Why R? Rich resources: covers almost all methods in data analysis for a variety of industries. Good expansibility: very convenient to write functions and packages, cross-platform, can be competent for complex data analysis, drawing beautiful graphics. Complete help system: each function has a uniform format of help, running instances. GNU software: free, software itself and package source code public.
R Comparison with other statistical software SAS: fast speed, a large number of statistical analysis modules, poor scalability, expensive. SPSS: Complex user graphical interface, easy to learn, but very difficult to program. Splus: Runs the S language, has a complex interface, is fully compatible with R, and is expensive. The user needs to be familiar with commands: working with code, memorizing common commands.
Memory: All data processing is carried out in memory, which is not suitable for processing large scale data.
Slightly slower: just-in-time compilation, about 1/20th as fast as C.
R is still much more efficient than clicking a mouse.
Advantage:
Can complete most of the data related analysis, statistics, mining, visualization and other work can work with big data solutions such as Hadoop disadvantages:
The obnoxious assignment symbol <- always appears when it comes to Chinese
Advantages and disadvantages of R language
Big data R language rapid development and practice
Content Introduction:
R language grammar is easy to understand, it is easy to learn and master the language grammar. And once we learn it, we can write our own functions to extend existing languages. This is why it updates much faster than general statistical software such as SPSS, SAS, etc. Most of the latest statistical methods and techniques are available directly in R.
As the most popular data mining development language in the world, R language attracts more and more data analysis enthusiasts with its unique openness, high scalability and top graphics functions.
Content List:
课时1: what is R language, advantages and resources of R
课时2: R installation, get help, workspace management 23:35
课时3: use of R package, reuse of results, how to deal with large data set 23:43
Lesson 4: Concepts of R data sets, Vectors, Matrices, and Arrays
Lecture 5: R Data boxes, factors, and lists 24:51
课时6: R’s common commands 17:38
课时7: the list of R
课时8: data source import method of R
Lesson 9: User – defined function of R
课时10: R access MySQL database 13:01
课时11: integrated development environment (IDE) for R –Rstudio 17:49
Lesson 12: R How to draw, figure parameters, symbols, lines and colors
课 程 13: Text Properties, Dimensions, Titles, and Custom Axes of R graphics
Lesson 14: R graph of the secondary scale line, reference line, legend and text annotation 30:25
Lesson 15: R graphic combination, fine control of graphic layout
Lesson 16: R Basic Data management — Variable creation, variable recoding, and variable renaming
课时17: R basic data management — how to deal with missing values, dates worth using, data type conversion
R Basic Data management — Extraction of data set union and subset and random sampling function
Lesson 19: R Advanced Data Management — Mathematical functions, Statistical functions and Probability Functions
Lesson 20: R Advanced Data Management — Character processing functions, applying functions to matrices and data boxes
R Advanced Data management — Repetition and loops, conditional execution, transpose 19:24
Lesson 22: R basic graphics — bar chart (stacking, grouping, mean), fine tuning of bar chart 26:36
课 hour 23: Basic graph of R — pie chart 17:04
课时24: R basic graphics — histogram 09:55
课时25: R basic graphics — nuclear density map 10:05
课 period 26: R Basic Graphics — Boxplot 08:27
Lesson 27: R examples — problem description and objectives for predicting algal populations, format 16:12
Lesson 28: R example — Data preprocessing for predicting algal populations
课 hour 29: R example — Acquisition prediction model for predicting the quantity of algae
Lesson 30: R Examples — Simplification and refinement of models for predicting algal populations
Course objectives
Master the use of R language and practical operation cases
Suits the crowd
Big data learners and developers
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