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Category: R

Thoughts on R’s New Pipe

Published 2020-12-09 by Kevin Feasel

John Mount has thoughts on the upcoming pipe operator in R:

There is a current active discussion on this prototype and some interesting points come up. Note the current proposal appears to disallow a |> f -> f(a), a currently popular transform.

1. This is a language feature presented as a soon-to-be-user-visible prototype, not an RFC.
2. Some are objecting to the term “pipe.”
3. Some call this sort of pipe function composition.
4. It is noticed that this sort of substitution is generally thought of as a “macro.”
5. There is a claim the proposed pipe seems to violate the beta-reduction rule of the lambda calculus: variables should be substitutable for values.

Read on for John’s take on this. I particularly appreciate his response to point number 2: other functional languages have pipes (in fact, |> is the F# pipe operator). Pipes are not unique to UNIX. John has a lot of interesting comments, so check them out.

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ETL with R in SQL Server

Published 2020-12-03 by Kevin Feasel

Rajendra Gupta shows one reason for using R inside SQL Server:

Data professionals get requests to import, export data into various formats. These formats can be such as Comma-separated data(.CSV), Excel, HTML, JSON, YAML, Tab-separated data(.TSV). Usually, we use SQL Server integration service ETL packages for data transformations, import or export data.

SQL Machine Learning can be useful in dealing with various file formats. In the article, External packages in R SQL Server, we explored the R services and the various external packages for performing tasks using R scripts.

In this article, we explore the useful Rio package developed by Thomas J. Leeper to simplify the data export and import process.

One thing I’d like to reiterate is that even though you’re using R to move this data, you don’t need to perform any data science activities on the data—R can be the easiest approach for getting and cleaning up certain types of data.

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Stochastic Processes in R

Published 2020-12-02 by Kevin Feasel

David Robinson takes us through simulation of a random walk in R:

What’s fun about this problem is that it’s an example of a random walk: a stochastic process made up of a sequence of random steps (in this case, left or right). What makes this a fun variation is that it’s a random walk in a circle- passing 5 to the left is the same as passing 15 to the right. I wasn’t previously familiar with a random walk in a circle, so I approached it through simulation to learn about its properties.

Click through for a simulation. Or 50,000 of them.

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Understanding Decision Trees

Published 2020-12-01 by Kevin Feasel

Ram Tavva walks us through the algorithm to design decision trees:

A decision tree is made up of several nodes:

1.Root Node: A Root Node represents the entire data and the starting point of the tree. From the above example the
First Node where we are checking the first condition, whether the movie belongs to Hollywood or not that is the
Rood node from which the entire tree grows
2.Leaf Node: A Leaf Node is the end node of the tree, which can’t split into further nodes.
From the above example ‘watch movie’ and ‘Don’t watch ‘are leaf nodes.
3.Parent/Child Nodes: A Node that splits into a further node will be the parent node for the successor nodes. The
nodes which are obtained from the previous node will be child nodes for the above node.

Read on for an example of implementation in R.

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The Effects of Undersampling and Oversampling on Predicted Probability

Published 2020-12-01 by Kevin Feasel

Bryan Shalloway has an interesting article for us:

In classification problems, under and over sampling techniques shift the distribution of predicted probabilities towards the minority class. If your problem requires accurate probabilities you will need to adjust your predictions in some way during post-processing (or at another step) to account for this.

Bryan has a clear example showing this problem in action.

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Image Modification with R in Machine Learning Services

Published 2020-11-27 by Kevin Feasel

Rajendra Gupta messes with The Mouse:

There is a famous adage in English: “A picture is worth a thousand words”. You can represent your information using the image in various formats such as JPEG, PNG, GIF. Usually, we use various client tools such as MS Paint, Photo, photoshop or other client applications for working with the images. You can convert image format, modify the size, applying various effects, multiple animated images.

SQL Machine Learning language – R makes us capable of working with the images directly with the SQL Server. In this article, we will use SQL Machine Learning using R scripts for image processing.

Click through for examples.

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AzureTableStor: Table Storage in R

Published 2020-11-19 by Kevin Feasel

Hong Ooi announces a new package on CRAN:

I’m pleased to announce that the AzureTableStor package, providing a simple yet powerful interface to the Azure table storage service, is now on CRAN. This is something that many people have requested since the initial release of the AzureR packages nearly two years ago.

Azure table storage is a service that stores structured NoSQL data in the cloud, providing a key/attribute store with a schemaless design. Because table storage is schemaless, it’s easy to adapt your data as the needs of your application evolve. Access to table storage data is fast and cost-effective for many types of applications, and is typically lower in cost than traditional SQL for similar volumes of data.

If that sounds like a fit for you, check out the package.

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Finding Function Names with Overlap in R

Published 2020-11-18 by Kevin Feasel

Tomaz Kastrun has a script for us:

So the following function does just that; if goes through the list of functions for all the installed packages in particular environment and finds duplicates. Giving the R programmer the insights where to be cautious and refer to scope and namespace.

This is a cautionary tale to use full object naming, like coin::show() rather than just show().

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Error Handling in R

Published 2020-11-16 by Kevin Feasel

Adi Sarid compares a few methods for error handling in R:

Error catching can be hard to catch at times (no pun intended). If you’re not used to error handling, this short post might help you do it elegantly.

There are many posts about error handling in R (and in fact the examples in the purrr package documentation are not bad either). In this sense, this post is not original.

However, I do demonstrate two approaches: both the base-R approach (tryCatch) and the purrr approach (safely and possibly). The post contains a concise summary of the two methods, with a very simple example.

Read the whole thing. H/T R-Bloggers

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