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The Guerilla Guide to R Update: Okay. I’ve uploaded a new template and things seem to be fine now. Update: I am aware the table of contents is not being displayed in bullet form as I intended. The web template I’m using seems to be buggy. It also seems to think this page is in Indonesian…Working on it! Table of Contents: About: Stack Overflow is awesome. This is why I’ve collated, The Guerilla Cookbook for R. The cool thing is, this “book” essentially writes itself since most of the experts (and peer-reviewers) are answering the questions. How Was The Content Selected? I personally searched through Stack Overflow to find my favorite questions and shared them here.

Code School - Try R Google Dev R Video Lectures I got this Google Developers R Programming Video Lectures from Stephen's blog - Getting Genetics Done. Very useful R tutorial for beginner! Short and efficient. Here is what I learned after watching the lectures: stop() and warning() function I was asked this question during a job interview. stop('message') will print out the error message and stop the function. warning('message') will print out the error message but continue the function. Passing additional arguments using an ellipsis(...), for example: myFunc <- function(a, return colMean(a, will allow us to call the function like myFun(a, na.rm=T), for example. You can also record the ellipsis arguments by args=list(...) return() vs. invisible() return() will return the values and print out in the screen. invisible will return the values but not print out to the screen. use recall() to call recursive function in R.

Curso gratuito sobre Introducción al tratamiento de datos con R y RStudio R es un lenguaje que estoy aprendiendo en un curso de Coursera. Me llama mucho la atención algunos tipos de datos que tiene y lo sencillo que es manipularlos comparando otros lenguajes. Por ejemplo las fechas es tan fácil usarlos en cualquier estructura como en listas, arrays, etc. En MiriadaX se estará realizando un curso sobre el tratamiento de datos con R y RStudio. RStudio es un IDE muy bueno para usar el lenguaje R. Este curso es una introducción al manejo de R y RStudio en la descripción, análisis y visualización de conjuntos de datos. El curso es gratuito, online, en español, dura 8 semanas y empieza el 3 de octubre de 2016. Módulos: Módulo 0: Presentación.Módulo 1: Introducción al R: instalación y uso básico.Módulo 2: Listas y matrices.Módulo 3: Tablas de datos.Módulo 4: Gráficos I.Módulo 5: Descripción de datos cualitativos y ordinales.Módulo 6: Descripción de datos cuantitativos.Módulo 7: Datos cuantitativos agrupados.Módulo 8. Inscribite Imagen: Centro de Educación Continua

Guide to Speeding Up R Code This is an overview of tools for speeding up your R code that I wrote for the Davis R Users’ Group. First, Ask “Why?” It’s customary to quote Donald Knuth at this point, but instead I’ll quote my twitter buddy Ted Hart to illustrate a point: I’m just going to say it.I like for loops in #Rstats, makes my code readable.All you [a-z]*ply snobs can shove it! — Ted Hart (@DistribEcology) March 12, 2013 Code optimization is a matter is a matter of personal taste and priorities. (1) Do you want your code to be readable? If you need to explain your code to yourself or others, or you will need to return to it in a few months time and understand what you wrote, it’s important that you write it in a way that is easy to understand. Some optimal code can be hard to read. (2) Do you want your code to be sharable? Most of the considerations of (1) apply here, but they have to be balanced with the fact that, if your code is painfully slow, others are not going to want or have time to use it. No? compare

ARIMA Modeling with R - Listen Data This tutorial explains the theoretical concepts of time series and ARIMA modeling and how we can forecast series using ARIMA with R. Time Series A time series is a data series consisting of several values over a time interval. e.g. daily Stock Exchange closing point, weekly sales and monthly profit of a company etc. Typically, in a time series it is assumed that value at any given point of time is a result of its historical values. This assumption is the basis of performing a time series analysis. So talking mathematically, Vt = p(Vt-n) + e Live Online Training :Data Science with R - Explain Advanced Algorithms in Simple English - Live Projects - Case Studies - Job Placement Assistance - Get 10% off till Sept 01, 2017 - Batch starts from October 8, 2017 It means value (V) at time "t" is a function of value at time "n" instance ago with an error (e). Example : Suppose Mr. ARIMA, as its full form indicates that it involves two components : 1. Lag - 2. Moving Average - ARIMA Modeling Steps Load Data

Creating your personal, portable R code library with GitHub As I discussed in a previous post, I have a few helper functions I’ve created that I commonly use in my work. Until recently, I manually included these functions at the start of my R scripts by either the tried and true copy-and-paste method, or by extracting them from a local file with the <code>source()</code> function. The former approach has the benefit of keeping the helper code inextricably attached to the main script, but it adds a good bit of code to wade through. The latter approach keeps the code cleaner, but requires that whoever is running the code always has access to the sourced file and that it is always in the same relative path – and that makes sharing or moving code more difficult. The resulting approach takes advantage of GitHub Gists and R’s ability to source via a web-based location to enable you to create a personal, portable library of R functions for private use or to share. The process is very straightforward.

Easy Book Publishing with R Markdown Style - Hadley Good coding style is like using correct punctuation. You can manage without it, but it sure makes things easier to read. As with styles of punctuation, there are many possible variations. The following guide describes the style that I use (in this book and elsewhere). It is based on Google’s R style guide, with a few tweaks. Good style is important because while your code only has one author, it’ll usually have multiple readers. The formatR package, by Yihui Xie, makes it easier to clean up poorly formatted code. Notation and naming File names File names should be meaningful and end in .R. # Good fit-models.R utility-functions.R # Bad foo.r stuff.r If files need to be run in sequence, prefix them with numbers: 0-download.R 1-parse.R 2-explore.R Object names “There are only two hard things in Computer Science: cache invalidation and naming things.”— Phil Karlton Variable and function names should be lowercase. # Good day_one day_1 # Bad first_day_of_the_month DayOne dayone djm1 Syntax Spacing