R Training Course

Course Code

rlang

Duration

21 hours (usually 3 days including breaks)

Overview

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining.

Course Outline

Day 1

Introduction and preliminaries

  • Making R more friendly, R and available GUIs
  • Rstudio
  • Related software and documentation
  • R and statistics
  • Using R interactively
  • An introductory session
  • Getting help with functions and features
  • R commands, case sensitivity, etc.
  • Recall and correction of previous commands
  • Executing commands from or diverting output to a file
  • Data permanency and removing objects

Simple manipulations; numbers and vectors

  • Vectors and assignment
  • Vector arithmetic
  • Generating regular sequences
  • Logical vectors
  • Missing values
  • Character vectors
  • Index vectors; selecting and modifying subsets of a data set
  • Other types of objects

Objects, their modes and attributes

  • Intrinsic attributes: mode and length
  • Changing the length of an object
  • Getting and setting attributes
  • The class of an object

Ordered and unordered factors

  • A specific example
  • The function tapply() and ragged arrays
  • Ordered factors

Arrays and matrices

  • Arrays
  • Array indexing. Subsections of an array
  • Index matrices
  • The array() function
    • Mixed vector and array arithmetic. The recycling rule
  • The outer product of two arrays
  • Generalized transpose of an array
  • Matrix facilities
    • Matrix multiplication
    • Linear equations and inversion
    • Eigenvalues and eigenvectors
    • Singular value decomposition and determinants
    • Least squares fitting and the QR decomposition
  • Forming partitioned matrices, cbind() and rbind()
  • The concatenation function, (), with arrays
  • Frequency tables from factors

Day 2

Lists and data frames

  • Lists
  • Constructing and modifying lists
    • Concatenating lists
  • Data frames
    • Making data frames
    • attach() and detach()
    • Working with data frames
    • Attaching arbitrary lists
    • Managing the search path

Data manipulation

  • Selecting, subsetting observations and variables          
  • Filtering, grouping
  • Recoding, transformations
  • Aggregation, combining data sets
  • Character manipulation, stringr package

Reading data

  • Txt files
  • CSV files
  • XLS, XLSX files
  • SPSS, SAS, Stata,… and other formats data
  • Exporting data to txt, csv and other formats
  • Accessing data from databases using SQL language

Probability distributions

  • R as a set of statistical tables
  • Examining the distribution of a set of data
  • One- and two-sample tests

Grouping, loops and conditional execution

  • Grouped expressions
  • Control statements
    • Conditional execution: if statements
    • Repetitive execution: for loops, repeat and while

Day 3

Writing your own functions

  • Simple examples
  • Defining new binary operators
  • Named arguments and defaults
  • The '...' argument
  • Assignments within functions
  • More advanced examples
    • Efficiency factors in block designs
    • Dropping all names in a printed array
    • Recursive numerical integration
  • Scope
  • Customizing the environment
  • Classes, generic functions and object orientation

Statistical analysis in R

  • Linear regression models
  • Generic functions for extracting model information
  • Updating fitted models
  • Generalized linear models
    • Families
    • The glm() function
  • Classification
    • Logistic Regression
    • Linear Discriminant Analysis
  • Unsupervised learning
    • Principal Components Analysis
    • Clustering Methods (k-means, hierarchical clustering, k-medoids)
  • Survival analysis
    • Survival objects in r
    • Kaplan-Meier estimate
    • Confidence bands
    • Cox PH models, constant covariates
    • Cox PH models, time-dependent covariates

Graphical procedures

  • High-level plotting commands
    • The plot() function
    • Displaying multivariate data
    • Display graphics
    • Arguments to high-level plotting functions
  • Basic visualisation graphs
  • Multivariate relations with lattice and ggplot package
  • Using graphics parameters
  • Graphics parameters list

Automated and interactive reporting

  • Combining output from R with text

Creating html, pdf documents

Testimonials

★★★★★
★★★★★

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