Package 'socialmixr'

Description

Filters a contact_survey object using an expression. The expression is evaluated against whichever table(s) contain the referenced columns (participants, contacts, or both). When participants are filtered, contacts are automatically pruned to matching part_ids.

Usage

## S3 method for class 'contact_survey'
x[i, ...]

Arguments

Value

a filtered contact_survey object

Examples

data(polymod)
polymod[country == "United Kingdom"]

Description

Inverse of limits_to_age_groups(). Extracts lower age limits from age group labels.

Usage

age_groups_to_limits(x)

Arguments

Value

a numeric vector of lower age limits

Examples

age_groups_to_limits(
  limits_to_age_groups(c(0, 5, 10), notation = "brackets")
)

Description

Aligns a population table to the groupings of a contact matrix produced by compute_matrix(), returning the survey_pop data frame that symmetrise(), split_matrix() and per_capita() expect.

The age grouping is rebinned to the matrix's age groups (via rebin_ages(), summing) within each combination of the other groupings; the population must be at least as fine as the matrix's age groups, otherwise rebin_ages() errors. Categorical groupings are aggregated to the matrix's levels by exact name; a level not present in the matrix is an error.

Usage

align_ages(pop, x)

Arguments

Value

a data frame with one column per grouping (named after the grouping, holding the matrix's levels) plus a population column, ready to pass as survey_pop

Examples

data(polymod)
result <- polymod |>
  (\(s) s[country == "United Kingdom"])() |>
  assign_age_groups(age_limits = c(0, 5, 15)) |>
  compute_matrix()
uk_pop <- data.frame(
  age = limits_to_age_groups(0:80, notation = "brackets"),
  population = rep(1e5, 81)
)
result |> symmetrise(survey_pop = align_ages(uk_pop, result))

Description

Checks that a survey fulfills all the requirements to work with the 'contact_matrix' function

Usage

as_contact_survey(
  x,
  id_column = "part_id",
  country_column = NULL,
  year_column = NULL,
  ...,
  id.column = deprecated(),
  country.column = deprecated(),
  year.column = deprecated()
)

Arguments

Value

invisibly returns a character vector of the relevant columns

Examples

data(polymod)
as_contact_survey(polymod)

Description

This function processes age data in a survey object. It imputes ages from ranges, handles missing values, and assigns age groups.

Usage

assign_age_groups(
  survey,
  age_limits = NULL,
  estimated_participant_age = c("mean", "sample", "missing"),
  estimated_contact_age = c("mean", "sample", "missing"),
  missing_participant_age = c("remove", "keep"),
  missing_contact_age = c("remove", "sample", "keep", "ignore")
)

Arguments

Value

The survey object with processed age data.

Examples

polymod_grouped <- assign_age_groups(polymod)
polymod_grouped
polymod_custom <- assign_age_groups(polymod, age_limits = c(0, 5, 10, 15))
polymod_custom

Description

Checks that a survey fulfills all the requirements to work with the 'contact_matrix' function

Usage

## S3 method for class 'contact_survey'
check(
  x,
  id.column = "part_id",
  participant.age.column = "part_age",
  country.column = "country",
  year.column = "year",
  contact.age.column = "cnt_age",
  ...
)

Arguments

Value

invisibly returns a character vector of the relevant columns

Examples

data(polymod)
try(check(polymod))

Description

Cleans survey data to work with the 'contact_matrix' function

Usage

## S3 method for class 'contact_survey'
clean(
  x,
  participant_age_column = "part_age",
  ...,
  participant.age.column = deprecated()
)

Arguments

Value

a cleaned survey in the correct format

Examples

data(polymod)
cleaned <- clean(polymod) # not really necessary, polymod is clean

Description

Computes a contact matrix from a contact_survey that has been processed by assign_age_groups() and optionally weigh(). This is the final step in the pipeline workflow.

For post-processing, pipe the result into symmetrise(), split_matrix(), or per_capita().

Usage

compute_matrix(survey, by = "age", counts = FALSE, weight_threshold = NULL)

Arguments

Value

a contact_matrix object with elements matrix (a rank-⁠2K⁠ array) and participants (a long table with one row per grouping combination)

Multi-dimensional matrices

Passing more than one entry to by produces a matrix of rank ⁠2K⁠, where K = length(by). The first K dimensions index participants and the last K dimensions index contacts, in the order given to by. For example, by = c("age", "gender") returns an array with dimensions ⁠(age, gender, age, gender)⁠ — age and gender of the participant first, then of the contact. Dim names carry the levels of each grouping.

