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Category: R

Digging Into The SQL Compute Context With R Services

Published 2018-05-21 by Kevin Feasel

Niels Berglund dives into how the SQL Compute Context works with R Services:

In the code above we use the RxInSqlServer() function to indicate we want to execute in a SQL context. The connectionString property defines where we execute, and the numTasks property sets the number of tasks (processes) to run for each computation, in Code Snippet 4 it is set to 1 which from a processing perspective should match what we do in Code Snippet 3. Before we execute the code in Code Snippet 4 we do what we did before we ran the code in Code Snippet 3:

  • Run Process Explorer as admin.
  • Navigate to the devenv.exe process in Process Explorer.
  • In addition, also look at the Launchpad.exe process in Process Explorer.

When we execute we see that the BxlServer.exe processes under the Microsoft.R.Host.exe processes are idling, but when we look at the Launchpad.exe process we see this:

This is a bit deep but interesting reading.

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Creating Choropleths With ggcounty

Published 2018-05-21 by Kevin Feasel

Sebastian Sauer has a quick example of using ggcounty to plot data on a map of US counties:

This posts shows how easy it can be to build an visually pleasing plot. We will use hrbrmster’s ggcounty, which is an R package at this Github repo. Graphics engine is as mostly in my plots, Hadley Wickhams ggplot. All build on R. Standing on shoulders…

Disclaimer: This example heavily draws on hrbrmster example on this page. All credit is due to Rudy, and those on whose work he built up on.

In just a few lines of code, you can have a pretty nice map.

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Using The Map Function In R

Published 2018-05-18 by Kevin Feasel

Nicolas Attalides on using purrr:

The best place to start when exploring the purrr package is the map function. The reader will notice that these functions are utilised in a very similar way to the apply family of functions. The subtle difference is that the purrr functions are consistent and the user can be assured of the output – as opposed to some cases when using for example sapply as I demonstrate later on.

My considered belief is, Always Be Purrring.  H/T R-bloggers

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Tuning xgboost Models In R

Published 2018-05-17 by Kevin Feasel

Gabriel Vasconcelos has a new series on tuning xgboost models:

My favourite Boosting package is the xgboost, which will be used in all examples below. Before going to the data let’s talk about some of the parameters I believe to be the most important. These parameters mostly are used to control how much the model may fit to the data. We would like to have a fit that captures the structure of the data but only the real structure. In other words, we do not want the model to fit noise because this will be translated in a poor out-of-sample performance.

  • eta: Learning (or shrinkage) parameter. It controls how much information from a new tree will be used in the Boosting. This parameter must be bigger than 0 and limited to 1. If it is close to zero we will use only a small piece of information from each new tree. If we set eta to 1 we will use all information from the new tree. Big values of eta result in a faster convergence and more over-fitting problems. Small values may need to many trees to converge.

  • colsample_bylevel: Just like Random Forests, some times it is good to look only at a few variables to grow each new node in a tree. If we look at all variables the algorithm needs less trees to converge, but looking at, for example, 2/3 of the variables may result in models more robust to over-fitting. There is a similar parameter called colsample_bytree that re-sample the variables in each new tree instead of each new node.

Read the whole thing.  H/T R-bloggers

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Converting Between Time Series Classes In R

Published 2018-05-16 by Kevin Feasel

Christoph Sax announces a new R library:

tsbox, now freshly on CRAN, provides a set of tools that are agnostic towards existing time series classes. It is built around a set of converters, which convert time series stored as ts, xts, data.frame, data.table, tibble, zoo, tsibble or timeSeries to each other.

If you have to work with time series data, this will be a useful library.  H/T R-Bloggers

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Power BI Custom Visuals In Excel

Published 2018-05-14 by Kevin Feasel

David Smith notes that Excel is getting a bit of an upgrade:

This week at the BUILD conference, Microsoft announced that Power BI custom visuals will soon be available as charts with Excel. You’ll be able to choose a range of data within an Excel workbook, and pass those data to one of the built-in Power BI custom visuals, or one you’ve created yourself using the API.

David’s point is that you can bring in R charts, but it extends to more than that.

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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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