Introduction to R
The explosion of data collection on the Internet and in all business processes has resulted in a dramatic increase in demand for data analysis and data science professionals. This course is designed to help both programmers who need to learn data analysis tools and data science professionals who know SAS or other tools and need to learn R. Examples and homework assignments will use digital marketing, economic, police/fire, and geographic data t illustrate techniques for preparing, cleaning, visualizing and analyzing the various data types and formats using the R statistical programming language.
The class will cover the following:
- Installing and getting started with R
- Understanding the strengths and limitations of R
- Data exploration and data preparation
- Common R functions and scripts
- Reading and writing data with R
- Programming efficiently in R
- Curve Fitting, prediction and interpolation
- Geostatistics, geocoding and mapping
- Advanced graphics building and communicating your case in graphics
- Using Shiny for simple interactive visualizations
- Advanced tools and packages and developing predictive models with R
The course assumes previous use of a programming language such as C/C++, Java, Perl, Python, SAS or PHP. The course also assumes knowledge of basic statistical concepts such as median, mean, standard deviation, and linear regression.