Merge branch 'main' into test-auto-doc-2

.Rbuildignore

@@ -21,3 +21,5 @@ allpopsamples_hlye.csv$
 ^serocalculator.*\.tgz$
 ^inst/extdata
 ^CRAN-SUBMISSION$
+^README\.qmd$
+^codecov\.yml$

DESCRIPTION

@@ -42,7 +42,8 @@ Suggests:
     readr,
     bookdown,
     ggbeeswarm,
-    DT
+    DT,
+    spelling
 LazyData: true
 Encoding: UTF-8
 URL: https://github.com/UCD-SERG/serocalculator, https://ucd-serg.github.io/serocalculator/

NAMESPACE

@@ -6,9 +6,18 @@ S3method(autoplot,pop_data)
 S3method(autoplot,seroincidence)
 S3method(autoplot,seroincidence.by)
 S3method(autoplot,summary.seroincidence.by)
+S3method(get_age,pop_data)
+S3method(get_age_var,pop_data)
+S3method(get_id,pop_data)
+S3method(get_id_var,pop_data)
+S3method(get_value,pop_data)
+S3method(get_value_var,pop_data)
 S3method(print,seroincidence)
 S3method(print,seroincidence.by)
 S3method(print,summary.seroincidence.by)
+S3method(set_age,pop_data)
+S3method(set_id,pop_data)
+S3method(set_value,pop_data)
 S3method(strata,seroincidence.by)
 S3method(summary,pop_data)
 S3method(summary,seroincidence)

R/load_pop_data.R

@@ -50,6 +50,7 @@ get_age <- function(object, ...) {
   UseMethod("get_age", object)
 }
 
+#' @export
 get_age.pop_data <- function(object, ...) {
   # get age data
   age_data <- object %>% pull(attr(object, "age_var"))
@@ -61,6 +62,7 @@ get_age_var <- function(object, ...) {
   UseMethod("get_age_var", object)
 }
 
+#' @export
 get_age_var.pop_data <- function(object, ...) {
   # get value attribute
   age_var <- attributes(object)$age_var
@@ -72,6 +74,7 @@ get_value <- function(object, ...) {
   UseMethod("get_value", object)
 }
 
+#' @export
 get_value.pop_data <- function(object, ...) {
   # get age data
   value_data <- object %>% pull(attr(object, "value_var"))
@@ -83,6 +86,7 @@ get_value_var <- function(object, ...) {
   UseMethod("get_value_var", object)
 }
 
+#' @export
 get_value_var.pop_data <- function(object, ...) {
   # get value attribute
   value_var <- attributes(object)$value_var
@@ -94,6 +98,7 @@ get_id <- function(object, ...) {
   UseMethod("get_id", object)
 }
 
+#' @export
 get_id.pop_data <- function(object, ...) {
   # get age data
   id_data <- object %>% pull(attr(object, "id_var"))
@@ -105,17 +110,20 @@ get_id_var <- function(object, ...) {
   UseMethod("get_id_var", object)
 }
 
+#' @export
 get_id_var.pop_data <- function(object, ...) {
   # get value attribute
   id_var <- attributes(object)$id_var
 
   return(id_var)
 }
 
+
 set_age <- function(object, ...) {
   UseMethod("set_age", object)
 }
 
+#' @export
 set_age.pop_data <- function(object, age = "Age", standardize = TRUE, ...) {
   # check if age column exists
   if (age %in% colnames(object)) {
@@ -163,6 +171,7 @@ set_value <- function(object, ...) {
   UseMethod("set_value", object)
 }
 
+#' @export
 set_value.pop_data <- function(object, value = "result", standardize = TRUE, ...) {
   # check if value column exists
   if (value %in% colnames(object)) {
@@ -209,6 +218,7 @@ set_id <- function(object, ...) {
   UseMethod("set_id", object)
 }
 
+#' @export
 set_id.pop_data <- function(object, id = "index_id", standardize = TRUE, ...) {
   # check if id column exists
   if (id %in% colnames(object)) {

