Posted on Categories Coding, TutorialsTags , , Leave a comment on Getting Started With rquery

Getting Started With rquery

To make getting started with rquery (an advanced query generator for R) easier we have re-worked the package README for various data-sources (including SparkR!).

Continue reading Getting Started With rquery

Posted on Categories Coding, OpinionTags , , , , Leave a comment on Playing With Pipe Notations

Playing With Pipe Notations

Recently Hadley Wickham prescribed pronouncing the magrittr pipe as “then” and using right-assignment as follows:

NewImage

I am not sure if it is a good or bad idea. But let’s play with it a bit, and perhaps readers can submit their experience and opinions in the comments section.

Continue reading Playing With Pipe Notations

Posted on Categories data science, Exciting Techniques, TutorialsTags , Leave a comment on Query Generation in R

Query Generation in R

R users have been enjoying the benefits of SQL query generators for quite some time, most notably using the dbplyr package. I would like to talk about some features of our own rquery query generator, concentrating on derived result re-use.

Continue reading Query Generation in R

Posted on Categories Administrativia, data science, Opinion, StatisticsTags , , Leave a comment on PDSwR2 Free Excerpt and New Discount Code

PDSwR2 Free Excerpt and New Discount Code

Manning has a new discount code and a free excerpt of our book Practical Data Science with R, 2nd Edition: here.

This section is elementary, but things really pick up speed as later on (also available in a paid preview).

Posted on Categories Exciting Techniques, Opinion, TutorialsTags , , Leave a comment on cdata Control Table Keys

cdata Control Table Keys

In our cdata R package and training materials we emphasize the record-oriented thinking and how to design a transform control table. We now have an additional exciting new feature: control table keys.

The user can now control which columns of a cdata control table are the keys, including now using composite keys (that is keys that are spread across more than one column). This is easiest to demonstrate with an example.

Continue reading cdata Control Table Keys

Posted on Categories Administrativia, data science, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, StatisticsTags 5 Comments on PDSwR2: New Chapters!

PDSwR2: New Chapters!

We have two new chapters of Practical Data Science with R, Second Edition online and available for review!

NewImage

The newly available chapters cover:

Data Engineering And Data Shaping – Explores how to use R to organize or wrangle data into a shape useful for analysis. The chapter covers applying data transforms, data manipulation packages, and more.

Choosing and Evaluating Models – The chapter starts with exploring machine learning approaches and then moves to studying key model evaluation topics like mapping business problems to machine learning tasks, evaluating model quality, and how to explain model predictions.

If you haven’t signed up for our book’s MEAP (Manning Early Access Program), we encourage you to do so. The MEAP includes a free copy of Practical Data Science with R, First Edition, as well as early access to chapter drafts of the second edition as we complete them.

For those of you who have already subscribed — thank you! We hope you enjoy the new chapters, and we look forward to your feedback.

Posted on Categories data science, Exciting Techniques, TutorialsTags , , , , 1 Comment on Function Objects and Pipelines in R

Function Objects and Pipelines in R

Composing functions and sequencing operations are core programming concepts.

Some notable realizations of sequencing or pipelining operations include:

The idea is: many important calculations can be considered as a sequence of transforms applied to a data set. Each step may be a function taking many arguments. It is often the case that only one of each function’s arguments is primary, and the rest are parameters. For data science applications this is particularly common, so having convenient pipeline notation can be a plus. An example of a non-trivial data processing pipeline can be found here.

In this note we will discuss the advanced R pipeline operator "dot arrow pipe" and an S4 class (wrapr::UnaryFn) that makes working with pipeline notation much more powerful and much easier.

Continue reading Function Objects and Pipelines in R