Examples

data(polymod)

# Single-grouping (age) — default
polymod |>
  assign_age_groups(age_limits = c(0, 5, 15)) |>
  compute_matrix()

# Two-grouping (age x gender)
polymod |>
  assign_age_groups(age_limits = c(0, 5, 15)) |>
  compute_matrix(by = c("age", "gender"))

Description

Returns a data.frame of (age, proportion) pairs representing how contact ages are distributed in the survey. This can be passed to assign_age_groups() as estimated_contact_age to impute ages from ranges using this distribution rather than uniform sampling.

Usage

contact_age_distribution(survey)

Arguments

Value

a data.frame with columns age (integer) and proportion (numeric, summing to 1)

Examples

data(polymod)
dist <- contact_age_distribution(polymod)
head(dist)
plot(dist$age, dist$proportion, type = "h",
     xlab = "Age", ylab = "Proportion")

Description

Computes a contact matrix from a diary survey in a single call, together with participant counts by age group. The demography comes too when any of symmetric, split, per_capita or weigh_age is TRUE, or when return_demography = TRUE; setting return_demography = FALSE suppresses it even then.

contact_matrix() is superseded: it is still maintained and is not going away, but new code is better written as the pipeline it wraps. The pipeline composes the same steps, and can group by more than age:

survey |>
  assign_age_groups(age_limits = c(0, 5, 15)) |>
  weigh_by_dayofweek() |>
  compute_matrix()

The weighing functions stand in for weigh_age and weigh_dayofweek, and take the survey. weigh_by_age() and weigh_by_dayofweek() belong after assign_age_groups(), which adds the age column weigh_by_age() needs and settles which participants the matrix is built from, and before compute_matrix(), which consumes the weights.

The post-processing functions stand in for symmetric, split and per_capita, and take the matrix: pipe the compute_matrix() result into symmetrise(), split_matrix() or per_capita().

Usage

contact_matrix(
  survey,
  countries = NULL,
  survey_pop = NULL,
  age_limits = NULL,
  filter = NULL,
  counts = FALSE,
  symmetric = FALSE,
  split = FALSE,
  sample_participants = FALSE,
  estimated_participant_age = c("mean", "sample", "missing"),
  estimated_contact_age = c("mean", "sample", "missing"),
  missing_participant_age = c("remove", "keep"),
  missing_contact_age = c("remove", "sample", "keep", "ignore"),
  weights = NULL,
  weigh_dayofweek = FALSE,
  weigh_age = FALSE,
  weight_threshold = NA,
  symmetric_norm_threshold = 2,
  sample_all_age_groups = FALSE,
  sample_participants_max_tries = 1000,
  return_part_weights = FALSE,
  return_demography = NA,
  per_capita = FALSE,
  ...,
  survey.pop = deprecated(),
  age.limits = deprecated(),
  sample.participants = deprecated(),
  estimated.participant.age = deprecated(),
  estimated.contact.age = deprecated(),
  missing.participant.age = deprecated(),
  missing.contact.age = deprecated(),
  weigh.dayofweek = deprecated(),
  weigh.age = deprecated(),
  weight.threshold = deprecated(),
  symmetric.norm.threshold = deprecated(),
  sample.all.age.groups = deprecated(),
  sample.participants.max.tries = deprecated(),
  return.part.weights = deprecated(),
  return.demography = deprecated(),
  per.capita = deprecated()
)

Arguments

Value

a list. It always holds matrix, the contact matrix, and participants, the participant counts by age group. It also holds demography under the conditions above; matrix.per.capita when per_capita = TRUE and neither counts nor split is; and participants.weights when return_part_weights = TRUE.

split = TRUE splits the matrix when counts is not set and the matrix has no missing entry and no missing group label. Most often a missing entry comes from an age group no participant falls into, and a missing label from keeping participants or contacts whose age is unknown, but any missing value has the same effect. The split adds mean.contacts, normalisation and contacts, and matrix then holds the assortativity matrix. When it is skipped contact_matrix() warns and matrix holds the contact matrix as usual.

