Humans are visual creatures. Thus, visualization is one of the most important tools for conveying information and data scientists should be adapt at selecting appropriate visualizations.
Discriminant analysis encompasses methods that can be used for both classification and dimensionality reduction. Linear discriminant analysis (LDA) is particularly popular because it is both a classifier and a dimensionality reduction technique. Quadratic discriminant analysis (QDA) is a variant of LDA that allows for non-linear separation of data. Finally, regularized discriminant analysis (RDA) is a compromise between LDA and QDA.
This post focuses mostly on LDA and explores its use as a classification and visualization technique, both in theory and in practice.
Radar plots visualize several variables using a radial layout. This plot is most suitable for visualizing and comparing the properties associated with individual objects. In the following, we will use a radar plot for comparing the characteristics of whiskeys from different distilleries.
A data set on whiskey Some of you may already know that radar plots are well-suited for visualizing whiskey flavors. I saw this type of visualization first, when I visited the Talisker distillery, the only whiskey distillery on the Isle of Skye.
Nowadays, infographics are everywhere. Fortunately, you do not have to be a professional designer to create them because there are several free platforms that assist you in creating engaging infographics. In this post, I compare three freely available tools for creating static infographics: Venngage, easelly, and Infogram. Each of the tools is reviewed according to three criteria:
Customizability: number of available templates, graphics, fonts and so on. User experience: how easy is it to design/deploy infographics?
Box plots are great as they do not only indicate the median value but also show the variation of the measurements in terms of the 1st and 3rd quartiles. There are, however, also plots that provide a bit of additional information. Here, we take a closer look at potential alternatives to the box plot: the beeswarm and the violin plot.
The beeswarm plot An implementation of the beeswarm plot is available via the beeswarm package.
The line plot is the go-to plot for visualizing time-series data (i.e. measurements for several points in time) as it allows for showing trends along time. Here, we’ll use stock market data to show how line plots can be created using native R, the MTS package, and ggplot.
Bar plots display quantities according to the height of bars. Since standard bar plots do not indicate the level of variation in the data, they are most appropriate for showing individual values (e.g. count data) rather than aggregates of several values (e.g. arithmetic means). Although variation can be shown through error bars, this is only appropriate if the data are normally distributed.
The box plot is useful for comparing the quartiles of quantitative variables. More specifically, lower and upper ends of a box (the hinges) are defined by the first (Q1) and third quartile (Q3). The median (Q2) is shown as a horizontal line within the box. Additionally, outliers are indicated by the whiskers of the boxes whose definition is implementation-dependent. For example, in geom_boxplot of ggplot2, whiskers are defined by the inter-quartile range (IQR = Q3 - Q1), extending no further than 1.5 * IQR.
The scatter plot is probably the most simple type of plot that is available because it doesn’t do anything more than to show individual measurements as points in a plot. The scatter plot is particularly useful for investigating whether two variables are associated.
It is always useful to spend some time exploring a new data set before processing it further and analyzing it. One of the most convenient ways to get a feel for the data is plotting a histogram. The histogram is a tool for visualizing the frequency of measurements in terms of a bar plot. Here we’ll take a closer look at how the histogram can be used in R.
