Learning R The R Project for Statistical Computing index • Git Cheatsheet • NDP Software stash workspace index local repository upstream repository status Displays paths that have differences between the index file and the current HEAD commit, paths that have differences between the workspace and the index file, and paths in the workspace that are not tracked by git. diff Displays the differences not added to the index. diff commit or branch View the changes you have in your workspace relative to the named <em>commit</em>. add file... or dir... Adds the current content of new or modified files to the index, thus staging that content for inclusion in the next commit. add -u Adds the current content of modified (NOT NEW) files to the index. rm file(s)... Remove a file from the workspace and the index. mv file(s)... Move file in the workspace and the index. commit -a -m 'msg' Commit all files changed since your last commit, except untracked files (ie. all files that are already listed in the index). checkout files(s)... or dir Updates the file or directory in the workspace. reset --soft HEAD^
Summer 2010 — R: ggplot2 Intro Contents Intro When it comes to producing graphics in R, there are basically three options for your average user. base graphics I've written up a pretty comprehensive description for use of base graphics here, and don't intend to extend beyond that. Both and make creating plots of multivariate data easier. The website for ggplot2 is here: Basics is meant to be an implementation of the Grammar of Graphics, hence gg-plot. Plots convey information through various aspects of their aesthetics. x position y position size of elements shape of elements color of elements The elements in a plot are geometric shapes, like points lines line segments bars text Some of these geometries have their own particular aesthetics. points point shape point size lines line type line weight bars y minimum y maximum fill color outline color text label value The values represented in the plot are the product of various statistics. Layer by Layer Displaying Statistics
R Tutorial Series: R Tutorial Series: Introduction to The R Project for Statistical Computing (Part 1) R is a free, cross-platform, open-source statistical analysis language and program. It is also an alternative to expensive commercial statistics software such as SPSS. The environment for R differs from the typical point and click interface found in most professional office applications. Although it does take some effort to become familiar with, R ultimately proves to be an affordable, customizable, and expandable statistical analysis solution. Acquiring R R is free, open-source software that runs on Mac OS, Windows, Linux, and Unix platforms. The R Interface The R interface is composed of three main parts. R Commands Commands are most commonly issued to R in the form of functions. q() The Working Directory One of the initial things that you want to do when you launch R for the first time is to set its working directory. > getwd()[1] "/Users/Admin/Documents/R" > setwd('/Users/Admin/Documents/R/newProject') Use getwd() again to verify that the change took place. Packages > available.packages()
developers:projects:gsoc2012:ropensci Summary: Dynamic access and visualization of scientific data repositories Description: rOpenSci is a collaborative effort to develop R-based tools for facilitating Open Science. Projects in rOpenSci fall into two categories: those for working with the scientific literature, and those for working directly with the databases. Visit the active development hub of each project on github, where you can see and download source-code, see updates, and follow or join the developer discussions of issues. Most of the packages work through an API provided by the resource (database, paper archive) to access data and bring it within reach of R’s powerful manipulation. See a complete list of our R packages currently in development. The student could choose to work on a package for a particular data repository of interest, or develop tools for visualization and exploration that could function across the existing packages.
In-depth introduction to machine learning in 15 hours of expert videos In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). I found it to be an excellent course in statistical learning (also known as "machine learning"), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book. If you are new to machine learning (and even if you are not an R user), I highly recommend reading ISLR from cover-to-cover to gain both a theoretical and practical understanding of many important methods for regression and classification. If you decide to attempt the exercises at the end of each chapter, there is a GitHub repository of solutions provided by students you can use to check your work. P.S.
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