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Introduction to R

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Descrição do Curso

This is the first course in R, designed to introduce students to the basics of the R language.
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  1. 1

    R Environment

    Grátis

    This is the first chapter in the Introduction to R course. You will get to know the R environment. You will also learn basic manipulations on R objects, which is essential for R. Let's get started!

    Reproduzir Capítulo Agora
    Variables assignment
    100 xp
    Inserting comments into your code
    100 xp
    Explore sessions in R
    100 xp
    More about R sessions
    100 xp
    Environment Variables
    100 xp
    Global Options Settings
    100 xp
    The R Workspace
    100 xp
    Data Objects in R
    100 xp
    Type, Mode, and Class of Objects
    100 xp
    R Object Attributes
    100 xp
    Implicit Class of Objects
    100 xp
    Object Coercion
    100 xp
  2. 2

    R Objects

    Grátis

    In this chapter details about R objects will be covered.

    Reproduzir Capítulo Agora
    Basic R Objects
    100 xp
    Character Strings
    100 xp
    Manipulating Strings
    100 xp
    Regular Expressions in R
    100 xp
    Vectors
    100 xp
    Arithmetic and Logical Operations on Vectors
    100 xp
    Naming and Manipulating Vectors
    100 xp
    Subsetting Vectors
    100 xp
    Filtering Vectors
    100 xp
    Factors
    100 xp
    Tables of Categorical Data
    100 xp
    Classifying Continuous Numeric Data Into Categories
    100 xp
    Classifying Continuous Numeric Data Into Categories, Continued
    100 xp
    Converting Numeric Data Into Factors Using cut()
    100 xp
    Matrices
    100 xp
    Matrix attributes
    100 xp
    Matrix Subsetting
    100 xp
  3. 3

    R Data Operations

    Grátis

    In this chapter you will learn to use the functional operations which is essential for more powerful operations. May the force be with you!

    Reproduzir Capítulo Agora
    Generating Pseudo-Random Numbers
    100 xp
    Generating Binomial Random Numbers
    100 xp
    Generating Random Samples and Permutations
    100 xp
    Statistical Estimators
    100 xp
    Functions in R
    100 xp
    Binding Arguments and Default Argument
    100 xp
    Return Values and Invisible Return
    100 xp
    Logical Operators
    100 xp
    Long Form Logical Operators
    100 xp
    Arithmetic Operators
    100 xp
    Comparing Objects With identical()
    100 xp
    Lookup and Matching
    100 xp
    Lookup and Matching (cont.)
    100 xp
    Assignment Operators
    100 xp
    The "if () else" Control Statement
    100 xp
    The switch() Control Statement
    100 xp
    Iteration Using for() and while() Loops
    100 xp
    Fibonacci Sequence Using for() Loop
    100 xp
    List
    100 xp
    Subsetting Lists
    100 xp
    Coercing Vectors Into Lists Using as.list()
    100 xp
    Data Frames
    100 xp
    Subsetting Data Frames
    100 xp
    Data Frames and Factors
    100 xp
    Exploring Data Frames
    100 xp
    Coercing Data Frames Into Matrices Using as.matrix()
    100 xp
    Coercing Matrices Into Data Frames and Lists
    100 xp
    The iris Data Frame
    100 xp
    'mtcars' data
    100 xp
    The Cars93 Data Frame
    100 xp
    Types of Bad Data
    100 xp
    Scrubbing Bad Data
    100 xp
    NULL Values
    100 xp
  4. 4

    R Functionals

    Grátis

    See details in R function settings

    Reproduzir Capítulo Agora
    Lazy Evaluation of Function Arguments
    100 xp
    The dots "..." Function Argument
    100 xp
    Functions that takes functions and returns functions
    100 xp
    Functionals
    100 xp
    Anonymous Functions and Functionals
    100 xp
    Executing Function Calls Using the do.call() Functional
    100 xp
    Performing Loops Using the apply() Functionals
    100 xp
    The apply() Functional with dots "..." Argument
    100 xp
    The apply() Functional with Anonymous Functions
    100 xp
    apply() Calling Functions with Multiple Arguments
    100 xp
    The lapply() and sapply() Functional
    100 xp
  5. 5

    More on matrix

    Grátis

    Matrix, and matrix-like structures are essential to R data manipulation and operation.

