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Beginner's guide to R: Painless data visualization

2013-09-09 12:00:00| InfoWorld: Top News

One of the most appealing things about R is its ability to create data visualizations with just a couple of lines of code. For example, it takes just one line of code -- and a short one at that -- to plot two variables in a scatterplot. Let's use as an example the mtcars data set installed with R by default. To plot the engine displacement column disp on the x axis and MPG on y: plot(mtcars$disp, mtcars$mpg)

Tags: data guide beginners visualization

 

Beginner's guide to R: Get your data into R

2013-09-09 12:00:00| InfoWorld: Top News

Once you've installed and configured R to your liking, it's time to start using it to work with data. Yes, you can type your data directly into R's interactive console. But for any kind of serious work, you're a lot more likely to already have data in a file somewhere, either locally or on the Web. Here are several ways to get data into R for further work.

Tags: your r data guide

 
 

Beginner's guide to R: Easy ways to do basic data analysis

2013-09-09 12:00:00| InfoWorld: Top News

So you've read your data into an R object. Now what? Examine your data objectBefore you start analyzing, you might want to take a look at your data object's structure and a few row entries. If it's a two-dimensional table of data stored in an R data frame object with rows and columns -- one of the more common structures you're likely to encounter -- here are some ideas. Many of these also work on one-dimensional vectors as well.

Tags: data guide easy basic

 

Beginner's guide to R: Syntax quirks you'll want to know

2013-09-09 12:00:00| InfoWorld: Top News

I mentioned at the outset that R syntax is a bit quirky, especially if your frame of reference is, well, pretty much any other programming language. Here are some unusual traits of the language you may find useful to understand as you embark on your journey to learn R.

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Beginner's guide to R: Introduction

2013-09-09 12:00:00| InfoWorld: Top News

R is hot. Whether measured by more than 4,400 add-on packages, the 18,000-plus members of LinkedIn's R group, or the close to 80 R Meetup groups currently in existence, there can be little doubt that interest in the R statistics language, especially for data analysis, is soaring.

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