determine whether a set of age labels refer to one-year, five-year, or life-table age groups.

Usage

age_group_type(x)
age_group_type(x)

Arguments

x

A vector of age labels

Details

The valid types of age labels are:

"single". One-year age groups, eg "0" or "55", and possibly an open age group, eg "90+".
"five". Five-year age groups, eg "0-4" or "55-59", and possibly an open age group, eg "100+".
"lt". Life table age groups, eg "0", "1-4", "5-9", "55-59", or "80+".

If x does not fit any of these descriptions, ⁠then age_group_type()⁠ throws an error.

If x could belong to more than one type, then age_group_type() prefers "single" to "five" and "lt", and prefers "five" to "lt".

Value

"single", "five", or "lt".

Examples

age_group_type(c("5-9", "0-4", "100+"))
age_group_type(c("2", "5", "1"))
age_group_type(c("0", "1-4"))

## could be any "single" or "lt"
age_group_type("0")

## could be "five" or "lt"
age_group_type("80-84")
age_group_type(c("5-9", "0-4", "100+"))
age_group_type(c("2", "5", "1"))
age_group_type(c("0", "1-4"))

## could be any "single" or "lt"
age_group_type("0")

## could be "five" or "lt"
age_group_type("80-84")

Create Age Labels

Description

Create labels for age groups. The labels depend on the type argument:

"single". One-year age groups, eg "0" or "55", and possibly an open age group, eg "90+".
"five". Five-year age groups, eg "0-4" or "55-59", and possibly an open age group, eg "100+".
"lt". Life table age groups, eg "0", "1-4", "5-9", "55-59", or "80+".

Usage

age_labels(type, min = 0, max = 100, open = NULL)
age_labels(type, min = 0, max = 100, open = NULL)

Arguments

type

Type of age group labels: "single", "five", or "lt".

min

Minimum age. Defaults to 0.

max

Maximum age for closed age groups. Defaults to 100.

open

Whether the last age group is "open", ie has no upper limit.

Details

The first age group starts at the age specified by min. If open is TRUE, then the final age group starts at the age specified by max. Otherwise, the final age group ends at the age specified by max.

open defaults to TRUE when min equals zero, and to FALSE otherwise.

Value

A character vector.

Examples

age_labels(type = "single", min = 15, max = 40)
age_labels(type = "five")
age_labels(type = "lt", max = 80)
age_labels(type = "single", min = 15, max = 40)
age_labels(type = "five")
age_labels(type = "lt", max = 80)

Lower Limits, Midpoints, and Upper Limits of Age Groups

Description

Given a vector x of age group labels, return a numeric vector.

age_lower() returns the lower limits of each age group,
age_mid() returns the midpoints, and
age_upper() returns the upper limits.

Vector x must describe 1-year, 5-year or life-table age groups: see age_labels() for examples. x can format these age groups in any way understood by reformat_age().

Usage

age_lower(x)

age_mid(x)

age_upper(x)
age_lower(x)

age_mid(x)

age_upper(x)

Arguments

x

A vector of age group labels.

Details

These functions can make age groups easier to work with. Lower and upper limits can be used for selecting on age. Replacing age group with midpoints can improve graphs.

Value

A numeric vector, the same length as x.

Examples

x <- c("15-19", "5-9", "50+")
age_lower(x)
age_mid(x)
age_upper(x)

## non-standard formats are OK
age_lower(c("infants", "100 and over"))

df <- data.frame(age = c("1-4", "10-14", "5-9", "0"),
                 rate = c(0.023, 0.015, 0.007, 0.068))
df
subset(df, age_lower(age) >= 5)
x <- c("15-19", "5-9", "50+")
age_lower(x)
age_mid(x)
age_upper(x)

## non-standard formats are OK
age_lower(c("infants", "100 and over"))

df <- data.frame(age = c("1-4", "10-14", "5-9", "0"),
                 rate = c(0.023, 0.015, 0.007, 0.068))
df
subset(df, age_lower(age) >= 5)

Booth Standard

Description

A "standard" distribution for age-specific fertility rates proposed in Booth (1984), and widely used for estimating fertility rates in settings with high fertility and limited data.

Usage

booth_standard
booth_standard

Format

A tibble with 8 rows and the following columns:

age Five-year age group, from "10-14" to "45-49"
value Proportion of total fertility occurring within the age group

Details

The key input for booth_standard is 'Yx' from table 2 of Booth (1984).

Source

Booth H (1984) Transforming Gompertz's function for fertility analysis: The development of a standard for the relational Gompertz function. Population Studies 38(3): 495-506.

Validity Checks for Age Labels

Description

Check that age labels can be parsed and, optionally, whether the labels are complete, unique, start at zero, and end with an open age group.

Usage

check_age(
  x,
  complete = FALSE,
  unique = FALSE,
  zero = FALSE,
  open = FALSE,
  closed = FALSE
)
check_age(
  x,
  complete = FALSE,
  unique = FALSE,
  zero = FALSE,
  open = FALSE,
  closed = FALSE
)

Arguments

x

A vector of age labels.

complete

If TRUE, test whether x has gaps.

unique

If TRUE, test whether x has duplicates.

zero

If TRUE, test whether youngest age group in x starts at 0.

open

If TRUE, test whether oldest age group in x is open.

closed

If TRUE, test whether oldest age group in x is closed.

Details

By default, check_age() only tests whether a set of labels can be parsed as single-year, five-year, or life table age groups. (See age_group_type() for more on the three types of age group.) However, it can also apply the following tests:

complete. Whether x includes all intermediate age groups, with no gaps. For instance, the labels c("10-14", "15-19", "5-9") are complete, while the labelsc("15-19", "5-9") are not (because they are missing "10-14".)
unique. Whether x has duplicated labels.
zero. Whether the youngest age group in x starts at age 0, ie whether it includes "0" or "0-4".
open. Whether the oldest age group in x is "open", with no upper limit, eg "100+" or "65+".
closed. Whether the oldest age group in x is "closed", with an upper limit, eg "100-104+" or "65".

Value

TRUE, invisibly, or raises an error if a test fails.

