R/feedback_plotting.R

In rubenarslan/formr: formr survey framework accompanying R package

#' Plot a normed value on the standard normal
#'
#' Pass in a z-standardised value (x - Mean)/SD,
#' get a standard normal distribution. 
#'
#' @param normed_value a z-standardised value
#' @param ylab Y-axis label, defaults to "Percentage of other people with this value"
#' @param xlab X-axis label, empty by default, useful for labeling the plotted trait
#' @param colour defaults to blue
#' @param x_ticks the ticks labels for -2,1,0,1 and 2 SDs around the mean, default to minuses, pluses and the average sign 
#' @export
#' @import ggplot2
#' @examples
#' normed_value = scale(x = 20, center = 14, scale = 5) # standardise value
#' qplot_on_normal(normed_value, xlab = "Extraversion")

qplot_on_normal = function(normed_value, ylab = "Percentage of other people with this value", 
                          xlab = '', colour = "blue", x_ticks = c('--','-','0','+','++')) 
{
  # Create base plot with proper labels
  ggplot(data.frame(x = -3:3), aes(x = .data$x)) +
    stat_function(fun = stats::dnorm, linewidth = 1) +
    geom_vline(xintercept = normed_value, colour = colour, linewidth = 1) +
    scale_x_continuous(breaks = c(-2:2), labels = x_ticks) +
    scale_y_continuous(labels = scales::percent_format()) +
    labs(x = xlab, y = ylab) +  # Use labs() to set labels properly
    theme_minimal() + 
    theme(text = element_text(size = 18))
}


#' Text feedback based on groups
#'
#' If you pass in a z-standardised value (x - Mean)/SD,
#' and a vector of feedback text chunks, that has either three
#' or five elements, the text chunks will be used in this order
#' \[very low\], low, average, high, \[very high\] corresponding to these
#' intervals \[low, -2\], \[-2, -1\], \[-1, 1\], \[1, 2\], \[2, high\]
#'
#' @param normed_value a z-standardised value
#' @param chunks a three or five element long character vector containing the text chunks for feedback
#' @export
#' @examples
#' feedback_chunk(normed_value = 0.7, chunks = c("You are rather introverted.",
#' "You're approximately as extraverted as most people.","You are rather extraverted."))

feedback_chunk = function(normed_value,  chunks) 
{
	chunks = as.character(chunks)
	if(! (length(chunks) %in% c(3,5))) stop("Have to provide either three or five chunks.")
	if(length(chunks) == 3) chunks = c(chunks[1], chunks, chunks[3]) # recycle
	
	if(normed_value <= -2)      chunks[1]
	else if(normed_value <= -1) chunks[2]
	else if(normed_value <= 1)  chunks[3]
	else if(normed_value <= 2)  chunks[4]
	else                        chunks[5]
}

#' Plot normed values as a barchart
#'
#' Pass in a data.frame with z-standardised values (x - Mean)/SD,
#' and variable names, get a bar chart. Getting your data.frame into this shape
#' probably will mean using tidyr and dplyr
#' If the data.frame has an se column or ymax/ymin columns, these will be displayed 
#' on top of the bars and the bars will become transparent.
#'
#' @param normed_data a dataset with a value column containing z-standardised value 
#' and a variable column containing labels for those values
#' @param ylab Y-axis label, defaults to "Percentage of other people with this value"
#' @param xlab X-axis label, empty by default, useful for labeling the plotted trait
#' @param title Plot title
#' @param y_ticks the ticks labels for -2,1,0,1 and 2 SDs around the mean, default to 
#' minuses, pluses and the average sign 
#' @export
#' @import ggplot2
#' @examples
#' normed_data = data.frame(variable = c("Extraversion","Openness",
#' "Agreeableness","Neuroticism","Conscientiousness"), 
#' value = c(-3,1,-1,0.5,2)) # standardise value
#' qplot_on_bar(normed_data, title = "Your personality")
#' normed_data = data.frame(variable = c("Extraversion","Openness",
#' "Agreeableness","Neuroticism","Conscientiousness"), 
#' value = c(-3,1,-1,0.5,2), se = c(0.2,0.3,0.2,0.25,0.4)) # standardise value
#' qplot_on_bar(normed_data, title = "Your personality")

