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1. 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