rename class vip_population -> popim_population

NAMESPACE

@@ -5,8 +5,8 @@ export(apply_vaccs)
 export(calc_pop_immunity)
 export(complete_vacc_activities)
 export(plot_immunity)
+export(popim_population)
 export(read_vacc_activities)
 export(vacc_from_immunity)
 export(vip_pop_from_file)
-export(vip_population)
 importFrom(rlang,.data)

R/apply_vacc.R

@@ -3,11 +3,11 @@
 ##' If the vaccination activity has non-missing entries for both
 ##' coverage and doses, the coverage is used - there is no test
 ##' whether the coverage and doses information are consistent with the
-##' target population size in the vip_population object to which the
+##' target population size in the popim_population object to which the
 ##' vaccination activity is applied.
 ##'
 ##' @param pop_df population dataframe object such as created by
-##'     function 'vip_population'
+##'     function 'popim_population'
 ##' @param region region of the vaccination activity
 ##' @param year year of the vaccination activity
 ##' @param age_first age of the youngest age group targeted
@@ -71,11 +71,11 @@ apply_vacc <- function(pop_df, region, year, age_first = 0, age_last = Inf,
 ##'
 ##' Takes the vaccination activities listed in the input object
 ##' vaccs_df (a data.frame object of class vip_vacc_activities), and
-##' applies them to the vip_population object pop_df, then returns
+##' applies them to the popim_population object pop_df, then returns
 ##' that updated object.
 ##'
-##' @param pop_df object of class vip_population such as created by
-##'     function 'vip_population'
+##' @param pop_df object of class popim_population such as created by
+##'     function 'popim_population'
 ##' @param vaccs_df object of class 'vip_vacc_activities' such as
 ##'     created by reading from file with function
 ##'     'read_vacc_activities'
@@ -106,8 +106,8 @@ apply_vaccs <- function(pop_df, vaccs_df) {
 ##' given number of doses. The target population is the sum of the
 ##' population of the targeted age groups in the targeted region(s).
 ##'
-##' @param pop_df object of class vip_population such as created by
-##'     function `vip_population`
+##' @param pop_df object of class popim_population such as created by
+##'     function `popim_population`
 ##' @param doses number of vaccine doses available for the vaccination
 ##'     activity
 ##' @param region region to be targeted
@@ -158,8 +158,8 @@ coverage_from_doses <- function(pop_df, doses, region, year, age_first = 0,
 ##' is the sum of the population of the targeted age groups in the
 ##' targeted region(s).
 ##'
-##' @param pop_df object of class vip_population such as created by
-##'     function `vip_population`
+##' @param pop_df object of class popim_population such as created by
+##'     function `popim_population`
 ##' @param coverage proportion of the target population to be covered,
 ##'     0 <= coverage <= 1
 ##' @param region region to be targeted

R/calc_new_immunity.R

@@ -57,11 +57,11 @@ assert_valid_targeting <- function(x, name = deparse(substitute(x))) {
 }
 
 ##' Calculate the overall population immunity (aggregating over age)
-##' from the supplied vip_population object
+##' from the supplied popim_population object
 ##'
-##' @param pop vip_population object for which the population size and
+##' @param pop popim_population object for which the population size and
 ##'     immunity will be aggregated over age.
-##' @return dataframe containing the vip_population aggregated by age.
+##' @return dataframe containing the popim_population aggregated by age.
 ##' @author Tini Garske
 ##' @export
 calc_pop_immunity <- function(pop) {

R/is_population.R

@@ -1,15 +1,15 @@
-##' Test if argument is a valid vip_population object
+##' Test if argument is a valid popim_population object
 ##'
 ##' This function tests whether the supplied argument is a valid
 ##' population dataframe such as might be generated by the function
-##' "vip_population".
+##' "popim_population".
 ##' @param x argument to be tested if it fulfils the requirements for
 ##'     a population dataframe.
 ##' @param tol numerical, determines the tolerance when comparing two
 ##'     numbers. Defaults to the square root of machine
 ##'     precision. This is used for checking if the cohort == year -
 ##'     age (as the cohort column is redundant).
-##' @return logical: TRUE if the argument is a valid vip_population
+##' @return logical: TRUE if the argument is a valid popim_population
 ##'     object, FALSE otherwise.
 ##' @author Tini Garske
 ##' @noRd