    Reproduzir Capítulo Agora
    Logical Operators Applied to Vectors and Matrices
    100 xp
    Coercing Vectors Into Matrices
    100 xp
    Coercing Matrices Into Other Types
    100 xp
    Binding Vectors and Matrices Together
    100 xp
    Replicating Objects Using rep()
    100 xp
    Multiplying Vectors and Matrices
    100 xp
    Matrix Inner Multiplication
    100 xp
    Matrix Transpose
    100 xp
    Matrix Outer Multiplication
    100 xp
    Flattening a List of Vectors to a Matrix Using do.call()
    100 xp
    Efficient Binding of Lists Into Matrices
    100 xp
  6. 6

    More on Data Frames

    Grátis

    Most data sets you will be working with will be stored as a data frame. Learn data frame operations will help your data analysis procedure.

    Reproduzir Capítulo Agora
    Filtering Data Frames Using subset()
    100 xp
    Splitting Data Frames Using factor Categorical Variables
    100 xp
    The split-apply-combine Procedure Example
    100 xp
    The tapply() Functional
    100 xp
    The split-apply-combine Returning Matrices Example
    100 xp
    Benchmarking the Speed of R Code
    100 xp
    Writing Fast R Code Using Compiled Functions
    100 xp
    Writing Fast R Code Without Method Dispatch
    100 xp
    Using apply() Instead of for() and while() Loops
    100 xp
    Increasing Speed of Loops by Pre-allocating Memory
    100 xp
    Vectorized Functions for Vector Computations
    100 xp
    Vectorized Functions for Matrix Computations
    100 xp
    Fast R Code for Matrix Computations
    100 xp
    Package matrixStats for Fast Matrix Computations
    100 xp
    Writing Fast R Code Using Vectorized Operations
    100 xp
    Vectorized Functions
    100 xp
    Performing sapply() Loops Over Function Parameters
    100 xp
    Creating Vectorized Functions
    100 xp
    The mapply() Functional
    100 xp
    Vectorized if-else Statements Using Function ifelse()
    100 xp
    Monte Carlo Simulation
    100 xp
    Simulating Brownian Motion Using while() Loops
    100 xp
    Simulating Brownian Motion Using Vectorized Functions
    100 xp
    Standard Errors of Estimators Using Bootstrap Simulation
    100 xp
    Standard Errors of Regression Coefficients Using Bootstrap
    100 xp
  7. 7

    Regression and tests

    Grátis

    Utilize R for statistical analysis.

    Reproduzir Capítulo Agora
    Hypothesis Testing
    100 xp
    Formula Objects
    100 xp
    Linear Regression Using lm()
    100 xp
    The Regression Scatterplot
    100 xp
    Regression Summary
    100 xp
    Interpreting the Regression Statistics
    100 xp
    Weak Regression
    100 xp
    Influence of Noise on Regression
    100 xp
    Influence of Noise on Regression Another Method
    100 xp
    Regression Diagnostic Plots
    100 xp
    Durbin-Watson Test of Autocorrelation of Residuals
    100 xp
    Omitted Variable Bias
    100 xp
    Spurious Time Series Regression
    100 xp
    Predictions Using Regression Models
    100 xp
  8. 8

    Exceptions

    Grátis

    In this chapter you will learn about several exceptions in R and how to handle with them.

    Reproduzir Capítulo Agora
    Exception Conditions: Errors and Warnings
    100 xp
    Validating Function Arguments
    100 xp
    Validating Assertions Inside Functions
    100 xp
    Validating Assertions Using stopifnot()
    100 xp
    Validating Function Arguments and Assertions
    100 xp
    The R Debugger Facility
    100 xp
    Debugging Using browser()
    100 xp
    Handling Exception Conditions
    100 xp
    Error Conditions and Exception Handling in Loops
    100 xp
  9. 9

    Parallel Computing

    Grátis

    Learn to ride the powerful technique in R!

    Reproduzir Capítulo Agora
    Parallel Computing Using Package parallel
    100 xp
    Performing Parallel Loops Using Package parallel
    100 xp
    Computing Overhead of Parallel Computing
    100 xp
    Parallel Computing Over Matrices
    100 xp
    Standard Errors of Regression Coefficients Using Bootstrap
    100 xp
    Distribution of Bootstrapped Regression Coefficients
    100 xp
    Bootstrapping Regressions Using Parallel Computing
    100 xp
  10. 10

    Logistic regression

    Grátis

    Have a taste of the machine learning techniques in R!

