It’s a long tradition in statistical graphics going from Tufte back to Tukey and Cleveland to advise against using bar charts. Many folks, including me, have pejoratively called the common (in ecology, at least) bar chart + SE a “dynamite plot”. Although Ben Bolker has questioned the wisdom of this sentiment , I think in most cases they’re worth avoiding. (I discuss this more in “When and here are dynamite plots appropriate” below.)

Last week, Tracey Weissgerber and colleagues extend this tradition, making a great set of concrete recommendations in a perspective for PLoS Biology. Importantly, the authors also provided a set of Excel templates on CTSpedia (a cool-looking site for sharing resources related to clinical trials) that implement their recommendations in Excel.

This is great because in Excel making good graphics is really hard. So people don’t do it. Best practices have little appeal if they also involve lots of work! Fortunately in R, the recommended alternatives are built in, and even easier to use.

Here, I’ll provide some minimal code to make plots similar to those Weissgerber et al recommend, both for independent groups and paired data, using the built-in graphics of R.

Independent groups

For convenience, I’m using the built in CO2 dataset:

##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4
## 3   Qn1 Quebec nonchilled  250   34.8
## 4   Qn1 Quebec nonchilled  350   37.2
## 5   Qn1 Quebec nonchilled  500   35.3
## 6   Qn1 Quebec nonchilled  675   39.2

These data come from an experiment on cold tolerance in grasses from different regions, but the specifics here don’t matter. The data were first published in Ecology in 1990. See ?CO2 in R if you’d like to know more.

Mostly, I’ll plot CO2 concentration versus uptake or Type, the plant’s source region.

First, a bad ol’ dynamite, er bar plot:

bad bar plot

Figure 1: bad bar plot

(I’m not including the code for this, because it’s what I’m recommending against. Nor did I add error bars, so it’s not really a dynamite plot. R’s base graphics make both producing this plot and, especially, adding error bars to it, tedious compared to box plots or strip charts. Maybe this is a feature. External libraries like ggplot or gplots make such graphics a lot easier. See link at the end of this post.)

Scatter plots

The first type of plot is a univariate scatter plot. Most often, you’d want to plot a response against some observational or experimental factors. Another name for this type of plot is stripchart, which is what R calls it:

CO2 <- within(CO2, conc_f <- factor(conc))
y_limits <- c(0, max(CO2$uptake) * 1.15)
point_col <- gray(0.4)
stripchart(uptake ~ conc_f, CO2, method='jitter', pch=19, col=point_col,
           xlab='concentration', ylim=y_limits, vertical=TRUE)
stripchart: a scatter plot v factors

Figure 2: stripchart: a scatter plot v factors

It’s easy to jitter the points, as Weissgerber et al recommend, by passing the argument method='jitter'. But there are other options. For cases where there really isn’t much data, method='stack' gives something closer to a Wilkinson dot plot. This more clearly shows the values that were observed more than once:

stripchart(uptake ~ conc_f, CO2, method='stack', pch=19, col=point_col,
           xlab='concentration',  ylim=y_limits, vertical=TRUE)
stripchart with stacking

Figure 3: stripchart with stacking

Box (and whisker) plots

For box plots, R makes it very easy.

boxplot( uptake ~ conc_f , CO2, ylab='uptake', xlab='concentration', ylim=y_limits)

Figure 4: boxplot

In this case, with small data the boxplot is a bit misleading. This is clear from the scatter plots above, but you can also overplot onto the boxes using stripchart with add = TRUE, vertical = TRUE:

  boxplot( uptake ~ conc_f , CO2, ylab='uptake', xlab='concentration', ylim=y_limits)
  stripchart(uptake ~ conc_f, CO2, method = 'jitter', add = TRUE, vertical = TRUE,
             pch = 19)
boxplot with points overplotted

Figure 5: boxplot with points overplotted

Even with base graphics boxplot, you can pass functions of multiple independent variables. This means you can visualize interactions between treatments in your raw data, and even overplot with stripchart!

  boxplot( uptake ~ conc_f : Treatment, CO2, ylab='uptake', ylim=y_limits,
          xlab = "concentration within chilling treatment")
  stripchart(uptake ~ conc_f : Treatment, CO2, method = 'jitter', add = TRUE, vertical = TRUE,
             pch = 19)
complex bar plot

Figure 6: complex bar plot

Note it would be easier to read the labels here if the plot were horizontal, for which there’s an argument you can pass. The graphics settings on this post aren’t playing well with long labels, so I don’t evaluate this here:

op <- par(las = 1, mar = c(4, 8, 2, 1)) # all axis labels horizontal
   boxplot( uptake ~ conc_f %in% Treatment, CO2, xlab='uptake', horizontal=TRUE)

Paired data

The CO2 data aren’t paired. To look at paired scatter plots, I’ll use the built in sleep data, which show extra sleep for subjects taking two sleep aids.

##   extra group ID
## 1   0.7     1  1
## 2  -1.6     1  2
## 3  -0.2     1  3
## 4  -1.2     1  4
## 5  -0.1     1  5
## 6   3.4     1  6

The easiest way to make plots that link paired data is to again use stripchart as a base. Then, to add lines illustrating the pairs, one can use split and lines:

  stripchart(extra ~ group, sleep, pch=19, col=point_col,
             vertical=TRUE, ylab='extra sleep', xlab='drug received')
  for(ID in split(sleep, sleep$ID))
    lines(extra ~ group, ID)
paired points connected by lines and marked by points

Figure 7: paired points connected by lines and marked by points

If you’d rather only have the lines, just suppress plotting of points within the initial call to stripchart:

  stripchart(extra ~ group, sleep, pch="", vertical=TRUE,
             ylab='extra sleep', xlab='drug received')
  for(ID in split(sleep, sleep$ID))
    lines(extra ~ group, ID)
paired locations connected by lines

Figure 8: paired locations connected by lines

Other plots

R easily produces many other plots, in addition to those Weissgerber et al for which provide templates.

For example, say you’d like a histograms across subsets. Here’s one for uptake from the CO2 data for grass plants receiving chilling or not:

op <- par(mfrow=c(2, 1))
for(v in levels(CO2$Treatment)) {
  subs <- subset(CO2, Treatment == v)
  hist(subs$uptake, main = v, col = point_col, xlab = 'uptake', xlim = y_limits)
histogram of uptake

Figure 9: histogram of uptake


References: plotting libraries and examples

When and where are dynamite plots appropriate?

In addition to Ben’s post linked above, Solomon Messing has some nice reasons to choose dot plots for estimates +/- SE (three paragraphs beginning with “Why do I use dot plots…”). These boil down to:

  • bar charts emphasize comparison to zero, which can make comparison of small differences difficult
  • bars are often used in histograms, which can confuse some audiences
  • dot plots use more ink, and cognition, which causes the eye to compare the estimate with the baseline

I agree with Ben that this last feature, the implied reference to a baseline, means bar charts, can be very useful. But there’s a corollary here: only use this strength when comparison to a baseline is the point. Further, then, if your graphics are to be honest, they must start at a meaningful zero. So, avoid bar charts for estimated quantities. Unless, your main comparison is between estimates with different, or with magnitudes very close to zero.