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StatKey. StatKey to accompany Statistics: Unlocking the Power of Data by Lock, Lock, Lock, Lock, and Lock.


ModernDive. Getting Started - For Students This book was written using the bookdown R package from Yihui Xie (Xie 2016).


In order to follow along and run the code in this book on your own, you’ll need to have access to R and RStudio. You can find more information on both of these with a simple Google search for “R” and for “RStudio.” An introduction to using R, RStudio, and R Markdown is also available in a free book here (Ismay 2016). It is recommended that you refer back to this book frequently as it has GIF screen recordings that you can follow along with as you learn. Think & Do: Technology Pages. Analysis of Variance. In an experiment study, various treatments are applied to test subjects and the response data is gathered for analysis.

Analysis of Variance

A critical tool for carrying out the analysis is the Analysis of Variance (ANOVA). It enables a researcher to differentiate treatment results based on easily computed statistical quantities from the treatment outcome. The statistical process is derived from estimates of the population variances via two separate approaches. The first approach is based on the variance of the sample means, and the second one is based on the mean of the sample variances.

Under the ANOVA assumptions as stated below, the ratio of the two statistical estimates follows the F distribution. Simple Linear Regression: A complete introduction with numeric example. Linear regression is a predictive modelling technique that aims to predict the value of an outcome variable based on one or more input predictor variables.

Simple Linear Regression: A complete introduction with numeric example

The aim is to establish a linear relationship (a mathematical formula) between the predictor variable(s) and the response variable, so we can use it to estimate the value of the response, when predictors values are known. Introduction. Flexdashboard: Easy interactive dashboards for R. RDocumentation. The R-Podcast. 5 plataformas para crear una página web gratis para usar en clase. Top 50 ggplot2 Visualizations - The Master List (With Full R Code) What type of visualization to use for what sort of problem?

Top 50 ggplot2 Visualizations - The Master List (With Full R Code)

This tutorial helps you choose the right type of chart for your specific objectives and how to implement it in R using ggplot2. This is part 3 of a three part tutorial on ggplot2, an aesthetically pleasing (and very popular) graphics framework in R. Advanced R Stats. Archive. 16 Jan 2017 » R Weekly 2017 Issue 3 09 Jan 2017 » R Weekly 2017 Issue 2 02 Jan 2017 » R Weekly 2017 Issue 1 26 Dec 2016 » Issue 31 19 Dec 2016 » Issue 30 12 Dec 2016 » Issue 29 05 Dec 2016 » Issue 28 28 Nov 2016 » Issue 27 21 Nov 2016 » Issue 26 14 Nov 2016 » Issue 25 07 Nov 2016 » Issue 24 31 Oct 2016 » Issue 23 24 Oct 2016 » Issue 22 17 Oct 2016 » Issue 21 10 Oct 2016 » Issue 20 03 Oct 2016 » Issue 19 26 Sep 2016 » Issue 18 19 Sep 2016 » Issue 17 12 Sep 2016 » Issue 16 05 Sep 2016 » Issue 15 29 Aug 2016 » Issue 14 22 Aug 2016 » Issue 13 15 Aug 2016 » Issue 12 08 Aug 2016 » Issue 11 01 Aug 2016 » Issue 10 25 Jul 2016 » Issue 9 18 Jul 2016 » Issue 8 11 Jul 2016 » Issue 7 04 Jul 2016 » Issue 6 03 Jul 2016 » R Weekly useR!


Bookdown: Easy Book Publishing with R Markdown. Authoring Books and Technical Documents with R Markdown. This short book introduces an R package, bookdown, to change your workflow of writing books.

Authoring Books and Technical Documents with R Markdown

It should be technically easy to write a book, visually pleasant to view the book, fun to interact with the book, convenient to navigate through the book, straightforward for readers to contribute or leave feedback to the book author(s), and more importantly, authors should not always be distracted by typesetting details. The bookdown package is built on top of R Markdown ( and inherits the simplicity of the Markdown syntax (you can learn the basics in five minutes; see Section 2.1), as well as the possibility of multiple types of output formats (PDF/HTML/Word/…). It has also added features like multi-page HTML output, numbering and cross-referencing figures/tables/sections/equations, inserting parts/appendices, and imported the GitBook style ( to create elegant and appealing HTML book pages.

