R Base and Advanced Programming - Dvanalytics
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R Base and Advanced Programming

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    R Programming Base and Advanced- Get an exposure to R statistical programming from Industry experts. In this course you will learn RDBMS programming to statistical exploratory data analysis

    Course Curriculum

    Session - 1
    What is R 00:00:00
    Installation guide to R 00:00:00
    Basic features of R 00:00:00
    R resources 00:00:00
    Limitation of R 00:00:00
    R Nuts and Bolts 00:00:00
    • Entering Input , Evaluation, R Objects, Numbers, Attributes, Creating Vectors, Mixing Objects, Explicit Coercion, Matrices, Lists, Factors, Missing Values, Data Frames, Names, Summary
    Session - 2
    R data reading importing and exporting 00:00:00
    • Reading and Writing Data
    • Reading Data Files with read.table()
    • Reading in Larger Datasets with read.table
    • Calculating Memory Requirements for R Objects
    Session - 3
    R packages 00:00:00
    Using Textual and Binary Formats for Storing Data 00:00:00
    File connections 00:00:00
    Reading Lines of a Text File 00:00:00
    Reading from a URL Connection 00:00:00
    Subsetting R Objects 00:00:00
    • Subsetting a Vector, Subsetting a Matrix, Subsetting a Lists, Subsetting Nested Elements of a List, Extracting Multiple Elements of a List, Partial Matching, Removing NA Values
    Session - 4
    R Functions (String, Numeric and Datetime) 00:00:00
    Loop Functions 00:00:00
    • Looping on the Command Line, lapply(), sapply(), split(), splitting a data frame, tapply(), apply(), Col/Row sums and means, Other ways to apply, Mapply(), Vectorizing a function
    Scoping rules of R 00:00:00
    Session - 5
    Managing Data Frames with the dplyr package 00:00:00
    • Data Frames
    • Merge Data Frames in R: Full and Partial Match
    • The dplyr Package
    • dplyr Grammar
    • Installing the dplyr Package
      • Select(), Filter(), Arrange(), Rename(), Mutate(), Group_by(), %>%, Summary
    Session - 6
    Control structure 00:00:00
    • If Else, For Loops, Nested for Loops, While Loops, Repeat Loops, Next, Break, Summary
    Debugging 00:00:00
    R profiling 00:00:00
    • Generating Random Numbers, Setting the random number seed, Simulation a Linear Model, Random Sampling