Package {DemogAnr}

DemogAnr: Demographic Analysis

Description

Tool for demographic modeling and analysis, combining stochastic simulation-based projections with classic demographic methods. Provides utilities for disaggregating population data using Karup-King interpolation, fitting Brass relational logit models, calculating fertility and mortality metrics, building life tables, and performing hierarchical subnational population projections. Relational life table estimation and interpolation methods are described in Brass (1975) "Methods for Estimating Fertility and Mortality from Limited and Defective Data", Preston et al. (2001, ISBN:978-0631226161) "Demography: Measuring and Modeling Population Processes", Siegel and Swanson (2004, ISBN:978-0126419559) "The Methods and Materials of Demography", and Raftery et al. (2012) "Bayesian probabilistic population projections for all countries" doi:10.1073/pnas.1211452109.

Author(s)

Maintainer: Edward Owusu Manu edward.manu@statsghana.gov.gh

Brass Relational Logit Model

Description

Fits the Brass relational logit model to an observed series of cumulative probabilities of dying q(x) = 1 - l(x)/l(0) using a standard life table. The observed logits are regressed on the logits of the standard, and the fitted relationship (alpha, beta) is used to predict a complete, smoothed series of q(x), including for ages where the observed value is missing (NA).

Usage

brass_logit(
  data,
  qx_col,
  age_col,
  standard,
  standards_data = NULL,
  verbose = FALSE
)

Arguments

Details

The Brass logit of a survivorship value is Y(x) = 0.5 \log\{(1 - l(x))/l(x)\} = 0.5 \log\{q(x)/(1 - q(x))\}, and predicted probabilities are recovered with the inverse transform q(x) = \exp(2Y)/(1 + \exp(2Y)).

Note that the input must be cumulative probabilities of dying by age x (i.e. 1 - l(x)), which increase with age, and not the age-interval probabilities {}_nq_x. Values of exactly 0 or 1 (for example q(0) = 0) are excluded from the fit.

Value

A list containing the fitted model (model), the alpha/beta coefficients, and data: the input data with a predicted_qx column of smoothed/completed cumulative probabilities and a predicted_lx column of the implied survivorship l(x) = 1 - q(x).

References

Brass, W. (1971). On the scale of mortality. In W. Brass (Ed.), Biological Aspects of Demography (pp. 69-110). London: Taylor & Francis.

Brass, W. (1975). Methods for Estimating Fertility and Mortality from Limited and Defective Data. Chapel Hill: Carolina Population Center, University of North Carolina.

Moultrie, T., Dorrington, R., Hill, A., Hill, K., Timaeus, I., & Zaba, B. (Eds.). (2013). Tools for Demographic Estimation. Paris: International Union for the Scientific Study of Population (IUSSP). https://demographicestimation.iussp.org/

Newell, C. (1988). Methods and Models in Demography. New York: Guilford Press. (Chapter on model life tables.)

Examples

# Observed cumulative probabilities of dying q(x) = 1 - l(x),
# with gaps (NA) to be filled by the model:
observed_data <- data.frame(
  age = c(1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50),
  qx = c(0.10, 0.15, NA, 0.19, 0.21, NA, 0.26, 0.30, NA, 0.38, 0.44)
)

model <- brass_logit(
  data = observed_data,
  qx_col = "qx",
  age_col = "age",
  standard = "African"
)

model$coefficients
model$data

Grouped population data by 5-year age groups

Description

Example population counts in 5-year age groups (0-4 to 75-79) for the years 2021 to 2035, used to demonstrate karup_king().

Usage

data

Format

A data frame with 16 rows (age groups) and 16 variables:

2021

Numeric. Population in the age group in 2021.

2022

Numeric. Population in the age group in 2022.

2023

Numeric. Population in the age group in 2023.

2024

Numeric. Population in the age group in 2024.

2025

Numeric. Population in the age group in 2025.

2026

Numeric. Population in the age group in 2026.

2027

Numeric. Population in the age group in 2027.

2028

Numeric. Population in the age group in 2028.

2029

Numeric. Population in the age group in 2029.

2030

Numeric. Population in the age group in 2030.

2031

Numeric. Population in the age group in 2031.

2032

Numeric. Population in the age group in 2032.

