Three Mighty Good Reasons to Learn R for Data Science

转载自：https://www.dataquest.io/blog/three-mighty-good-reasons-to-learn-r-for-data-science/

Rose Martin
发表于

Ahoy, mateys! Happy International Talk Like A Pirate Day

You may think a pirate’s life sounds like fun, but it isn’t all buried treasure and singing yo-ho-ho. Pirates have lots to do:

  • Predicting profitability of plundering events based on crew size and ship features

  • Optimizing trade routes to avoid the law, storms, and other pirates

  • Analyzing large data sets containing millions of hints to hone in on buried treasure chests

Aye, you see, the best pirates are also data scientists. And, of course, they use R for their data science!

Here are some of our favorite reasons to learn R:

  1. Yarrr, statistics: R was originally designed by statisticians for doing statistical analysis, and it remains the programming choice of most statisticians today. R’s syntax makes it easy to create complex statistical models with just a few lines of code. Since so many statisticians use and contribute to R packages, you’re likely to be able to find support for any statistical analysis you need to perform, such as:

  • Looking for correlations between peg leg length and plundering ability to take the guesswork out of crew recruiting

  • Comparing the amount of treasure you found while plundering several islands to decide which one to return to using an analysis of variance model

  • Building a linear regression model to predict the amount of booty in a treasure chest based on its dimensions and burial depth

  1. Shiver me timbers! Data visualizations to impress: R’s streamlined syntax for creating visualizations allows you to quickly construct charts for exploratory data visualization, as well as create high-quality, publication-worthy visuals to communicate your findings:
  • You can make a fine map to buried treasure for yer ship’s captain using ggmap
  1. A swashbucklin’ community of data buccaneers: Like buried treasure, R is free, and anyone who knows where to look can download it and contribute to it in the form of packages
  • You can take the “Blimey!” out of data manipulation and cleaning using the dplyr package

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