Examples

try(
  check_age(c("10-14", "0-4", "15+"),
            complete = TRUE)  
)

try(
  check_age(c("10-14", "5-9", "0-4", "5-9", "15+"),
            unique = TRUE)
)

try(
  check_age(c("10-14", "5-9", "15+"),
            zero = TRUE)
)

try(
  check_age(c("10-14", "0-4", "5-9"),
            open = TRUE)
)

try(
  check_age(c("10+", "0-4", "5-9"),
            closed = TRUE)
)
try(
  check_age(c("10-14", "0-4", "15+"),
            complete = TRUE)  
)

try(
  check_age(c("10-14", "5-9", "0-4", "5-9", "15+"),
            unique = TRUE)
)

try(
  check_age(c("10-14", "5-9", "15+"),
            zero = TRUE)
)

try(
  check_age(c("10-14", "0-4", "5-9"),
            open = TRUE)
)

try(
  check_age(c("10+", "0-4", "5-9"),
            closed = TRUE)
)

Check that Arguments have Same Length

Description

Check that x and y have the same length.

Usage

check_equal_length(x, y, nm_x, nm_y)
check_equal_length(x, y, nm_x, nm_y)

Arguments

x, y

Arguments to compare

nm_x, nm_y

Names to use in error message

Value

'TRUE', invisibly.

Examples

x <- 1:3
y <- 3:1
check_equal_length(x = x,
                   y = y,
                   nm_x = "x",
                   nm_y = "y")
x <- 1:3
y <- 3:1
check_equal_length(x = x,
                   y = y,
                   nm_x = "x",
                   nm_y = "y")

Check Whole Number

Description

Check that n is finite, non-NA scalar that is an integer or integerish (ie is equal to round(n)), and optionally within a specified range and divisible by a specified number.

Usage

check_n(n, nm_n, min, max, divisible_by)
check_n(n, nm_n, min, max, divisible_by)

Arguments

n

A whole number

nm_n

Name for 'n' to be used in error messages

min

Minimum value 'n' can take. Can be NULL.

max

Maximum values 'n' can take. Can be NULL.

divisible_by

'n' must be divisible by this. Can be NULL.

Value

If all tests pass, check_n() returns TRUE invisibly. Otherwise it throws an error.

Examples

check_n(10, nm_n = "count", min = 0, max = NULL, divisible_by = 1)
check_n(10, nm_n = "count", min = NULL, max = NULL, divisible_by = NULL)
check_n(10, nm_n = "n", min = 5, max = 10, divisible_by = 2)
check_n(10, nm_n = "count", min = 0, max = NULL, divisible_by = 1)
check_n(10, nm_n = "count", min = NULL, max = NULL, divisible_by = NULL)
check_n(10, nm_n = "n", min = 5, max = 10, divisible_by = 2)

Check that Colnum Vectors do not Overlap

Description

Given a named list of colnum vectors, like those produced by tidyselect::eval_select(), throw an error if there is an overlap.

Usage

check_no_overlap_colnums(x)
check_no_overlap_colnums(x)

Arguments

x

A named list of integer vectors.

Value

TRUE, invisibly

Examples

x <- list(arg1 = c(age = 1L),
          arg2 = c(gender = 4L, region = 5L))
check_no_overlap_colnums(x)
x <- list(arg1 = c(age = 1L),
          arg2 = c(gender = 4L, region = 5L))
check_no_overlap_colnums(x)

Aggregate Age Group Labels

Description

Convert age group labels to a less detailed classification. The three classifications recognized by combine_age() are "single", "five", and "lt", as defined on age_labels(). The following conversions are permitted:

"single" —> "lt"
"single" —> "five"
"lt" —> "five"

Usage

combine_age(x, to = c("five", "lt"))
combine_age(x, to = c("five", "lt"))

Arguments

x

A vector of age labels

to

Type of age classification to convert to: "five" or "lt". Defaults to "five".

Value

If x is a factor, then combine_age() returns a factor; otherwise it returns a character vector.

Examples

x <- c("0", "5", "3", "12")
combine_age(x)
combine_age(x, to = "lt")
x <- c("0", "5", "3", "12")
combine_age(x)
combine_age(x, to = "lt")

Derive Life Tables that Match Life Expectancies, using a Brass Logit Model

Description

Turn life expectancies at birth into full life tables, using the Brass logit model. The method is simple and is designed for simulations or for settings with little or no data on age-specific mortality rates. In settings where data on age-specific mortality is available, other methods might be more appropriate.

Usage

e0_to_lifetab_logit(
  target,
  standard,
  infant = c("constant", "linear", "CD", "AK"),
  child = c("constant", "linear", "CD"),
  closed = c("constant", "linear"),
  open = "constant",
  radix = 1e+05,
  suffix = NULL
)
e0_to_lifetab_logit(
  target,
  standard,
  infant = c("constant", "linear", "CD", "AK"),
  child = c("constant", "linear", "CD"),
  closed = c("constant", "linear"),
  open = "constant",
  radix = 1e+05,
  suffix = NULL
)

Arguments

target

A data frame containing a variable called "e0", and possibly other varibles. See Details.

standard

A data frame containing variables called age and lx, and possibly others. See Details.

infant, child, closed, open

Methods used to calculate life expectancy. See lifetab() for details.

radix

Initial population for the lx column in the derived life table(s). Default is 100000.

suffix

Optional suffix added to life table columns.

Value

A tibble.

Method

The method implemented by e0_to_lifetab_logit() is based on the observation that, if populations A and B are demographically similar, then, in many cases,

$\text{logit}(q_x^{\text{B}}) \approx \alpha + \beta \text{logit}(q_x^{\text{A}})$

where $q_x$ is the life table probability of dying between birth and age $x$ . By definition, $q_x = 1 - l_x$ , where $l_x$ is the standard life table function, with radix ( $l_0$ ) equal to 1.

Given (i) target life expectancy, (ii) a set of $l_x^{\text{A}}$ ), (referred to as a "standard"), and (iii) a value for $\beta$ , e0_to_lifetab_logit() finds a value for $\alpha$ that yields a set of $q_x^{\text{B}}$ ) with the required life expectancy. If populations A and B are similar, then $beta$ is likely to close to 1.

The `target` argument

target is a data frame specifying life expectancies for each population being modelled, and, optionally, inputs to the calculations, and 'by' variables.

target contains the following variables:

A variable called "e0" giving life expectancy at birth.
Optionally, a variable called "beta" with values for $\beta$ . Can be an ordinary numeric vector or an rvec. If target does not include a "beta" variable, then e0_to_lifetab_logit() sets $\beta$ to 1.
A variable called "sex". The "sex" variable must be supplied if the infant argument to e0_to_lifetab_logit() is "CD" or "AK", or if the child argument is "CD".
Optionally, 'by' variables. Typical examples are time, region, and model variant.

