By Thomas Mailund
- Perform information technology and analytics utilizing facts and the R programming language
- Visualize and discover info, together with operating with huge information units present in tremendous data
- Build an R package
- Test and fee your code
- Practice model control
- Profile and optimize your code
Read or Download Beginning Data Science in R: Data Analysis, Visualization, and Modelling for the Data Scientist PDF
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Extra resources for Beginning Data Science in R: Data Analysis, Visualization, and Modelling for the Data Scientist
V <- 1:4 names(v) <- LETTERS[1:4] v ## A B C D ## 1 2 3 4 (ff <- factor(LETTERS[1:4])) 17 Chapter 1 ■ Introduction to R Programming ##  A B C D ## Levels: A B C D v[ff] ## A B C D ## 1 2 3 4 We are lucky to get the expected result, though. Because this expression is not indexing using the names we might expect it to use. Read the following even more carefully! (ff <- factor(LETTERS[1:4], levels = rev(LETTERS[1:4]))) ##  A B C D ## Levels: D C B A v[ff] ## D C B A ## 4 3 2 1 This time ff is still a vector with the categories A to D in that order, but we have specified that the levels are D, C, B, and A, in that order.
The results of a data analysis project is typically a report describing models and analysis results, and it is natural to think of this document as the primary product. So the documentation is already the main focus. The only thing needed to use literate programming is a way of putting the analysis code inside the documentation report. Many programming languages have support for this. com/ mathematica/) has always had notebooks where you could write code together with documentation. org), the descendant of iPython Notebook, lets you write notebooks with documentation and graphics interspersed with executable code.
While you would get an error if you called a function with a variable name that doesn’t exist, you won’t necessarily get a simple error. If you just call a function with incorrect data, you might not notice it, but it would probably give you the wrong result. It would not be an error easy to debug later. There is slightly less of a problem with reassigning to a variable. It is mostly an issue when you work with R interactively. There, if you want to go back and change part of the program you are writing, you have to go all the way back to the start, where the data is imported.