R/underhill_single_curves.R

In davidclarance/africabirdmap: Functions to clean, analyze and plot Africa Bird Map data.

#' Plot the underhill smoother and associated confidence intervals for a species.
#'
#' Fit a line through the reporting rates and its associated 1 and 2 sigma confidence intervals. 1 and 2 sigma roughly correspond to 67 percent and 95 percent confidence intervals. The line is over Pentades defined in the `underhill_smoother()` function.
#'
#' The dark, thinner interval is the 1 sigma interval and corresponds to 67% confidence, whereas the 2 sigma interval corresponds to a 95% confidence.
#'
#' This function only takes in one dataframe and is intended for deep study of one species. To make multi species comparisons use `underhill_multiple_curves`
#'
#' @param underhill_smoother The dataframe returned by the `underhill_smoother function`.

#' @return A ggplot plot
#' @examples
#'
#' \dontrun{
#'
#'
#' underhill_curves(underhill_smoother)
#'
#' }
#'
#'
underhill_single_curves <- function(underhill_smoother){

  species_id = underhill_smoother$SpeciesId[1]
  species_name = underhill_smoother$SpeciesName[1]

  output_plot <- underhill_smoother %>%
    ggplot(aes(y = fit)) +
    theme_light() +
    # plot points, line and the intervals
    geom_point(aes(x = DateInYear, y = ReportingRate)) +
    geom_smooth(aes(x = DateInYear, ymin = fit - 2*se, ymax = fit + 2*se), stat = 'identity') +
    geom_smooth(aes(x = DateInYear, ymin = fit - 1*se, ymax = fit + 1*se), stat = 'identity') +
    # fix scales
    scale_x_date(labels = date_format("%b"), date_breaks = 'month') +
    scale_y_continuous(labels = percent_format(), limits = c(0, 1)) +
    # add labels
    xlab('Month') +
    ylab('Reporting rate') +
    ggtitle(paste0(species_id, ": ", species_name))

  output_plot
}