The `standard` argument

standard is a data frame specifying the $l_x$ to be used with each life expectancy in target, and, optionally, values for the average age person-years lived by people who die in each group, $_na_x$ . Values in standard are age-specific.

standard contains the following variables:

A variable called "age", with labels that can be parsed by reformat_age().
A variable called "lx". Cannot be an rvec.
Additional variables used to match rows in standard to rows in target.

References

Brass W, Coale AJ. 1968. “Methods of analysis and estimation,” in Brass, W, Coale AJ, Demeny P, Heisel DF, et al. (eds). The Demography of Tropical Africa. Princeton NJ: Princeton University Press, pp. 88–139.

Moultrie TA, Timæus IM. 2013. Introduction to Model Life Tables. In Moultrie T, Dorrington R, Hill A, Hill K, Timæus I, Zaba B. (eds). Tools for Demographic Estimation. Paris: International Union for the Scientific Study of Population. online version.

Examples

## create new life tables based on level-1
## 'West' model life tables, but with lower
## life expectancy

library(dplyr, warn.conflicts = FALSE)

target <- data.frame(sex = c("Female", "Male"), 
                     e0 = c(17.5, 15.6))

standard <- west_lifetab |>
    filter(level == 1) |>
    select(sex, age, lx)
    
e0_to_lifetab_logit(target = target,
                    standard = standard,
                    infant = "CD",
                    child = "CD")

## target is an rvec
library(rvec, warn.conflicts = FALSE)
target_rvec <- data.frame(sex = c("Female", "Male"), 
                          e0 = rnorm_rvec(n = 2,
                                          mean = c(17.5, 15.6),
                                          n_draw = 1000))
e0_to_lifetab_logit(target = target_rvec,
                    standard = standard)
## create new life tables based on level-1
## 'West' model life tables, but with lower
## life expectancy

library(dplyr, warn.conflicts = FALSE)

target <- data.frame(sex = c("Female", "Male"), 
                     e0 = c(17.5, 15.6))

standard <- west_lifetab |>
    filter(level == 1) |>
    select(sex, age, lx)
    
e0_to_lifetab_logit(target = target,
                    standard = standard,
                    infant = "CD",
                    child = "CD")

## target is an rvec
library(rvec, warn.conflicts = FALSE)
target_rvec <- data.frame(sex = c("Female", "Male"), 
                          e0 = rnorm_rvec(n = 2,
                                          mean = c(17.5, 15.6),
                                          n_draw = 1000))
e0_to_lifetab_logit(target = target_rvec,
                    standard = standard)

Derive Life Tables that Match Life Expectancies, using a Brass Logit Model

Description

This function contained an error, and is deprecated. Please use function e0_to_lifetab_logit() instead.

Usage

ex_to_lifetab_brass(
  target,
  standard,
  infant = c("constant", "linear", "CD", "AK"),
  child = c("constant", "linear", "CD"),
  closed = c("constant", "linear"),
  open = "constant",
  radix = 1e+05,
  suffix = NULL
)
ex_to_lifetab_brass(
  target,
  standard,
  infant = c("constant", "linear", "CD", "AK"),
  child = c("constant", "linear", "CD"),
  closed = c("constant", "linear"),
  open = "constant",
  radix = 1e+05,
  suffix = NULL
)

Arguments

target

A data frame containing a variable called "ex", and possibly other varibles. See Details.

standard

A data frame containing variables called age and lx, and possibly others. See Details.

infant, child, closed, open

Methods used to calculate life expectancy. See lifetab() for details.

radix

Initial population for the lx column in the derived life table(s). Default is 100000.

suffix

Optional suffix added to life table columns.

Details

Function ex_to_lifetab_brass() used formula

$\text{logit}(l_x^{\text{B}}) \approx \alpha + \beta \text{logit}(l_x^{\text{A}})$

instead of the conventional

$\text{logit}(1-l_x^{\text{B}}) \approx \alpha + \beta \text{logit}(1-l_x^{\text{A}})$

Value

A tibble.

Identify Sex or Gender Labels Referring to Females

Description

Given labels for sex or gender, try to infer which (if any) refer to females. If no elements look like a label for females, or if two or more elements do, then return NULL.

Usage

find_label_female(nms)
find_label_female(nms)

Arguments

nms

A character vector

Value

An element of nms or NULL.

Examples

find_label_female(c("Female", "Male")) ## one valid
find_label_female(c("0-4", "5-9"))     ## none valid
find_label_female(c("F", "Fem"))       ## two valid
find_label_female(c("Female", "Male")) ## one valid
find_label_female(c("0-4", "5-9"))     ## none valid
find_label_female(c("F", "Fem"))       ## two valid

Identify Sex or Gender Labels Referring to Males

Description

Given labels for sex or gender, try to infer which (if any) refer to males. If no elements look like a label for males, or if two or more elements do, then return NULL.

Usage

find_label_male(nms)
find_label_male(nms)

Arguments

nms

A character vector

Value

An element of nms or NULL.

Examples

find_label_male(c("Female", "Male")) ## one valid
find_label_male(c("0-4", "5-9"))     ## none valid
find_label_male(c("male", "m"))      ## two valid
find_label_male(c("Female", "Male")) ## one valid
find_label_male(c("0-4", "5-9"))     ## none valid
find_label_male(c("male", "m"))      ## two valid

Identify an Age Variable

Description

Find the element of nms that looks like an age variable. If no elements look like an age variable, or if two or more elements do, then return NULL.

Usage

find_var_age(nms)
find_var_age(nms)

Arguments

nms

A character vector

Value

An element of nms, or NULL.

Examples

find_var_age(c("Sex", "Year", "AgeGroup", NA)) ## one valid
find_var_age(c("Sex", "Year"))                 ## none valid
find_var_age(c("age", "age.years"))            ## two valid
find_var_age(c("Sex", "Year", "AgeGroup", NA)) ## one valid
find_var_age(c("Sex", "Year"))                 ## none valid
find_var_age(c("age", "age.years"))            ## two valid

Identify a Sex or Gender Variable

Description

Find the element of nms that looks like a sex or gender variable. If no elements look like a sex or gender variable, or if two or more elements do, then return NULL.

Usage

find_var_sexgender(nms)
find_var_sexgender(nms)

Arguments

nms

A character vector

Value

An element of nms, or NULL.

