Kristian Larsen demonstrates the corrplot package in R:

First we need to read the packages into the R library. For descriptive statistics of the dataset we use the skimr package and for visualization of correlation matrix we use the corrplot package. We will work with windspeed dataset from the bReeze package:

# Read packages into R library
library(bReeze)
library(corrplot)
library(skimr)

Click through for the demo.

Nina Zumel shows how to use the cdata package to create faceted ggplot2 plots:

First, load the packages and data:

library("ggplot2")library("cdata")
iris <- data.frame(iris)

Now define the data-shaping transform, or control table. The control table is basically a picture that sketches out the final data shape that I want. I want to specify the x and y columns of the plot (call these the value columns of the data frame) and the column that I am faceting by (call this the key column of the data frame). And I also need to specify how the key and value columns relate to the existing columns of the original data frame.

Read on to see how you can use cdata to tie together different faceted plots.

John Mount shows how you can use the wrapr pipe to perform data processing and building a ggplot2 visual:

Now we can run a single pipeline that combines data processing steps and ggplot plot construction.

Check it out.

Kristian Larsen has a quick demo of three R visualization packages, ggplot2, dygraphs, and plotly:

Another value generating visualisation package in R is dygraphs. This package focuses on creating interactive visualisations with elegant interactive coding modules. Furthermore, the package specialises in creating visualisations for machine learning methods. The below coding generates different visualisation graphs with dygraphs:

Three useful libraries to learn.  Two more which might be useful are ggvis and rbokeh.

Sebastian Sauer gives us an example of modifying a standard ggplot2 theme:

ggplot2 is customizeable. Frankly, one can change a heap of details – not everything probably, but a lot. Of course, one can add a theme to the ggplot call, in order to change the theme. However, a more catch-it-all approach would be to change the standard theme of ggplot itself. In this post, we’ll investigate this option.

To date, I’ve only used themes others have created, but if you need to customize a theme, there’s a lot you can do here.

Bernando Lares shows off some stuff his lares can do around visualizing time series data:

The overall idea of these functions is to visualize your stocks and portfolio’s performance with a just a few lines of simple code. I’ve created individual functions for each of the calculations and plots, and some other functions that gather all of them into a single list of objects for further use.

On the other hand, the lares package is “my personal library used to automate and speed my everyday work on Analysis and Machine Learning tasks”. I am more than happy to share it with you for your personal use. Feel free to install, use, and comment on any of its code and functionalities and I’ll happy to help you with it. I have previously shared other uses of the library in other posts which might also interest you: Visualizing ML Results (binary), Visualizing ML Results (continuous)and AutoML to understand datasets.

• NOTE 1: The following post was written by a non-economist or professional investor. I am open to your comments and technical corrections if needed. Glad to learn as always!

• NOTE 2: I will be using the less customizable functions in this post so we can focus more on the outputs than in the coding part; but once again, feel free to use the functions and dive into the library to understand or change them!

• NOTE 3: All currency units are USD ($).

It does seem to be easy to use for this scenario.

David Smith looks at an interesting package in R:

Tyler describes the rayshader package in a gorgeous blog post: his goal was to generate 3-D representations of landscape data that “looked like a paper weight”. (Incidentally, you can use this package to produce actual paper weights with 3-D printing.) To this end, he went beyond simply visualizing a 3-D surface in rgl and added a rectangular “base” to the surface as well as shadows cast by the geographic features. He also added support for detecting (or specifying) a water level: useful for representing lakes or oceans (like the map of the Monterey submarine canyon shown below) and for visualizing the effect of changing water levels like this animation of draining Lake Mead.

It looks great.

Laura Ellis shows us how to create formatted tables using the formattable package in R:

We are going to narrow down the data set to focus on 4 key health metrics. Specifically the prevalence of obesity, tobacco use, cardiovascular disease and obesity. We are then going to select only the indicator name and yearly KPI value columns. Finally we are going to make extra columns to display the 2011 to 2016 yearly average and the 2011 to 2016 metric improvements.

Tables are an area of data visualization that we tend to forget at our own peril.