Help for package propop

Title:

Project Population Growth in Switzerland Using the Cohort Component Method

Version:

2.2.1

Description:

Project the development of the population at different spatial levels (e.g., cantons, districts, municipalities) using the cohort component method, broken down by 101 one-year age classes, gender, and nationality (see e.g. Preston et al. (2000, ISBN: 978-1-557-86451-2)). Includes helper functions to download required population data and projection parameters as open data for Switzerland, plus tools for model evaluation against benchmarks such as population records or a base model.

URL:

https://statistik-aargau.github.io/propop/

BugReports:

https://github.com/statistik-aargau/propop/issues

License:

GPL (≥ 3)

Depends:

R (≥ 4.1.0)

Imports:

assertthat, BFS, cli, curl, dplyr, stringi, tidyr, vctrs, progressr, purrr

Suggests:

devtools, DT, ggplot2, htmltools, tibble, knitr, rmarkdown, stringr, testthat, rlang, lifecycle, colorspace

VignetteBuilder:

knitr

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.3.3

Config/testthat/edition:

NeedsCompilation:

Packaged:

2026-10-28 06:52:28 UTC; nefo

Author:

Norah Efosa [aut, cre], Adrian Gadient

[aut], Tina Cornioley [aut], Jan Wunder [aut], Niklas Haffert [aut], Andrea Plüss [ctb], Nadine Herrmann [ctb], Lutz Benson [ctb], Eric Meyer [ctb], Thomas Knecht [ctb], Moana Stadelmann [ctb], Statistik Aargau [fnd, cph]

Maintainer:

Norah Efosa <norah.efosa@ag.ch>

Repository:

CRAN

Date/Publication:

2026-11-12 19:20:02 UTC

propop: Project Population Growth in Switzerland Using the Cohort Component Method

Description

Author(s)

Maintainer: Norah Efosa norah.efosa@ag.ch

Authors:

Adrian Gadient adrian.gadient@ag.ch (ORCID)
Tina Cornioley tina.cornioley@vd.ch
Jan Wunder jan.wunder@sg.ch
Niklas Haffert niklas.haffert@sg.ch

Other contributors:

Andrea Plüss andrea.pluess@ag.ch [contributor]
Nadine Herrmann [contributor]
Lutz Benson [contributor]
Eric Meyer [contributor]
Thomas Knecht [contributor]
Moana Stadelmann [contributor]
Statistik Aargau [funder, copyright holder]

Subregional migration records for the canton of Aargau (five subregions)

Description

Raw migration data from the canton of Aargau divided into five subregions.

Usage

ag_migration_subregional

Format

The example data includes historic migration records for immigration and emigration. Dimensions correspond to the number of people of each demographic group (nationality (2) X sex (2) X age classes (101)) for five spatial units of the canton of Aargau in 2025.

Population data for the canton of Aargau (five subregions)

Description

Population from the canton of Aargau divided into five subregions.

Usage

ag_population_subregional

Format

The example population records include the number of people of each demographic group (nationality (2) X sex (2) X age classes (101)) for five spatial units of the canton of Aargau in 2025.

Aggregate evaluation measures

Description

Returns descriptive summary statistics of model accuracy and bias measures across demographic groups and years. The returned statistics are particularly useful for comparing the model performance for different groups or different models.

Usage

aggregate_measures(data, weight_groups = NULL)

Arguments

data

data frame created with function compute_measures.

weight_groups

character, optional argument indicating one or more column names to obtain evaluation criteria weighted for specific groups (e.g., age groups, nationality).

Value

A data frame. The data frame includes the following summary measures:

mpe is the mean percentage error (mpe; or mean algebraic percentage error malpe); it is a bias indicator as it takes the direction of the error into account. Positive values indicate that the projections were, overall, too high. Negative values indicate that the projections were, overall, too low. The closer the value is too zero, the lower the bias.
medpe is the median (or middle value) of the percentage error (medpe). Particularly useful for small samples or skewed distributions. The closer the value is too zero, the lower the bias.
mape is the mean absolute percentage / proportional error (mape). It considers variance (or amplitude) and can be seen as a measure of precision. The smaller the value, the lower is the average error.
medape is the median (or middle value) of the absolute percentage error (medape). Particularly useful for small samples or skewed distributions. The smaller the value, the lower is the average error.
rmse is the root mean square error; it is an indication of the robustness or quality of the projection. The smaller the value, the more robust the projection.
wmape is the weighted mean absolute percentage error (wmape); in contrast to mape, this measure weights each absolute percentage error according to the population size of the "focal" group (e.g., nationality, age group) and thus considers domain size. Put differently, errors count more in populous groups than in less populous groups. This measure is particularly useful when population sizes vary strongly. The closer the value, the more precise is the projection.
n_measure is the number of times a summary measure occurs (per weight group if requested).
ape_under_1 is a measure to gauge the error distribution; it indicates the proportion of observations that have absolute percentage errors smaller than 1%.
ape_under_5 is a measure to gauge the error distribution; it indicates the proportion of observations that have absolute percentage errors smaller than 5%.