Examples

find_var_sexgender(c("Sex", "Year", "AgeGroup", NA)) ## one valid
find_var_sexgender(c("Age", "Region"))               ## none valid
find_var_sexgender(c("sexgender", "sexes"))          ## two valid
find_var_sexgender(c("Sex", "Year", "AgeGroup", NA)) ## one valid
find_var_sexgender(c("Age", "Region"))               ## none valid
find_var_sexgender(c("sexgender", "sexes"))          ## two valid

Identify a Time Variable

Description

Find the element of nms that looks like an time variable. If no elements look like a time variable, or if two or more elements do, then return NULL.

Usage

find_var_time(nms)
find_var_time(nms)

Arguments

nms

A character vector

Value

An element of nms, or NULL.

Examples

find_var_time(c("Sex", "Year", "AgeGroup", NA)) ## one valid
find_var_time(c("Sex", "Region"))               ## none valid
find_var_time(c("time", "year"))                ## two valid
find_var_time(c("Sex", "Year", "AgeGroup", NA)) ## one valid
find_var_time(c("Sex", "Region"))               ## none valid
find_var_time(c("time", "year"))                ## two valid

Get a named vector of column indices for the grouping variables in a grouped data frame

Description

Constructed a named vector of indices equivalent to the vectors produced by tidyselect::eval_select, but for the grouping variables in an object of class "grouped_df".

Usage

groups_colnums(data)
groups_colnums(data)

Arguments

data

A data frame.

Details

If data is not grouped, then groups_colnums returns a zero-length vector.

Value

A named integer vector.

Examples

library(dplyr)
df <- data.frame(x = 1:4,
                 g = c(1, 1, 2, 2))
groups_colnums(df)
df <- group_by(df, g)
groups_colnums(df)
library(dplyr)
df <- data.frame(x = 1:4,
                 g = c(1, 1, 2, 2))
groups_colnums(df)
df <- group_by(df, g)
groups_colnums(df)

Age-Specific Fertility Rates in Iran

Description

Estimates of age-specific fertility rates, (births per 1000 person-years lived) for rural and urban areas, in Iran, 1986-2000. Calculated by Mohammad Jalal Abbasi-Shavazi and Peter McDonald from data from the 2000 Iran Demographic and Health Survey.

Usage

irn_fert
irn_fert

Format

A tibble with 2010 rows and the following columns:

time Calendar year
age Five-year age group from "15-19" to "45-49"
area "Rural" or "Urban"
rate Age-specific fertility rate

Source

Tables 4.1 and 4.2 of Abbasi-Shavazi, M J, McDonald, P (2005). National and provincial level fertility trends in Iran, 1972–2006. Australian National University. Working Papers in Demography no. 94.

Calculate Life Tables or Life Expectancies

Description

Calculate life table quantities. Function lifetab() returns an entire life table. Function lifeexp() returns life expectancy at birth. The inputs can be mortality rates (mx) or probabilities of dying (qx), though not both.

Usage

lifetab(
  data,
  mx = NULL,
  qx = NULL,
  age = age,
  sex = NULL,
  ax = NULL,
  by = NULL,
  infant = c("constant", "linear", "CD", "AK"),
  child = c("constant", "linear", "CD"),
  closed = c("constant", "linear"),
  open = "constant",
  radix = 1e+05,
  suffix = NULL,
  n_core = 1
)

lifeexp(
  data,
  mx = NULL,
  qx = NULL,
  at = 0,
  age = age,
  sex = NULL,
  ax = NULL,
  by = NULL,
  infant = c("constant", "linear", "CD", "AK"),
  child = c("constant", "linear", "CD"),
  closed = c("constant", "linear"),
  open = "constant",
  suffix = NULL,
  n_core = 1
)
lifetab(
  data,
  mx = NULL,
  qx = NULL,
  age = age,
  sex = NULL,
  ax = NULL,
  by = NULL,
  infant = c("constant", "linear", "CD", "AK"),
  child = c("constant", "linear", "CD"),
  closed = c("constant", "linear"),
  open = "constant",
  radix = 1e+05,
  suffix = NULL,
  n_core = 1
)

lifeexp(
  data,
  mx = NULL,
  qx = NULL,
  at = 0,
  age = age,
  sex = NULL,
  ax = NULL,
  by = NULL,
  infant = c("constant", "linear", "CD", "AK"),
  child = c("constant", "linear", "CD"),
  closed = c("constant", "linear"),
  open = "constant",
  suffix = NULL,
  n_core = 1
)

Arguments

data

Data frame with mortality data.

mx

<tidyselect> Mortality rates, expressed as deaths per person-year lived. Possibly an rvec.

qx

<tidyselect> Probability of dying within age interval. An alternative to mx. Possibly an rvec.

age

<tidyselect> Age group labels. The labels must be interpretable by functions such as reformat_age() and age_group_type(). The first age group must start at age 0, and the last age group must be "open", with no upper limit.

sex

<tidyselect> Biological sex, with labels that can be interpreted by reformat_sex(). Needed only when infant is "CD" or "AK", or child is "CD".

ax

<tidyselect> Average age at death within age group. Optional. See Details.

by

<tidyselect> Separate life tables, or life expectancies, calculated for each combination the by variables. If a sex variable was specified, then that variable is automatically included among the by variables. If data is a grouped data frame, then the grouping variables take precedence over by.

infant

Method used to calculate life table values in age group "0". Ignored if age does not include age group "0". Default is "constant".

child

Method used to calculate life table values in age group "1-4". Ignored if age does not include age group "0". Default is "constant".

closed

Method used to calculate life table values in closed age intervals other than "0" and "1-4" (ie intervals such as "10-14" or "12"). Default is "constant".

open

Method used to calculate life table values in the final, open age group (eg "80+" or "110+"). Currently the only option is '"constant".

radix

Initial population for the lx column. Default is 100000.

suffix

Optional suffix added to new columns in result.

n_core

Number of cores to use for parallel processing. If n_core is 1 (the default), no parallel processing is done.

at

Age at which life expectancy is calculated (⁠lifeexp() only). Default is ⁠0'. Can be a vector with length > 1.

Value

A tibble.

Definitions of life table quantities

mx Deaths per person-year lived.
qx Probability of surviving from the start of age group 'x' to the end.
lx Number of people alive at the start of age group x.
dx Number of deaths in age group x
Lx Expected number of person years lived in age group x.
ex Life expectancy, calculated at the start of age group x.

