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Descripción del curso
This is the first course in R, designed to introduce students to the basics of the R language.
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R Environment
GratuitoThis 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!
Variables assignment100 xpInserting comments into your code100 xpExplore sessions in R100 xpMore about R sessions100 xpEnvironment Variables100 xpGlobal Options Settings100 xpThe R Workspace100 xpData Objects in R100 xpType, Mode, and Class of Objects100 xpR Object Attributes100 xpImplicit Class of Objects100 xpObject Coercion100 xp - 2
R Objects
GratuitoIn this chapter details about R objects will be covered.
Basic R Objects100 xpCharacter Strings100 xpManipulating Strings100 xpRegular Expressions in R100 xpVectors100 xpArithmetic and Logical Operations on Vectors100 xpNaming and Manipulating Vectors100 xpSubsetting Vectors100 xpFiltering Vectors100 xpFactors100 xpTables of Categorical Data100 xpClassifying Continuous Numeric Data Into Categories100 xpClassifying Continuous Numeric Data Into Categories, Continued100 xpConverting Numeric Data Into Factors Using cut()100 xpMatrices100 xpMatrix attributes100 xpMatrix Subsetting100 xp - 3
R Data Operations
GratuitoIn this chapter you will learn to use the functional operations which is essential for more powerful operations. May the force be with you!
Generating Pseudo-Random Numbers100 xpGenerating Binomial Random Numbers100 xpGenerating Random Samples and Permutations100 xpStatistical Estimators100 xpFunctions in R100 xpBinding Arguments and Default Argument100 xpReturn Values and Invisible Return100 xpLogical Operators100 xpLong Form Logical Operators100 xpArithmetic Operators100 xpComparing Objects With identical()100 xpLookup and Matching100 xpLookup and Matching (cont.)100 xpAssignment Operators100 xpThe "if () else" Control Statement100 xpThe switch() Control Statement100 xpIteration Using for() and while() Loops100 xpFibonacci Sequence Using for() Loop100 xpList100 xpSubsetting Lists100 xpCoercing Vectors Into Lists Using as.list()100 xpData Frames100 xpSubsetting Data Frames100 xpData Frames and Factors100 xpExploring Data Frames100 xpCoercing Data Frames Into Matrices Using as.matrix()100 xpCoercing Matrices Into Data Frames and Lists100 xpThe iris Data Frame100 xp'mtcars' data100 xpThe Cars93 Data Frame100 xpTypes of Bad Data100 xpScrubbing Bad Data100 xpNULL Values100 xp - 4
R Functionals
GratuitoSee details in R function settings
Lazy Evaluation of Function Arguments100 xpThe dots "..." Function Argument100 xpFunctions that takes functions and returns functions100 xpFunctionals100 xpAnonymous Functions and Functionals100 xpExecuting Function Calls Using the do.call() Functional100 xpPerforming Loops Using the apply() Functionals100 xpThe apply() Functional with dots "..." Argument100 xpThe apply() Functional with Anonymous Functions100 xpapply() Calling Functions with Multiple Arguments100 xpThe lapply() and sapply() Functional100 xp - 5
More on matrix
GratuitoMatrix, and matrix-like structures are essential to R data manipulation and operation.
Logical Operators Applied to Vectors and Matrices100 xpCoercing Vectors Into Matrices100 xpCoercing Matrices Into Other Types100 xpBinding Vectors and Matrices Together100 xpReplicating Objects Using rep()100 xpMultiplying Vectors and Matrices100 xpMatrix Inner Multiplication100 xpMatrix Transpose100 xpMatrix Outer Multiplication100 xpFlattening a List of Vectors to a Matrix Using do.call()100 xpEfficient Binding of Lists Into Matrices100 xp - 6
More on Data Frames
GratuitoMost data sets you will be working with will be stored as a data frame. Learn data frame operations will help your data analysis procedure.