2033

Numeric. Population in the age group in 2033.

2034

Numeric. Population in the age group in 2034.

2035

Numeric. Population in the age group in 2035.

age_col

Character. The 5-year age group, e.g. "0-4", "5-9", ..., "75-79".

Source

Simulated data based on population structures from the Ghana Statistical Service.

Calculate CDR and ASDR

Description

This function calculates the Crude Death Rate (CDR) and Age-Specific Death Rates (ASDR).

Usage

dem.cdr(
  data,
  type,
  age_col = NULL,
  population_col,
  deaths_col,
  verbose = FALSE
)

Arguments

Value

A list of named outputs for the requested death rate calculations.

References

Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and Modeling Population Processes. Oxford: Blackwell Publishers. ISBN 978-0631226161. (Chapter 2: Age-Specific Rates and Probabilities.)

Newell, C. (1988). Methods and Models in Demography. New York: Guilford Press.

Siegel, J. S., & Swanson, D. A. (Eds.). (2004). The Methods and Materials of Demography (2nd ed.). San Diego: Elsevier Academic Press. ISBN 978-0126419559. (Chapter on mortality measures.)

Examples

data <- data.frame(
  age = c("0-4", "5-9", "10-14"),
  population = c(10000, 12000, 11000),
  deaths = c(100, 50, 30)
)
dem.cdr(data, type = "all", age_col = "age",
                    population_col = "population", deaths_col = "deaths")

Calculate Fertility Metrics

Description

This function calculates various fertility metrics (CBR, GFR, ASFR, TFR) based on user input and a dataframe with demographic data.

Usage

dem.fert(
  data,
  type,
  age_col = NULL,
  population_col,
  women_col = NULL,
  births_col,
  age_interval = 5,
  verbose = FALSE
)

Arguments

Value

A list of named outputs for the requested fertility calculations and the modified dataframe with ASFR.

References

Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and Modeling Population Processes. Oxford: Blackwell Publishers. ISBN 978-0631226161. (Chapter 5: Fertility and Reproduction.)

Newell, C. (1988). Methods and Models in Demography. New York: Guilford Press.

Siegel, J. S., & Swanson, D. A. (Eds.). (2004). The Methods and Materials of Demography (2nd ed.). San Diego: Elsevier Academic Press. ISBN 978-0126419559. (Chapter on fertility measures.)

Examples

demo_data <- data.frame(
  age = c("15-19", "20-24", "25-29"),
  population = c(10000, 12000, 11000),
  women = c(5000, 6000, 5500),
  live_births = c(200, 400, 300)
)
# Calculate ASFR
dem.fert(demo_data, type = "ASFR", age_col = "age",
                    population_col = "population", women_col = "women", births_col = "live_births")
# Calculate all metrics
dem.fert(demo_data, type = "all", age_col = "age",
                    population_col = "population", women_col = "women", births_col = "live_births")

Calculate Maternal Mortality Metrics

Description

This function calculates the maternal mortality rate (maternal deaths per 100,000 women of reproductive age) and the maternal mortality ratio (maternal deaths per 100,000 live births).

Usage

dem.mmr(data, deaths_col, births_col, pop_women, verbose = FALSE)

Arguments

Value

An object of class dem_mmr: a list with results (the maternal mortality rate MMR per 100,000 women, and the maternal mortality ratio MMR_Ratio per 100,000 live births) and modified_data (the input data with per-row mm_rate and mm_ratio columns).

References

World Health Organization (1992). International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10). Geneva: World Health Organization. (Definitions of maternal death and maternal mortality measures.)

Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and Modeling Population Processes. Oxford: Blackwell Publishers. ISBN 978-0631226161.

Siegel, J. S., & Swanson, D. A. (Eds.). (2004). The Methods and Materials of Demography (2nd ed.). San Diego: Elsevier Academic Press. ISBN 978-0126419559. (Chapter on health demography and maternal mortality.)

Examples

demo_data <- data.frame(
  age=c("15-24", "25-34", "35-44"),
  maternal_deaths = c(5, 10, 15),
  live_births = c(5000, 7000, 6000),
  women =c(20000, 22000, 32000)
)
dem.mmr(demo_data, deaths_col = "maternal_deaths", births_col = "live_births", pop_women = "women")

Calculate Child Mortality Metrics by Age

Description

This function calculates child mortality metrics (neonatal, infant, child, under-5) based on single-age data. It requires columns for age, denominator (live births or population), and deaths.