Mortality rates mx are sometimes expressed as deaths per 1000 person-years lived, or per 100,000 person-years lived. lifetab() and lifeexp() assumed that they are expressed as deaths per person-year lived.

Calculation methods

lifetab() and lifeexp() implement several methods for calculating life table quantities from mortality rates. Each method makes different assumptions about the way that mortality rates vary within age intervals:

"constant" Mortality rates are constant within each interval.
"linear". Life table quantity lx is a straight line within each interval. Equivalently, deaths are distributed uniformly within each interval.
"CD". Used only with age groups "0" and "1-4". Mortality rates decline over the age interval, with the slope depending on the absolute level of infant mortality. The formulas were developed by Coale and Demeny (1983), and used in Preston et al (2001).
"AK". Used only with age group "0". Mortality rates decline over the age interval, with the slope depending on the absolute level of infant mortality. The formulas were formulas developed by Andreev and Kingkade (2015), and are used in the Human Mortality Database methods protocol.

For a detailed description of the methods, see the vignette for poputils.

ax

ax is the average number of years lived in an age interval by people who die in that interval. Demographers sometimes refer to it as the 'separation factor'. If a non-NA value of ax is supplied for an age group, then the results for that age group are based on the formula

$m_x = d_x / (n_x l_x + a_x d_x)$

(where n_x is the width of the age interval), over-riding any methods specified via the infant, child, closed and open arguments.

Open age group when inputs are qx

The probability of dying, qx, is always 1 in the final (open) age group. qx therefore provides no direct information on mortality conditions within the final age group. lifetab() and lifeexp() use conditions in the second-to-final age group as a proxy for conditions in the final age group. When open is "constant" (which is currently the only option), and no value for ax in the final age group is provided, lifetab() and lifeexp() assume that $m_A = m_{A-1}$ , and set $L_{A} = l_A / m_A$ .

In practice, mortality is likely to be higher in the final age group than in the second-to-final age group, so the default procedure is likely to lead to inaccuracies. When the size of the final age group is very small, these inaccuracies will be inconsequential. But in other cases, it may be necessary to supply an explicit value for ax for the final age group, or to use mx rather than qx as inputs.

Using rvecs to represent uncertainty

An rvec is a 'random vector', holding multiple draws from a distribution. Using an rvec for the mx argument to lifetab() or lifeexp() is a way of representing uncertainty. This uncertainty is propagated through to the life table values, which will also be rvecs.

Parallel processing

Calculations can be slow when working with rvecs and many combinations of 'by' variables. In these cases, setting n_core to a number greater than 1, which triggers parallel processing, may help.

References

Preston SH, Heuveline P, and Guillot M. 2001. Demography: Measuring and Modeling Population Processes Oxford: Blackwell.
Coale AJ, Demeny P, and Vaughn B. 1983. Regional model life tables and stable populations New York: Academic Press.
Andreev, E.M. and Kingkade, W.W., 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, pp.363-390.
Human Mortality Database Methods Protocol.
Tools for Demographic Estimation.

Examples

library(dplyr)

## life table for females based on 'level 1'
## mortality rates "West" model life table
west_lifetab |>
    filter(sex == "Female",
           level == 1) |>
    lifetab(mx = mx)

## change method for infant and children from
## default ("constant") to "CD"
west_lifetab |>
    filter(sex == "Female",
           level == 1) |>
    lifetab(mx = mx,
            sex = sex,
            infant = "CD",
            child = "CD")

## calculate life expectancies
## for all levels, using the 'by'
## argument to distinguish levels
west_lifetab |>
    lifeexp(mx = mx,
            sex = sex,
            infant = "CD",
            child = "CD",
            by = level)

## obtain the same result using
## 'group_by'
west_lifetab |>
  group_by(level) |>
  lifeexp(mx = mx,
          sex = sex,
          infant = "CD",
          child = "CD")

## calculations based on 'qx'
west_lifetab |>
  lifeexp(qx = qx,
          sex = sex,
          by = level)

## life expectancy at age 60
west_lifetab |>
  filter(level == 10) |>
  lifeexp(mx = mx,
          at = 60,
          sex = sex)

## life expectancy at ages 0 and 60
west_lifetab |>
  filter(level == 10) |>
  lifeexp(mx = mx,
          at = c(0, 60),
          sex = sex)
library(dplyr)

## life table for females based on 'level 1'
## mortality rates "West" model life table
west_lifetab |>
    filter(sex == "Female",
           level == 1) |>
    lifetab(mx = mx)

## change method for infant and children from
## default ("constant") to "CD"
west_lifetab |>
    filter(sex == "Female",
           level == 1) |>
    lifetab(mx = mx,
            sex = sex,
            infant = "CD",
            child = "CD")

## calculate life expectancies
## for all levels, using the 'by'
## argument to distinguish levels
west_lifetab |>
    lifeexp(mx = mx,
            sex = sex,
            infant = "CD",
            child = "CD",
            by = level)

## obtain the same result using
## 'group_by'
west_lifetab |>
  group_by(level) |>
  lifeexp(mx = mx,
          sex = sex,
          infant = "CD",
          child = "CD")

## calculations based on 'qx'
west_lifetab |>
  lifeexp(qx = qx,
          sex = sex,
          by = level)

## life expectancy at age 60
west_lifetab |>
  filter(level == 10) |>
  lifeexp(mx = mx,
          at = 60,
          sex = sex)

## life expectancy at ages 0 and 60
west_lifetab |>
  filter(level == 10) |>
  lifeexp(mx = mx,
          at = c(0, 60),
          sex = sex)

Logit and Inverse-Logit Functions

Description

Transform values to and from the logit scale. logit() calculates

Usage

logit(p)

invlogit(x)
logit(p)

invlogit(x)

Arguments

p

Values between 0 and 1. Can be an atomic vector, a matrix, or an rvec.

x

Values in the interval ⁠(-Inf, Inf)⁠. Can be an atomic vector, a matrix, or an rvec.

Details

$x = \log \left(\frac{p}{1 - p}\right)$

and invlogit() calculates

$p = \frac{e^x}{1 + e^x}$

To avoid overflow, invlogit() uses $p = \frac{1}{1 + e^{-x}}$ internally for $x$ where $x > 0$ .

In some of the demographic literature, the logit function is defined as

$x = \frac{1}{2} \log \left(\frac{p}{1 - p}\right).$

logit() and invlogit() follow the conventions in statistics and machine learning, and omit the $\frac{1}{2}$ .

