Best products from r/rstats

1. The R Book is probably the most widely used text for beginners, but if you know what types of data you will be working with and the types of analyses you want to run, there are more specific texts that can help walk you through. On the other hand, there are a ton of online resources you can access for free. The only time I've bought a book for R was for timeseries analysis. I bought it because I needed an introduction to the topic and it was handy having the accompanying script being written in R.
   
   
2. R works perfectly fine with both mac and windows. Unlike most people, though, I prefer not to use RStudio. To me it's just easier to keep everything organized by use the developer's console and writing and organizing my output in Notepad++.
   
   For hardware demands, large datasets won't be as big of an issue as model complexity, but given that modern computing demands keep going up, I think 16 GB should be standard for anyone getting a new computer. You'll be less likely to need to make an upgrade in the near future when OS and other software demands require you to do so. The bigger thing to consider is the read/write speed of your hard drive, so definitely go with an SSD. This will also help with power consumption and durability. For your CPU, R doesn't default to using multiple cores, so you'll want to look at single-core benchmarks when picking out your machine. With that being said, R does use multiple threads on a single core, so I wouldn't settle for an i5. But if you're using MCMC, a multicore CPU can be a lifesaver since you can assign your chains to run on different cores in parallel.
   
   I hope that helps. Good luck with your purchase!

From one R newbie to another, I have found the website Quick-R to be the most helpful. I was roped into using R on my thesis and have had to learn everything as I go, so I don't have the background that a lot of other R users do. That said, it's nice to have a condensed, plain-english reference without all the alphabet soup of theory.

I also use A Beginner's Guide to R to figure out the basic stuff and R in Action for more advanced topics. I don't know what your statistical background is, but you should also try to find a solid stats book related to your field.

As a last bit, and this may not be a popular sentiment on this sub, but I have found very little help through the r-help files, mailing lists, or stackoverflow. Most times the posts are written by and for people with much greater ability than mine so I have a really hard time gleaning any useful information. Also, I have found that there are a lot of condescending attitudes throughout and it's soured me to those sources. In my experience, the R community at large is not very friendly towards beginners.

Good luck on your adventuRe.

Quick and dirty answer: speaking very broadly, random forests -- found in the "randomForest" package -- tend to win battle-of-the-algorithms type studies. If you just want to play with a single model, I'd recommend starting with that and looking at the help for it.

Longer and better answer: Your best bet to answering all these questions and getting a good handle on data mining/predictive analytics is this book: Applied Predictive Modeling. The book references the "caret" package quite a bit, since the package's author is the same person. With it, you can train a lot of different types of models for regression or classification optimizing for accuracy, RMSE, ROC, etc. It provides a standard API for playing with models and makes your life much, much easier. It has its own website here.

A faculty member at my university put together a workshop and actually included stuff about implementing lavaan for Latent Growth Curve Models (LGCM). Notes for that workshop can be found here. I would also second using anything by Grimm. I took a class on LGCM and it was taught with this book by Grimm and it was extremely helpful.

I have become the go to person in my lab (psychology) for using lavaan and have helped my peers become more familiar with LGCM. If you have any specific questions, or would want someone to take a look at your code, I would be happy to! I really enjoy this type of stuff. Google and the lavaan Project will be super helpful! There are plenty of additional resources out there!

If you want to know just the practical use:

The closest the R2 goes to 1, the better the model. For example, an R2 of, say, 0.82 means: "82% of the variation of the dependent variable is explained by the variation of the dependent variables". Conversely, the less the R2, the less the predictive power of the dependent variables.

In order to evaluate the significance of each variable, you have to check the P values. The practical meaning is: the lower the p-value, the more useful is the variable. For example, a p-value of, say, 0.01 means the variable is very significant, and therefore should be implemented in the model. The level of significance of course depends on your work.

edit: If you want to know the theory, take a look at this. It is one of the best books. https://www.amazon.com/Basic-Econometrics-Damodar-Gujarati/dp/0073375772

My advice would be to find a dataset for yourself that you wish to analyse in some manner and work through that using the references you have and Stackoverflow to search for solutions to problems you encounter (and asking when you can't find a solution).

This is because, in my experience, most exercises are canned and have perfect working solutions provided, yet when you get to the real world working on your own data things are never perfect and you will have a lot of problems to solve.

One very useful thing I can recommend is to start learning how to use Knitr as a basis for making your work-flow from importing data through to producing reports (in LaTeX or HTML via R markdown) completely reproducible.

A couple of useful references though are the following books which all have R examples...

