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Category: R

Building Flow Charts In R

Published 2018-05-10 by Kevin Feasel

Alan Haynes shows how to build flow charts in R using the grid Gmisc packages:

Flow charts are an important part of a clinical trial report. Making them can be a pain though. One good way to do it seems to be with the grid and Gmisc packages in R. X and Y coordinates can be designated based on the center of the boxes in normalized device coordinates (proportions of the device space – 0.5 is this middle) which saves a lot of messing around with corners of boxes and arrows.

A very basic flow chart, based very roughly on the CONSORT version, can be generated as follows…

Click through for sample code and a resulting image.  H/T R-bloggers

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Building Palettes From Pictures In R

Published 2018-05-10 by Kevin Feasel

Andrea Cirillo takes inspiration from the great works to build palettes:

If you see this painting you will find a profound of colours with a great equilibrium between different hues, the hardy usage of complementary colours and the ability expressed in the “chiaroscuro” technique. While I was looking at the painting I started, wondering how we moved from this wisdom to the ugly charts you can easily find within today’s corporate reports ( find a great sample on the WTF visualization website)

This is where Paletter comes from: bring the Renaissance wisdom and beauty within the plots we produce every day.

Introducing paletter

PaletteR is a lean R package which lets you draw from any custom image an optimized palette of colours. The package extracts a custom number of representative colours from the image. Let’s try to apply it on the “Vergine con il Bambino, angeli e Santi” before looking into its functional specification.

It’s an interesting package.  I’ll have to play around with it.

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Data Types In R

Published 2018-05-09 by Kevin Feasel

Ellen Talbot gives us an overview of the different data types in R:

Now here’s something we didn’t cover in the video and is especially helpful if something just WILL NOT work and you’ve spent all morning panic eating biscuits.

You can write checks to see if something is numeric, or an integer, with is.numeric() or is.integer().

The general “‘is.XXXXX()’” function will take many of the data types we cover here and more, and can be a real time/life saver.

We could also use class() here and inspect the result.^[You might recall that class(1) had the result of “numeric” – R was not by default considering 1 as an integer for the purpose of the class() function. ### Special numbers As well as i to denote imaginary numbers, there are some additional symbols you might encounter or want to use.

There’s a video as well as a full blog post.

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Building Control Charts With R

Published 2018-05-09 by Kevin Feasel

Kamal Kumar covers one of my favorite types of charts:

Control charts are used during the Control phase of DMAIC methodology. Control charts, also known as Shewhart charts or process-behavior charts, are a statistical process control tool used to determine if a manufacturing or business process is in a state of control. If analysis of the control chart indicates that the process is currently under control, then no corrections or changes to process control parameters are needed. Moreover, data from the method can be used to predict the future performance of the process. If the control chart indicates that the process is not in control, analysis of the chart can help determine the sources of variation, as this will result in degradation of process performance.

There are many packages in R, which can be used for analysis related to Six Sigma. Here, we will go through qcc package (R package for statistical quality control charts) and learn “How to create control chart (to know whether the process is in control)”.

Control charts are great for telling if a process has changed in some important way—if your machine is boring holes outside of tolerances, if your busy web server is getting closer to the breaking point, etc.

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ggplot2 Coordinate Systems

Published 2018-05-08 by Kevin Feasel

Lea Waniek walks us through coordinate systems in ggplot2:

The coordinate system can be manipulated by adding one of ggplot’s different coordinate systems. When you are imagining a coordinate system, you are most likely thinking of a Cartesian one. The Cartesian coordinate system combines x and y dimension orthogonally and is ggplots default (coord_cartesian).

There also are several varaitions of the familiar Cartesian coordinate system in ggplot, namely coord_fixed, coord_flip and coord_trans. For all of them, the displayed section of the data can be specified by defining the maximal value depicted on the x (xlim =) and y (ylim =) axis. This allows to “zoom in” or “zoom out” of a plot. It is a great advantage, that all manipulations of the coordinate system only alter the depiction of the data but not the data itself.