References

Baker, J., et al. (2015). Sub-county population estimates using administrative records: A municipal-level case study in New Mexico. In M. N. Hoque & L. B. Potter (Eds.), Emerging techniques in applied demography (pp. 63-79). Springer, doi:10.1007/978-94-017-8990-5_6

Bérard-Chagnon, J. (2015) Using tax data to estimate the number of families and households in Canada. In M. N. Hoque & L. B. Potter (Eds.), Emerging techniques in applied demography (pp. 137-153). Springer, doi:10.1007/978-94-017-8990-5_10

Reinhold M. & Thomsen, S. L. (2015) Subnational population projections by age: An evaluation of combined forecast techniques, Population Research and Policy Review, 34, 593-613, doi:10.1007/s11113-015-9362-0

Wilson, T. (2012). Forecast accuracy and uncertainty of Australian Bureau of Statistics state and territory population projections, International Journal of Population Research, 1, 419824, doi:10.1155/2012/419824

Wilson, T. (2016). Evaluation of alternative cohort-component models for local area population forecasts, Population Research and Policy Review, 35, 241-261, doi:10.1007/s11113-015-9380-y

Examples

evaluation <- tibble::tibble(
  year = rep(2020L, 3L),
  n_benchmark = c(2605, 2679, 2650),
  n_projected = c(2526, 2558, 2594),
  error = c(-79, -121, -56),
  pe = c(-3.032629558541267, -4.516610675625234, -2.1132),
  ape = c(3.03, 4.516610675625234, 2.1132075471698113),
)

aggregate_measures(evaluation)

Calculate emigration rates to subregions

Description

The function calculates the mean of past emigration to other municipalities within the canton for all municipalities and all possible combinations of age group, sex, and nationality.

Usage

calculate_rates(
  past_migration,
  n_jan,
  births,
  emi_n,
  spatial_unit,
  method,
  year_range = NULL,
  age_group = 1,
  binational = TRUE,
  two_sex = TRUE
)

Arguments

past_migration

data frame, containing aggregated migration records. The data frame must include one row for each combination of year, spatial unit, nationality, sex, and age.

n_jan

character, column containing the starting population (typically the number of people per group in January).

births

character, column containing the number of births in the group of 0-year olds.

emi_n

character, column containing the total number of people emigrating per spatial unit and demographic group.

spatial_unit

character, column containing the spatial units.

method

character, method to calculate average shares, i.e. mean or median.

year_range

(optional) vector, years taken into consideration to calculate historical shares. Default uses all years present in the data.

age_group

(optional) integer, divides continuous age values into intervals for calculating shares. If the argument is not specified, the default uses 1-year age groups.

binational

(optional) boolean, TRUE indicates that the calculation discriminates between two groups of nationalities. FALSE indicates that the calculation does not distinguish between nationalities.

two_sex

(optional) boolean, TRUE indicates that the calculation discriminates between two sexes. FALSE indicates that the calculation does not distinguish between sexes.

Value

A data frame that includes the average emigration rate.

Examples

# Compute mean emigration rate
calculate_rates(
  past_migration = ag_migration_subregional,
  n_jan = n_jan,
  births = births,
  emi_n = hist_emi_sub_n,
  spatial_unit = spatial_unit,
  method = "mean",
  year_range = c(2022:2024),
  age_group = 5,
  binational = TRUE,
  two_sex = TRUE
)

Calculate shares for distributing immigration among subregions

Description

Calculates historical immigration shares across spatial units within a region.
To compute the shares, the counts are summed within each spatial unit and across all spatial units combined, across all years. Dividing the spatial unit count by the total gives the group's share.
These shares are used to allocate emigrants moving from one subregion to another subregion (which can be done with calculate_rates).

