Category: R

Nicola Rennie explains the purpose of a new standard for object-oriented programming in R:

The two main OOP systems in R, S3 and S4, both have their advantages and their limitations. For example, in S3 there’s no systematic object validation to make sure an object’s class is correct. In S4, the syntax for defining classes is rather unusual and relies on side effects. Issues such as these mean that, unlike other programming languages, there isn’t a dominant approach to OOP in R.

Now imagine you could take the best bits of S3 and the best bits of S4. That’s where R7 comes in. 

Read on to learn more about how R7 compares to other object-oriented paradigms in R, such as S3, S4, and R6.

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Thomas Williams makes a database call:

R code (including in R Markdown and Shiny) can connect to databases, for both reading and writing. This opens up a whole world of powerful analysis – however, it can sometimes be tricky to deal with drivers, error handling and closing connections. In this post, I’ll demonstrate how to connect to a SQL Server database and run a query; in part 2, I’ll cover some of the things you might want to do with the query results.

Click through for two common libraries and a few tips.

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Benjamin Smith’s ideas are intriguing to me and I wish to subscribe to his newsletter:

Jinja is a powerful templating engine that is useful in a variety of contexts. Recently, I discovered how its possible to use the power of Jinja syntax in R with the jinjar package written by David C Hall. With jinjar and the tidyRSS package by Robert Myles it is possible to make an email template that can provide short and informative updates. In his blog, I’m going to share how the jinjar and tidyRSS packages work and show how to combine them to make a simple daily email newsletter.

Read on to learn how.

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Tomaz Kastrun shuffles things around:

The problem is described as:

Given a signed 32-bit integer x, return x with its digits reversed. If reversing x causes the value to go outside the signed 32-bit integer range [-2^31, 2^31 – 1], then return 0.

For example:

x = 120; reversed_x = 21
x = -2310; reversed_x = -132

Read on to see how you can implement this in R.

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Sebastian Sauer looks at Google search data:

You cannot download as much data as you like, there are some restrictions, again, from the same source as above:

Google has incorporated quota limits for Trends searches. This limits the number of search attempts available per user/IP/device. Details of quota limits have not yet been provided, but it may depend on geographical location or browser privacy settings. It has been reported in some cases that this quota is reached very quickly if one is not logged into a Google account before trying to access the Trends service.[52]

Click through to see how you can access this data. In this case, the example focuses on specific categories but there’s a lot more within Google Trends.

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