I tend to avoid polar coordinates, but that’s mostly because I don’t work in a space which benefits from it.

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Limitations Of Mapping In Power BI

Published 2018-05-08 by Kevin Feasel

David Stelfox points out a limitation in Power BI and tries to circumvent it with R to some limited effect:

This results in a row per ride and visualises pretty well in SSMS. If you are familiar with the geography of London you can make out the river Thames toward the centre of the image and Regents Park towards the top left:

This could be overlaid on a shape file of London or a map from another provider such as Google Maps or Mapbox.

However, when you try to load the dataset into Power BI, you find that Power BI does not natively support Geography data types. There is an idea you can vote on here to get them supported: https://ideas.powerbi.com/forums/265200-power-bi-ideas/suggestions/12257955-support-sql-server-geometry-geography-data-types-i

Hit up that idea link if you want to see geography type support within Power BI.

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Installing R Tools for Visual Studio 2017

Published 2018-05-07 by Kevin Feasel

Tech Junkie Blog shows how to install R tooling within Visual Studio 2017:

In this post we are going to go over the steps to install R Tools For Visual Studio 2017.  RStudio has a development environment that is bare bones for the free version.  Visual Studio 2017 offers a more robust development environment if you download the R Tools feature.

Here are the steps to install R Tools for Visual Studio:

R Tools for Visual Studio 2015 is still a separate download.

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Microsoft R Open 3.4.4

Published 2018-05-03 by Kevin Feasel

David Smith announces Microsoft R Open 3.4.4:

An update to Microsoft R Open (MRO) is now available for download on Windows, Mac and Linux. This release upgrades the R language engine to version 3.4.4, which addresses some minor issues with timezone detection and some edge cases in some statistics functions. As a maintenance release, it’s backwards-compatible with scripts and packages from the prior release of MRO.

MRO 3.4.4 points to a fixed CRAN snapshot taken on April 1 2018, and you can see some highlights of new packages released since the prior version of MRO on the Spotlights page. As always, you can use the built-in checkpoint package to access packages from an earlier date (for reproducibility) or a later date (to access new and updated packages).

David also spills the beans on when we’ll see MRO 3.5.0.

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Toward Interpretable Machine Learning

Published 2018-05-02 by Kevin Feasel

Cristoph Molnar shows off a couple of R packages which help interpret ML models:

Machine learning models repeatedly outperform interpretable, parametric models like the linear regression model. The gains in performance have a price: The models operate as black boxes which are not interpretable.

Fortunately, there are many methods that can make machine learning models interpretable. The R package imlprovides tools for analysing any black box machine learning model:

  • Feature importance: Which were the most important features?
  • Feature effects: How does a feature influence the prediction? (Partial dependence plots and individual conditional expectation curves)
  • Explanations for single predictions: How did the feature values of a single data point affect its prediction? (LIME and Shapley value)
  • Surrogate trees: Can we approximate the underlying black box model with a short decision tree?
  • The iml package works for any classification and regression machine learning model: random forests, linear models, neural networks, xgboost, etc.

This is a must-read if you’re getting into model-building. H/T R-Bloggers

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Creating Seaborn Plots With R

Published 2018-04-26 by Kevin Feasel

Abdul Majed Raja shows how to call Python from R and build plots using the Seaborn Python package:

The reticulate package provides a comprehensive set of tools for interoperability between Python and R. The package includes facilities for:

  • Calling Python from R in a variety of ways including R Markdown, sourcing Python scripts, importing Python modules, and using Python interactively within an R session.
  • Translation between R and Python objects (for example, between R and Pandas data frames, or between R matrices and NumPy arrays).
  • Flexible binding to different versions of Python including virtual environments and Conda environments.

Reticulate embeds a Python session within your R session, enabling seamless, high-performance interoperability.

The more common use of reticulate I’ve seen is running TensorFlow neural networks from R.

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