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Introduction to R Programming is a hands-on course covers the manipulation of objects in R including reading data, accessing R packages, writing R functions, and making informative graphs.

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Unlimited Duration

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March 2, 2021

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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:

· 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)

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 • 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 • Matrix multiplication • Linear equations and inversion • Eigenvalues and eigenvectors • Singular value decomposition and determinants • 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 • Concatenating lists 00:00:00
    • Data frames • Making data frames • attach() and detach() • Working with data frames • Attaching arbitrary lists • Managing the search path 00:00:00
    • The read.table()function 00:00:00
    • The scan() function 00:00:00
    • Accessing builtin datasets • 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 two-sample tests 00:00:00
    • Grouped expressions 00:00:00
    • Control statements • Conditional execution: if statements • 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 • Efficiency factors in block designs • Dropping all names in a printed array • 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 • Contrasts 00:00:00
    • Linear models 00:00:00
    • Generic functions for extracting model information 00:00:00
    • Analysis of variance and model comparison • ANOVA tables 00:00:00
    • Updating fitted models 00:00:00
    • Generalized linear models 00:00:00
    • Nonlinear least squares and maximum likelihood models • Least squares • Maximum likelihood 00:00:00
    • Some non-standard models 00:00:00
    • High-level plotting commands • The plot() function • Displaying multivariate data • Display graphics • Arguments to high-level plotting functions 00:00:00
    • Low-level plotting commands • Mathematical annotation • Hershey vector fonts 00:00:00
    • Interacting with graphics 00:00:00
    • Using graphics parameters • Permanent changes: The par() function • Temporary changes: Arguments to graphics functions 00:00:00
    • Graphics parameters list • Graphical elements • Axes and tick marks • Figure margins • Multiple figure environment 00:00:00
    • Device drivers • PostScript diagrams for typeset documents • 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

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