Broad Guidelines for Graphs

Here are some broad guidelines for Graphs from EIA.gov , so you can say these are the official graphical guidelines of USA Gov

They can be really useful for sites planning to get into the Tableau Software/NYT /Guardian Infographic mode- or even for communities of blogs that have recurrent needs to display graphical plots- particularly since communication, statistical and design specialists are different areas/expertise/people.

Energy Information Administration Standard

Broad Guidelines for Graphs-I am reproducing an example from EIA ‘s guidelines for graphs-
http://www.eia.gov/about/eia_standards.cfm#Standard25

Energy Information Administration Standard 2009-25

Title: Statistical Graphs
Superseded Version: Standard 2002-25
Purpose: To ensure the utility (usefulness to intended users) and objectivity (accuracy, clarity, completeness, and lack of bias) of energy information presented in statistical graphs.
Applicability: All EIA information products.
Required Actions:

  1. Graphs should be used to show and compare changes, trends and/or relationships, and to assist users in visualizing the conclusions drawn from the data represented.
  2. A graph should contain sufficient Continue reading “Broad Guidelines for Graphs”

Top Ten Business Analytics Graphs-Line Charts (2/10)

A line chart is one of the most commonly used charts in business analytics and metrics reporting. It basically consists of two variables plotted along the axes with the adjacent points being joined by line segments. Most often used with time series on the x-axis, line charts are simple to understand and use.
Variations on the line graph can include fan charts in time series which include joining line chart of historic data with ranges of future projections. Another common variation is to plot the linear regression or trend line between the two variables  and superimpose it on the graph.
The slope of the line chart shows the rate of change at that particular point , and can also be used to highlight areas of discontinuity or irregular change between two variables.

The basic syntax of line graph is created by first using Plot() function to plot the points and then lines () function to plot the lines between the points.

> str(cars)
‘data.frame’:   50 obs. of  2 variables:
$ speed: num  4 4 7 7 8 9 10 10 10 11 …
$ dist : num  2 10 4 22 16 10 18 26 34 17 …
> plot(cars)
> lines(cars,type=”o”, pch=20, lty=2, col=”green”)
> title(main=”Example Automobiles”, col.main=”blue”, font.main=2)

An example of Time Series Forecasting graph  or fan chart is http://addictedtor.free.fr/graphiques/RGraphGallery.php?graph=51

Top Ten Graphs for Business Analytics -Pie Charts (1/10)

I have not been really posting or writing worthwhile on the website for some time, as I am still busy writing ” R for Business Analytics” which I hope to get out before year end. However while doing research for that, I came across many types of graphs and what struck me is the actual usage of some kinds of graphs is very different in business analytics as compared to statistical computing.

The criterion of top ten graphs is as follows-

1) Usage-The order in which they appear is not strictly in terms of desirability but actual frequency of usage. So a frequently used graph like box plot would be recommended above say a violin plot.

2) Adequacy- Data Visualization paradigms change over time- but the need for accurate conveying of maximum information in a minium space without overwhelming reader or misleading data perceptions.

3) Ease of creation- A simpler graph created by a single function is more preferrable to writing 4-5 lines of code to create an elaborate graph.

4) Aesthetics– Aesthetics is relative and  in addition studies have shown visual perception varies across cultures and geographies. However , beauty is universally appreciated and a pretty graph is sometimes and often preferred over a not so pretty graph. Here being pretty is in both visual appeal without compromising perceptual inference from graphical analysis.

 

so When do we use a bar chart versus a line graph versus a pie chart? When is a mosaic plot more handy and when should histograms be used with density plots? The list tries to capture most of these practicalities.

Let me elaborate on some specific graphs-

1) Pie Chart- While Pie Chart is not really used much in stats computing, and indeed it is considered a misleading example of data visualization especially the skewed or two dimensional charts. However when it comes to evaluating market share at a particular instance, a pie chart is simple to understand. At the most two pie charts are needed for comparing two different snapshots, but three or more pie charts on same data at different points of time is definitely a bad case.

In R you can create piechart, by just using pie(dataset$variable)

As per official documentation, pie charts are not  recommended at all.

http://stat.ethz.ch/R-manual/R-patched/library/graphics/html/pie.html

Pie charts are a very bad way of displaying information. The eye is good at judging linear measures and bad at judging relative areas. A bar chart or dot chart is a preferable way of displaying this type of data.

Cleveland (1985), page 264: “Data that can be shown by pie charts always can be shown by a dot chart. This means that judgements of position along a common scale can be made instead of the less accurate angle judgements.” This statement is based on the empirical investigations of Cleveland and McGill as well as investigations by perceptual psychologists.

—-

Despite this, pie charts are frequently used as an important metric they inevitably convey is market share. Market share remains an important analytical metric for business.

The pie3D( ) function in the plotrix package provides 3D exploded pie charts.An exploded pie chart remains a very commonly used (or misused) chart.

From http://lilt.ilstu.edu/jpda/charts/chart%20tips/Chartstip%202.htm#Rules

we see some rules for using Pie charts.

 

  1. Avoid using pie charts.
  2. Use pie charts only for data that add up to some meaningful total.
  3. Never ever use three-dimensional pie charts; they are even worse than two-dimensional pies.
  4. Avoid forcing comparisons across more than one pie chart

 

From the R Graph Gallery (a slightly outdated but still very comprehensive graphical repository)

http://addictedtor.free.fr/graphiques/RGraphGallery.php?graph=4

par(bg="gray")
pie(rep(1,24), col=rainbow(24), radius=0.9)
title(main="Color Wheel", cex.main=1.4, font.main=3)
title(xlab="(test)", cex.lab=0.8, font.lab=3)
(Note adding a grey background is quite easy in the basic graphics device as well without using an advanced graphical package)