Author(s)

Sebastian Funk

See Also

compute_matrix() for the pipeline this function wraps

Examples

data(polymod)
contact_matrix(
  survey = polymod,
  countries = "United Kingdom",
  age_limits = c(0, 1, 5, 15)
)

Description

download_survey() is defunct. Use contactsurveys::download_survey() instead.

Usage

download_survey(survey, dir = NULL, sleep = 1)

Arguments

Value

Always errors.

See Also

load_survey

Examples

## Not run: 
peru_survey <- contactsurveys::download_survey(
  "https://doi.org/10.5281/zenodo.1095664"
)

## End(Not run)

Description

Returns the contact matrix in the flattened representation of Manna et al. — a ⁠T x T⁠ matrix where each axis enumerates the Cartesian product of grouping levels. For a single-grouping matrix this is the matrix itself.

Row/column names join the grouping levels with a colon, e.g. "[0,5):F" for the age group ⁠[0,5)⁠ combined with gender level F. The participant axes vary fastest in the first grouping (column-major reshape).

Usage

flatten(x)

Arguments

Value

a numeric ⁠T x T⁠ matrix

References

Manna A, Dall'Amico L, Tizzoni M, Karsai M, Perra N (2024). Generalized contact matrices allow integrating socioeconomic variables into epidemic models. Science Advances 10(41), eadk4606. doi:10.1126/sciadv.adk4606

Examples

data(polymod)
polymod |>
  (\(s) s[country == "United Kingdom"])() |>
  assign_age_groups(age_limits = c(0, 5, 15)) |>
  compute_matrix(by = c("age", "gender")) |>
  flatten()

Description

get_citation() is defunct. Use contactsurveys::get_citation() instead.

Usage

get_citation(x)

Arguments

Value

Always errors.

Examples

## Not run: 
data(polymod)
citation <- contactsurveys::get_citation(polymod)
print(citation)

## End(Not run)

Description

get_survey() is defunct. Use contactsurveys::download_survey() and then load_survey() instead.

Usage

get_survey(survey, clear_cache = FALSE, ...)

Arguments

Value

Always errors.

Examples

## Not run: 
peru_doi <- "https://doi.org/10.5281/zenodo.1095664"
peru_survey <- contactsurveys::download_survey(peru_doi)
peru_data <- load_survey(peru_survey)

## End(Not run)

Description

Mostly used for plot labelling

Usage

limits_to_age_groups(
  x,
  limits = sort(unique(x)),
  notation = c("dashes", "brackets")
)

Arguments

Value

Age groups as specified in notation

Examples

limits_to_age_groups(c(0, 5, 10))

Description

list_surveys() is defunct. Use contactsurveys::list_surveys() instead.

Usage

list_surveys(clear_cache = FALSE)

Arguments

Value

Always errors.

Examples

## Not run: 
contactsurveys::list_surveys()

## End(Not run)

Description

Loads a survey from a local file system. Tables are expected as csv files, and a reference (if present) as JSON.

Usage

load_survey(files, participant_key = NULL, ...)

Arguments

Value

a survey in the correct format. For longitudinal surveys with multiple observations per participant, the returned object includes an observation_key field containing the column names (excluding part_id) that distinguish observations for the same participant.

Examples

## Not run: 
peru_files <- contactsurveys::download_survey(
  "https://doi.org/10.5281/zenodo.1095664"
)
peru_survey <- load_survey(peru_files)

# For longitudinal surveys, specify the unique key explicitly:
france_files <- contactsurveys::download_survey(
  "https://doi.org/10.5281/zenodo.1157918"
)
france_survey <- load_survey(france_files,
  participant_key = c("part_id", "wave", "studyDay")
)

## End(Not run)

Description

This function combines the R image.plot function with numeric contact rates in the matrix cells.

Usage

matrix_plot(
  mij,
  min.legend = 0,
  max.legend = NA,
  num.digits = 2,
  num.colors = 50,
  main,
  xlab,
  ylab,
  legend.width,
  legend.mar,
  legend.shrink,
  cex.lab,
  cex.axis,
  cex.text,
  color.palette = heat.colors
)

Arguments

Details

This is a function using basic R graphics to visualise a social contact matrix.

