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Introduction to R Programming is a handson course covers the manipulation of objects in R including reading data, accessing R packages, writing R functions, and making informative graphs.
Unlimited Duration
March 2, 2021
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. In this course you will learn about:
Â· R Language and Mathematics
Â· How to work with R Vectors
Â· How to read and write data from files, and how to categorize data in factors
Â· How to work with Dates and perform Date math
Â· How to work with multiple dimensions and DataFrame essentials
Â· Essential Data Science and how to use R with it
Â· Visualization in R
Â· How R can be used in Spark (Optional / Overview)
Course Curriculum

 Making R more friendly, R and available GUIs 00:00:00
 The R environment 00:00:00
 Related software and documentation 00:00:00
 R and statistics 00:00:00
 Using R interactively 00:00:00
 An introductory session 00:00:00
 Getting help with functions and features 00:00:00
 R commands, case sensitivity, etc. 00:00:00
 Recall and correction of previous commands 00:00:00
 Executing commands from or diverting output to a file 00:00:00
 Data permanency and removing objects 00:00:00

 Vectors and assignment 00:00:00
 Vector arithmetic 00:00:00
 Generating regular sequences 00:00:00
 Logical vectors 00:00:00
 Missing values 00:00:00
 Character vectors 00:00:00
 Index vectors; selecting and modifying subsets of a data set 00:00:00

 Intrinsic attributes: mode and length 00:00:00
 Changing the length of an object 00:00:00
 Getting and setting attributes 00:00:00
 The class of an object 00:00:00

 A specific example 00:00:00
 The function tapply() and ragged arrays 00:00:00
 Ordered factors 00:00:00

 Arrays 00:00:00
 Array indexing. Subsections of an array 00:00:00
 Index matrices 00:00:00
 The array() function â€¢ Mixed vector and array arithmetic. The recycling rule 00:00:00
 The outer product of two arrays 00:00:00
 Generalized transpose of an array 00:00:00
 Matrix facilities â€¢ Matrix multiplication â€¢ Linear equations and inversion â€¢ Eigenvalues and eigenvectors â€¢ Singular value decomposition and determinants â€¢ Least squares fitting and the QR decomposition 00:00:00
 Forming partitioned matrices, cbind() and rbind() 00:00:00
 The concatenation function, (), with arrays 00:00:00
 Frequency tables from factors 00:00:00

 Lists 00:00:00
 Constructing and modifying lists â€¢ Concatenating lists 00:00:00
 Data frames â€¢ Making data frames â€¢ attach() and detach() â€¢ Working with data frames â€¢ Attaching arbitrary lists â€¢ Managing the search path 00:00:00

 The read.table()function 00:00:00
 The scan() function 00:00:00
 Accessing builtin datasets â€¢ Loading data from other R packages 00:00:00
 Editing data 00:00:00

 R as a set of statistical tables 00:00:00
 Examining the distribution of a set of data 00:00:00
 One and twosample tests 00:00:00

 Grouped expressions 00:00:00
 Control statements â€¢ Conditional execution: if statements â€¢ Repetitive execution: for loops, repeat and while 00:00:00

 Simple examples 00:00:00
 Defining new binary operators 00:00:00
 Named arguments and defaults 00:00:00
 The ‘…’ argument 00:00:00
 Assignments within functions 00:00:00
 More advanced examples â€¢ Efficiency factors in block designs â€¢ Dropping all names in a printed array â€¢ Recursive numerical integration 00:00:00
 Scope 00:00:00
 Customizing the environment 00:00:00
 Classes, generic functions and object orientation 00:00:00

 Defining statistical models; formulae â€¢ Contrasts 00:00:00
 Linear models 00:00:00
 Generic functions for extracting model information 00:00:00
 Analysis of variance and model comparison â€¢ ANOVA tables 00:00:00
 Updating fitted models 00:00:00
 Generalized linear models 00:00:00
 Nonlinear least squares and maximum likelihood models â€¢ Least squares â€¢ Maximum likelihood 00:00:00
 Some nonstandard models 00:00:00

 Highlevel plotting commands â€¢ The plot() function â€¢ Displaying multivariate data â€¢ Display graphics â€¢ Arguments to highlevel plotting functions 00:00:00
 Lowlevel plotting commands â€¢ Mathematical annotation â€¢ Hershey vector fonts 00:00:00
 Interacting with graphics 00:00:00
 Using graphics parameters â€¢ Permanent changes: The par() function â€¢ Temporary changes: Arguments to graphics functions 00:00:00
 Graphics parameters list â€¢ Graphical elements â€¢ Axes and tick marks â€¢ Figure margins â€¢ Multiple figure environment 00:00:00
 Device drivers â€¢ PostScript diagrams for typeset documents â€¢ Multiple graphics devices 00:00:00
 Dynamic graphics 00:00:00

 Standard packages 00:00:00
 Contributed packages and CRAN 00:00:00
 Namespaces 00:00:00

Course Reviews
Students