Data Visualization – ggplot2 – the anatomy of a barchart – dimensions of the bar

As mentioned in my first post” this post is about how to create the chart below, or at least how to create a chart that looks similar to the one below.

anatomy of charts - part 1 - the barchart - 6

But before I come up with the R code that creates a similar chart, I want to provide some theory about bars in data visualization.

In his book “Show me the numbers (2nd edition)” Stephen Few describes a bar as

“… a line with the second dimension of width added to the line’s single dimension of length, which transforms the line into a rectangle. The width of the bar doesn’t usually mean anything; it simply makes each bar easy to see and compare to the others. The quantitative information represented by a bar is encoded by its length and also by its end in relation to a quantitative scale along an axis.

And then there is the article “Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods”  by William S. Cleveland and Robert McGill. In this article the authors order graphical representations of data by the accuracy these representations provide to their audience (the reader of a chart) regarding the “decoding” of information (this article can be found here and is in my opinion a must read, even it was written decades ago).

Cleveland and McGill describe that graphical representations of data can be best decoded if they

  • are positioned along a common scale (xaxis and yaxis) and if
  • the data is encoded by length

According to this article decoding from an area or volume is not that accurate.

And finally there is this concise statement from Hadley Wickham

“A Data Visualization is simply this: a mapping of data to aesthetics (color, shape, size) of geometric objects (points, lines, bars)” taken from “ggplot2 – Elegant Graphics for Data Analysis”.

The above mentioned statements in my mind and all the bar charts I created for different audiences and also looked at as a member of the targeted audience make me think that using just one quantitative variable is often a waste of precious “space”.

For this reason I often try to use one of the other two dimensions (besides the height, there are the width, and the area of a rectangle) to provide meaning. Commonly we interpret a column with a greater length / height in a bar chart more important than columns with a lesser height. Due to the fact, that the area of a graphical object is one of the graphical representations that provide lesser accuracy, I do not recommend using all three dimensions of a bar to provide some meaning like the following following: amount of goods sold (height) * average price (width) = sales (area).

I commonly use just a second “quantitative” variable to provide additional information, adding some extra insight: e.g. sales (height) and number of distinct customer contributing to the sales (width).

Before starting with some out of the box thinking, I’m using some data to create a simple stacked barchart – why stacked: a stacked barchart is able to encode 2 qualitative variables and one quantitave variable (okay – the same way as a dodged barchart, but I prefer stacked barcharts 🙂 ).

The data

dt.source <- data.table(

category = c(“A”, “A”, “B”, “B”, “C”, “C”, “D”),

anothercategory = c(“m”, “f”, “m”, “f”, “m”, “f”, “m”),

values = c(60, 40, 35, 25, 30, 55, 120),

values2 = c(20,20, 30,10,10, 90,2)


The R code to create a stacked barchart (using the ggplot2 package):

p <- ggplot()

p <- p + geom_bar(data = dt.source,

aes(x = attribute, y = measure, fill = attribute2)

,stat = “identity”

,position = “stack”



The code above creates the following chart:

a simple stacked barchart

If I want to use another quantitative variable (measure 2)  and change the R script a little

… aes(x = attribute, y = measure, fill = attribute2, width = measure2)

I got an error message. Simply said, it is not possible to use geom_bar(…) and give each individual segment an individual width.

But there is another geom_… that can be used to create the following chart – geom_linerange(…):

the anatomy of a chart - part 2  - geom_linerange


I have to admit that maybe it seems a little odd to use the geom_linerange(…) to create a barchart with 2 quantitave and 2 qualitative variables, but this is just another example of the endless possibilities of ggplot2.

Just some out of the box thinking.

First I have to add some additional columns, this is due to the parameters that have to be passed to the geom:

ymin (the starting point of a linerange) and
ymax (the ending point of a linerange)

ymin is simply calculated by determine previous value within a group

dt.source[, ymin := c(0, head(measure, -1)), by = list(attribute)]

The group is determined by “by = list(attribute)“, the by statement subsets the data.table (maybe it is helpful to picture a subset of a data.table as some kind of a SQL Window using a SQL statement like OVER(PARTITION BY …), if you think like that, than ” c(0, head(measure, -1))” is similar to the SQL statement LAG(measure, 1,0).