Author(s)

Lander Willem

Examples

## Not run: 
data(polymod)
mij <- contact_matrix(
  polymod,
  countries = "United Kingdom",
  age_limits = c(0, 18, 65)
)$matrix
matrix_plot(mij)

## End(Not run)

Description

Divides each column of the contact matrix by the population of the contacted group, so that entry (a, b) becomes the mean number of contacts a member of group a makes with a single individual of group b. Multi-grouping matrices are handled the same way, with each combination of grouping levels treated as a group.

Usage

per_capita(x, survey_pop)

Arguments

Value

x with ⁠$matrix⁠ replaced by the per-capita version

Population data

survey_pop is a data frame with one column per grouping, named after the grouping (e.g. age, gender) and holding that grouping's levels as they appear in the matrix, plus a population column with the size of each combination. One row per combination of levels is required, and levels are matched to the matrix exactly, without interpolation.

Use align_ages() to build this from a raw population table: it aggregates each grouping to the matrix's levels, coarsening the age grouping where needed, and labels the columns to match. A population coarser than the matrix's age groups is an error rather than being split.

Examples

data(polymod)
result <- polymod |>
  (\(s) s[country == "United Kingdom"])() |>
  assign_age_groups(age_limits = c(0, 5, 15)) |>
  compute_matrix()
uk_pop <- data.frame(
  age = limits_to_age_groups(0:80, notation = "brackets"),
  population = rep(1e5, 81)
)
result |> per_capita(survey_pop = align_ages(uk_pop, result))

Description

A dataset containing social mixing diary data from 8 European countries: Belgium, Germany, Finland, Great Britain, Italy, Luxembourg, The Netherlands and Poland. The Data are fully described in Mossong J, Hens N, Jit M, Beutels P, Auranen K, Mikolajczyk R, et al. (2008) Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases. PLoS Med 5(3): e74.

Usage

polymod

Format

A list of two data frames:

participants

the study participant, with age, country, year and day of the week (starting with 1 = Monday)

contacts

reported contacts of the study participants. The variable phys_contact has two levels (1 denotes physical contact while 2 denotes non-physical contact), duration_multi has five levels (1 is less than 5 minutes while 5 is more than 4 hours, increasing in the order found in Figure 1 in Mossong et al.), and frequency_multi has five levels (1 is daily, 2 is weekly, 3 is monthly, 4 is less often, and 5 is first time)

All other variables are described on the Zenodo repository of the data, available at doi:10.5281/zenodo.1043437

Source

doi:10.1371/journal.pmed.0050074

Description

Rebins a population table to the coarser age groups defined by age_limits, summing populations into each group. Errors if age_limits are finer than the population data, since splitting a band would require assuming a within-band age distribution. Operates on an age column of age-group labels (e.g. from limits_to_age_groups() or assign_age_groups()) and returns the same form.

To align a population to a contact matrix's groupings for the post-processing functions, use align_ages() instead.

Usage

rebin_ages(pop, age_limits)

Arguments

Value

a data frame with an age column of age-group labels and a population column

Examples

it_pop <- data.frame(
  age = limits_to_age_groups(seq(0, 80, by = 5), notation = "brackets"),
  population = c(rep(2.5e6, 4), rep(3.5e6, 4), rep(5e6, 6), 5e6, 7e6, 4e6)
)
# rebin into 10-year age groups
rebin_ages(it_pop, age_limits = seq(0, 100, by = 10))

Description

Operates on lower limits

Usage

reduce_age_groups(x, limits)

Arguments

Value

vector with the new age groups

Examples

reduce_age_groups(seq_len(20), c(0, 5, 10))

Description

Splits the contact matrix into the mean number of contacts across the whole population (mean.contacts), a normalisation constant (normalisation), age-specific contact rates (contacts), and an assortativity matrix (replacing ⁠$matrix⁠). For details, see the "Getting Started" vignette.

Usage

split_matrix(x, survey_pop)

Arguments

Details

split_matrix() supports single-grouping (rank-2) matrices only.

Value

x with ⁠$matrix⁠ replaced by the assortativity matrix, plus additional elements ⁠$mean.contacts⁠, ⁠$normalisation⁠, and ⁠$contacts⁠

Population data

survey_pop is a data frame with one column per grouping, named after the grouping (e.g. age, gender) and holding that grouping's levels as they appear in the matrix, plus a population column with the size of each combination. One row per combination of levels is required, and levels are matched to the matrix exactly, without interpolation.