Value

A vector of doubles, if p or x is a vector.
A matrix of doubles, if p or x is a matrix.
An object of class rvec_dbl, if p or x is an rvec.

Examples

p <- c(0.5, 1, 0.2)
logit(p)
invlogit(logit(p))
p <- c(0.5, 1, 0.2)
logit(p)
invlogit(logit(p))

Turn a Matrix Into a List of Columns or Rows

Description

Given a matrix, create a list, each element of which contains a column or row from the matrix.

Usage

matrix_to_list_of_cols(m)

matrix_to_list_of_rows(m)
matrix_to_list_of_cols(m)

matrix_to_list_of_rows(m)

Arguments

m

A matrix

Details

matrix_to_list_of_cols() and 'matrix_to_list_of_rows() are internal functions, for use by developers, and would not normally be called directly by end users.

Value

matrix_to_list_of_cols() A list of vectors, each of which is a column from x.
matrix_to_list_of_rows(), A list of vectors, each of which is a row from x.

Examples

m <- matrix(1:12, nrow = 3)
matrix_to_list_of_cols(m)
matrix_to_list_of_rows(m)
m <- matrix(1:12, nrow = 3)
matrix_to_list_of_cols(m)
matrix_to_list_of_rows(m)

Mortality Data for New Zealand

Description

Counts of deaths and population, by age, sex, and calendar year, plus mortality rates, for New Zealand, 2021-2022.

Usage

nzl_mort
nzl_mort

Format

A data frame with 84 rows and the following variables:

year Calendar year.
gender "Female", and "Male".
age Age, in life table age groups, with an open age group of 95+.
deaths Counts of deaths, randomly rounded to base 3.
popn Estimates of average annual population.
mx Mortality rates (deaths / popn).

Source

Modified from data in tables "Deaths by age and sex (Annual-Dec)" and "Estimated Resident Population by Age and Sex (1991+) (Annual-Dec)" from Stats NZ online database Infoshare, downloaded on 24 September 2023.

Mortality Data and Probabilistic Rates for New Zealand

Description

A modified version of link{nzl_mort} where mx columns is an rvec, rather than an ordinary R vector. The rvec holds the random draws from the posterior distribution obtained from by a Bayesian statistical model.

Usage

nzl_mort_rvec
nzl_mort_rvec

Format

An object of class tbl_df (inherits from tbl, data.frame) with 84 rows and 4 columns.

Convert q0 to m0

Description

Convert the probability of dying during infancy (q0) to the mortality rate for infancy (m0).

Usage

q0_to_m0(
  q0,
  sex = NULL,
  a0 = NULL,
  infant = c("constant", "linear", "CD", "AK")
)
q0_to_m0(
  q0,
  sex = NULL,
  a0 = NULL,
  infant = c("constant", "linear", "CD", "AK")
)

Arguments

q0

Probability of dying in first year of life. A numeric vector or an rvec.

sex

Biological sex. A vector the same length as q0, with labels that can be interpreted by reformat_sex(). Needed only when infant is "CD" or "AK".

a0

Average age at death for infants who die. Optional. See help for lifetab().

infant

Calculation method. See help for lifetab(). Default is "constant".

Value

A numeric vector or rvec.

Warning

The term "infant mortality rate" is ambiguous. Demographers sometimes use it to refer to m0 (which is an actual rate) and sometimes use it to refer to q0 (which is a probability.)

Examples

library(dplyr, warn.conflicts = FALSE)
west_lifetab |>
 filter(age == 0, level <= 5) |>
 select(level, sex, age, mx, qx) |>
 mutate(m0 = q0_to_m0(q0 = qx, sex = sex, infant = "CD"))
library(dplyr, warn.conflicts = FALSE)
west_lifetab |>
 filter(age == 0, level <= 5) |>
 select(level, sex, age, mx, qx) |>
 mutate(m0 = q0_to_m0(q0 = qx, sex = sex, infant = "CD"))

Reformat Age Group Labels

Description

Convert age group labels to one of three formats:

Single-year age groups, eg "0", "1", ..., "99", "100+".
Life table age groups, eg "0", ⁠"1-4", ⁠"5-9"⁠, ..., ⁠"95-99"⁠, ⁠"100+"'.
Five-year age groups, eg "0-4", "5-9", ..., "95-99", "100+".

By default reformat_age() returns a factor that includes all intermediate age groups. See below for examples.

Usage

reformat_age(x, factor = TRUE)
reformat_age(x, factor = TRUE)

Arguments

x

A vector.

factor

Whether the return value should be a factor.

Details

reformat_age() applies the following algorithm:

Tidy and translate text, eg convert "20 to 24 years" to "20-24", convert "infant" to "0", or convert "100 or more" to "100+".
Check whether the resulting labels could have been produced by age_labels(). If not, throw an error.
If factor is TRUE (the default), then return a factor. The levels of this factor include all intermediate age groups. Otherwise return a character vector.

When x consists entirely of numbers, reformat_age() also checks for two special cases:

If every element of x is a multiple of 5, and if max(x) >= 50, then x is assumed to describe 5-year age groups
If every element of x is 0, 1, or a multiple of 5, with max(x) >= 50, then x is assumed to describe life table age groups.

Value

If factor is TRUE, then reformat_age() returns a factor; otherwise it returns a character vector.

Examples

reformat_age(c("80 to 84", "90 or more", "85 to 89"))

## factor contains intermediate level missing from 'x'
reformat_age(c("80 to 84", "90 or more"))

## non-factor
reformat_age(c("80 to 84", "90 or more"),
          factor = FALSE)

## single
reformat_age(c("80", "90plus"))

## life table
reformat_age(c("0",
            "30-34",
            "10--14",
            "1-4 years"))
reformat_age(c("80 to 84", "90 or more", "85 to 89"))

## factor contains intermediate level missing from 'x'
reformat_age(c("80 to 84", "90 or more"))

## non-factor
reformat_age(c("80 to 84", "90 or more"),
          factor = FALSE)

## single
reformat_age(c("80", "90plus"))

## life table
reformat_age(c("0",
            "30-34",
            "10--14",
            "1-4 years"))

Reformat a Binary Sex Variable

Description

Reformat a binary sex variable so that it consists entirely of values "Female", "Male", and possibly NA and any values included in except.

Usage

reformat_sex(x, except = NULL, factor = TRUE)
reformat_sex(x, except = NULL, factor = TRUE)

Arguments

x

A vector.

except

Values to exclude when reformatting.

factor

Whether the return value should be a factor.

