NobleProg

Data Analytics With R Course

Course Author: Dr Chibisi Chima-Okereke
Copyright 2017 Dr Chibisi Chima-Okereke

Course Introduction

Welcome to the Data Analytics with R course with NobelProg. The aim of this course is to provide an introduction to the R programming language . It covers language fundamentals, working with data structures and file i/o, and includes basic concepts in working with data visualization and data analysis.

The schedule for the course outline is given below. The course is very interactive and includes many practical exercises throughout.

• Day One: Language Basics
  • Course Introduction
  • What is Data Science
    • Data Science Definition
    • Process of Doing Data Science
  • Introduction to the R language
    • What is R? Dynamic interpreted statistical programming languages
    • Information about R on the web
    • R programming environments
    • The R interpreter
  • Variables and basic types in R
    • Creating variables
    • Class, mode, typeof and Vectors: numeric, integer, character, logical, matrices, arrays
    • Operators in R
    • Subsetting vectors, matrices and arrays
    • Missing values (NA) in vectors
    • Factors in R
  • Control Structures
    • for, while, break, next, repeat if, else, ifelse, switch
  • String and Text Manipulation
    • paste, grep, gsub, strsplit, casefold, ...
    • File I/O: save, load, readRDS, saveRDS
  • Lists, data.frames, and environments
    • Lists as R's generic object
    • DataFrames as a special type of list, creating, selecting and subsetting.
  • Functions (Functional Programming)
    • Introduction to functions, higher order functions
    • Anonymous functions
    • Operators as functions
    • Recursive functions
    • Lists of functions
    • Map, Reduce, Filter, Find, Negate, Position, lapply, sapply, vapply, mapply, replicate, sweep
    • Pipelining/UFCS and magrittr
  • Mathematical functions in R
    • sin, cos, tan, log, sqrt, ...
• Day Two: Data Manipulation & Visualization with R
  • Data file I/O
    • read.table, write.table, read.csv, write.csv, save, load, readRDS, saveRDS, readLines
  • Preparing data using the data.table package
    • Installing and loading R packages
    • Reading in data, and converting DataFrames to DataTables
    • Transforming and cleaning data using the DataTable package
    • Dealing with missing values (NA)
  • Data Exploration
    • Data Exploration using the dplyr packages
  • R's built in datasets
  • Data Visualization
    • Introduction to R's graphics packages
    • Basic plots with graphics: (scatters & line) plot, lines, barplot, boxplot
    • Data visualization using ggplot2: qplot, ggplot, points, lines, bar, density, and so on
• Day Three: Data Analysis with R
  • Statistical Modelling with R
    • Summary statistics: mean, mode, median, variance, standard deviation, correlation
    • Statistical distributions in R:
      • ?Distributions
      • Density, probability and quantiles of distributions Normal, Gamma, Log-Normal, Poisson, Binomial.
      • Random number generation of statistical distributions, set.seed
    • Regression:
      • Multiple linear regression:
        • Useful functions model.matrix, model.frame, model.extract, model.response
        • Linear regression as a matrix operation
        • Creating linear regression models using the lm function
        • Transforming variables (Box-Cox), model disgnostics, prediction, error measurement
  • Clustering
    • Foundation Concepts in Clustering
    • KMeans Clustering
  • Classification
    • Foundation Concepts in Classification
    • Naive Bayes
    • Decision Trees
    • Logistics Regression
    • Model validation
  • Text Processing with (tm package/Wordclouds)