Usage

calculate_shares(
  past_migration,
  imm_n,
  year_range = NULL,
  age_group = 1,
  binational = TRUE,
  two_sex = TRUE
)

Arguments

past_migration

data frame, historical records (e.g., immigration from other cantons, countries or subregions). Required columns are: year, spatial_unit, age and a column that contains aggregated historical migration records. The columns nat and sex are optional.

imm_n

character, name of the column which contains the data for aggregated historical migration records.

year_range

(optional) vector, years taken into consideration to calculate historical shares. Default uses all years present in the data.

age_group

(optional) integer, divides continuous age values into intervals for calculating shares. If the argument is not specified, the default uses 1-year age groups.

binational

(optional) boolean, TRUE indicates that the calculation discriminates between two groups of nationalities. FALSE indicates that the calculation does not distinguish between nationalities.

two_sex

(optional) boolean, TRUE indicates that the calculation discriminates between two sexes. FALSE indicates that the calculation does not distinguish between sexes.

Value

A data frame that includes the average share per demographic group and spatial unit. imm_share can be used as imm_sub parameter when propop::propop() uses subregional = "rate".

Examples

# Calculate shares to distribute subregional immigration among spatial units
calculate_shares(
  past_migration = ag_migration_subregional,
  imm_n = hist_imm_sub_n,
  year_range = c(2022:2024),
  age_group = 10,
  binational = TRUE,
  two_sex = TRUE
)

Check if population equation components add up within one year

Description

Function to check if the components in the propop::propop() output add up. Takes the population at the beginning of the year (n_jan), adds all obligatory components (births - mor_n - emi_int_n - emi_nat_n + imm_int_n + imm_nat_n) and optional components (acq_n, mig_sub, emi_sub_n, imm_sub_n) and checks if the sum is equal to the population at the end of the year (n_dec).

Usage

check_balance(.data)

Arguments

.data

data frame containing population projections; can be created with propop::propop().

Value

data frame containing summary statistics.

Examples

propop(
  parameters = fso_parameters,
  year_first = 2026,
  year_last = 2027,
  population = fso_population,
  subregional = FALSE,
  binational = TRUE
) |>
  check_balance()

Compute evaluation measures

Description

Uses the differences between the results from a projection and a benchmark to compute performance measures.

Usage

compute_measures(combined, weight_groups = NULL)

Arguments

combined

data frame created with propop::prepare_evaluation().

weight_groups

character, optional argument indicating one or more column names to obtain evaluation criteria weighted for specific groups (e.g., age groups, nationality).

Details

The input is a data frame created with propop::prepare_evaluation(). It includes a benchmark (typically the observed population records, i.e., the number of people per spatial unit, demographic group, and year) and the corresponding projected number of people. The input can range from low resolution (e.g., total number of people per municipality) to high resolution (e.g., 101 age classes, nationality, sex).

For more details on usage, see vignette("evaluate", package = "propop").

Value

A data frame. The following evaluation criteria can directly be interpreted and used for descriptive comparisons:

error is the forecast error; it quantifies the level of under-projection (negative values) and over-projection (positive values) relative to the benchmark n_benchmark.
pe is the percentage error and expresses the under- / over-projection in percent of the benchmark n_benchmark.
ape is the absolute percentage error; it is the absolute deviation in percent of the benchmark n_benchmark, thus only showing the extent of the error but not the direction.
w_ape is the weighted absolute percentage error; it weighs each absolute percentage error according to the population size of the focal group (e.g., nationality, age group). The weighted version is useful as an aggregated measure when groups vary strongly in terms of population size. Only returned when the argument weight_groups contains at least one grouping variable.

The following helper variables are used to compute aggregate measures. They are only returned when weight groups are provided via the argument weight_groups.

n_tot is the total number of people (i.e., sum of the number of people in all demographic groups); used to compute the weighted absolute percentage error.
group_tot is the number of people in the focal group; used to compute the weighted absolute percentage error.
weight is the share of the (optional) focal group (e.g., municipality type / size, nationality, age group) relative to all people; used to compute the weighted absolute percentage error.

References

Wilson, T. (2016). Evaluation of alternative cohort-component models for local area population forecasts, Population Research and Policy Review, 35, 241-261, doi:10.1007/s11113-015-9380-y

Examples

combined <- tibble::tibble(
  year = 2020,
  spatial_unit = "Aargau",
  sex = "f",
  age = c("age_00_19", "age_20_64", "age_65_"),
  n_benchmark = c(50020, 17917, 53005),
  n_projected = c(49691, 18519, 52395),
)

compute_measures(combined)

Sample parameters to run population projection

Description

Data frame containing the rates and number of people from the Federal Statistical Office (FSO) required to project the development of four demographic groups for a selected canton (Aargau). The parameters are from the model published in 2025. The sample data include three scenarios for the years 2025-2055.

