Learn R. Upload mybringback.com mybringback Loading... Working... ► Play all Learn R mybringback23 videos17,852 viewsLast updated on Jun 26, 2014 Play all Sign in to YouTube Sign in History Sign in to add this to Watch Later Add to Loading playlists... Why R is Hard to Learn. By Bob Muenchen R has a reputation of being hard to learn. Some of that is due to the fact that it is radically different from other analytics software. Some is an unavoidable byproduct of its extreme power and flexibility. And, as with any software, some is due to design decisions that, in hindsight, could have been better. If you have experience with other analytics tools, you may at first find R very alien. Training and documentation that leverages your existing knowledge and which points out where your previous knowledge is likely to mislead you can save much frustration.
This is the approach I use in my books, R for SAS and SPSS Users and R for Stata Users as well as the workshops that are based on them. Below is a list of complaints about R that I commonly hear from people taking my R workshops. Unhelpful Help R’s help files are often thorough and usually contain many working examples. Misleading Function or Parameter Names Too Many Commands Inconsistent Syntax Identity Crisis. Quick-R: Home Page. An R Introduction to Statistics | R Tutorial.
Swirl: Learn R, in R. Code School - Try R. Beginner's guide to R: Introduction. R is hot. Whether measured by more than 4,400 add-on packages, the 18,000+ members of LinkedIn's R group or the close to 80 R Meetup groups currently in existence, there can be little doubt that interest in the R statistics language, especially for data analysis, is soaring. Why R? It's free, open source, powerful and highly extensible.
"You have a lot of prepackaged stuff that's already available, so you're standing on the shoulders of giants," Google's chief economist told The New York Times back in 2009. Because it's a programmable environment that uses command-line scripting, you can store a series of complex data-analysis steps in R. That lets you re-use your analysis work on similar data more easily than if you were using a point-and-click interface, notes Hadley Wickham, author of several popular R packages and chief scientist with RStudio.
Sure, you can easily examine complex formulas on a spreadsheet. Indeed, the mantra of "Make sure your work is reproducible! " Why not R? R Tutorial — R Tutorial. DataVisualization. Statistical Learning. About This Course This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).
This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data analysis. Computing is done in R. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter. Course Staff. Coursera - Free R Progamming Course.