Details

When parsing labels, reformat_sex() ignores case: "FEMALE" and "fEmAlE" are equivalent.

White space is removed from the beginning and end of labels.

reformat_sex() does not try to interpreting numeric codes (eg 1, 2).

Value

If factor is TRUE, then reformat_age() returns a factor; otherwise it returns a character vector.

Examples

reformat_sex(c("F", "female", NA, "MALES"))

## values supplied for 'except'
reformat_sex(c("Fem", "Other", "Male", "M"),
             except = c("Other", "Diverse"))

## return an ordinary character vector
reformat_sex(c("F", "female", NA, "MALES"),
             factor = FALSE)
reformat_sex(c("F", "female", NA, "MALES"))

## values supplied for 'except'
reformat_sex(c("Fem", "Other", "Male", "M"),
             except = c("Other", "Diverse"))

## return an ordinary character vector
reformat_sex(c("F", "female", NA, "MALES"),
             factor = FALSE)

Randomly Round A Vector of Integers to Base 3

Description

Apply the 'Random Round to Base 3' (RR3) algorithm to a vector of integers (or doubles where round(x) == x.)

Usage

rr3(x)
rr3(x)

Arguments

x

A vector of integers (in the sense that round(x) == x.) Can be an rvec.

Details

The RR3 algorithm is used by statistical agencies to confidentialize data. Under the RR3 algorithm, an integer $n$ is randomly rounded as follows:

If $n$ is divisible by 3, leave it unchanged
If dividing $n$ by 3 leaves a remainder of 1, then round down (subtract 1) with probability 2/3, and round up (add 2) with probability 1/3.
If dividing $n$ by 3 leaves a remainder of 1, then round down (subtract 2) with probability 1/3, and round up (add 1) with probability 2/3.

RR3 has some nice properties:

The randomly-rounded version of $n$ has expected value $n$ .
If $n$ non-negative, then the randomly rounded version of $n$ is non-negative.
If $n$ is non-positive, then the randomly rounded version of $n$ is non-positive.

Value

A randomly-rounded version of x.

Examples

x <- c(1, 5, 2, 0, -1, 3, NA)
rr3(x)
x <- c(1, 5, 2, 0, -1, 3, NA)
rr3(x)

Specify Open Age Group

Description

Set the lower limit of the open age group. Given a vector of age group labels, recode all age groups with a lower limit greater than or equal to ⁠<lower>⁠ to ⁠<lower>+⁠.

Usage

set_age_open(x, lower)
set_age_open(x, lower)

Arguments

x

A vector of age labels.

lower

An integer. The lower limit for the open age group.

Details

set_age_open() requires that x and the return value have a a five-year, single-year, or life table format, as described in age_labels().

Value

A modified version of x.

Examples

x <- c("100+", "80-84", "95-99", "20-24")
set_age_open(x, 90)
set_age_open(x, 25)
x <- c("100+", "80-84", "95-99", "20-24")
set_age_open(x, 90)
set_age_open(x, 25)

Calculate Total Fertility Rates

Description

Calculate the total fertility rate (TFR) from age-specific fertility rates.

Usage

tfr(
  data,
  asfr = asfr,
  age = age,
  sex = NULL,
  by = NULL,
  denominator = 1,
  suffix = NULL
)
tfr(
  data,
  asfr = asfr,
  age = age,
  sex = NULL,
  by = NULL,
  denominator = 1,
  suffix = NULL
)

Arguments

data

Data frame with age-specific fertility rates and age

asfr

<tidyselect> Age-specific fertility rates. Possibly an rvec. Default is asfr.

age

<tidyselect> Age group labels. The labels must be interpretable by functions such as reformat_age() and age_group_type(). The age groups must not have gaps, and the highest age group must be "closed" (ie have an upper limit.) Default is age.

sex

<tidyselect> Sex/gender of the child (not the parent).

by

<tidyselect> Separate total fertility rates are calculated for each combination the by variables. If data is a grouped data frame, then the grouping variables take precedence over by.

denominator

The denominator used to calculate asfr. Default is 1.

suffix

Optional suffix added to "tfr" column in result.

Details

The total fertility rate is a summary measures for current fertility levels that removes the effect of age structure. Is obtained by summing up age-specific fertility rates, multiplying each rate by the width of the corresponding age group. For instance, the rate for age group "15-19" is multiplied by 5, and the rate for age group "15" is multiplied by 1.

The total fertility rate can be interpreted as the number of average children that a person would have, under prevailing fertility rates, if the person survived to the maximum age of reproduction. The hypothetical person is normally a woman, since age-specific fertility rates normally use person-years lived by women as the denominator. But it can apply to men, if the age-specific fertility rates are "paternity rates", ie rates that use person-years lived by men as the denominator.

Value

A tibble.

Sex-specific fertility rates

Age-specific fertility rates do not normally specify the sex of the children who are born. In cases where they do, however, rates have to be summed across sexes to give the total fertility rates. If tfr() is supplied with a sex argument, it assumes that sex applies to the births, and sums over the sexes.

Denominator

Published tables of age-specific fertility rates often express the rates as births per 1000 person-years lived, rather than per person-year lived. (Sometimes this is expressed as "births per 1000 women".) In these cases

Using rvecs to represent uncertainty

An rvec is a 'random vector', holding multiple draws from a distribution. Using an rvec for the asfr argument to tfr() is a way of representing uncertainty. This uncertainty is propagated through to the TFR, which will also be rvecs.

Examples

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

Derive Age-Specific Fertility Rates that Match Total Fertility Rates by Scaling

Description

Turn total fertility rates (TFRs) into sets of age-specific fertility rates, by scaling a set of standard rates upwards or downwards.

Usage

tfr_to_asfr_scale(target, standard, suffix = NULL)
tfr_to_asfr_scale(target, standard, suffix = NULL)

Arguments

target

A data frame containing a variable called "tfr", and possibly others. See Details.

standard

A data frame containing variables called age and asfr, and possibly others. See Details.

suffix

Optional suffix added to asfr column in results.

Value

A tibble.

Method

Let ${}_nf_x$ be the age-specific fertility rate for people aged between $x$ and $x+n$ . Values for ${}_nf_x$ are obtained by scaling the standard rates ${}_nf_x^{\mathrm{std}}$ so that they agree with the target total fertility rate $F$ . That is, tfr_to_asfr_scale() sets

${}_nf_x = \alpha \times {}_nf_x^{\mathrm{std}}$

where

$\alpha = \frac{F}{\sum_x n \times {}_nf_x^{\mathrm{std}}}$

The `target` argument

target is a data frame specifying total fertility rates for each population being modelled.

target contains the following variables:

A variable called "tfr". An ordinary numeric vector or an rvec().
Optionally, 'by' variables. Typical examples are time, region, and model variant.