Usage

fso_parameters

Format

The example data include the required parameters for each demographic group (nationality (2) X sex (2) X age classes (101)) for the years 2025-2055.

Demographic groups

The returned data frame includes parameters for each unique combination of the following demographic variables:

nat: ch = Swiss; int = foreign / international.
sex: f = female, m = male.
age: 101 one-year age classes, ranging from 0 to 100 (including those older than 100).

Parameters

The following parameters are included in the returned data frame:

start_n: numeric, number of people in the corresponding demographic group on 1st of January.
year: numeric, year of projection.
scen: character, projection scenario.
spatial_unit: character, indicating the user requested spatial unit(s).
birthrate: numeric, number of birth per mother by age, nationality and year.
int_mothers: numeric, proportion of children with Swiss nationality born to non-Swiss mothers.
mor: numeric, prospective mortality (probability of death).
emi_int: numeric, rate of people emigrating to other countries.
emi_nat: numeric, rate of people emigrating to other cantons.
acq: numeric, rate of acquisition of Swiss citizenship.
imm_int_n: numeric, number of people immigrating from abroad..
imm_nat_n: numeric, number of people immigrating from other cantons.
emi_nat_n: numeric, number of people emigrating to other cantons.
mig_nat_n: numeric, national / inter-cantonal net migration.

Projected population

n_projected is the number of people per demographic group and year on December 31 (as projected by the FSO in the 2025 model).

Details about calculated variables

births_int_ch is calculated by dividing the number of live newborns with Swiss citizenship born to non-Swiss mothers by the number of all live newborns born to non-Swiss mothers.

mig_ch is calculated as the difference between the immigration from other cantons and the emigration to other cantons.

Source

Data obtained from the Swiss Federal Statistical Office (FSO):

Sample population data from the Federal Statistical Office

Description

Example data frame containing the starting population required to project the development of four demographic groups for a selected canton (Aargau). Population data can be downloaded from the FSO using the get_population.

Usage

fso_population

Format

The example population records include the number of people of each demographic group (nationality (2) X sex (2) X age classes (101)) for the canton of Aargau in 2024.

Value

A data frame. For each of the four demographic groups (female / male, Swiss / foreign nationals), there are 101 age classes, resulting in a total of 404 rows per requested year and spatial unit. Columns included in the returned data frame:

year: numeric, year in which the population was recorded.
spatial_unit: character, indicating the spatial entities (e.g., cantons, districts, municipalities).
nat: character, ch = Swiss, int = foreign / international.
sex: character f = female, m = male.
age: numeric, 101 one-year age classes, ranging from 0 to 100 (including those older than 100).
n: numeric, number of people per year, spatial entity, and demographic group.

Source

Federal Statistical Office: https://www.pxweb.bfs.admin.ch/pxweb/en/px-x-0102010000_101/-/px-x-0102010000_101.px/

Get projection parameters from FSO

Description

Users who do not have the mandatory projection parameters for propop::propop() can use this convenience function to download them from the Federal Statistical Office (FSO). The parameters are only available on the level of cantons. For smaller-scale projections, the parameters must be scaled down. In addition to the parameters, the function also returns the projected population (i.e., number of people estimated in the FSO model released in 2025). All parameters and projections are from the FSO model published in 2025. The parameters are available for the years 2025-2055. The variables int_mothers and mig_nat_n are not directly available from the FSO. They are calculated within this function.

To get projection parameters, you must use the spelling defined in the corresponding FSO table. See vignette("prepare_data", package = "propop"). Inspecting the column 'valueTexts' in the following package data may also help:

data('stattab_101_snap')
data('stattab_102_snap')
data('stattab_103_snap')
data('stattab_106_snap')
data('stattab_109_snap')

Changes to the API interface may break this function. If problems occur, we recommend following the step-by-step procedure described in vignette("prepare_data", package = "propop").