    Reproduzir Capítulo Agora
    The Logistic Function
    100 xp
    Performing Logistic Regression Using the Function glm()
    100 xp
    Package ISLR With Datasets for Machine Learning
    100 xp
    The Default Dataset
    100 xp
    Boxplots of the Default Dataset
    100 xp
    Modeling Credit Defaults Using Logistic Regression
    100 xp
    Modeling Cumulative Defaults Using Logistic Regression
    100 xp
    Multifactor Logistic Regression
    100 xp
    Confounding Variables in Multifactor Logistic Regression
    100 xp
    Forecasting of Credit Defaults using Logistic Regression
    100 xp
  11. 11

    Optimization

    Grátis

    Use various optimization functions to get your best result!

    Reproduzir Capítulo Agora
    One-dimensional Optimization
    100 xp
    The Log-likelihood Function
    100 xp
    Perspective Plot of Likelihood Function
    100 xp
    Optimization of Objective Function
    100 xp
    Mixture Model Likelihood Function
    100 xp
    Optimization of Mixture Model
    100 xp
  12. 12

    Import and Export

    Grátis

    Import external data files and export r data objects.

    Reproduzir Capítulo Agora
    Writing Text Strings
    100 xp
    Displaying Numeric Data
    100 xp
    Reading Text from Files
    100 xp
    Reading and Writing Data Frames from Text Files
    100 xp
    Copying Data Frames Between the clipboard and R
    100 xp
    Reading and Writing Data Frames from csv Files
    100 xp
    Reading and Writing Matrices from csv Files
    100 xp
    Reading and Writing Matrices (cont.)
    100 xp
    Reading Matrices Containing Bad Data
    100 xp
    Reading and Writing zoo Series From Text Files
    100 xp
    Reading and Writing zoo Series With Date-time Index
    100 xp
    Reading and Writing zoo Series From csv Files
    100 xp
    Passing Arguments to the save() Function
    100 xp
    Reading and Writing Lists of Objects
    100 xp
    Saving Output of R to a File
    100 xp
  13. 13

    Numbers and Dates

    Grátis

    A bit more details about numeric variables and date-time objects.

    Reproduzir Capítulo Agora
    Floating Point Numbers
    100 xp
    Floating Point Calculations
    100 xp
    Modular Arithmetic Operators
    100 xp
    Determining the Memory Usage of R Objects
    100 xp
    Date Object
    100 xp
    POSIXct Date-time Objects
    100 xp
    Operations on POSIXct Objects
    100 xp
    Moment of Time and Clock Time
    100 xp
    Methods for Manipulating POSIXct Objects
    100 xp
    POSIXlt Date-time Objects
    100 xp
    Time Zones and Date-time Conversion
    100 xp
    Manipulating Date-time Objects Using lubridate
    100 xp
    Time Zones Using lubridate
    100 xp
    lubridate Time Span Objects
    100 xp
    End-of-month Dates
    100 xp
    RQuantLib Calendar Functions
    100 xp
    Review of Date-time Classes in R
    100 xp
    EuStockMarkets Data
    100 xp
    Boxplots
    100 xp
    Managing NA Values
    100 xp
  14. 14

    Time Series

    Grátis

    Present and investigate time series data in R.

    Reproduzir Capítulo Agora
    xts Time Series Objects
    100 xp
    Coercing zoo Time Series Into Class xts
    100 xp
    Plotting Multiple xts Using Packages xts and quantmod
    100 xp
    Plotting xts Using Package ggplot2
    100 xp
    Plotting Multiple xts Using Package ggplot2
    100 xp
    Interactive Time Series Plots Using Package dygraphs
    100 xp
    Interactive Time Series Plots Using Package plotly
    100 xp
    Subsetting xts Time Series
    100 xp
    Subsetting Recurring xts Time Intervals
    100 xp
    Properties of xts Time Series
    100 xp
    lag() and diff() Operations on xts Time Series
    100 xp
    Converting xts to Lower Periodicity
    100 xp
    Plotting OHLC Time Series Using xts
    100 xp
    Time Series Classes in R
    100 xp
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