#' @title Bland-Altman histogram and density plot
#'
#' @description Generates a combined histogram and density curve for Bland-Altman differences
#'
#' @author Deepankar Datta <deepankar.datta@gmail.com>
#'
#' @param statistics.results A list of statistics generated by the blandr.statistics function: see the function's return list to see what variables are passed to this function
#'
#' @include blandr.statistics.r
#'
#' @export
blandr.plot.normality <- function( statistics.results ) {
# We could do a histogram and density plot by the following
# hist( statistics.results$differences )
# plot( density( statistics.results$differences ) )
# qqnorm( results$differences )
# qqline( results$differences, col = 2 )
# However ggplot2 is so much more customisable
# ggplot can't use lists, so need to convert the results to a dataframe
results <- data.frame( statistics.results$differences )
# and rename
names(results)[1] <- "differences"
# Note that having ..density.. below results in a CRAN note
# NULLing it first to handle it -> is a bit hacky but works
# See: https://stackoverflow.com/questions/9439256/how-can-i-handle-r-cmd-check-no-visible-binding-for-global-variable-notes-when#12429344
..density.. <- NULL
# Create the histogram
normality.plot <- ggplot( results , aes( x = results$differences ) ) +
geom_histogram( aes(y=..density..) , colour="black", fill="white" ) +
geom_density( colour="red" ) +
ylab( "Density" ) +
xlab( "Differences") +
ggtitle("Histogram and density plot of differences")
return(normality.plot)
# END OF FUNCTION
}
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