R/util_assert.R

@@ -125,8 +125,8 @@ assert_is <- function(x, what, name = deparse(substitute(x))) {
 }
 
 assert_population <- function(x, name = deparse(substitute(x))) {
-    if(!inherits(x, "vip_population"))
-        stop(sprintf("'%s' must be of class 'vip_population'", name),
+    if(!inherits(x, "popim_population"))
+        stop(sprintf("'%s' must be of class 'popim_population'", name),
              call. = FALSE)
 }
 

R/vacc_from_immunity.R

@@ -1,6 +1,6 @@
 ##' Infer vaccination activities from population
 ##'
-##' Given a vip_population object and an assumption of how vaccine is
+##' Given a popim_population object and an assumption of how vaccine is
 ##' targeted in a partially immune population, this function infers
 ##' the vaccination activities that returns a vip_vacc_activities
 ##' object that details the vaccination activities that have given
@@ -26,7 +26,7 @@
 ##' also never be picked up as this age group will have aged out of
 ##' the population (i.e., died) before the immunity is updated in the
 ##' next year.
-##' @param pop vip_population object for which vaccination activities
+##' @param pop popim_population object for which vaccination activities
 ##'     are to be inferred
 ##' @param targeting string to determine the assumption of how doses
 ##'     are allocated. Valid options are "random", "correlated",
@@ -68,10 +68,10 @@ vacc_from_immunity <- function(pop, targeting = "random", n_digits = 10) {
     vaccs
 }
 
-##' Add the rate of immunity change to a vip_population object
+##' Add the rate of immunity change to a popim_population object
 ##'
-##' @param pop vip_population_object
-##' @return the input vip_population object with an added column
+##' @param pop popim_population_object
+##' @return the input popim_population object with an added column
 ##'     `immunity_diff` that holds the difference in immunity between
 ##'     the current and next year.
 ##' @author Tini Garske

R/vip_vacc_activities.R

@@ -2,7 +2,7 @@
 ##'
 ##' The "vip_vacc_activities" object is a dataframe that holds
 ##' information on vaccination activities that are typically meant to
-##' be applied to a "vip_population" object.
+##' be applied to a "popim_population" object.
 ##'
 ##' The input parameters are the columns of the "vip_vacc_activities"
 ##' object to be returned, they should be vectors of the same length,
@@ -118,8 +118,8 @@ validate_vacc_activities <- function(x, name = deparse(substitute(x))) {
 ##' "vip_vacc_activities" object generated here), these can be
 ##' converted into each other, when both are given, they may be
 ##' inconsistent with each other once applied to a specific
-##' "vip_population" object. The consistency between these two colums
-##' cannot be confirmed in without reference to a vip_population
+##' "popim_population" object. The consistency between these two colums
+##' cannot be confirmed in without reference to a popim_population
 ##' object, but this function requires that at least one of these is
 ##' non-missing in each row.
 ##'
@@ -168,13 +168,13 @@ read_vacc_activities <- function(file) {
 ##'
 ##' For each line in the "vip_vacc_activities" object the given
 ##' information of coverage is converted to doses, or vice versa,
-##' using the target population size implied by the "vip_population"
+##' using the target population size implied by the "popim_population"
 ##' object supplied. If both coverage and doses are given for any
 ##' activity, the function checks if they are consistent with the
 ##' population size, and fails if there are any inconsistencies.
 ##'
 ##' @param vaccs vip_vaccination_activities object
-##' @param pop_df vip_population object
+##' @param pop_df popim_population object
 ##' @return vip_vaccination_activities object, updated to have both
 ##'     doses and coverage information
 ##' @author Tini Garske

README.Rmd

@@ -13,7 +13,7 @@ knitr::opts_chunk$set(
 )
 ```
 
-# vip
+# popim
 
 <!-- badges: start -->
 [![R-CMD-check](https://github.com/mrc-ide/vip/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/mrc-ide/vip/actions/workflows/R-CMD-check.yaml)
@@ -29,29 +29,29 @@ target specific age groups.
 
 ## Installation
 
-You can install the development version of vip from
+You can install the development version of popim from
 [GitHub](https://github.com/) with:
 
