That lets you re-use your analysis work on similar data more easily than if you were using a point-and-click interface, notes Hadley Wickham, author of several popular R packages and chief scientist with RStudio.
This is an excellent and thorough introduction to statistical analysis. It is easy to follow and provides complete coverage of key concepts in introductory courses. Editorial Reviews. Review. This is an excellent and thorough introduction to statistical analysis. It is easy to follow and provides complete coverage of key.
The error itself wasn't a surprise, blogs Christopher Gandrud , who earned a doctorate in quantitative research methodology from the London School of Economics. Sure, you can easily examine complex formulas on a spreadsheet. But it's not nearly as easy to run multiple data sets through spreadsheet formulas to check results as it is to put several data sets through a script, he explains.
Indeed, the mantra of "Make sure your work is reproducible! Why not R?
Well, R can appear daunting at first. That's often because R syntax is different from that of many other languages, not necessarily because it's any more difficult than others. Cook in a Web post about R programming for those coming from other languages.
And so, this guide. Our aim here isn't R mastery, but giving you a path to start using R for basic data work: Extracting key statistics out of a data set, exploring a data set with basic graphics and reshaping data to make it easier to analyze.
Error rating book. Testing categorical variable independence in contingency tables. Bunga Pratiwi Bunga. Table of Contents General Information. Knowledge of a basic course in statistics would be beneficial, but not necessary. Percentiles of the t-distribution. Graphical trend of variable association.
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Some more basic commands are given below - try them in turn and see what they do. R is a full programming language, and before long, you are likely to want to add your own functions. Consider the following declaration.
The second line declares a local variable x to be a matrix with n rows and p columns, whose elements are standard normal random variables. The next line forms a new matrix y whose elements are the squares of the elements in x. The last line computes the matrix-vector product of y and a vector of p ones, then coerces the resulting n by 1 matrix into a vector. The result of the last line of the function body is the return result of the function. In fact, this function provides a fairly efficient way of simulating Chi-squared random quantities with p degrees of freedom, but that isn't particularly important.
The function is just another R object, and hence can be viewed by entering rchi on a line by itself.
It can be edited by doing fix rchi. You might want to look at my R Hints and Tips for a few tips on working with functions in R. Of course, in order to use R for data analysis, it is necessary to be able to read data into R from other sources. It is often also desirable to be able to output data from R in a format that can be read by other applications.
Unsurprisingly, R has a range of functions for accomplishing these tasks, but we shall just look here at the two simplest.
The next thing to work through is the official Introduction to R , which covers more stuff in more depth than this very quick intro.