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.
- 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
- Map projections and rgdal
Map Components in R
- 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
- 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
- Database-backed designs for large surveys
- Full description of multistage surveys
- Creating replicate weights for a design: as.svrepdesign()
- Computing summary statistics and design effects.
- Extracting information from result objects
- Tables of summary statistics
- Contingency tables: svychisq(), svyloglin()
- Boxplots, histograms, plots of tabular data.
- Strategies for weighting in scatterplots: bubble plots, hexagonal binning, transparency
- Scatterplot smoothers.
- Linear models
- Generalized linear models
- Proportional odds and other cumulative link models
- Survival analysis