R training at Aimia

R INTRODUCTION COURSEBasics of Data Analysis and Visualisation in R

Ali Arsalan Kazmi

STRUCTURE FOR THE SESSION

For Discussion For Practical work

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

INTRODUCTION

• Initial Thoughts on 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?


• GNU Free





A BIT ABOUT R

• Four (essential) freedoms granted

• Share the spirit


• GNU Free





A BIT ABOUT R

• Clustering – Sophisticated and others

• Supervised Learning

• Deep Learning

• Integration with Hadoop, Spark, Storm

• Many more

A BIT ABOUT R


• GNU Free





A BIT ABOUT R

• Currently: 7,284 packages

• Strong presence on the web

• R Consortium

• Google, Ebay, Facebook, NYT, etc.


• GNU Free





A BIT ABOUT R

• Link: http://spectrum.ieee.org/computing/software/the-2015-top-ten-programming-languages

• Ranked along with the general purpose languages

http://spectrum.ieee.org/computing/software/the-2015-top-ten-programming-languages


• GNU Free





A BIT ABOUT R

• Link: http://blog.revolutionanalytics.com/2015/05/r-in-sql-server.html

http://blog.revolutionanalytics.com/2015/05/r-in-sql-server.html

FUNDAMENTALS

• 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

• Data Types

• Data Structures


• Functions

FUNDAMENTALS

• How to store data? Logico-Computational considerations…

• In R: Atomic vectors; Lists; Matrices and Arrays; Dataframes

• Data Types

• Data Structures


• Functions

FUNDAMENTALS

• Control the flow of a programme’s/function’s logic

• If; IfElse; For; While; Repeat

• Data Types

• Data 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)

PRACTICAL SESSION

DATA I/O

• Sources for Data

• 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


• Types of Data

• Base R for I/O


DATA I/O

• .txt; .csv; .xlsx; .Rdata

• .html; .json; xml

• .xpt (SAS); .sav (SPSS); .dta (Stata)


• Types of Data

• Base R for I/O


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


• Types of Data

• Base R for I/O


DATA I/O

• readr – fast import for .txt, .csv

• readxl – fast import for .xlsx

• R-commander for GUI-based import

PRACTICAL SESSION

DATA MANIPULATION & ANALYSIS

• Subsetting Data

• Split-Apply-Combine

• Merging Data

• sqldf

• dplyr

• R-commander


• Subsetting ≡ SELECT & WHERE in SQL

• Subset operators: [, [[, $

• Numeric or logical indexes are used to subset data

• Subsetting Data


• Merging Data

• sqldf

• dplyr

• R-commander


• 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

• Subsetting Data


• Merging Data

• sqldf

• dplyr

• R-commander


• Merge ≡ JOINs in SQL

• Dataframes’ specific

• Subsetting Data


• Merging Data

• sqldf

• dplyr

• R-commander


• Link: https://cran.r-project.org/web/packages/sqldf/sqldf.pdf

• Write SQL in R

• Dataframes’ specific

• Limited to Data analysis and manipulation operations

https://cran.r-project.org/web/packages/sqldf/sqldf.pdf

• Subsetting Data


• Merging Data

• sqldf

• dplyr

• R-commander


• 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

https://cran.r-project.org/web/packages/dplyr/dplyr.pdf

• Subsetting Data


• Merging Data

• sqldf

• dplyr

• R-commander


• GUI

• Can assist in learning R

PRACTICAL SESSION

VISUALISATION

• Grammar of Graphics

• 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


• ggplot2


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


• ggplot2


VISUALISATION

• Graph is formed of well-defined constituents

• Grammar enables succinct definition of constituents

• Insights into graphs’ structure

• Encourages Creativity


• ggplot2


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

http://docs.ggplot2.org/current/


• ggplot2


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

http://d3js.org/

PRACTICAL SESSION

R training at Aimia

Data & Analytics

Transcript of R training at Aimia