Heat maps are great to compare observations with lots of variables (which must be comparable in terms of unit, domain, etc.). In some cases however, traditional heat maps might not suffice, for example when you want to compare multiple groups of observations. One solution is to use facets. Another solution, which I want to explain here, is to make a “ballon plot” with a fixed grid of rows and columns.

## Parallel Coordinate Plots for Discrete and Categorical Data in R — A Comparison

Parallel Coordinate Plots are useful to visualize multivariate data. R provides several packages/functions to draw Parallel Coordinate Plots (PCPs):

*ggparcoord*in the package*GGally*- the package
*ggparallel* - plain
*ggplot2*with*geom_path*

In this post I will compare these approaches using a randomly generated data set with three discrete variables.

## Dynamic column/variable names with dplyr using Standard Evaluation functions

Data manipulation works like a charm in R when using a library like dplyr. An often overlooked feature of this library is called *Standard Evaluation (SE)* which is also described in the vignette about the related *Non-standard Evaluation*. It basically allows you to use dynamic arguments in many dplyr functions (“verbs”).