The `standard` argument

standard is a data frame specifying standard fertility schedules to be used with each life expectancy in target. Values in standard are age-specific.

standard contains the following variables:

A variable called "age", with labels that can be parsed by reformat_age().
A variable called "value", containing non-negative values. Cannot be an rvec.
Additional variables used to match rows in standard to rows in target.

Examples

## create age-specific fertility rates
## based on the [Booth standard][booth_standard]
library(dplyr, warn.conflicts = FALSE)
target <- data.frame(region = c("A", "B"), 
                     tfr = c(5.5, 4.7))
asfr <- tfr_to_asfr_scale(target = target,
                          standard = booth_standard)
asfr

## check consistency with original TFRs
asfr |>
  tfr(asfr = asfr, by = region)

## target is an rvec
library(rvec, warn.conflicts = FALSE)
target_rvec <- data.frame(region = c("A", "B"), 
                          tfr = rnorm_rvec(n = 2,
                                           mean = c(5.5, 4.7),
                                           n_draw = 1000))
tfr_to_asfr_scale(target = target_rvec,
                  standard = booth_standard)
## create age-specific fertility rates
## based on the [Booth standard][booth_standard]
library(dplyr, warn.conflicts = FALSE)
target <- data.frame(region = c("A", "B"), 
                     tfr = c(5.5, 4.7))
asfr <- tfr_to_asfr_scale(target = target,
                          standard = booth_standard)
asfr

## check consistency with original TFRs
asfr |>
  tfr(asfr = asfr, by = region)

## target is an rvec
library(rvec, warn.conflicts = FALSE)
target_rvec <- data.frame(region = c("A", "B"), 
                          tfr = rnorm_rvec(n = 2,
                                           mean = c(5.5, 4.7),
                                           n_draw = 1000))
tfr_to_asfr_scale(target = target_rvec,
                  standard = booth_standard)

Build a Matrix from Measure and ID Variables

Description

Build a matrix where the elements are values of a measure variable, and the rows and columns are formed by observed combinations of ID variables. The ID variables picked out by rows and cols must uniquely identify cells. to_matrix(), unlike stats::xtabs(), does not sum across multiple combinations of ID variables.

Usage

to_matrix(x, rows, cols, measure)
to_matrix(x, rows, cols, measure)

Arguments

x

A data frame.

rows

The ID variable(s) used to distinguish rows in the matrix.

cols

The ID variable(s) used to distinguish columns in the matrix.

measure

The measure variable, eg rates or counts.

Value

A matrix

Examples

x <- expand.grid(age = c(0, 1, 2),
                 sex = c("F", "M"),
                 region = c("A", "B"),
                 year = 2000:2001)
x$count <- 1:24

to_matrix(x,
          rows = c(age, sex),
          cols = c(region, year),
          measure = count)

to_matrix(x,
          rows = c(age, sex, region),
          cols = year,
          measure = count)

## cells not uniquely identified
try(
to_matrix(x,
          rows = age,
          cols = sex,
          measure = count)
)
x <- expand.grid(age = c(0, 1, 2),
                 sex = c("F", "M"),
                 region = c("A", "B"),
                 year = 2000:2001)
x$count <- 1:24

to_matrix(x,
          rows = c(age, sex),
          cols = c(region, year),
          measure = count)

to_matrix(x,
          rows = c(age, sex, region),
          cols = year,
          measure = count)

## cells not uniquely identified
try(
to_matrix(x,
          rows = age,
          cols = sex,
          measure = count)
)

Trim Values So They Are Between 0 and 1

Description

Trim a vector so that all values are greater than 0 and less than 1.

Usage

trim_01(x)
trim_01(x)

Arguments

x

A numeric vector. Can be an rvec.

Details

min is lowest element of x that is higher than 0, and
max is the highest element of x that is lower than 1, then trim_01()
shifts all elements of x that are lower than min upwards, so that they equal min, and
shifts all elements of x that are higher than max downwards, so that they equal max.

Value

A trimmed version of x

Examples

x <- c(1, 0.98, -0.001, 0.5, 0.01)
trim_01(x)
x <- c(1, 0.98, -0.001, 0.5, 0.01)
trim_01(x)

Coale-Demeny West Model Life Tables

Description

Life table quantities from the "West" family of Coale-Demeny model life tables.

Usage

west_lifetab
west_lifetab

Format

A data frame with 1,050 rows and the following variables:

level Index for life table. Lower level implies lower life expectancy.
sex "Female", and "Male".
age Age, in life table age groups, with an open age group of 95+.
mx Mortality rate.
ax Average years lived in age interval by people who die in that interval.
qx Probability some alive at start of age interval dies during interval.
lx Number of people still alive at start of age interval.
dx Number of people dying during age interval.
Lx Number of person-years lived during age interval.
ex Expectation of life at start of age interval.

Source

Coale A, Demeny P, and Vaughn B. 1983. Regional model life tables and stable populations. 2nd ed. New York: Academic Press, accessed via demogR::cdmltw().

Package 'poputils'

Help Index

Calculate Intrinsic Growth Rate

Description

Usage

Arguments

Details

Value

Examples

Infer Age Label Type

Description

Usage

Arguments

Details

Value

Examples

Create Age Labels

Description

Usage

Arguments

Details

Value

See Also

Examples

Lower Limits, Midpoints, and Upper Limits of Age Groups

Description

Usage

Arguments

Details

Value

See Also

Examples

Booth Standard

Description

Usage

Format

Details

Source

Validity Checks for Age Labels

Description

Usage

Arguments

Details

Value

See Also

Examples

Check that Arguments have Same Length

Description

Usage

Arguments

Value

Examples

Check Whole Number

Description

Usage

Arguments

Value

Examples

Check that Colnum Vectors do not Overlap

Description

Usage

Arguments

Value

See Also

Examples

Aggregate Age Group Labels

Description

Usage

Arguments

Value

See Also

Examples

Derive Life Tables that Match Life Expectancies, using a Brass Logit Model

Description

Usage

Arguments

Value

Method

The target argument

The standard argument

The `target` argument

The `standard` argument