Learning R: A Step-by-Step Function Guide to Data Analysis by Richard Cotton

By Richard Cotton

How to practice facts research with the R language and software program surroundings, whether you've got very little programming event. With the tutorials during this hands-on consultant, you will find out how to use the basic R instruments you want to comprehend to research info, together with info kinds and programming options. the second one 1/2 studying R exhibits you actual information research in motion by means of protecting every little thing from uploading info to publishing your effects. each one bankruptcy within the ebook encompasses a quiz on what you've gotten realized, and concludes with routines, so much of which contain writing R code.

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That is, all vectors have a length, which tells us how many elements they contain. This is a nonnegative inte‐ ger1 (yes, zero-length vectors are allowed), and you can access this value with the length function. Missing values still count toward the length: length(1:5) ## [1] 5 length(c(TRUE, FALSE, NA)) ## [1] 3 One possible source of confusion is character vectors. With these, the length is the number of strings, not the number of characters in each string. For that, we should use nchar: sn <- c("Sheena", "leads", "Sheila", "needs") length(sn) ## [1] 4 nchar(sn) ## [1] 6 5 6 5 It is also possible to assign a new length to a vector, but this is an unusual thing to do, and probably indicates bad code.

This is, as you might expect, a combination of the ls and str functions, and is very useful during debugging sessions (see “Debugging” on page 292 in Chap‐ ter 16). info After working for a while, especially while exploring data, our workspace can become quite cluttered. We can clean it up by using the rm function to remove variables: rm(peach, plum, pear) rm(list = ls()) #Removes everything. Use with caution! Summary • All variables have a class. • You test if an object has a particular class using the is function, or one of its classspecific variants.

Use comments to document your code, so that you can remember what you were doing six months ago. search do the same things as ? , respectively, but with these you always need to enclose your arguments in quotes. search("regression model") The apropos function4 finds variables (including functions) that match its input. This is really useful if you can only half-remember the name of a variable that you’ve created, or a function that you want to use. vector" The results contain the variable you just created, a_vector, and all other variables that contain the string vector.

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