Usage

get_parameters(
  number_fso_ref = "px-x-0104020000_101",
  number_fso_high = "px-x-0104020000_102",
  number_fso_low = "px-x-0104020000_103",
  number_fso_rates = "px-x-0104020000_109",
  number_fso_births = "px-x-0104020000_106",
  year_first,
  year_last,
  spatial_units
)

Arguments

number_fso_ref

character, px-x table ID for number parameters (reference scenario), defaults to "px-x-0104020000_101".

number_fso_high

character, px-x table ID for number parameters (high growth scenario), defaults to "px-x-0104020000_102".

number_fso_low

character, px-x table ID for for number parameters (low growth scenario, defaults to "px-x-0104020000_103".

number_fso_rates

character, px-x table ID for rate parameters, defaults to "px-x-0104020000_109".

number_fso_births

character, px-x table ID required to compute the share of Swiss newborns from non-Swiss mothers, defaults to "px-x-0104020000_106".

year_first

numeric, first year for which the parameters and projections are to be downloaded.

year_last

numeric, last year for which the parameters and projections are to be downloaded.

spatial_units

character vector, indicating at least one spatial entity for which the projection will be run. Typically a canton.

Value

A data frame with the rates and number of people from the Federal Statistical Office (FSO) required to project the population development of the requested spatial entities. For each of the four demographic groups (nationality x sex), there are 101 age classes, resulting in a total of 404 rows per requested year and spatial unit.

Demographic groups

The returned data frame includes parameters for each unique combination of the following demographic variables:

nat: ch = Swiss; int = foreign / international.
sex: f = female, m = male.
age: 101 one-year age classes, ranging from 0 to 100 (including those older than 100).

Parameters

The following parameters are included in the returned data frame:

start_n: numeric, number of people in the corresponding demographic group on 1st of January.
year: numeric, year of projection.
scen: character, projection scenario.
spatial_unit: character, indicating the user requested spatial unit(s).
birthrate: numeric, number of birth per mother by age, nationality and year.
int_mothers: numeric, proportion of children with Swiss nationality born to non-Swiss mothers.
mor: numeric, prospective mortality (probability of death).
emi_int: numeric, rate of people emigrating to other countries.
emi_nat: numeric, rate of people emigrating to other cantons.
acq: numeric, rate of acquisition of Swiss citizenship.
imm_int_n: numeric, number of people immigrating from abroad..
imm_nat_n: numeric, number of people immigrating from other cantons.
emi_nat_n: numeric, number of people emigrating to other cantons.
mig_nat_n: numeric, national / inter-cantonal net migration.

Projected population

n_projected is the number of people per demographic group and year on December 31 (as projected by the FSO in the 2025 model).

Details about calculated variables

births_int_ch is calculated by dividing the number of live newborns with Swiss citizenship born to non-Swiss mothers by the number of all live newborns born to non-Swiss mothers.

mig_ch is calculated as the difference between the immigration from other cantons and the emigration to other cantons.

Source

Data obtained from the Swiss Federal Statistical Office (FSO):

Examples


two_cantons_4years <- get_parameters(
  year_first = 2025,
  year_last = 2028,
  spatial_units = c("Aargau", "Zug")
)

Get population data from FSO

Description

Users who do not have the required population data can use this convenience function to get the mandatory starting population for propop::propop() from the Federal Statistical Office (FSO). The function can also be used to obtain the population records for several years (e.g., for model performance evaluations). This function can be used to obtain data at various spatial levels (e.g., cantons, municipalities). The most recent data are usually about 6 to 18 months old.

To get the population data, you must use the spelling defined in the corresponding FSO table (STATTAB cube px-x-0102010000_101). Inspecting the column 'valueTexts' in the following package data may also help: data('stattab_pop_snap').

Changes to the API interface may break this function.

Usage

get_population(
  number_fso = "px-x-0102010000_101",
  year,
  year_last = NULL,
  spatial_units
)

Arguments

number_fso

character, px-x table ID for population records, defaults to ⁠px-x-0102010000_101⁠.

year

numeric, year for which the population records are to be downloaded. This usually is the starting population. To download longer time periods, use year to indicate the first year of the period.

year_last

numeric (optional); specifies the final year of the time period for which data will be downloaded.

spatial_units

character vector, indicating at least one spatial entity for which the projection will be run. Typically a canton, several districts or municipalities.

Value

year: numeric, year in which the population was recorded.
spatial_unit: character, indicating the spatial entities (e.g., cantons, districts, municipalities).
nat: character, ch = Swiss, int = foreign / international.
sex: character f = female, m = male.
age: numeric, 101 one-year age classes, ranging from 0 to 100 (including those older than 100).
n: numeric, number of people per year, spatial entity, and demographic group.