Use align_ages() to build this from a raw population table: it aggregates each grouping to the matrix's levels, coarsening the age grouping where needed, and labels the columns to match. A population coarser than the matrix's age groups is an error rather than being split.

Examples

data(polymod)
result <- polymod |>
  (\(s) s[country == "United Kingdom"])() |>
  assign_age_groups(age_limits = c(0, 5, 15)) |>
  compute_matrix()
uk_pop <- data.frame(
  age = limits_to_age_groups(0:80, notation = "brackets"),
  population = rep(1e5, 81)
)
result |> split_matrix(survey_pop = align_ages(uk_pop, result))

Description

survey() is defunct. Use as_contact_survey() instead.

Usage

survey(participants, contacts, reference = NULL)

Arguments

Value

Always errors.

Author(s)

Sebastian Funk

See Also

as_contact_survey()

Description

Usage

survey_countries(survey, country.column = "country", ...)

Arguments

Details

survey_countries() is defunct. Use contactsurveys::download_survey() and load_survey() and then explore the country column yourself.

Value

Always errors.

Examples

## Not run: 
doi_peru <- "10.5281/zenodo.1095664" # nolint
# download the data with the contactsurveys package
peru_survey <- contactsurveys::download_survey(doi_peru)
# load the survey with socialmixr
peru_data <- socialmixr::load_survey(peru_survey)
# find the unique country - assuming your data has a "country" column:
unique(peru_data$participants$country)

## End(Not run)

Description

survey_country_population() is defunct alongside wpp_age(), which it wrapped. Construct a data.frame with columns lower.age.limit and population from a current source (e.g. the wpp2024 package from GitHub) and pass it to contact_matrix() via the survey_pop argument instead.

Usage

survey_country_population(survey, countries = NULL)

Arguments

Value

Always errors.

Examples

## Not run: 
survey_country_population(polymod, countries = "Belgium")

## End(Not run)

Description

Makes a contact matrix symmetric so that $c_{ab} N_a = c_{ba} N_b$, where $c_{ab}$ is the (a, b) entry and $N_a$ is the population of group a. Each pair is replaced by half their sum, weighted by population size. Reciprocity requires that each grouping has the same levels on the participant and contact side; if not, the function aborts.

Usage

symmetrise(x, survey_pop, symmetric_norm_threshold = 2)

Arguments

Value

x with ⁠$matrix⁠ replaced by the symmetrised version

Population data

survey_pop is a data frame with one column per grouping, named after the grouping (e.g. age, gender) and holding that grouping's levels as they appear in the matrix, plus a population column with the size of each combination. One row per combination of levels is required, and levels are matched to the matrix exactly, without interpolation.

Use align_ages() to build this from a raw population table: it aggregates each grouping to the matrix's levels, coarsening the age grouping where needed, and labels the columns to match. A population coarser than the matrix's age groups is an error rather than being split.

Examples

data(polymod)
result <- polymod |>
  (\(s) s[country == "United Kingdom"])() |>
  assign_age_groups(age_limits = c(0, 5, 15)) |>
  compute_matrix()
uk_pop <- data.frame(
  age = limits_to_age_groups(0:80, notation = "brackets"),
  population = rep(1e5, 81)
)
result |> symmetrise(survey_pop = align_ages(uk_pop, result))

Description

weigh() multiplies participant weights by values looked up from a target. The existing weight column is multiplied in place, so multiple calls compose; if no weight column is present, one is created with value 1.

weigh_by_dayofweek() and weigh_by_age() are thin convenience wrappers around the two most common recipes — the weekly weekday/weekend split and age post-stratification against a reference population. See the dedicated sections below for what they compute exactly.

Usage

weigh(survey, by, target = NULL, groups = NULL, ...)

weigh_by_dayofweek(survey)

weigh_by_age(survey, pop, ...)

Arguments

Value

the survey object with updated participant weights

Target shapes accepted by weigh()

• target = NULL (the default) — multiply the numeric column by directly into weight. Useful when participants already carry a precomputed weight column.

• a two-column data frame whose key column is named by — pure discrete join: multiply the value column into weight where the key matches. Unmatched values get NA (with a warning).

• an unnamed numeric vector together with groups — each element of target is the total weight assigned across participants matching the corresponding entry in groups. The per-participant factor is target[g] / n_in_group.

• a named numeric vector — same as above but names(target) are matched against values of the by column.

