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Code School - Try R

Code School - Try R

http://tryr.codeschool.com/

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R Bootcamp — Jared Knowles Intro Welcome to the R Bootcamp. Here you can find all the materials used for the Second R Bootcamp for Education at the Wisconsin Department of Public Instruction. These slides represent the slides presented on December 3rd-5th of 2012. However, the slides are being further developed to improve the relevance and usefulness of the material based on feedback received at each bootcamp. Exploratory Data Analysis Using R Lesson 1: What is EDA? (1 hour) We'll start by learn about what exploratory data analysis (EDA) is and why it is important. Free Statistical Programming Courses This list is a little different than the others. Most of this site is focused on R materials that you can consume quickly. The links below are for online coursework to help further your understanding of statistical programming. If you have the time to devote to regular study of this material, you'll definitely be glad that you did.

Academic Phrasebank – Writing Conclusions Conclusions are shorter sections of academic texts which usually serve two functions. The first is to summarise and bring together the main areas covered in the writing, which might be called ‘looking back’; and the second is to give a final comment or judgement on this. The final comment may also include making suggestions for improvement and speculating on future directions. In dissertations and research papers, conclusions tend to be more complex and will also include sections on the significance of the findings and recommendations for future work. Conclusions may be optional in research articles where consolidation of the study and general implications are covered in the Discussion section.

What is R? During the last decade, the momentum coming from both academia and industry has lifted the R programming language to become the single most important tool for computational statistics, visualization and data science. Worldwide, millions of statisticians and data scientists use R to solve their most challenging problems in fields ranging from computational biology to quantitative marketing. R has become the most popular language for data science and an essential tool for Finance and analytics-driven companies such as Google, Facebook, and LinkedIn. Watch this 90 second video for an introduction to R This video is free to download, remix and share!

Very gentle resource for speeding up R code Nathan Uyttendaele has written a great beginner’s guide to speeding up your R code. Abstract: Most calculations performed by the average R user are unremarkable in the sense that nowadays, any computer can crush the related code in a matter of seconds. But more and more often, heavy calculations are also performed using R, something especially true in some fields such as statistics. The user then faces total execution times of his codes that are hard to work with: hours, days, even weeks. Design of Experiments – Full Factorial Designs In designs where there are multiple factors, all with a discrete group of level settings, the full enumeration of all combinations of factor levels is referred to as a full factorial design. As the number of factors increases, potentially along with the settings for the factors, the total number of experimental units increases rapidly. In many cases each factor takes only two levels, often referred to as the low and high levels, the design is known as a 2^k experiment. Given a three factor setup where each factor takes two levels we can create the full factorial design using the expand.grid function: expand.grid(Factor1 = c("Low", "High"), Factor2 = c("Low", "High"), Factor3 = c("Low", "High"))

Academic Phrasebank – Discussing Findings The term ‘discussion’ has a variety of meanings in English. In academic writing, however, it usually refers to two types of activity: a) considering both sides of an issue, or question before reaching a conclusion; b) considering the results of research and the implications of these. Discussion sections in dissertations and research articles are probably the most complex sections in terms of their elements. They normally centre around a ‘statement of result’ or an important ‘finding’. As there is usually more than one result, discussion sections are often structured into a series of discussion cycles. Julia Studio Beginner These tutorials will help you to familiarize yourself with the Julia Studio environment and the basics of the language. Hello, World! 1 Star This tutorial will walk you through creating a project in Julia Studio and have your write your first program. Start Tutorial Prime Numbers 1 Star Learn how to define functions and get your hands dirty with some actual math. Start Tutorial Quadratic Equation Solver 2 Stars Julia gives you all the power of a functional programming language, with the performance you would expect from C++.

R Basics For exploring data and doing open-ended statistical analysis on it, nothing beats the R language. Over the years, this open-source tool has come to dominate the way we do analysis and visualization; It has attracted a rich and varied collection of third-party libraries that has given it remarkable versatility: But how do you get started? Casimir explains how to get started, and get familiar with the way it works. As mentioned on the simple-talk blog, Microsoft recently acquired Revolution Analytics, a leading commercial provider of software and services for the open source R programming language. GrapheR: A GUI for base graphics in R How did I miss the GrapheR package? The author, Maxime Hervé, published an article about the package [1] in the same issue of the R Journal as we did on googleVis. Yet, it took me a package update notification on CRANbeeries to look into GrapheR in more detail - 3 years later! And what a wonderful gem GrapheR is.

Academic Phrasebank – Reporting Results The standard approach to this section of a research article or dissertation is to present and describe the results in a systematic and detailed way. When reporting qualitative results, the researcher will highlight and comment on the themes that emerge from the analysis. These comments will often be illustrated with excerpts from the raw data. In text based studies, this may comprise quotations from primary sources.

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