The R Trader » Blog Archive » BERT: a newcomer in the R Excel connection. A few months ago a reader point me out this new way of connecting R and Excel.

The R Trader » Blog Archive » BERT: a newcomer in the R Excel connection

I don’t know for how long this has been around, but I never came across it and I’ve never seen any blog post or article about it. So I decided to write a post as the tool is really worth it and before anyone asks, I’m not related to the company in any way. BERT stands for Basic Excel R Toolkit. It’s free (licensed under the GPL v2) and it has been developed by Structured Data LLC. The R-Podcast. Learning Path - Data Science, Analytics, BI, Big Data. We live in a world of Information overload!

Learning Path - Data Science, Analytics, BI, Big Data

Google throws 1,270,000,000 billion in 0.39 seconds, when I search it for “Learn R”! This still does not reveal the entire picture – it probably hasn’t searched through all the YouTube videos, the GitHub repos, the presentations on SlideShare, numerous blogs and discussions happening on the topic! We come across people daily who are following too many things and chasing too many directions in their attempt to learn data science. LeaRning Path on R - Step by Step Guide to Learn Data Science on R. One of the common problems people face in learning R is lack of a structured path.

LeaRning Path on R - Step by Step Guide to Learn Data Science on R

They don’t know, from where to start, how to proceed, which track to choose? Though, there is an overload of good free resources available on the Internet, this could be overwhelming as well as confusing at the same time. To create this R learning path, Analytics Vidhya and DataCamp sat together and selected a comprehensive set of resources to help you learn R from scratch. This learning path is a great introduction for anyone new to data science or R, and if you are a more experienced R user you will be updated on some of the latest advancements. This will help you learn R quickly and efficiently. OnePageR – Togaware. A Survival Guide to Data Science with R These draft chapters weave together a collection of tools for the data scientist—tools that are all part of the R Statistical Software Suite. Each chapter is a collection of one (or more) pages that cover particular aspects of the topic.

Sci-Hub: removing barriers in the way of science. Statistical Analysis of List Experiments. The validity of empirical research often relies upon the accuracy of self-reported behavior and beliefs. Yet, eliciting truthful answers in surveys is challenging especially when studying sensitive issues such as racial prejudice, corruption, and support for militant groups. List experiments have attracted much attention recently as a potential solution to this measurement problem. Importing Data into R – RStudio. Plotting background data for groups with ggplot2. This tweet by mikefc alerted me to a mind-blowingly simple but amazing trick using the ggplot2 package: to visualise data for different groups in a facetted plot with all of the data plotted in the background.

Here’s an example that we’ll learn to make in this post so you know what I’m talking about: Credit where credit’s due Before continuing, I’d be remiss for not mentioning that the origin of this ingenious suggestion is Hadley Wickham. The tip comes in his latest ggplot book, for which hardcopies are available online at places like Amazon, and the code and text behind it are freely available on Hadley’s Github at this repository. Some motivating examples. Plotting individual observations and group means with ggplot2. @drsimonj here to share my approach for visualizing individual observations with group means in the same plot. Here are some examples of what we’ll be creating: I find these sorts of plots to be incredibly useful for visualizing and gaining insight into our data. BlogR. Understanding Pie Charts. Pie charts are perhaps the most ubiquitous chart type; they can be found in newspapers, business reports, and many other places. But few people actually understand the function of the pie chart and how to use it properly.

In addition to issues stemming from using too many categories, the biggest problem is getting the basic premise: that the pie slices sum up to a meaningful whole. The Part-Whole Relationship The pie chart is actually a very clever visual design that conveys one fact above all others with a minimum of visual cues: that the circle (the “pie”) represents some kind of whole, which is made up of the slices. Analytics Discussions. Creating a Treemap in R. Embedding R-generated Interactive HTML pages in MS PowerPoint. The importance of self-care. Tim Urban: Inside the mind of a master procrastinator.

Datacamp. Package pxR. MicroDatosEs. Ejercicios de mi clase de R – datanalytics. TrelliscopeJS with Plotly. Computing Sample Size for Variance Estimation. Inspiration and Help concerning R graphics. Exploratory Data Analysis Using R (Part-I) Explore R and ScaleR in 25 functions. Top 10 TED Talks for the Data Scientists.

R for Data Science. 7 Visualizations You Should Learn in R. What data patterns can lie behind a correlation coefficient? Jan Vanhove.