R/serosurvey_viz.R

In avallecam/serosurvey: Serological Survey Analysis For Prevalence Estimation Under Misclassification

#' @title Visualization of proportions
#'
#' @description visualization of proportions
#'
#' @describeIn ggplot_prevalence ggplot2 visualization of proportions
#'
#' @param data input tibble
#' @param denominator_level - denominator values column
#' @param numerator - numerator variable name column
#' @param proportion point estimate
#' @param proportion_upp upper interval
#' @param proportion_low lower interval
#' #param breaks_n number of breaks in axis
#'
#' @import skimr
#' @import tidyverse
#' @import ggplot2
#'
#' @export ggplot_prevalence
#'
#' @examples
#'
#' # not yet
#' # go to workflow:
#' # https://avallecam.github.io/serosurvey/articles/howto-reprex.html
#'

ggplot_prevalence <- function(data,
                              denominator_level,
                              numerator,
                              proportion,
                              proportion_upp,
                              proportion_low#,
                              # breaks_n=5
                              ) {
  data %>%
    ggplot(aes(x = {{denominator_level}},
               y = {{proportion}},
               color={{numerator}},
               group={{numerator}})) +
    # geom_point(aes(size=proportion_cv),position = position_dodge(width = 0.5)) +
    geom_point(position = position_dodge(width = 0.5)) +
    geom_errorbar(aes(max={{proportion_upp}},min={{proportion_low}}),
                  position = position_dodge(width = 0.5)) #+
  # scale_y_continuous(#labels = scales::percent_format(accuracy = 1),
  # breaks = scales::pretty_breaks(n = {{breaks_n}})) #+
  # scale_size_continuous(labels = scales::percent_format()) +
  # theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
}


#' #' #param category denominator level
#' #param outcome numerator variable
#' #export ggplot_prevalence_ii
#' ggplot_prevalence <- function(data,category,outcome,
#'                               proportion,proportion_upp,proportion_low,
#'                               breaks_n=5) {
#'   data %>%
#'     ggplot(aes(x = {{category}},
#'                y = {{proportion}},
#'                color={{outcome}},
#'                group={{outcome}})) +
#'     # geom_point(aes(size=proportion_cv),position = position_dodge(width = 0.5)) +
#'     geom_point(position = position_dodge(width = 0.5)) +
#'     geom_errorbar(aes(max={{proportion_upp}},min={{proportion_low}}),
#'                   position = position_dodge(width = 0.5)) +
#'     scale_y_continuous(labels = scales::percent_format(accuracy = 1),
#'                        breaks = scales::pretty_breaks(n = {{breaks_n}})) +
#'     # scale_size_continuous(labels = scales::percent_format()) +
#'     theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#' }
#'
#' #' describeIn ggplot_prevalence ggplot_prevalence with new arguments
#' #' inheritParams ggplot_prevalence
#' #' param denominator_level - denominator values column
#' #' param numerator - numerator variable name column