Source

Federal Statistical Office: https://www.pxweb.bfs.admin.ch/pxweb/en/px-x-0102010000_101/-/px-x-0102010000_101.px/

Examples


get_population(
  year = 2025,
  spatial_units = c("- Aargau", "......0301 Aarberg")
)

Prepare data for evaluation

Description

Takes population projections and benchmark data (e.g., population records) and prepares a combined data frame to evaluate the performance of the projection. For more details on usage, see vignette("evaluate", package = "propop").

Usage

prepare_evaluation(
  data_benchmark,
  n_benchmark,
  data_projected,
  n_projected,
  age_groups = NULL
)

Arguments

data_benchmark

data frame containing benchmark data (e.g., actual / official population records; can be obtained with propop::get_population()).

n_benchmark

numeric column containing the benchmark population of each demographic group.

data_projected

data frame containing population projections; can be created with propop::propop().

n_projected

numeric column containing the projected size of each demographic group.

age_groups

character, optional argument with options "age_groups_3" or "age_groups_5". The option "age_groups_3" groups the data into three age ranges (0-19, 20-64, 65 years and older). The option "age_groups_5" groups the data into five age ranges (0-19, 20-39, 40-59, 60-79, 80 years and older). Using aggregated groups will lead to smaller projection errors than using 101 age classes. Defaults to using 101 one-year age classes if no option is chosen.

Value

Returns a data frame with the number of people from the benchmark and from the projection. Each row contains a unique combination of year, spatial unit, and demographic group.

Input data and variables

Both input data frames must contain the following variables for the same range of years:

year: character, year in which the population was recorded.
spatial_unit: character, indicating the projected spatial entities (e.g., cantons, districts, municipalities).
nat: character, ch = Swiss, int = foreign / international.
sex: character, f = female, m = male.
age: numeric, 101 one-year age classes, ranging from 0 to 100 (including those older than 100).
n: numeric, number of people per year, spatial entity, and demographic group.

Examples

data_benchmark <- tibble::tibble(
  year = 2030,
  spatial_unit = "Aargau",
  nat = c("ch", "ch"),
  sex = c("m", "f"),
  age = c(42, 42),
  n = c(3551, 3655)
)

data_projected <- tibble::tibble(
  year = 2030,
  spatial_unit = "Aargau",
  nat = c("ch", "ch"),
  sex = c("m", "f"),
  age = c(42, 42),
  n = c(3474, 3536)
)

combined <- prepare_evaluation(
  data_benchmark = data_benchmark,
  n_benchmark = "n",
  data_projected = data_projected,
  n_projected = "n"
)

Project population development

Description

Wrapper function to project population development using the cohort component method. This function iterates across years and spatial units calling the function project_population.R, which performs the calculations.

You can either use your own parameters and starting population or download these data from the Swiss Federal Statistical Office (FSO). For instructions on how to download this information from STAT-TAB, see vignette("prepare_data", package = "propop").

The projection parameters need to be passed to propop::propop() as a single data frame (with the parameters as columns). The column types, names, and factor levels need to match the specifications listed below under parameters.

If nothing else is indicated in argument scenarios, propop() runs and returns all scenarios provided via parameters.

For more details on how to use this function to project the population development on the level of a canton, see vignette("project_single_region", package = "propop").

Usage

propop(
  parameters,
  population,
  year_first,
  year_last,
  scenarios = NULL,
  age_groups = 101,
  fert_first = 16,
  fert_last = 50,
  share_born_female = 100/205,
  subregional = FALSE,
  binational = TRUE,
  spatial_unit = "spatial_unit"
)

Arguments

parameters

data frame containing the FSO rates and numbers to run the projection for a specific spatial level (e.g., canton, municipality).