Usage

dm.chm(
  data,
  age_col,
  live_births,
  deaths_col,
  type = c("neonatal", "infant", "child", "under5"),
  age_in_months = NULL,
  verbose = FALSE
)

Arguments

Details

The result is expressed per 1,000 of the supplied denominator. If the denominator is live births (as is conventional for neonatal and infant mortality), the result is a rate per 1,000 live births. If a mid-year population is supplied instead (often the only option for the child and under-5 groups from census data), the result is a death rate per 1,000 population, which is not identical to the cohort probability of dying (e.g. 5q0) reported by survey programmes such as the DHS.

Value

A numeric value representing the mortality rate per 1,000 live births.

References

Pollard, A. H., Yusuf, F., & Pollard, G. N. (1990). Demographic Techniques (3rd ed.). Sydney: Pergamon Press.

Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and Modeling Population Processes. Oxford: Blackwell Publishers. ISBN 978-0631226161. (Chapters 1-2: rates versus probabilities of dying.)

Examples

demo_data <- data.frame(
  age = 0:10,
  population = c(1000, 950, 900, 850, 800, 750, 700, 650, 600, 550, 500),
  deaths = c(30, 20, 15, 10, 5, 2, 1, 1, 0, 0, 0)
)
dm.chm(demo_data,age_col = "age",
                                 live_births = "population",
                                 deaths_col = "deaths",
                                 type = "under5")

Karup-King coefficients for the first age group

Description

The standard Karup-King third-difference osculatory interpolation multipliers used to split the first 5-year age group into single years. Rows are the five single-year ages within the group; columns are the weights applied to the first, second, and third 5-year groups.

Usage

first_coef

Format

A 5 x 3 data frame of numeric coefficients.

Source

Siegel, J. S., & Swanson, D. A. (Eds.). (2004). The Methods and Materials of Demography (2nd ed.), Appendix C.

Ghana 2010 Population and Housing Census mortality data

Description

Population and deaths by age group from the Ghana 2010 Population and Housing Census, used to demonstrate lifetable().

Usage

gphc2010

Format

A data frame with 21 rows and 3 variables:

Age

Numeric. Lower bound of the age group (0, 1, 5, 10, ...).

Pop

Numeric. Population in the age group.

Deaths

Numeric. Deaths in the age group.

Source

Ghana Statistical Service, 2010 Population and Housing Census.

Smart Interpolation Function

Description

Performs interpolation on numeric data, supporting linear, quadratic, Lagrange, and cubic spline methods. Can handle both vector and dataframe inputs.

Usage

interpolation(
  x,
  y,
  x_new,
  data = NULL,
  y_cols = NULL,
  method = c("auto", "linear", "quadratic", "lagrange", "spline"),
  verbose = FALSE
)

Arguments

Value

If vectors x and y are provided, returns a named numeric vector of interpolated values. If a dataframe is provided, returns the original dataframe with additional interpolated columns.

References

Shryock, H. S., Siegel, J. S., & Larmon, E. A. (1973). The Methods and Materials of Demography. Washington, DC: US Bureau of the Census. (Appendix C: Interpolation and graduation.)

Siegel, J. S., & Swanson, D. A. (Eds.). (2004). The Methods and Materials of Demography (2nd ed.). San Diego: Elsevier Academic Press. ISBN 978-0126419559. (Appendix C: Selected General Methods.)

Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (2007). Numerical Recipes: The Art of Scientific Computing (3rd ed.). Cambridge: Cambridge University Press. ISBN 978-0521880688. (Chapter 3: Interpolation and Extrapolation.)

