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Welcome to a Little Book of R for Time Series! — Time Series 0.2 documentation. SQL and R. Not only can you easily retrieve data from SQL Sources for analysis and visualisation in R, but you can also use SQL to create, clean, filter, query and otherwise manipulate datasets within R, using a wide choice of relational databases.

SQL and R

There is no reason to abandon your hard-earned SQL skills! The R platform and programming language supports a vast array of data science techniq. With decades of history and over 7,000 packages available on CRAN it can be overwhelming to determine where to start. The R-Basics and Visualizing Data with R articles provide initial direction, but don’t go into much detail about how to manipulate datasets within R. Fortunately, database professionals can be productive quickly in this realm by leveraging their well-honed SQL skills. There is a great deal of excitement regarding Microsoft’s acquisition of Revolution Analytics that subsequently lead to R being integrated into SQLServer 2016. The RSQLite package The “mtcars” dataset is included by default in R. R. Learn R. R. Go/r-resources.md at master · datasciencemasters/go.

Sweave. Resources and Tutorials. RWeb. Resources and Tutorials. R-Node ( WebUI4R Built on NodeJS ) R.NET - Home. R Markdown — Dynamic Documents for R. Videos. Data-Driven Security: Analysis, Visualization and Dashboards. Benrothke writes There is a not so fine line between data dashboards and other information displays that provide pretty but otherwise useless and unactionable information; and those that provide effective answers to key questions.

Data-Driven Security: Analysis, Visualization and Dashboards

Data-Driven Security: Analysis, Visualization and Dashboards is all about the later. In this extremely valuable book, authors Jay Jacobs and Bob Rudis show you how to find security patterns in your data logs and extract enough information from it to create effective information security countermeasures. By using data correctly and truly understanding what that data means, the authors show how you can achieve much greater levels of security. Keep reading for the rest of Ben's review. The book is meant for a serious reader who is willing to put in the time and effort to learn the programming necessary (mainly in Python and R) to truly understand what information exists deep in the recesses of their logs.

Reviewed by Ben Rothke. Ten Steps to Building an R package under Windows. If you’ve spent any amount of time working in R there comes a point when you want to create your own package in R.

Ten Steps to Building an R package under Windows

I’ve switched back and forth between running R on my MAC and running it on Windows and I thought I’d give package development on Windows a try. What I’ll try to do on these pages is document that process step by step for the raw beginner. First off, I need to thank all the R experts who have help put all the tools and documentation in place. Without them none of this would be possible. How to Build a Package in Windows Use the guide below to jump to any section you want. Guide to Getting Started in Machine Learning. Someone at work recently asked how he should go about studying machine learning on his own.

Guide to Getting Started in Machine Learning

So I’m putting together a little guide. This post will be a living document…I’ll keep adding to it, so please suggest additions and make comments. Fortunately, there’s a ton of great resources that are free and on the web. The very best way to get started that I can think of is to read chapter one of The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2009 edition). The pdf is available online. Once you’ve read the first chapter, download R. Once you’ve installed R, maybe played around a little, then check out this page which describes the major machine learning packages in R. Oh, by the way, if you want to start playing around with machine learning in R, you’ll need data.

I’d suggest next reading more of The Elements of Statistical Learning. Another great resource is the machine learning course MIT has posted on their OpenCourseWare site. I’ll stop here now. Machine Learning in R, in a nutshell. Machine learning - R and Data Mining. R as a Programming Language. The R Language The Good The Bad And The Ugly - John Cook. Introduction to R Programming: Download, Install and Setup R & RStudio (R Tutorial 1.0) Devtools - RStudio. Devtoolsbill@rstudio.com2014-04-10T14:07:14+00:00 Painless package development for R Devtools makes package development a breeze: it works with R’s existing conventions for code structure, adding efficient tools to support the cycle of package development.

devtools - RStudio

With devtools, developing a package becomes so easy that it will be your default layout whenever you’re writing a significant amount of code. Before you get started be sure to check out: Getting started Install devtools by running: install.packages(“devtools”) If you’ve never developed a package before, start by reading about package basics.

R Programming Tutorial Lesson 1: Downloading and Installing. Roxygen. R Commander. John Fox and Milan Bouchet-Valat Please Read the Rcmdr Installation Notes (click on the image for a larger view) For more details, see my paper on the R Commander in the Journal of Statistical Software (which is somewhat out of date) and the introductory manual distributed with the package (accessible via the Help -> Introduction to the R Commander menu).

R Commander

The R-Commander GUI consists of a window containing several menus, buttons, and information fields. (The menu tree, etc., are shown below.) The menus lead to simple dialog boxes, the general contents of which are more or less obvious from the names of the menu items. By default, commands generated via the dialogs are posted to the output window, along with printed output, and to the script window. T Distribution in R (R Tutorial 3.4) Introduction to R Webinar 1 of 7. Introduction to R Webinar - Full.

YouTube. R programming books (updated) R Programming Language. RSeek.org R-project Search Engine. The R Book, 2nd Edition - Michael J. Crawley. The R programming language for programmers coming from other programming languages. IntroductionAssignment and underscoreVariable name gotchasVectorsSequencesTypesBoolean operatorsListsMatricesMissing values and NaNsCommentsFunctionsScopeMisc.Other resources Ukrainian translation Other languages: Powered by Translate Introduction I have written software professionally in perhaps a dozen programming languages, and the hardest language for me to learn has been R.

The R programming language for programmers coming from other programming languages

R is more than a programming language. This document is a work in progress. R Statistical Programming Language. Since R is freely available there is quite a bit of educational material available for free as well.

R Statistical Programming Language

While there are dozens online, not all are worth your time. You'll be safe with any (or all) of these. R For Beginners by Emmanuel Paradis - Excellent book available through CRAN. Introduction to R. YouTube. R 1.1 - Initial Setup and Navigation. YouTube. Shiny.

R Help

A GUI for R - Deducer Manual. An R Graphical User Interface (GUI) for Everyone Deducer is designed to be a free easy to use alternative to proprietary data analysis software such as SPSS, JMP, and Minitab.

A GUI for R - Deducer Manual

It has a menu system to do common data manipulation and analysis tasks, and an excel-like spreadsheet in which to view and edit data frames. The goal of the project is two fold. Provide an intuitive graphical user interface (GUI) for R, encouraging non-technical users to learn and perform analyses without programming getting in their way. Increase the efficiency of expert R users when performing common tasks by replacing hundreds of keystrokes with a few mouse clicks. Deducer is designed to be used with the Java based R console JGR, though it supports a number of other R environments (e.g. Download and Install Join the Help/Discussion Group Screen shots. Knitr: Elegant, flexible and fast dynamic report generation with R. Overview The knitr package was designed to be a transparent engine for dynamic report generation with R, solve some long-standing problems in Sweave, and combine features in other add-on packages into one package (knitr ≈ Sweave + cacheSweave + pgfSweave + weaver + animation::saveLatex + R2HTML::RweaveHTML + highlight::HighlightWeaveLatex + 0.2 * brew + 0.1 * SweaveListingUtils + more).

knitr: Elegant, flexible and fast dynamic report generation with R

This package is developed on GitHub; for installation instructions and FAQ’s, see README. This website serves as the full documentation of knitr, and you can find the main manual, the graphics manual and other demos / examples here. For a more organized reference, see the knitr book. Motivation One of the difficulties with extending Sweave is we have to copy a large amount of code from the utils package (the file SweaveDrivers.R has more than 700 lines of R code), and this is what the two packages mentioned above have done.

Features Acknowledgements Misc. The Comprehensive R Archive Network. RStudio - Home. The R Project for Statistical Computing.