Visualizing Survey Data : Comparison Between Observations. Cybersecurity is a domain that really likes survey, or at the very least it has many folks within it that like to conduct and report on surveys.
One recent survey on threat intelligence is in it’s second year, so it sets about comparing answers across years. Rather than go ingo the many technical/statistical issues with this survey, I’d like to focus on alternate ways to visualize the comparison across years. We’ll use the data that makes up this chart (Figure 3 from the report): since it’s pretty representative of the remainder of the figures.
Let’s start by reproducing this figure with ggplot2: Now, the survey does caveat the findings and talks about non-response bias, sampling-frame bias and self-reporting bias. They are both roughly 3.65% so let’s take a look at our dodged bar chart again with this new information: Hrm. Spaghetti plots with ggplot2 and ggvis. This post was motivated by this article that discusses the graphics and statistical analysis for a two treatment, two period, two sequence (2x2x2) crossover drug interaction study of a new drug versus the standard.
Every observation in a dataset is represented with a polyline that crosses a set of parallel axes corresponding to variables in the dataset. You can create such plots in R using a function parcoord in package MASS. For example, we can create such plot for the built-in dataset mtcars: R Graph Gallery. R Graph Gallery. Not unlike the stars() plot, a profile plot allows one to generate a profile of several items and how they compare on various axes. Using this plot one can quickly see how items stack up on multiple dimensions. For example, when attempting to compare three brands of cars, one might want to compare their styles on scales such as "modern vs traditional," "sporty vs elegant," and "compact vs large.
" Using a profile plot one can then plot the profiles of each of the cars on these dimensions so that we can see trends. For example, if one were to plot all of Ferrari's cars and all of Audi's cars on the plot, we would see that all of the Ferrari's would be very high on the "sporty" scale and only a few of the Audi models would score highly.
The code provided is an updated version of the profile (image) plot provided by Detlev Reymann that encapsulates the plotting code in an easy to use function. Steep decline in market capitalization. R Graph Gallery. These are two scripts which I wrote because I did not find any tool, which is able to produce these kinds of graphs. For marketing purposes you need so called profile diagrammes and image profiles. Both types of diagrammes have to show the position of each object, which can be a brand, an enterprise, a person or whatsoever in comparison to other objects. The graph is a kind of traditional XY-graph rotated 90 degrees. Profile diagrammes are used in marketing to show the position of research objects. The research criterea or items are shown on the y-axis and the values on the x-axis. These scripts are non-interactive versions of a originally interactive R-script just to demonstrate the idea of the script.
The interactive scripts hve been developed as part of a marketing book. The interactive scripts are available in german only in the moment but if there is interest, I will produce english versions as well. R Graph Gallery.