year, character, projection year.
spatial_unit, character, ID of spatial entity (e.g., canton, municipality) for which to run the projections.
scen, character, one or several projection scenario(s). The main scenarios are usually "reference", "low" growth, and "high" growth.
nat, character, nationality (ch = Swiss; int = foreign / international). Required if binational = TRUE.
sex, character (f = female, m = male).
age, numeric, typically ranging from 0 to 100 (incl. >100).
birthrate, numeric, number of birth per mother by age, nationality and year.
int_mothers, numeric, proportion of children with Swiss nationality born to non-Swiss mothers. Required if binational = TRUE.
mor, numeric, prospective mortality rate (probability of death).
acq, numeric, rate of acquisition of Swiss citizenship. Required if binational = TRUE.
emi_int, numeric, rate of people emigrating abroad (number of immigrants - number of emigrants).
emi_nat, rate of people emigrating to other cantons.
imm_int_n, numeric, number of people immigrating from abroad.
imm_nat_n, numeric, number of people immigrating from other cantons.
mig_sub (optional), numeric, net migration per subregion; this is the migration from / to other subregions (e.g., municipalities, districts) within the main superordinate projection unit (e.g., a canton). Accounts for movements between different subregions. Needs to be provided by the user.
emi_sub (optional), numeric, emigration rate per subregion; Accounts for movements between different subregions. Needs to be provided by the user. * imm_sub (optional), numeric, immigration rate per subregion; Accounts for movements between different subregions. Needs to be provided by the user.

population

data frame including the starting population of each demographic group and each spatial unit. Possible values are the same as in parameters (apart from year). The data frame only includes one year, usually the one preceding the first year of the projection.

year character, should be year_first - 1.
spatial_unit character.
nat character.
sex character.
age numeric.
n numeric, number of people per demographic group.

year_first

numeric, first year to be projected.

year_last

numeric, last year to be projected.

scenarios

(optional), character, indicating which projection scenario(s) shall be run; the corresponding information must be available in parameters. Defaults to the values in variable scen in parameters.

age_groups

numeric, number of age classes. Creates a vector with 1-year age classes running from 0 to (age_groups - 1). Must currently be set to ⁠= 101⁠ (FSO standard number of age groups).

fert_first

numeric, first year of female fertility. Defaults to 16 (FSO standard value).

fert_last

numeric, last year of female fertility. Defaults to 50 (FSO standard value).

share_born_female

numeric, fraction of female babies. Defaults to 100 / 205 (FSO standard value).

subregional

character or FALSE, indicates if subregional migration patterns (e.g., movement between municipalities within a canton) are part of the projection (default subregional = FALSE). Requires input on the level of subregions (in parameters and population). Two calculation methods are supported to distribute people between subregions: With subregional = "net", the net migration between subregions is added to the population balance. Net migration numbers must be specified in a data column mig_sub in parameters. With subregional = "rate", the numbers for subregional emigrants are subtracted from the population balance, then redistributed back to all subregional units as subregional immigration; parameters must contain the columns emi_sub and imm_sub.

binational

boolean, TRUE indicates that projections discriminate between two groups of nationalities. FALSE indicates that the projection is run without distinguishing between nationalities.

spatial_unit

character, name of variable containing the names of the region or subregions for which the projection shall be performed.

Value

Returns a data frame that includes the number of people for each demographic group per year (for projected years) and spatial unit. The number of rows is the product of all scenarios times all years times all demographic groups times all spatial units. The output includes several identifiers that indicate to which scenario, demographic group, year, and spatial unit the results in the rows refer to:

year

integer, indicating the projected years.

scen

character, indicating the projected scenario(s).

spatial_unit

factor, spatial units for which the projection was run (e.g., canton, districts, municipalities).

age

integer, ranging from 0n to 100 years (including those older than 100).

sex

factor, female (f) and male (m).

nat

factor, indicates if the nationality is Swiss (ch) or international / foreign (int). This variable is only returned if binational = TRUE.

The output also includes columns related to the size and change of the population:

n_jan

numric, start-of-year population per demographic group.

n_dec

numeric, end-of-year population per demographic group.

delta_n

numeric, population change per demographic group from the start to the end of the year in absolute numbers.

delta_perc

numeric, population change per demographic group from the start to the end of the year in percentages.

The components that are used to project the development of the population are also included in the output:

births

numeric, number of births (non-zero values are only available for age = 0).

mor_n

numeric, number of deaths.

emi_int_n

numeric, number of people who emigrate to other countries.

emi_nat_n

numeric, number of people who emigrate to other cantons.

imm_int_n

numeric, number of people who immigrate from other countries.

imm_nat_n

numeric, number of people who immigrate from other cantons.

acq_n

numeric, number of people who acquire Swiss citizenship (only returned if binational = TRUE.)

mig_sub

numeric, migration between subregions (only returned if subregional = "net".)

emi_sub_n

numeric, emigration from subregions (only returned if subregional = "rate".)

imm_sub_n

numeric, immigration from subregions (only returned if subregional = "rate".)

