Download e-book for kindle: Beginning Data Science in R: Data Analysis, Visualization, by Thomas Mailund

By Thomas Mailund

ISBN-10: 1484226712

ISBN-13: 9781484226711

Discover top practices for information research and software program improvement in R and begin at the route to turning into a fully-fledged information scientist. This booklet teaches you concepts for either info manipulation and visualization and indicates you the way in which for constructing new software program programs for R.
Beginning facts technological know-how in R info how facts technology is a mixture of facts, computational technological know-how, and desktop studying. You’ll see tips to successfully constitution and mine info to extract precious styles and construct mathematical types. This calls for computational equipment and programming, and R is a perfect programming language for this. 
This e-book relies on a few lecture notes for sessions the writer has taught on information technology and statistical programming utilizing the R programming language. sleek facts research calls for computational abilities and customarily no less than programming. 
What you'll Learn

  • 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

Who This booklet Is For

Those with a few info technological know-how or analytics historical past, yet now not inevitably adventure with the R programming language.

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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.

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Beginning Data Science in R: Data Analysis, Visualization, and Modelling for the Data Scientist by Thomas Mailund

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