Examples

# Example 1: Interpolating between points (vector input)
x <- c(1, 2, 3, 4, 5)
y <- c(2, 4, 6, 8, 10)
x_new <- c(2.5, 3.5, 4.5)
interpolation(x, y, x_new, method = "linear")

# Example 2: Interpolating for a dataframe
data <- data.frame(
  category = c("A", "B", "C"),
  y1 = c(2, 5, 9),
  y2 = c(3, 6, 10)
)
x_new <- c(2.5, 3.5)
interpolation(x = c(1, 2), data = data, y_cols = c("y1", "y2"), x_new = x_new, method = "spline")

Split Age Groups into Single-Year Ages

Description

This function takes grouped population data (e.g., 5-year age groups) and distributes the population into single-year age groups using the Karup-King third-difference osculatory interpolation coefficients. The user can specify the column containing age groups, one or more population columns, and (optionally) custom coefficient matrices for the first, middle, and last groups.

Usage

karup_king(
  df,
  age_col = "age_group",
  pops = "population",
  first_coef = NULL,
  middle_coef = NULL,
  last_coef = NULL,
  verbose = FALSE
)

Arguments

Details

Each coefficient matrix has 5 rows (one per single year within the 5-year group) and 3 columns (weights applied to the neighbouring 5-year groups). The defaults are the standard Karup-King multipliers (Siegel & Swanson 2004, Appendix C): first_coef for the first group (columns: current, next, next+1), middle_coef for interior groups (columns: previous, current, next), and last_coef for the last group (columns: previous-1, previous, current).

Value

A data frame with single-year ages and corresponding population estimates for each specified population column. Ages beyond the range of the input age groups (overflow ages) are removed.

References

Siegel, J. S., & Swanson, D. A. (Eds.). (2004). The Methods and Materials of Demography (2nd ed.). San Diego: Elsevier Academic Press. ISBN 978-0126419559. (Appendix C: Karup-King osculatory interpolation multipliers.)

Shryock, H. S., Siegel, J. S., & Larmon, E. A. (1973). The Methods and Materials of Demography. Washington, DC: US Bureau of the Census. (Appendix C: Interpolation and graduation.)

Examples

data(data)
head(data)

# Split the 5-year age groups into single years for the 2021 population,
# using the packaged Karup-King coefficients:
single <- karup_king(df = data, age_col = "age_col", pops = "2021")
head(single)

# Population totals are (approximately) preserved:
sum(data[["2021"]])
sum(single[["2021"]])

Karup-King coefficients for the last age group

Description

The standard Karup-King third-difference osculatory interpolation multipliers used to split the last 5-year age group into single years. Rows are the five single-year ages within the group; columns are the weights applied to the antepenultimate, penultimate, and last 5-year groups.

Usage

last_coef

Format

A 5 x 3 data frame of numeric coefficients.

Source

Siegel, J. S., & Swanson, D. A. (Eds.). (2004). The Methods and Materials of Demography (2nd ed.), Appendix C.

lifetable

Description

This function computes a complete life table based on given mortality rates or raw mortality data. It takes a dataframe with the necessary inputs and computes standard life table metrics.

Usage

lifetable(
  data,
  age = "Age",
  nMx = NULL,
  pop = NULL,
  Dx = NULL,
  sex = c("male", "female"),
  radix = 1e+05,
  verbose = FALSE
)

Arguments

Details

Interval widths n are derived from the age column (e.g. ages 0, 1, 5, 10, ... give widths 1, 4, 5, ...). Probabilities of dying are obtained from the death rates with the standard conversion {}_nq_x = n \cdot {}_nM_x / (1 + (n - {}_na_x) \cdot {}_nM_x). The average person-years lived by those dying in the interval ({}_na_x) use the Coale-Demeny model values for ages 0 and 1-4 (which depend on sex and the level of infant mortality; see Preston et al. 2001, Table 3.3), n/2 for other closed intervals, and 1/{}_nM_x for the open-ended interval. Person-years lived are {}_nL_x = n \cdot l_{x+n} + {}_na_x \cdot {}_nd_x for closed intervals and l_x / M_x for the open-ended interval.

Value

A list containing:

metrics

A list with the life expectancy at birth and the total life table deaths.

lifetable

A dataframe of the complete life table.

References

Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and Modeling Population Processes. Oxford: Blackwell Publishers. ISBN 978-0631226161. (Chapter 3: The Life Table and Single Decrement Processes; Table 3.3 for the Coale-Demeny separation factors.)

Chiang, C. L. (1984). The Life Table and Its Applications. Malabar, FL: Robert E. Krieger Publishing.

