R Programming

Learn how to program in R and how to use R for effective data analysis. 

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

Why Learn Business Analytics with R?
‘Business Analytics with R’ at Treesoft technologies will prepare you to:

1. Learn R programming language and use it in analytical projects including multiple industrial domains and scenarios.
2. Become an R user and learn to think like a data scientist/business analyst.
3. Get exposure into the latest analytics techniques including forecasting, social network analytics and text mining.
4. Add-on to your existing analytics knowledge and methodology.
5. Acquire advanced knowledge of analytics in web analytics, social media analytics and Industry norms.

Course Syllabus

  • Overview of R, R data types and objects, reading and writing data
  • Control structures, functions, scoping rules, dates and times
  • Loop functions, debugging tools
  • Simulation, code profiling

Spatial Objects in R

  • Types of Spatial Objects
  • Getting shapefiles into R
  • Attributes
  • Map projections and rgdal

Map Components in R

  • Legends
  • Scales, North Arrows, Labels
  • Colouring schemes
  • Can a red/green colorblind person read your map?

Mapping Data From the Internet

  • The RgoogleMaps Library
  • The Google coordinate system
  • Sources of geographical data and APIs
  • A live map in a function

Further Topics

  • Other approaches to mapping: ggmap
  • Incorporating maps in 3d graphics
  • Basic GIS operations via rgeos
  • Basic map topology with spdep

Describing the Survey Design to R

  • The usual ‘with-replacement’ approximation
  • svydesign()
  • svrepdesign()
  • Database-backed designs for large surveys
  • Full description of multistage surveys
  • Creating replicate weights for a design: as.svrepdesign()

Summary Statistics

  • Computing summary statistics and design effects.
  • Extracting information from result objects
  • Tables of summary statistics
  • Contingency tables: svychisq(), svyloglin()

Graphics

  • Boxplots, histograms, plots of tabular data.
  • Strategies for weighting in scatterplots: bubble plots, hexagonal binning, transparency
  • Scatterplot smoothers.

Regression

  • Linear models
  • Generalized linear models
  • Proportional odds and other cumulative link models
  • Survival analysis