Advanced R: Data Programming and the Cloud by Matt Wiley

Advanced R: Data Programming and the Cloud by Matt Wiley

By Matt Wiley

Software for information research utilizing R and study useful talents to make your paintings extra effective. This ebook covers how one can automate operating code and the construction of stories to percentage your effects, in addition to writing capabilities and programs. complex R isn't really designed to educate complex R programming nor to coach the idea in the back of statistical tactics. quite, it really is designed to be a pragmatic advisor relocating past simply utilizing R to programming in R to automate initiatives. This booklet will enable you to manage info in sleek R constructions and contains connecting R to info bases similar to SQLite, PostgeSQL, and MongoDB. The booklet closes with a hands-on part to get R operating within the cloud. every one bankruptcy additionally contains a targeted bibliography with references to investigate articles and different assets that hide appropriate conceptual and theoretical themes.

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Keep in mind that the PowerPoint files are less about the files themselves (neither author imagines using R for such files genuinely), and more about the fact that the nuances of file manipulation, in general, deserve treatment with due caution. pptx") [1] TRUE Figure 2-2. The first operation was successful according to R and worked upon opening the file Figure 2-3. The second operation was successful according to R yet did not work upon opening the file 21 CHAPTER 2 ■ PROGRAMMING UTILITIES You can not only manipulate files with R, but also create directories, or file folders, as well.

However, there is usually more than one way to do things, and it would be silly to ignore those. time() in our code to measure different times; we will calculate the runtime by finding the difference between the stop and start time. Take a look at the following code and output: © Matt Wiley and Joshua F. Wiley 2016 M. Wiley and J. F. 25 You can see that the numbers 1 through 100,000 are in the variable x (the first six display by calling the head() function). Next, we create a variable to hold the cubes of all those variables.

The following is an example of a function that calculates Cohen’s d effect size, which for a single group is defined as the mean of a variable divided by the standard deviation. Cohen’s d can also be calculated for repeated measures, such as a group measured before and after an intervention. This is done by calculating the difference of the two variables, and then proceeding as before. call(). call() captures the entire function call. This might be easier to demonstrate than to explain. The following little function calculates the coefficient of variation, the sample standard deviation divided by the sample mean.

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