Coale, A. J., & Demeny, P. (1983). Regional Model Life Tables and Stable Populations (2nd ed.). New York: Academic Press. (Source of the a0 and 4a1 separation factors.)

Examples

# Example computing rates from deaths and population
data(gphc2010)
res <- lifetable(data = gphc2010, age = "Age", pop = "Pop", Dx = "Deaths")
res$metrics
head(res$lifetable)

Compute Life Table Using Age and nqx

Description

This function computes a life table from a data frame containing age groups and age-interval probabilities of dying (nqx). It calculates key life table components such as lx, dx, Lx, Tx, and ex.

Usage

lifetable_nqx(data, age, nqx, l0 = 1e+05, verbose = FALSE)

Arguments

Details

Interval widths n are derived from the age column (e.g. ages 0, 1, 5, 10, ... give widths 1, 4, 5, ...). Person-years lived in each closed interval are approximated as {}_nL_x = n(l_x - d_x/2), i.e. deaths are assumed to occur at the midpoint of the interval. For the open-ended last age group, person-years are approximated with the classical rule of thumb L_{x+} = l_x \log_{10}(l_x) (equivalent to e_x \approx \log_{10} l_x), which is used when no death rate for the open interval is available. If death rates are available, lifetable() provides a more precise treatment of the open interval.

Value

A data frame with the computed life table, including:

Age

Age groups.

n

Width of each age interval (Inf for the last, open-ended group).

nqx

Probability of dying between age x and x+n.

lx

Number of survivors at the beginning of each age group.

dx

Number of deaths within each age group.

Lx

Person-years lived within each age group.

Tx

Cumulative person-years lived above a specific age group.

ex

Life expectancy at the beginning of each age group.

References

Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and Modeling Population Processes. Oxford: Blackwell Publishers. ISBN 978-0631226161. (Chapter 3: The Life Table and Single Decrement Processes.)

Chiang, C. L. (1984). The Life Table and Its Applications. Malabar, FL: Robert E. Krieger Publishing.

Examples

# Example data
data <- data.frame(
  age = c(0, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85),
  nqx = c(0.05, 0.01, 0.005, 0.002, 0.003, 0.004, 0.005, 0.007, 0.010, 0.015,
          0.020, 0.030, 0.050, 0.070, 0.100, 0.150, 0.200, 0.300, 1.000)
)
life_table <- lifetable_nqx(data, age = "age", nqx = "nqx")
print(life_table)

Hierarchical Subnational Population Projection

Description

Projects lower-level geographies (e.g., Districts) while constraining them to higher-level control totals (e.g., Regions or National) provided within the dataset.

Usage

math_project(
  data,
  sub_group_col,
  parent_group_col = NULL,
  pop_cols,
  time_vals,
  target_times,
  method = c("linear", "exponential", "geometric", "constant_share", "shift_share",
    "share_of_growth"),
  parent_totals_cols = NULL,
  verbose = FALSE
)

Arguments

Value

A dataframe appending the projected columns to the original data.

References

Smith, S. K., Tayman, J., & Swanson, D. A. (2013). A Practitioner's Guide to State and Local Population Projections. Dordrecht: Springer. doi:10.1007/978-94-007-7551-0 (Trend extrapolation, constant-share, shift-share, and share-of-growth methods.)

Siegel, J. S., & Swanson, D. A. (Eds.). (2004). The Methods and Materials of Demography (2nd ed.). San Diego: Elsevier Academic Press. ISBN 978-0126419559. (Chapter on population projections.)

Rowland, D. T. (2003). Demographic Methods and Concepts. Oxford: Oxford University Press. ISBN 978-0198752639. (Chapter on population projections.)

Examples

districts <- data.frame(
  Region = c("A", "A", "B", "B"),
  District = c("A1", "A2", "B1", "B2"),
  pop2010 = c(10000, 20000, 15000, 5000),
  pop2020 = c(12000, 26000, 16500, 5500)
)

# Exponential extrapolation of each district
math_project(districts,
  sub_group_col = "District", parent_group_col = "Region",
  pop_cols = c("pop2010", "pop2020"), time_vals = c(2010, 2020),
  target_times = c(2025, 2030), method = "exponential"
)

Karup-King coefficients for middle age groups

Description

The standard Karup-King third-difference osculatory interpolation multipliers used to split interior 5-year age groups into single years. Rows are the five single-year ages within the group; columns are the weights applied to the previous, current, and next 5-year groups.

