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原创 【undergoing】快速学习使用LaTeX!

//2014/11/19 周二 周五开始美赛模拟,第一次做,还很不了解情况。

2014-11-19 09:44:40 741

原创 将博客搬至CSDN

搬家啦!

2014-03-16 18:53:09 523 1

R for Everyone Advanced Analytics and Graphics, Lander, 2014

开源的R语言是全世界数据科学家使用最广泛的语言。 --- Statistical Computation for Programmers, Scientists, Quants, Users, and Other Professionals Using the R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. COVERAGE INCLUDES Exploring R, RStudio, and R packages Using R for math: variable types, vectors, calling functions, and more Exploiting data structures, including data.frames, matrices, and lists Creating attractive, intuitive statistical graphics Writing user-defined functions Controlling program flow with if, ifelse, and complex checks Improving program efficiency with group manipulations Combining and reshaping multiple datasets Manipulating strings using R’s facilities and regular expressions Creating normal, binomial, and Poisson probability distributions Programming basic statistics: mean, standard deviation, and t-tests Building linear, generalized linear, and n

2014-11-19

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