R数据分析 – Bear This In Mind..

R is a language and environment for statistical computing and graphics. It is a GNU project which is comparable to the S language and environment that was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be looked at as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

R provides a multitude of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and it is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

Certainly one of R’s strengths will be the ease that well-designed publication-quality plots can be manufactured, including mathematical symbols and formulae where needed. Great care has become bought out the defaults for your minor design choices in R代写, however the user retains full control.

R can be obtained as Free Software under the regards to the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a multitude of UNIX platforms and other systems (including FreeBSD and Linux), Windows and MacOS.

The R environment – R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It provides

* a highly effective data handling and storage facility,

* a suite of operators for calculations on arrays, in particular matrices,

* a sizable, coherent, integrated variety of intermediate tools for data analysis,

* graphical facilities for data analysis and display either on-screen or on hardcopy, and

* a well-developed, simple and effective programming language which include conditionals, loops, user-defined recursive functions and input and output facilities.

The word “environment” is intended to characterize it as a completely planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is also frequently the case along with other data analysis software.

R, like S, was created around a real computer language, plus it allows users to incorporate additional functionality by defining new functions. Much of the system is itself written in the R dialect of S, making it easier for users to follow along with the algorithmic choices made. For computationally-intensive tasks, C, C and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.

Many users think of R as a statistics system. We would rather consider it an environment within which statistical techniques are implemented. R can be extended (easily) via packages. There are approximately eight packages supplied with the R distribution and much more are available from the CRAN group of Internet sites covering a very wide range of recent statistics. R has its own LaTeX-like documentation format, which is often used to offer comprehensive documentation, both on-line in a number of formats as well as in hardcopy.

Should you choose R? Data scientist can use two excellent tools: R and Python. You may not have access to time to learn both of them, particularly if you get going to learn data science. Learning statistical modeling and algorithm is way more important rather than to become familiar with a programming language. A programming language is really a tool to compute and communicate your discovery. The most significant task in rhibij science is how you handle the info: import, clean, prep, feature engineering, feature selection. This should be your primary focus. Should you be learning R and Python simultaneously without a solid background in statistics, its plain stupid. Data scientist usually are not programmers. Their job is to understand the data, manipulate it and expose the most effective approach. Should you be considering which language to find out, let’s see which language is regarded as the right for you.

The principal audience for data science is business professional. In the market, one big implication is communication. There are lots of methods to communicate: report, web app, dashboard. You want a tool that does all this together.

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