A data frame target that does not have a column named by but does have lower.age.limit and population is and errors. Use weigh_by_age() instead, which post-stratifies by age explicitly. It takes the population with an age column of group labels, so convert a lower.age.limit table with limits_to_age_groups() first. It weights at the population's own age bands, where the old path first rebinned to single years, so the two agree for a population already in single-year bands and diverge for a coarser one.

weigh_by_dayofweek()

Rescales weights so that weekday participants together carry a total weight of 5 and weekend participants a total weight of 2 — the weekly 5/2 split that corrects for the typical over-representation of weekdays in diary surveys. Concretely, each weekday participant gets 5 / n_weekday and each weekend participant 2 / n_weekend; participants with NA day-of-week get the neutral average 7 / N. The dayofweek column is taken to use 0 = Sunday through 6 = Saturday (the POLYMOD convention).

Equivalent to: weigh(survey, "dayofweek", target = c(5, 2), groups = list(1:5, c(0, 6)))

weigh_by_age()

Convenience wrapper for age post-stratification. Participants are binned into the reference population's own age bands (whatever resolution pop is supplied at) and, for each band $b$, the weight becomes

$w_b = \frac{P_b / P}{N_b / N},$

where $P_b$ is the target population in band $b$, $P$ the total, and $N_b$, $N$ the corresponding sample counts. No interpolation is performed, so the weighting resolution is that of the supplied reference population.

survey must already have been processed by assign_age_groups() so that a part_age column is available for the join.

Examples

data(polymod)
uk <- polymod[country == "United Kingdom"] |>
  assign_age_groups(age_limits = c(0, 5, 15))

# --- target = NULL ---
# Multiply an existing numeric column directly into the weight:
uk |> weigh("hh_size")

# --- data-frame target (discrete join) ---
# The key column of `target` must match `by`. Each participant
# has its weight multiplied by the matching value column.
age_target <- data.frame(
  age.group = c("[0,5)", "[5,15)", "[15,Inf)"),
  p = c(0.06, 0.12, 0.82)
)
uk |> weigh("age.group", target = age_target)

# Same idea, joining on `country` to pool participants across studies
# by a target population share:
country_target <- data.frame(
  country = c("United Kingdom", "Germany", "Italy"),
  p = c(0.3, 0.4, 0.3)
)
polymod |>
  assign_age_groups(age_limits = c(0, 5, 15)) |>
  weigh("country", target = country_target)

# --- unnamed vector + groups (total-weight semantics) ---
# Each `target[g]` is the *total* weight assigned to participants in
# `groups[[g]]`. Here weekdays together carry weight 5, weekend days
# together carry weight 2:
uk |> weigh("dayofweek", target = c(5, 2), groups = list(1:5, c(0, 6)))

# The same is available as the convenience:
uk |> weigh_by_dayofweek()

# --- named vector ---
# `names(target)` are matched against `by` values; each value is the
# total weight for participants with that key.
uk$participants[, agecat := ifelse(part_age < 18, "child", "adult")]
uk |> weigh("agecat", target = c(child = 0.25, adult = 0.75))

# --- age post-stratification ---
uk_pop <- data.frame(
  age = limits_to_age_groups(c(0, 5, 15, 65), notation = "brackets"),
  population = c(3500000, 6000000, 40000000, 10000000)
)
uk |> weigh_by_age(uk_pop)

Description

wpp_age() is defunct. Pass population data directly to contact_matrix() via the survey_pop argument instead, as a data frame with columns lower.age.limit and population.

Usage

wpp_age(countries, years)

Arguments

Value

Always errors.

Examples

## Not run: 
# population data now comes from a source of your choosing, for example
# the wpp2024 package (GitHub only):
# remotes::install_github("PPgp/wpp2024")
library(wpp2024)
data(popAge1dt)
uk_pop <- popAge1dt[
  name == "United Kingdom" & year == 2020,
  .(lower.age.limit = age, population = pop * 1000)
]
contact_matrix(
  polymod,
  countries = "United Kingdom",
  survey_pop = uk_pop,
  symmetric = TRUE
)

## End(Not run)

Description

wpp_countries() is defunct. Pass population data directly to contact_matrix() via the survey_pop argument instead, which removes the need for a country list.

Usage

wpp_countries()

Value

Always errors.

Examples

## Not run: 
wpp_countries()

## End(Not run)