Examples

# Run projection for the sample data (whole canton of Aargau)
propop(
  parameters = fso_parameters,
  year_first = 2026,
  year_last = 2027,
  population = fso_population,
  subregional = FALSE,
  binational = TRUE
)
propop(
  parameters = fso_parameters |>
    dplyr::filter(scen == "reference" | scen == "high"),
  year_first = 2026,
  year_last = 2027,
  scenarios = c("reference", "high"),
  population = fso_population,
  subregional = FALSE,
  binational = TRUE
)

Snapshot of STAT-TAB px 101

Description

Used for tests and to make it easier for users to look up the spelling used on STAT-TAB.

Usage

stattab_101_snap

Format

An object of class tbl_df (inherits from tbl, data.frame) with 177 rows and 6 columns.

Source

Data obtained from the Swiss Federal Statistical Office (FSO):

https://www.pxweb.bfs.admin.ch/pxweb/en/px-x-0104020000_101/-/px-x-0104020000_101.px/

Snapshot of STAT-TAB px 102

Description

Used for tests and to make it easier for users to look up the spelling used on STAT-TAB.

Usage

stattab_102_snap

Format

An object of class tbl_df (inherits from tbl, data.frame) with 177 rows and 6 columns.

Source

Data obtained from the Swiss Federal Statistical Office (FSO):

https://www.pxweb.bfs.admin.ch/pxweb/en/px-x-0104020000_102/-/px-x-0104020000_102.px

Snapshot of STAT-TAB px 103

Description

Used for tests and to make it easier for users to look up the spelling used on STAT-TAB.

Usage

stattab_103_snap

Format

An object of class tbl_df (inherits from tbl, data.frame) with 177 rows and 6 columns.

Source

Data obtained from the Swiss Federal Statistical Office (FSO):

https://www.pxweb.bfs.admin.ch/pxweb/en/px-x-0104020000_106/-/px-x-0104020000_106.px/

Snapshot of STAT-TAB px 106

Description

Used for tests and to make it easier for users to look up the spelling used on STAT-TAB.

Usage

stattab_106_snap

Format

An object of class tbl_df (inherits from tbl, data.frame) with 103 rows and 6 columns.

Source

Data obtained from the Swiss Federal Statistical Office (FSO):

https://www.pxweb.bfs.admin.ch/pxweb/en/px-x-0104020000_106/-/px-x-0104020000_106.px/

Snapshot of STAT-TAB px 109

Description

Used for tests and to make it easier for users to look up the spelling used on STAT-TAB.

Usage

stattab_109_snap

Format

An object of class tbl_df (inherits from tbl, data.frame) with 175 rows and 6 columns.

Source

Data obtained from the Swiss Federal Statistical Office (FSO):

https://www.pxweb.bfs.admin.ch/pxweb/en/px-x-0104020000_109/-/px-x-0104020000_109.px/

Snapshot of STAT-TAB that includes the population records

Description

Used for tests and to make it easier for users to look up the spelling used on STAT-TAB.

Usage

stattab_pop_snap

Format

An object of class tbl_df (inherits from tbl, data.frame) with 2412 rows and 7 columns.

Source

Federal Statistical Office: https://www.pxweb.bfs.admin.ch/pxweb/en/px-x-0102010000_101/-/px-x-0102010000_101.px/

Package {propop}

propop: Project Population Growth in Switzerland Using the Cohort Component Method

Description

Author(s)

See Also

Subregional migration records for the canton of Aargau (five subregions)

Description

Usage

Format

Population data for the canton of Aargau (five subregions)

Description

Usage

Format

Aggregate evaluation measures

Description

Usage

Arguments

Value

References

Examples

Calculate emigration rates to subregions

Description

Usage

Arguments

Value

Examples

Calculate shares for distributing immigration among subregions

Description

Usage

Arguments

Value

See Also

Examples

Check if population equation components add up within one year

Description

Usage

Arguments

Value

Examples

Compute evaluation measures

Description

Usage

Arguments

Details

Value

References

Examples

Sample parameters to run population projection

Description

Usage

Format

Demographic groups

Parameters

Projected population

Details about calculated variables

Source

Sample population data from the Federal Statistical Office

Description

Usage

Format

Value

Source

Get projection parameters from FSO

Description

Usage

Arguments

Value

Demographic groups

Parameters

Projected population

Details about calculated variables

Source

Examples

Get population data from FSO

Description

Usage

Arguments

Value

Source

Examples