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Get an introduction to the R programming language in order to perform data manipulation, statistical analysis and graphics applications. Thousands of libraries or packages are available for everything from time-series analysis and machine learning to data visualization. As a result, R is rapidly becoming the language of choice for engineers, scientists, researchers and business analysts who engage in data preparation and analysis to generate high-quality typographical and graphical results.
Course Outline
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Course Objectives
- Install and configure R and essential R development tools, write R programs, and run them to generate tabular and graphical results.
- Use R to create, or read-in external datasets, storing data in all of the commonly used R data structures (vectors, matrices, arrays, data frames, factors and lists).
- Manage and manipulate data; perform data type conversions; merge data sets; deal with missing values; and extract, delete, or transform subsets of data based on logical criteria.
- Use basic R language constructs such as variables, branching and looping statements, write and call programmer defined, built-in and externally installed (package) functions.
- Employ R to perform basic data analysis using data exploration, statistical analysis and machine-learning techniques.
What You Learn
- Writing and running R programs
- Creating datasets
- R language elements
- R functions
- Basic graphs; bar plots, pie charts, histograms, density, box and dot plots
- Descriptive statistics
- Correlation and hypothesis testing; regression, variance
- Advanced graphing
- Statistical modeling: linear models, regression, classification trees
How You Learn
- Lectures
- In-class exercises
- Online discussion boards
- In-class exams
Is This Course Right for You?
If you want to learn the fundamentals of the R programming language and gain a solid foundation for future study, then this course is geared to your needs. Also, those who want to learn R to perform data exploration and analysis using statistical and machine learning techniques should enroll. Scientists, engineers, business analysts and social science researchers who explore and analyze data, and wish to present their results in well-formatted textual or graphical forms will find this course a fit.