• Regression Modelling Strategies (also has the rms package
  
• An Introduction to Statistical Learning with Applications in R <- Free PDF version
  
• Elements of Statistical Learning <- Free PDF version (more in-depth than Intro to Stats Learning).
  
  The authors of the last two books do an online course Statistical Learning based on the Intro to Stat Learning book if you want to work through a course. I did this last year but am yet to put much of it into practice yet (and consequently have forgotten the majority of it, but the PDFs linked above will be very useful when I get round to using the methods).

I can recommend Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence by Judith Singer & John Willett.

The second half of the book considers survival analysis, and R code for the examples from each chapter of the book are available here -- in addition to code for SAS, SPSS Stata, etc.

Full disclosure: I have only worked through the first half of the book so far (growth curve modeling), but the text is very complete and the code on that webpage has been really helpful. I imagine the section on survival analysis is similarly helpful.

Another excellent book is CAR. I really like it. In addition to the regression content it teaches a good number of practices and tricks in working with R.

I would recommend Discovering Statistics Using R. It goes through the math of the stats in a pretty solid way with example experiments and available data files if you want to work along with it. I have used the SPSS version with my intro and intermediate stats classes, so using it with R would be great. Plus, Fields is funny as hell.

Deep Learning With Python is very good for practical application, as is the course at fast.ai. For theory, people love Goodfellow.

> I'd like to know, how did you learn to use R?

My batshit crazy lovable thesis advisor was teaching intro datascience in R.

He can't really lecture and he have high expectation. The class was for everybody including people that don't know how to program. The class book was advance R http://adv-r.had.co.nz/... (red flag).

We only survived this class because I had a cs undergrad background and I gave the class a crash course once. Our whole class was more about how to implement his version of random forest.

I learned R because we had to implement a version of Random forest with Rpart package and then create a package for it.

Before this a dabble in R for summer research. It was mostly cleaning data.

So my advice would be to have a project and use R.

>how did you learn statistics?

Master program using the wackerly book and chegg/slader. (https://www.amazon.com/Mathematical-Statistics-Applications-Dennis-Wackerly/dp/0495110817)

It's a real grind. You need to learn probability first before even going into stat. Wackerly was the only real book that break down the 3 possible transformations (pdf,cdf, mgf).

I really liked Yihui's Dynamic Documents with R and Knitr and I personally think it has all the information you need in order to rule over the power of Knitr. If you prefer to have all information you need in an easy to access place, it's worth it.

I recommend you to first study probability theory, (formal definitions, variable independence, moments, limit theorems, and some distributions) this is my favorite course, but it's in spanish http://lya.fciencias.unam.mx/lars/0625/, then I recommend this course in MIT: http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041sc-probabilistic-systems-analysis-and-applied-probability-fall-2013/ it's going to take you a couple months to finish it, it's a lot of material, you can complement the material with this course http://oli.cmu.edu/courses/free-open/statistics-course-details/ (it has some r exercises). and in the road, try to make all the ocw homeworks on r, using Rmd, and some latex. Use the R book as a reference http://www.amazon.com/The-Book-Michael-J-Crawley/dp/0470973927

Learning R, is more about learning the math behind probability and the basic concepts of statistics than learning the language.

1. Google resources for beginners R and also google introduction R (I particularly recommend google for this search).
   
   
2. Try R-bloggers, such as this search
   
   
3. There's this document, now also a book
   
   
4. There's a lot of help to be found on the stackexchange network. e.g. 1
   , 2, 3, 4, 5, 6, 7, 8, 9
   
   
5. 
   descriptives: summary, by, aggregate, apply, tapply, sapply etc
   ...... also plot, hist, boxplot, stripchart, barplot, dotchart etc
   t-tests: t.test (paired-t, independent samples Welch t, independent samples equal-variance t),
   ANOVA: lm, aov. Also anova itself produces anova tables from a variety of model output
   correlation: cor
   regression: lm
   
   
   

I didn't know about MLR until this post. So without having spent any time with it whatsoever, I would only say that one of the nice things about the caret package is that you can also leverage Kuhn and Johnson's book, Applied Predictive Modeling, as well as YouTube videos of Max Kuhn discussing caret.

you check the following book

Forecasting models – an overview with the help of R software : Time series - Past ,Present and Future

https://www.amazon.co.uk/dp/B07VFY53B1 (E-Book)

https://www.amazon.com/dp/1081552808 (Paperback)

ISBN: 9781081552800

This should get you started to port over some existing skills. You can then fill in the rest with one of the suggested references.

R for SAS and SPSS Users (Statistics and Computing) https://www.amazon.com/dp/1461406846/ref=cm_sw_r_cp_awd_2doPwbMVQTRYH