Download to read offline
Uploaded byAli Arsalan Kazmi
R training at Aimia
The document outlines a comprehensive introduction course on data analysis and visualization using R, covering fundamental topics such as data import, manipulation, and visual representation. It highlights R's significance in the programming community, notably its ranking in 2015 and its integration with various platforms. The course includes practical sessions focused on data I/O, manipulation techniques, and leveraging ggplot2 for creating visualizations.
More Related Content
R training at Aimia
- 1. R INTRODUCTION COURSE Basicsof Data Analysis and Visualisation in R Ali Arsalan Kazmi
- 2. STRUCTURE FOR THESESSION For Discussion For Practical work
- 3. 1. Introduction 2. Fundamentals 3.Data Import and Export in R 4. Data Analysis and Manipulation 5. Data Visualisation ROADMAP Each section contains: 1. Subsections 2. Some Theory 3. Practical work
- 4.
- 5. • Initial Thoughtson R • GNU Free • Data Analysis and Superior Visualisation • Burgeoning community of useRs • #6 in IEEE 2015 Top Programming Languages • Integration of R in SQL Server 2016 A BIT ABOUT R • Your first impression about R? • What do you already know about R?
- 6. • Initial Thoughtson R • GNU Free • Data Analysis and Superior Visualisation • Burgeoning community of useRs • #6 in IEEE 2015 Top Programming Languages • Integration of R in SQL Server 2016 A BIT ABOUT R • Four (essential) freedoms granted • Share the spirit
- 7. • Initial Thoughtson R • GNU Free • Data Analysis and Superior Visualisation • Burgeoning community of useRs • #6 in IEEE 2015 Top Programming Languages • Integration of R in SQL Server 2016 A BIT ABOUT R • Clustering – Sophisticated and others • Supervised Learning • Deep Learning • Integration with Hadoop, Spark, Storm • Many more
- 8.
- 9.
- 10.
- 11.
- 12. • Initial Thoughtson R • GNU Free • Data Analysis and Superior Visualisation • Burgeoning community of useRs • #6 in IEEE 2015 Top Programming Languages • Integration of R in SQL Server 2016 A BIT ABOUT R • Currently: 7,284 packages • Strong presence on the web • R Consortium • Google, Ebay, Facebook, NYT, etc.
- 13. • Initial Thoughtson R • GNU Free • Data Analysis and Superior Visualisation • Burgeoning community of useRs • #6 in IEEE 2015 Top Programming Languages • Integration of R in SQL Server 2016 A BIT ABOUT R • Link: http://spectrum.ieee.org/computing/software/the-2015-top-ten-programming- languages • Ranked along with the general purpose languages
- 14. • Initial Thoughtson R • GNU Free • Data Analysis and Superior Visualisation • Burgeoning community of useRs • #6 in IEEE 2015 Top Programming Languages • Integration of R in SQL Server 2016 A BIT ABOUT R • Link: http://blog.revolutionanalytics.com/2015/05/r-in-sql-server.html
- 15.
- 16. • Data Types •Data Structures • Control Structures • Functions FUNDAMENTALS • Think the commonly used data types for Stats • In R: Numeric/Double; Integer; Logical; Character; Factor • Many more
- 17. • Data Types •Data Structures • Control Structures • Functions FUNDAMENTALS • How to store data? Logico-Computational considerations… • In R: Atomic vectors; Lists; Matrices and Arrays; Dataframes
- 18. • Data Types •Data Structures • Control Structures • Functions FUNDAMENTALS • Control the flow of a programme’s/function’s logic • If; IfElse; For; While; Repeat
- 19. • Data Types •Data Structures • Control Structures • Functions FUNDAMENTALS • “Every process in R is the result of a Function call” – John Chambers • “Everything in R is an R object” – John Chambers • Modularise; Customise; Optimise; Automate • Transition from a useR to a programmeR (and on to a developeR)
- 20.
- 21.
