R is not just about data analysis-though we will mostly use it this way. The resulting ecosystem is vast, and though it can be difficult to navigate at times, when we run into an R-related problem the chances are that the answer is already written down somewhere 1. Now, there are probably hundreds of books about different aspects of R, online tutorials written by enthusiasts, and many websites that exist solely to help people learn R. In the early 2000s there were very few books about R and the main way to access help online was through the widely-feared R mailing lists. The size of this community has increased steadily since R was created, but this growth has really increased up in the last 5-10 years or so. In short, R is a very productive environment for doing data analysis.īecause R is such a good environment for data analysis, a very large community of users has grown up around it. With sufficient expertise, we can make pretty much any type of figure we need (e.g. scatter plots, phylogenetic trees, spatial maps, or even volcanoes). R also has the best graphics and plotting facilities of any platform. This means a competent R user can always access the latest, cutting edge analysis tools. These days, when statisticians and computer scientists develop a new analysis tool, they often implement it in R first. We can carry out any standard statistical analysis in R, as well as access a huge array of more sophisticated tools with impressive names like “structural equation model”, “random forests” and “penalized regression”. When a typical R user talks about “R” they are often referring to two things at once, the GNU R language and the ecosystem that exists around the language: It is useful to understand why so many people have turned to R to meet their data analysis needs. R is a functional programming language, it supports object orientation, etc etc… but these kinds of explanations are only helpful to someone who already knows about computer languages. We could go on and on about the various features that R possesses. That language quickly evolved into something that looked more and more S-like, which we now know as R (GNU R, to be overly precise). Their motivation was to create an open source language to enable researchers in computational statistics to explore new ideas. Development of R was begun in the late 1990s by two academics, Ross Ihaka and Robert Gentleman, at the University of Auckland. S-Plus came first, and although it is still around, it is used less each year. There are essentially two widely used versions of S (though others have started to appear), a commercial one called S-Plus, and the open source implementation known as R. It was designed to offer an interactive computing environment for statisticians and scientists to carry out data analysis. R is a dialect of the S language, which was developed by John Chambers and colleagues at Bell Laboratories in the mid 1970s. The answer to this question very much depends on who we ask. 22.3.3 Alternatives to box and whiskers plots.22.3 Categorical-numerical associations.22.2 Associations between categorical variables. 22.1 Associations between numeric variables.21.2 Graphical summaries of categorical variables.21.1 Understanding categorical variables.19.2 Working with layer specific position adjustments.19.1.1 The relationship between aesthetics and geom properties.19.1 Working with layer specific geom properties.18.3 Increasing the information density. 18.2.2 The standard way of using ggplot2.17.3 Populations, samples and distributions.17.2.1 Numeric vs. categorical variables.15.1 Summarising variables with summarise.13.3 Reording observations with arrange.12.3.1 Transforming and dropping variables.12.2.1 Renaming variables with select and rename.10.5 Importing data with RStudio (Avoid this!).
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