From marc_schwartz at me.com  Fri Jan 27 16:27:43 2017
From: marc_schwartz at me.com (Marc Schwartz)
Date: Fri, 27 Jan 2017 09:27:43 -0600
Subject: [Rd] Suggestion: barplot function
In-Reply-To: <519488518da576043b52b5bd2c962a7a@lycos.com>
References: <519488518da576043b52b5bd2c962a7a@lycos.com>
Message-ID: <CA798A7B-B6C5-407B-9F1D-72CD080CE2C7@me.com>


> On Jan 27, 2017, at 8:30 AM, danielrenato at lycos.com wrote:
> 
> Hello developers folks!
> 
> First, congratulations for the wonderful work with R.
> 
> For science, barplots with error bars are very important. We were wondering that is so easy to use the boxplot function:
> 
> boxplot(Spores~treatment, col=treatment_colors)
> 
> But there is no such function for barplots with standard deviation or standard error. It becomes a "journey" to plot a simple graph (e.g. https://www.r-bloggers.com/building-barplots-with-error-bars/).
> 
> The same way that is easy to use the boxplot function, do you think it is possible to upgrade the barplot function: i.e.: barplot(Spores~treatment, error.bar=standard_error, col=treatment_colors)
> 
> Thank you so much!
> Daniel, FU-Berlin


Hi,

With the caveat that I do not speak on behalf of R Core:

Boxplots are specifically designed to include "whiskers" (NOT error bars) that aid to visually describe the distribution of continuous data. The whiskers do not represent standard deviations (SDs). Thus, that the boxplot() function contains the code to draw the whiskers automatically is not relevant to barplot().

Barplots are best used to visually present tabulations of categorical data (e.g. counts or percentages), in which case, the "error" bars would typically represent binomial or similar confidence intervals. Even there, many will advocate that dotplots be used instead as a better presentation format, as barplots, much like pie charts, have a high "ink to data" ratio.

Barplots should not really be used to present continuous data (e.g. means and SDs).

You will find a great deal of disagreement with your premise that barplots with error bars are very important to science. If you do a Google search for "Dynamite Plot", especially where only the upper error bar is included, you will find a variety of critical discussions on that point, such as:

  http://biostat.mc.vanderbilt.edu/wiki/pub/Main/TatsukiRcode/Poster3.pdf <http://biostat.mc.vanderbilt.edu/wiki/pub/Main/TatsukiRcode/Poster3.pdf>

You pointed to one example of how easy it is to actually add error bars to a barplot in R, and that approach, of incrementally building plots using multiple functions, is an integral part of R's philosophy. There is also an example in ?barplot.

Generally, R's default approaches to most analyses are extremely well reasoned. Thus, if you don't see something in a function by default, there is generally strong logic behind what is being done, or as in this case, not being done.

If you wanted to, it would be a reasonable exercise for you to create your own plotting function that wraps barplot() and either segments() or arrows() in a single function call, where you can pass arguments that contain the values for the various components and draw the plot as you desire. That is how a lot of R code is created.

There are other graphic functions in R packages, such as ggplot2 (https://www.r-bloggers.com/using-r-barplot-with-ggplot2/ <https://www.r-bloggers.com/using-r-barplot-with-ggplot2/>) and others on CRAN that offer methods to add error bars to barplots that others have created if you wanted to research those.

As a result of all of the above, I am not sure that, after all these years, error bars would be added to barplot() as a standard feature.

Regards,

Marc Schwartz


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[Rd] Suggestion: barplot function
Marc Schwartz