How to learn good R language? Featured Collections

methods of #学习 R

Knowledge and patience are the only ways to be strong.

-Learn by reading.
It includes reading classics, code, papers, and learning public lessons.
-Learn by the cattle people.
Including peer gatherings, discussions, Daniel's blogs, Weibo, Twitter, RSS.
-Learn by practicing.
It includes code exercises, participation in kaggle competitions, and solving problems in practical work.
-Learn by sharing.
It includes writing notes, writing blogs, writing books, translating books, sharing with peers and training new people.

# Read the list #

First, Beginners:
"R in Action"
From the statistical point of view, divided into three parts of high and low schools to explain how to use R to achieve statistical analysis.
"The Art of_r Programming"
From the point of view of program writing, the characteristics of R itself are clearly introduced.
"Learning R"
The book does not simply speak grammar, but with the flow of data analysis , from data acquisition to data processing to analysis and reporting, there is a sense of one go, in addition to the last two chapters on how to write robust R code and write packages are very exciting.

Second, statistics advanced:
"A Handbook of Statistical analyses_using_r"
"Modern applied Statistics with S"
These two books basically cover some high-level content of statistics, such as multivariate analysis, multilevel regression model, meta-analysis, survival analysis and so on. Case rich, the formula is not many, it is worth repeating study reference.

Third, scientific calculation:
"Introduction to scientific programming and Simulation Using R"
"Mastering Scientific Computing with R"
In addition to statistical analysis, the unique point is to use R to do numerical analysis, such as root, optimization, numerical integration. Some common simulation techniques are also included. The post-book exercises and the final case are very useful.

Iv. Data Mining:
"Practical Data Science with R"
Starting with the expansion packages and functions of R itself, the system introduces all aspects of data science.
"An Introduction to statistical learning" This book can be said to be another data mining masterpiece "The Elements of statistical learning" R implementation Manual, the architecture is basically consistent, More emphasis on using R to achieve, the more rare place is to provide a good exercise.
"Data Mining with R learning with case studies"
"Machine Learning for Hackers"
Two r books focusing on data mining, all based on the case, the demonstration code is very large. It will be a great achievement to follow it down.
"Data Mining explain using R"
Using basic functions to implement a variety of machine learning algorithms is helpful in understanding the underlying algorithm.
"Data Science in R"
A case-based book requires a certain basis for data mining.

Five, data mapping:
"Ggplot2 Elegant Graphics for Data analysis"
Ggplot2 what else to say, the best drawing package in R, but because of the recent upgrade of the package, the book seems a bit outdated. Fortunately, the Chinese version has been greatly updated and is coming soon.
"R Graphics Cookbook" This book is also Rstudio company's people, seems to be Hadley students, mainly a variety of ggplot2 package examples, but also includes the use of other packages to draw, it is recommended to read through.

Vi. Reference Manual:
"R Cookbook"
"R in a Nutshell"
Sometimes we need a desk reference book like a dictionary for easy access at any time. Or you can read it over again to check the gaps. Although the above two books are somewhat thicker, they are still recommended. The latter's Chinese version is also in the translation state.

Seven, advanced Programming:
"R Programming for Bioinformatics"
"Software for data analysis programming with R"
If you are a beginner, don't look at the two books above. If you want to advance to expert R users, then you need to take them in. The former explains the lesser known aspects of R, such as character processing, regular expression and XML, as well as error handling and interaction with other languages. The latter is a biblical guide to writing production-level code.
"Advanced R Programming" Hadley of the masterpiece, clearly explained the function of R programming ideas and write R package of various details, to enter the R master, have to read.

Li and Xiao Kai's works "R Language in Data Science" (published in June)

# Read Suggestions #

-Take notes while reading to write down some points or tips
-When reading the code, type the code and understand its meaning in the R environment
-Keep practicing and try to use the data around you for application analysis
-Understand the rationale behind expansion packages and functions (citation paper? GLM)

# network Resources #

-[Official R language Station] (http://www.r-project.org/)
-[R-blogger] (http://www.r-bloggers.com/)
-[Summary of R language resources] (Https://github.com/qinwf/awesome-R)
-[R language search engine] (http://www.rseek.org/)
-[R function online Help] (http://www.rdocumentation.org/)
-[Question and answer website about R] (HTTP://STACKOVERFLOW.COM/QUESTIONS/TAGGED/R)
-[An entry-level R online Tutorial] (http://tryr.codeschool.com/)
-[Interactive R online Tutorial] (https://www.datacamp.com)
-[Statistical capital] (http://cos.name/)
-[My blog] (http://xccds1977.blogspot.com)
-[Introduction to beginners of R language provided by the American Computer World magazine] (http://www.computerworld.com/s/article/9239625/Beginner_s_guide_to_R_Introduction)
-Various Cheatsheet
Http://cran.r-project.org/doc/contrib/Short-refcard.pdf
http://www.rstudio.com/resources/cheatsheets/

# Video Resources #

-[Google developers ' Intro to R] (http://www.youtube.com/watch?v=iffR3fWv4xw&list= PLOU2XLYXMSIK9QQFZTXEYBPHVRU-TRQAP)
-[Twotorials] (http://www.twotorials.com/)
-[Coursera]
-"Real data analyst " R Language Series (http://study.163.com/course/introduction/967017.htm)

# Practice List #

-Euler Project
https://projecteuler.net/

-Kaggle
http://www.kaggle.com/

How to learn good R language? Featured Collections