Usage

middle_coef

Format

A 5 x 3 data frame of numeric coefficients.

Source

Siegel, J. S., & Swanson, D. A. (Eds.). (2004). The Methods and Materials of Demography (2nd ed.), Appendix C.

Basic Probabilistic Population Projection

Description

This function projects population trajectories for multiple administrative regions using a simple stochastic (simulation-based) exponential growth model, inspired by the probabilistic projection literature (e.g. Raftery et al. 2012), but without a full Bayesian model.

For each region, num_samples growth-rate trajectories are simulated. The annual growth rate of each trajectory is the sum of a random baseline, a fertility effect (increasing in TFR), a mortality effect (decreasing in the death rate), a migration effect (with the sign of net migration), and random noise. Each trajectory is propagated independently through all years, so that uncertainty accumulates over the projection horizon, and the results are summarised per region and year.

Usage

project_population(
  data,
  future_year,
  base_year,
  region_var = "region",
  subregion_var = "subregion",
  base_pop_var = "base_pop",
  TFR_var = "TFR",
  death_rate_var = "death_rate",
  net_migration_var = "net_migration",
  num_samples = 2000,
  random_seed = 42,
  verbose = FALSE
)

Arguments

Value

A data frame with one row per region, subregion, and year, containing the summary statistics of the simulated population trajectories: lower (25th percentile), median, mean, and upper (75th percentile).

References

Raftery, A. E., Li, N., Sevcikova, H., Gerland, P., & Heilig, G. K. (2012). Bayesian probabilistic population projections for all countries. Proceedings of the National Academy of Sciences, 109(35), 13915-13921. doi:10.1073/pnas.1211452109 (Inspiration for the stochastic/probabilistic projection logic.)

Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and Modeling Population Processes. Oxford: Blackwell Publishers. ISBN 978-0631226161. (Chapter 6: Population Projections.)

Rowland, D. T. (2003). Demographic Methods and Concepts. Oxford: Oxford University Press. ISBN 978-0198752639. (Chapter on population projections.)

Examples

data(region2000)
proj <- project_population(region2000,
  base_year = 2000, future_year = 2009,
  TFR_var = "TFR", base_pop_var = "base_pop",
  region_var = "Country", death_rate_var = "death_rate",
  net_migration_var = "net_migration",
  subregion_var = "Region",
  num_samples = 1000
)
head(proj)

Regional projection parameters, Ghana 2000

Description

Base-year demographic parameters for the regions of Ghana in 2000, used to demonstrate project_population().

Usage

region2000

Format

A data frame with 16 rows and 6 variables:

Country

Character. Country name.

Region

Character. Region name.

base_pop

Numeric. Base-year population.

TFR

Numeric. Total fertility rate.

death_rate

Numeric. Crude death rate.

net_migration

Numeric. Net migration (persons).

Source

Derived from Ghana Statistical Service census publications.

Brass standard life tables

Description

Survivorship values l(x) (radix 1) and their Brass logits for the African and General United Nations standards, used by brass_logit().

Usage

standards

Format

A data frame with 24 rows and 5 variables:

Age

Numeric. Exact age x.

GeneralUN

Numeric. l(x) of the General UN standard.

GeneralUN_logit

Numeric. Brass logit of the General UN standard.

African

Numeric. l(x) of the African standard.

African_logit

Numeric. Brass logit of the African standard.

Source

Brass, W. (1971). On the scale of mortality. In Biological Aspects of Demography. London: Taylor & Francis. United Nations model life table publications.

District-level demographic test data

Description

Simulated district-level demographic panel data (population, births, deaths, migration) by age group and sex, for testing subnational projection workflows such as math_project().

Usage

testdata

Format

A data frame with 1224 rows and 10 variables:

region

Character. Region name.

district

Character. District name.

year

Numeric. Calendar year.

age_group

Character. Age group label.

sex

Character. "Male" or "Female".

population

Numeric. Population count.

births

Numeric. Live births.

deaths

Numeric. Deaths.

in_migration

Numeric. In-migrants.

out_migration

Numeric. Out-migrants.

Source

Simulated data.