inst/WORDLIST

@@ -1,134 +1,137 @@
-Aga
 AKU
-al
+Aga
 Ang
 Anik
 BMC
 Bonačić
 Bordetella
+CMD
 Campylobacter
-campylobacteriosis
 Ceper
 Chiang
-CMD
 De
-de
-der
-devtools
-df
 Dhulikhel
 Diekmann
-doi
 Duynhoven
 Eijkeren
-epidem
-et
-Falkenhorst
 FIC
+Falkenhorst
 Fogarty
 Friesema
-fx
 Gageldonk
 Graaf
 Gras
 Grolemund
 Hassen
 Heijden
-hemolysin
 Hessians
 HlyE
 IgA
 IgG
 IgM
-isos
-isotype
-isotypes
 Jernberg
-jinf
-jitter
 Juba
 Juma
-Kamenskaya
-kDa
 KGH
+Kamenskaya
 Kretzschmar
 Krogfelt
 Kuusi
 Linneberg
 Marinović
-mc
-mcmc
 Melker
 Mertens
-modelled
 Molbak
 Monge
 Mughini
 Muktadir
-multicohort
 Munira
-Nadu
 NIAID
 NLL
-nr
-Orientia
+Nadu
 OSF
-param
-params
-Pebody
+Orientia
 PLoS
-pmed
+Pebody
 Polina
+RStudio
 Rai
-recombinant
 Roord
-RStudio
 Rtools
 Rumunu
+SErologic
 Sadkowska
 Saha
 Saiful
 Sajib
 Sarkar
-savePath
 Schellekens
 Seidman
 Senjuti
 Sero
+Seroincidence
+Serological
+Simonsen
+Sira
+Sium
+Strid
+Syed
+TW
+Todys
+Valk
+Vellore
+Versteegh
+Wetering
+Wiens
+Zota
+al
+behaviour
+campylobacteriosis
+de
+der
+devtools
+doi
+epidem
+et
+fx
+hemolysin
+isos
+isotype
+isotypes
+jinf
+jitter
+kDa
+mc
+mcmc
+modelled
+multicohort
+nr
+param
+params
+pmed
+recombinant
+savePath
 seroconversion
 seroconversions
 seroepidemiology
-Seroincidence
 seroincidence
 seroincidences
-SErologic
 serologic
-Serological
 serological
 seroresponse
 seroresponses
 serosurvey
 serosurveys
-Simonsen
-Sira
-Sium
 smp
 smpl
-Strid
 subfigures
-Syed
-tbl
 tibble
 titers
-Todys
 tsutsugamushi
-TW
 unstratified
-Valk
 varepsilon
-Vellore
-Versteegh
-Wetering
-Wiens
-Zota
-
+SeroEpidemiology
+callout
+codecov
+geoms
+ggproto

tests/spelling.R

@@ -0,0 +1,3 @@
+if(requireNamespace('spelling', quietly = TRUE))
+  spelling::spell_check_test(vignettes = TRUE, error = TRUE,
+                             skip_on_cran = TRUE)

vignettes/articles/enteric_fever_example.Rmd

@@ -15,9 +15,9 @@ bibliography: references.bib
 ---
 ## Introduction
 
-This vignette provides users with an example analysis using the [**serocalculator**](https://github.com/UCD-SERG/serocalculator) package by reproducing the analysis for: [**Estimating typhoid incidence from community-based serosurveys: a multicohort study**](https://www.thelancet.com/journals/lanmic/article/PIIS2666-5247(22)00114-8/fulltext) (@Aiemjoy_2022_Lancet). We review the methods underlying the analysis and then walk through an example of enteric fever incidence in Pakistan. Note that because this is a simplied version of the analysis, the results here will differ slightly from those presented in the publication.
+This vignette provides users with an example analysis using the [**serocalculator**](https://github.com/UCD-SERG/serocalculator) package by reproducing the analysis for: [**Estimating typhoid incidence from community-based serosurveys: a multicohort study**](https://www.thelancet.com/journals/lanmic/article/PIIS2666-5247(22)00114-8/fulltext) (@Aiemjoy_2022_Lancet). We review the methods underlying the analysis and then walk through an example of enteric fever incidence in Pakistan. Note that because this is a simplified version of the analysis, the results here will differ slightly from those presented in the publication.
 
-In this example, users will determine the seroincidence of enteric fever in cross-sectional serosurveys conducted as part of the the SeroEpidemiology and Environmental Surveillance (SEES) for enteric fever study in Bangladesh, Nepal, and Pakistan. Longitudinal antibody responses were modeled from 1420 blood culture-confirmed enteric fever cases enrolled from the same countries. 
+In this example, users will determine the seroincidence of enteric fever in cross-sectional serosurveys conducted as part of the SeroEpidemiology and Environmental Surveillance (SEES) for enteric fever study in Bangladesh, Nepal, and Pakistan. Longitudinal antibody responses were modeled from 1420 blood culture-confirmed enteric fever cases enrolled from the same countries. 
 
 
 ```{r, include = FALSE}
@@ -33,8 +33,8 @@ knitr::opts_chunk$set(
 ### Load packages
 The first step in conducting this analysis is to load our necessary packages. If you haven't installed already, you will need to do so before loading. 
 
-```{r setup, message=FALSE}
-# devtools::install_github("ucd-serg/serocalculator", eval=FALSE)
+```{r setup, eval=FALSE}
+# devtools::install_github("ucd-serg/serocalculator")
 ```