- 22. • Sources forData • Types of Data • Base R for I/O • Packages for Data Import DATA I/O • Online Sources: Web; APIs; Dropbox; GitHub • Offline Sources: Databases; flat files; zipped files
- 23. • Sources forData • Types of Data • Base R for I/O • Packages for Data Import DATA I/O • .txt; .csv; .xlsx; .Rdata • .html; .json; xml • .xpt (SAS); .sav (SPSS); .dta (Stata)
- 24. • Sources forData • Types of Data • Base R for I/O • Packages for Data Import DATA I/O • You can use base R to read a variety of data • Can be slow with large data • For exotic file types, use dedicated packages
- 25. • Sources forData • Types of Data • Base R for I/O • Packages for Data Import DATA I/O • readr – fast import for .txt, .csv • readxl – fast import for .xlsx • R-commander for GUI-based import
- 26.
- 27.
- 28. • Subsetting Data •Split-Apply-Combine • Merging Data • sqldf • dplyr • R-commander DATA MANIPULATION & ANALYSIS • Subsetting ≡ SELECT & WHERE in SQL • Subset operators: [, [[, $ • Numeric or logical indexes are used to subset data
- 29. • Subsetting Data •Split-Apply-Combine • Merging Data • sqldf • dplyr • R-commander DATA MANIPULATION & ANALYSIS • Split a collection of data, Apply a function to each partition, Combine the result and present • Collection ≡ data structure • Splitting is different for data structures and data types • Combination is different for data structures
- 30. • Subsetting Data •Split-Apply-Combine • Merging Data • sqldf • dplyr • R-commander DATA MANIPULATION & ANALYSIS • Merge ≡ JOINs in SQL • Dataframes’ specific
- 31. • Subsetting Data •Split-Apply-Combine • Merging Data • sqldf • dplyr • R-commander DATA MANIPULATION & ANALYSIS • Link: https://cran.r-project.org/web/packages/sqldf/sqldf.pdf • Write SQL in R • Dataframes’ specific • Limited to Data analysis and manipulation operations
- 32. • Subsetting Data •Split-Apply-Combine • Merging Data • sqldf • dplyr • R-commander DATA MANIPULATION & ANALYSIS • Link: https://cran.r-project.org/web/packages/dplyr/dplyr.pdf • Expressive for most data manipulation • Very efficient • Consistent coding • Directly connect with some RDBMS
- 33. • Subsetting Data •Split-Apply-Combine • Merging Data • sqldf • dplyr • R-commander DATA MANIPULATION & ANALYSIS • GUI • Can assist in learning R
- 34.
- 35.
- 36. • Grammar ofGraphics • ggplot2 • Bonus: Interactive Visualisation VISUALISATION • Graph is formed of well-defined constituents • Grammar enables succinct definition of constituents • Layer(s) • Scale(s) • Coordinate System • Facetting/Trellis Graphics
- 37. • Grammar ofGraphics • ggplot2 • Bonus: Interactive Visualisation VISUALISATION • Layer(s) • Data • Aesthetics (positions on x/y axes; colours, size, etc.) • Statistical Transformation (none; Log; Squared; etc.) • Geometric Object(s) • Position Adjustment • Scale(s) – control how data are mapped to each aesthetic • Coordinate System • Facetting/Trellis Graphics
- 38. • Grammar ofGraphics • ggplot2 • Bonus: Interactive Visualisation VISUALISATION • Graph is formed of well-defined constituents • Grammar enables succinct definition of constituents • Insights into graphs’ structure • Encourages Creativity
- 39. • Grammar ofGraphics • ggplot2 • Bonus: Interactive Visualisation VISUALISATION • Link: http://docs.ggplot2.org/current/ • An implementation of (layered) Grammar of Graphics • Elegant graphics • Typical Stat graphs + more exotic graphs • Works with dataframes • Static graphics
- 40. • Grammar ofGraphics • ggplot2 • Bonus: Interactive Visualisation VISUALISATION • Intended for the Web – HTML files • Mostly based on D3 – Data Driven Documents • Based on contributed packages • Some under active development • Not limited to dataframe datasets
- 41.