# Introduction to R | JumpStart to R Programming

**Learning Objectives**

This course provides indoctrination in the practical use of the umbrella of technologies that are on the leading edge of data science development focused on R and related tools. Working in a hands-on learning environment, led by our expert practitioner, students will learn R and its ecosystem, and where it’s a better a tool than Excel.

This course is approximately **50% hands-on**, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current "on-the-job" experience into every classroom. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore:

- R Language and Mathematics
- How to work with R Vectors
- How to read and write data from files, and how to categorize data in factors
- How to work with Dates and perform Date math
- How to work with multiple dimensions and DataFrame essentials
- Essential Data Science and how to use R with it
- Visualization in R
- How R can be used in Spark (Optional / Overview)

**Audience & Pre-Requisites**

Attendees for this course should have prior practical hands-on experience with another programming language. Prior exposure to working with statistics and probability, as well as hands-on working knowledge of Excel would also be helpful but is not required. We will collaborate with you to design the best solution to ensure your needs are met, whether we customize the material, or devise a different educational path to help your team best prepare for this training.

- Getting Started with R

- Making R more friendly, R and available GUIs
- The R environment
- 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

- 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

- 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

- Reading data from files

- The read.table()function
- The scan() function
- Accessing builtin datasets
- Loading data from other R packages
- Editing data

- 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

- 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 models in R

- Defining statistical models; formulae
- Contrasts
- Linear models
- Generic functions for extracting model information
- Analysis of variance and model comparison
- ANOVA tables
- Updating fitted models
- Generalized linear models
- Nonlinear least squares and maximum likelihood models
- Least squares
- Maximum likelihood
- Some non-standard models

- Graphical procedures

- High-level plotting commands
- The plot() function
- Displaying multivariate data
- Display graphics
- Arguments to high-level plotting functions
- Low-level plotting commands
- Mathematical annotation
- Hershey vector fonts
- Interacting with graphics
- Using graphics parameters
- Permanent changes: The par() function
- Temporary changes: Arguments to graphics functions
- Graphics parameters list
- Graphical elements
- Axes and tick marks
- Figure margins
- Multiple figure environment
- Device drivers
- PostScript diagrams for typeset documents
- Multiple graphics devices
- Dynamic graphics

- Packages

- Standard packages
- Contributed packages and CRAN
- Namespaces

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