R Training Course

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