One line of the resulting dataset will look like this

   attribute attribute2 Measure  measure2 ymin  ymax
A m 60 20 0 60

The next step is to calculate the percentage of measure2 wihtin the group:

dt.source[,measure2.weighted := measure2 / sum(measure2),by = list(attribute)]

Basically thats all, the following R script

p <- ggplot()

p <- p + ggtitle(“A barchart where the width of \nthe bar has meaning”)

p <- p + geom_linerange(data = dt.source, aes(x = attribute, ymin = ymin, ymax = ymax, color = attribute2, size = as.factor(size)))


draws this chart

the anatomy of a chart - part 2 - a simple geom_linerange

Admittedly, this looks not completely like the chart I want to share with my audience, but all data preparation (data wrangling, data mincing) has been done.

Everything else is to provide the finishing touch.

I use 2 geom_text() geoms to add labels to the segments:

p <- p + geom_text(data = dt.source, aes(label = measure, x = attribute, y = measure.prev.val + measure), size = 3, color = “white”, vjust = 1.2)

p <- p + geom_text(data = dt.source2, aes(label = sumOfOuterGroup, x = attribute, y = sumOfOuterGroup), size = 3, color = “black”, vjust = -0.8)

and I’m fiddling with the “size” aesthetic of a linerange:

sizeFactor <- 100

size <- dt.source[order(measure2.weighted* 10), measure2.weighted]

size.label <- round(size * sizeFactor,2)

p <- p + scale_size_discrete(range = c(size)*sizeFactor , labels = unique(size.label) ,guide = guide_legend(title=”% of measure2\nwithin each group”, override.aes = list(color = “lightgrey”), order = 2))

and I’m fiddling with the color (this is definitely not for the fainthearted. I will cover this in much more detail in one of my upcoming posts 🙂

p <- p + scale_color_manual(values = c(m = “#3F5151”, f = “#9B110E”), guide = guide_legend(title = “the qualitative variable called:\nattribute2”, override.aes = list(size = 10), order = 1 ))

C’est ca!

You can download the complete R script from this Dropbox link

A final word.

I’m using a development version of the ggplot2 package from github due to the fact that the legend is not properly displayed for the aesthetic size (at least not as expected) using the geom_linerange.

The R script shows how I handle the usage of different versions of a R package in one script – in this case ggplot2. This works with my environment, this does not has to work with your environment. If you are not familiar with the R environment you also can use the ggplot2 package from CRAN or one of its mirrors. Just the legend for size will not look the same.

The R script also reference the wesanderson library that provides my favorite color palettes. In the R script I do not use the palette or any function directly just two colors from the “BottleRocket” palette.

My next post will explain how to create a chart inspired by one of the older IBCS standards (

 a bar chart inspired by an older IBCS standard

Thanks for reading!

They walk in line SQL Server 2016 Reporting Services and R charting using ggplot2

Some Weeks ago I started blogging, and started with the first part of a series about the not that obvious aspects of charting using the well known (not to say famous) R package gglot2 (developed by Hadley Wickham).

This post is not part of this series, but just due to my enthusiasm for the integration of R into SQL Server 2016 and the possibilities that come with this integration.

If you want to try it by yourself you can find the preview version of SQL Server 2016 here:

Please be aware that this is the CTP (Community Technology Preview) 3.0 and for this reason, you should not use this release in a production environment and also not on a machine that is used for development earning money to pay your rent. If you want to use R from T-SQL (meaning as an external script 🙂 ) please make sure that you select the feature “Advanced Analytics” within the feature selection list during installation.

There are also some sample files available:

The zip-archive “SQLServer2016CTP3Samples” contains the document “Getting Started.docx” in the folder “Advanced Analytics”. This document explains how to install the additional components that are necessary to get your R integration up and running (pretty straightforward explanation).

The above mentioned components can be found here:

My first experiment using the R integration from SQL Server 2016 CTP 3.0 was inspired by one of the older IBCS (International Business Charting Standards) Templates from 2012 that can be found here:

The result of my first experiment:


I hope that by the end of the week I have finished the 2nd part of the ggplot2 series and also the 3rd part that already explains how to create the chart above using R charting and SQL Server Reporting Services 2016.

Keep on charting, it’s Rsome 🙂