Tag: boxplot

Avoid bar plots for continuous data! Do this instead:

Tracey Weissgerber, Natasa Milic, Stacey Winham, and Vesna Garovic wrote this interesting 2015 paper on bar graphs. By a systematic review of physiology research, they demonstrate we need to reconsider how we present continuous data in small samples.

Bar and line plots are commonly used to display continuous data. This is problematic, as many different data distributions can lead to the same bar or line graph. Nevertheless, the rarely used scatterplots, box plots, and histograms much better allow users to critically evaluate continuous data.

They provide many interesting visuals that underline their argument.

For instance, the four datasets below (B, C, D, and E) will all result in the same barplot (A), whereas they demonstrate quite different characteristics.

Alternatively, bar plots are often used for to display group means when observations within groups may not be independent. For instance, it could be that the bars below represent two measurement occassians, and that each of our sampled observations occurs in both. In that case, the scatterplots with connected dots may be more suitable. While the bars in plot A would represent datasets B, C, and D, these are clearly different when viewed in scatterplots.

Also, a lot of meaningful information is typically lost in bar plots. For instance, the number of observations in a group. But also the distribution of values. While the former can be added (see B below), the latter can much better be shown in a scatter plot like C (below).

Actually, in a later blog post, lead researcher Tracey Weissgerber shares the below visual. It highlights the distractive irrelevance of bar plot and the information that is lost (becomes invisible) when opting for a bar chart.

Tracey refactored this into a similar visual of her own:

So what can you do instead, you may ask yourself. To this question too, Tracey has an answer, sharing the below overview of alternatives options:

She made another overview which may help you pick the best visual for your data. This one takes your intention behind the visual as a starting point, though is unfortunately a bit low quality:

Add a self-explantory legend to your ggplot2 boxplots

Laura DeCicco found that non-R users keep asking her what her box plots exactly mean or demonstrate. In a recent blog post, she therefore breaks down the calculations into easy-to-follow chunks of code. Even better, she included the source code to make boxplots that come with a very elaborate default legend:

As you can see, the above contains much more and easier to understand information than the original ggplot2 boxplot below.

Laura wrote the custom function ggplot_box_legend() (see source code below and in Laura’s blog), which uses the cowplot package to paste the explanation to the box plot. All you need to do is call the legend function just before you run your ggplot2 boxplot call.

ggplot_box_legend <- function(family = "serif"){
  
  # Create data to use in the boxplot legend:
  set.seed(100)

  sample_df <- data.frame(parameter = "test",
                        values = sample(500))

  # Extend the top whisker a bit:
  sample_df$values[1:100] <- 701:800
  # Make sure there's only 1 lower outlier:
  sample_df$values[1] <- -350
  
  # Function to calculate important values:
  ggplot2_boxplot <- function(x){
  
    quartiles <- as.numeric(quantile(x, 
                                     probs = c(0.25, 0.5, 0.75)))
    
    names(quartiles) <- c("25th percentile", 
                          "50th percentile\n(median)",
                          "75th percentile")
    
    IQR <- diff(quartiles[c(1,3)])
  
    upper_whisker <- max(x[x < (quartiles[3] + 1.5 * IQR)])
    lower_whisker <- min(x[x > (quartiles[1] - 1.5 * IQR)])
      
    upper_dots <- x[x > (quartiles[3] + 1.5*IQR)]
    lower_dots <- x[x < (quartiles[1] - 1.5*IQR)]
  
    return(list("quartiles" = quartiles,
                "25th percentile" = as.numeric(quartiles[1]),
                "50th percentile\n(median)" = as.numeric(quartiles[2]),
                "75th percentile" = as.numeric(quartiles[3]),
                "IQR" = IQR,
                "upper_whisker" = upper_whisker,
                "lower_whisker" = lower_whisker,
                "upper_dots" = upper_dots,
                "lower_dots" = lower_dots))
  }
  
  # Get those values:
  ggplot_output <- ggplot2_boxplot(sample_df$values)
  
  # Lots of text in the legend, make it smaller and consistent font:
  update_geom_defaults("text", 
                     list(size = 3, 
                          hjust = 0,
                          family = family))
  # Labels don't inherit text:
  update_geom_defaults("label", 
                     list(size = 3, 
                          hjust = 0,
                          family = family))
  
  # Create the legend:
  # The main elements of the plot (the boxplot, error bars, and count)
  # are the easy part.
  # The text describing each of those takes a lot of fiddling to
  # get the location and style just right:
  explain_plot <- ggplot() +     stat_boxplot(data = sample_df,                  aes(x = parameter, y=values),                  geom ='errorbar', width = 0.3) +     geom_boxplot(data = sample_df,                  aes(x = parameter, y=values),                   width = 0.3, fill = "lightgrey") +     geom_text(aes(x = 1, y = 950, label = "500"), hjust = 0.5) +     geom_text(aes(x = 1.17, y = 950,                   label = "Number of values"),               fontface = "bold", vjust = 0.4) +     theme_minimal(base_size = 5, base_family = family) +     geom_segment(aes(x = 2.3, xend = 2.3,                       y = ggplot_output[["25th percentile"]],                       yend = ggplot_output[["75th percentile"]])) +     geom_segment(aes(x = 1.2, xend = 2.3,                       y = ggplot_output[["25th percentile"]],                       yend = ggplot_output[["25th percentile"]])) +     geom_segment(aes(x = 1.2, xend = 2.3,                       y = ggplot_output[["75th percentile"]],                       yend = ggplot_output[["75th percentile"]])) +     geom_text(aes(x = 2.4, y = ggplot_output[["50th percentile\n(median)"]]),                label = "Interquartile\nrange", fontface = "bold",               vjust = 0.4) +     geom_text(aes(x = c(1.17,1.17),                    y = c(ggplot_output[["upper_whisker"]],                         ggplot_output[["lower_whisker"]]),                    label = c("Largest value within 1.5 times\ninterquartile range above\n75th percentile",                             "Smallest value within 1.5 times\ninterquartile range below\n25th percentile")),                   fontface = "bold", vjust = 0.9) +     geom_text(aes(x = c(1.17),                    y =  ggplot_output[["lower_dots"]],                    label = "Outside value"),                vjust = 0.5, fontface = "bold") +     geom_text(aes(x = c(1.9),                    y =  ggplot_output[["lower_dots"]],                    label = "-Value is >1.5 times and"), 
              vjust = 0.5) +
    geom_text(aes(x = 1.17, 
                  y = ggplot_output[["lower_dots"]], 
                  label = "<3 times the interquartile range\nbeyond either end of the box"), 
              vjust = 1.5) +
    geom_label(aes(x = 1.17, y = ggplot_output[["quartiles"]], 
                  label = names(ggplot_output[["quartiles"]])),
              vjust = c(0.4,0.85,0.4), 
              fill = "white", label.size = 0) +
    ylab("") + xlab("") +
    theme(axis.text = element_blank(),
          axis.ticks = element_blank(),
          panel.grid = element_blank(),
          aspect.ratio = 4/3,
          plot.title = element_text(hjust = 0.5, size = 10)) +
    coord_cartesian(xlim = c(1.4,3.1), ylim = c(-600, 900)) +
    labs(title = "EXPLANATION")

  return(explain_plot) 
  
}

ggplot_box_legend()

The Dataviz Project: Find just the right visualization

Do you have a bunch of data but you can’t seem to figure out how to display it? Or looking for that one specific visualization of which you can’t remember the name?

www.datavizproject.com provides a most comprehensive overview of all the different ways to visualize your data. You can sort all options by Family, Input, Function, and Shape to find that one dataviz that best conveys your message.