qplot_on_bar = function(normed_data, ylab = "Your value", xlab = "Trait", title = '', 
                       y_ticks = c('--','-','0','+','++'))
{
  if(!all(c("value","variable") %in% names(normed_data))) stop("Malformed file, check help.")
  if(exists("se", where = normed_data))
  {
    normed_data$ymin = normed_data$value - normed_data$se
    normed_data$ymax = normed_data$value + normed_data$se
  }
  plot = 
    ggplot(normed_data, aes(x = .data$variable, y = .data$value, fill = .data$variable)) +
    labs(x = xlab, y = ylab, title = title) +  # Use labs() to set labels properly
    scale_fill_brewer("", palette = "Set1") +
    scale_y_continuous(breaks = c(-2,-1,0,1,2), labels = y_ticks) +
    theme_minimal() + 
    theme(text = element_text(size = 18)) +
    expand_limits(y = c(-2.5,2.5))
  
  if(exists("ymin", where = normed_data)) {
    plot + 
      geom_linerange(aes(ymin = .data$ymin, ymax = .data$ymax, colour = .data$variable), linewidth = 1) + 
      scale_colour_brewer("", palette = "Set1") + 
      geom_bar(stat = "identity", position = position_dodge(), alpha = 0.7)
  } else {
    plot + geom_bar(stat = "identity", position = position_dodge())
  }
}


#' Time-polar plot
#'
#' Pass in a data.frame with z-standardised values (x - Mean)/SD,
#' and variable names, get a bar chart. Getting your data.frame into this shape
#' probably will mean using tidyr + dplyr.
#' If the data.frame has an se column or ymax/ymin columns, these will be displayed on top of the bars and the bars will become transparent.
#'
#' @param normed_data a dataset with a value column containing z-standardised value and a variable column containing labels for those values
#' @param ylab Y-axis label, defaults to "Percentage of other people with this value"
#' @param title Plot title
#' @export
#' @import ggplot2
#' @examples
#' weekdays = c("Monday","Tuesday","Wednesday","Thursday","Friday","Saturday","Sunday")
#' normed_data = data.frame(variable = factor(weekdays, weekdays), 
#' 	value = c(0,1,0.2,0.5,1.5,2,1)) # standardise value
#' qplot_on_polar(normed_data, title = "Your alcohol consumption across the week")
#' normed_data = data.frame(variable = factor(1:24,1:24), 
#' 	value = 3+rnorm(24), se = rep(0.2,24)) # standardise value
#' qplot_on_polar(normed_data, title = "Your mood around the clock")

qplot_on_polar = function(normed_data, ylab = "Your value", title = '')
{
  if(!all(c("value","variable") %in% names(normed_data))) stop("Malformed file, check help.")
  
  if(exists("se", where = normed_data))
  {
    normed_data$ymin = normed_data$value - normed_data$se
    normed_data$ymax = normed_data$value + normed_data$se
  }
  plot = 
    ggplot(normed_data, aes(x = .data$variable, y = .data$value, fill = .data$value)) +
    ggtitle(title) +
    scale_y_continuous("", breaks = c()) +
    xlab("") +
    scale_fill_continuous(name = ylab) +
    theme_minimal() + 
    theme(text = element_text(size = 18)) +
    coord_polar()
  
  if(exists("ymin", where = normed_data)) {
    plot + 
      geom_linerange(aes(ymin = .data$ymin, ymax = .data$ymax, colour = .data$value), linewidth = 1) + 
      geom_bar(stat = "identity", position = position_dodge(), alpha = 0.7) + 
      scale_colour_continuous(name = ylab)
  } else {
    plot + geom_bar(stat = "identity", position = position_dodge())
  }
}