
By Thomas Mailund
ISBN-10: 1484226712
ISBN-13: 9781484226711
- Perform facts technological know-how and analytics utilizing facts and the R programming language
- Visualize and discover info, together with operating with huge information units present in immense data
- Build an R package
- Test and payment 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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Additional info for Beginning Data Science in R: Data Analysis, Visualization, and Modelling for the Data Scientist
Example text
Often they provide LaTeX templates, and you can modify these to work with Markdown. There isn’t much support for this in RStudio, but for HTML documents, you can use the Output Options command (click on the tooth-wheel) to choose different output formatting. 39 Chapter 2 ■ Reproducible Analysis Running R Code in Markdown Documents The formatting so far is all Markdown (and YAML). Where it combines with R and makes it R Markdown is through knitr. When you format a document, the first step evaluates R code to create a Markdown document.
If you just write R Markdown documents, then RStudio will let you compile them into different types of output documents. But because the pipeline goes from R Markdown via knitr to Markdown and then via pandoc to the various output formats, you do have access to a very powerful tool for creating documents. I have written this book in R Markdown where each chapter is a separate document that I can run through knitr independently. I then have pandoc with some options take the resulting Markdown documents, combine them, and produce both output and Epub output.
Markdown is used a lot by people who document programs, so there is a notation for getting code highlighted in verbatim blocks. The convention is to write the name of the programming language after the three backticks, then the program used for formatting the document will highlight the code when it can. For R code you write r, so this block: ```r f <- function(x) ifelse(x %% 2 == 0, x**2, x**3) f(2) ``` Is formatted like this: f <- function(x) ifelse(x %% 2 == 0, x**2, x**3) f(2) The only thing this markup of blocks does is highlight the code.
Beginning Data Science in R: Data Analysis, Visualization, and Modelling for the Data Scientist by Thomas Mailund
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