Course Introduction
R is a language and operating environment for statistical analysis, mapping, a free, free, open source software for the GNU system, an excellent tool for statistical computing and statistical mapping.
The R language grammar is easy to understand and can easily learn and master the grammar of language. And after learning, we can develop our own functions to extend the existing language. This is why it is much faster to update than the general statistical software, such as SPSS, SAS and so on. Most of the latest statistical methods and techniques can be obtained directly from R.
As the most popular data mining development language in the world today,--r language attracts more and more data analysis enthusiasts with its unique openness, high scalability and top charting capabilities.
Course Objectives
- Master the use of R language and the actual operation case
Suitable for people
- Big Data learner, developer
Lesson List
- Lesson 1:r What language is, advantages of R, resources
- Lesson 2:R installation, Access Help, Workspace management
- Lesson 3:r use of packages, reuse of results, how to handle large datasets
- Lesson 4:r Concepts, vectors, matrices, and arrays of datasets
- Lesson 5:R data frames, factors, and lists
- Common commands for Class 6:r
- The list of lessons 7:r is detailed
- Data source Import method for lesson 8:r
- User-defined functions for class 9:r
- Lesson 10:r accessing MySQL Database
- Lesson 11:R Integrated Development Environment (IDE)--rstudio
- Lesson 12:r How to draw, graphic parameters, symbols, lines and colors
- Lesson 13:r Text Properties, dimensions, headings, and custom axes for a graphic
- Lesson 14:r Minor tick marks, guides, legends, and text callouts for graphs
- The graphic combination and the fine control of the graphic layout of the class 15:r
- Lesson 16:R Basic Data management-creating variables, variable re-encoding, and renaming
- Lesson 17:R Basic Data Management-how to handle missing values, dates worth using, data type conversions
- Lesson 18:R Basic Data Management--data set merging, subset extraction and random sampling function
- Lessons 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 frames
- Lesson 21:R Advanced Data Management-repetition and looping, conditional execution, transpose
- Lesson 22:r Basic Graphics-bar chart (stacking, grouping, mean), fine-tuning of bar charts
- The basic graphic of the class 23:r--Pie chart
- Lesson 24:R Basic Graph--histogram
- Lesson 25:r Basic Graphics--Kernel density map
- Lesson 26:r Basic Graphics--Box line diagram
- Lesson 27:r Example--predicting the quantity of algae: problem description and target, data set format
- Lesson 28:r Example--pretreatment of data for predicting algae quantity
- Lesson 29:r Example--prediction model for predicting the quantity of algae
- Lesson 30:r Example--simplification and tuning of model for predicting algae quantity
Start learning: http://click.aliyun.com/m/27931/
Big data of the R language Express and actual combat, for data analysis enthusiasts