 ``` r
 # install.packages("devtools")
-devtools::install_github("mrc-ide/vip")
+devtools::install_github("mrc-ide/popim")
 ```
 
 ## Example 1
 
-This package defines an S3 class `vip_population` which is a dataframe
+This package defines an S3 class `popim_population` which is a dataframe
 with at least the columns year, age, cohort (the year of birth for the
 individuals tracked in this row), and immunity (as a proportion of
 this cohort). Year, age and cohort are tracked as annual time steps
 and age groups.
 
 A basic, totally naive population dataframe can be setup using the
-function `vip_population()`:
+function `popim_population()`:
 
 ```{r example}
-library(vip)
+library(popim)
 
-pop <- vip_population(region = c("UK", "FRA"), year_min = 2000, year_max = 2010,
+pop <- popim_population(region = c("UK", "FRA"), year_min = 2000, year_max = 2010,
                         age_min = 0, age_max = 10)
 
 dim(pop)
@@ -112,7 +112,7 @@ diagonal fashion.
 ## Example 2
 
 A more realistic scenario is to read in some real population data to
-set up the vip_population, and then apply some vaccination activities
+set up the popim_population, and then apply some vaccination activities
 to this. Information on the vaccination activities may be given in
 coverage (typical for routine infant vaccination) or doses (typical
 for mass vaccination campaigns).
@@ -198,7 +198,7 @@ ggplot(pop_agg, aes(x = year, y = immunity, col = region)) +
 
 ```
 
-Given a `vip_population` object, the vaccination activities that would
+Given a `popim_population` object, the vaccination activities that would
 be needed to achieve the specified population immunity can be inferred
 with the function `vacc_from_immunity()`, given an assumption on the
 targeting method of the vaccination activities. Using this function on

README.md

@@ -1,10 +1,11 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-# vip
+# popim
 
 <!-- badges: start -->
 
+[![R-CMD-check](https://github.com/mrc-ide/vip/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/mrc-ide/vip/actions/workflows/R-CMD-check.yaml)
 <!-- badges: end -->
 
 **Vaccine Immunity Propagation**
@@ -17,29 +18,29 @@ age groups.
 
 ## Installation
 
-You can install the development version of vip from
+You can install the development version of popim from
 [GitHub](https://github.com/) with:
 
 ``` r
 # install.packages("devtools")
-devtools::install_github("mrc-ide/vip")
+devtools::install_github("mrc-ide/popim")
 ```
 
 ## Example 1
 
-This package defines an S3 class `vip_population` which is a dataframe
+This package defines an S3 class `popim_population` which is a dataframe
 with at least the columns year, age, cohort (the year of birth for the
 individuals tracked in this row), and immunity (as a proportion of this
 cohort). Year, age and cohort are tracked as annual time steps and age
 groups.
 
 A basic, totally naive population dataframe can be setup using the
-function `vip_population()`:
+function `popim_population()`:
 
 ``` r
-library(vip)
+library(popim)
 
-pop <- vip_population(region = c("UK", "FRA"), year_min = 2000, year_max = 2010,
+pop <- popim_population(region = c("UK", "FRA"), year_min = 2000, year_max = 2010,
                         age_min = 0, age_max = 10)
 
 dim(pop)
@@ -115,7 +116,7 @@ fashion.
 ## Example 2
 
 A more realistic scenario is to read in some real population data to set
-up the vip\_population, and then apply some vaccination activities to
+up the popim\_population, and then apply some vaccination activities to
 this. Information on the vaccination activities may be given in coverage
 (typical for routine infant vaccination) or doses (typical for mass
 vaccination campaigns).
@@ -274,7 +275,7 @@ ggplot(pop_agg, aes(x = year, y = immunity, col = region)) +
 
 <img src="man/figures/README-calc_pop_immunity-1.png" width="100%" />
 
-Given a `vip_population` object, the vaccination activities that would
+Given a `popim_population` object, the vaccination activities that would
 be needed to achieve the specified population immunity can be inferred
 with the function `vacc_from_immunity()`, given an assumption on the
 targeting method of the vaccination activities. Using this function on