Filtering Data Frames Using subset()100 xpSplitting Data Frames Using factor Categorical Variables100 xpThe split-apply-combine Procedure Example100 xpThe tapply() Functional100 xpThe split-apply-combine Returning Matrices Example100 xpBenchmarking the Speed of R Code100 xpWriting Fast R Code Using Compiled Functions100 xpWriting Fast R Code Without Method Dispatch100 xpUsing apply() Instead of for() and while() Loops100 xpIncreasing Speed of Loops by Pre-allocating Memory100 xpVectorized Functions for Vector Computations100 xpVectorized Functions for Matrix Computations100 xpFast R Code for Matrix Computations100 xpPackage matrixStats for Fast Matrix Computations100 xpWriting Fast R Code Using Vectorized Operations100 xpVectorized Functions100 xpPerforming sapply() Loops Over Function Parameters100 xpCreating Vectorized Functions100 xpThe mapply() Functional100 xpVectorized if-else Statements Using Function ifelse()100 xpMonte Carlo Simulation100 xpSimulating Brownian Motion Using while() Loops100 xpSimulating Brownian Motion Using Vectorized Functions100 xpStandard Errors of Estimators Using Bootstrap Simulation100 xpStandard Errors of Regression Coefficients Using Bootstrap100 xp - 7
Regression and tests
GratuitoUtilize R for statistical analysis.
Hypothesis Testing100 xpFormula Objects100 xpLinear Regression Using lm()100 xpThe Regression Scatterplot100 xpRegression Summary100 xpInterpreting the Regression Statistics100 xpWeak Regression100 xpInfluence of Noise on Regression100 xpInfluence of Noise on Regression Another Method100 xpRegression Diagnostic Plots100 xpDurbin-Watson Test of Autocorrelation of Residuals100 xpOmitted Variable Bias100 xpSpurious Time Series Regression100 xpPredictions Using Regression Models100 xp - 8
Exceptions
GratuitoIn this chapter you will learn about several exceptions in R and how to handle with them.
Exception Conditions: Errors and Warnings100 xpValidating Function Arguments100 xpValidating Assertions Inside Functions100 xpValidating Assertions Using stopifnot()100 xpValidating Function Arguments and Assertions100 xpThe R Debugger Facility100 xpDebugging Using browser()100 xpHandling Exception Conditions100 xpError Conditions and Exception Handling in Loops100 xp - 9
Parallel Computing
GratuitoLearn to ride the powerful technique in R!
Parallel Computing Using Package parallel100 xpPerforming Parallel Loops Using Package parallel100 xpComputing Overhead of Parallel Computing100 xpParallel Computing Over Matrices100 xpStandard Errors of Regression Coefficients Using Bootstrap100 xpDistribution of Bootstrapped Regression Coefficients100 xpBootstrapping Regressions Using Parallel Computing100 xp - 10
Logistic regression
GratuitoHave a taste of the machine learning techniques in R!
The Logistic Function100 xpPerforming Logistic Regression Using the Function glm()100 xpPackage ISLR With Datasets for Machine Learning100 xpThe Default Dataset100 xpBoxplots of the Default Dataset100 xpModeling Credit Defaults Using Logistic Regression100 xpModeling Cumulative Defaults Using Logistic Regression100 xpMultifactor Logistic Regression100 xpConfounding Variables in Multifactor Logistic Regression100 xpForecasting of Credit Defaults using Logistic Regression100 xp - 11
Optimization
GratuitoUse various optimization functions to get your best result!
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Import and Export
GratuitoImport external data files and export r data objects.
Writing Text Strings100 xpDisplaying Numeric Data100 xpReading Text from Files100 xpReading and Writing Data Frames from Text Files100 xpCopying Data Frames Between the clipboard and R100 xpReading and Writing Data Frames from csv Files100 xpReading and Writing Matrices from csv Files100 xpReading and Writing Matrices (cont.)100 xpReading Matrices Containing Bad Data100 xpReading and Writing zoo Series From Text Files100 xpReading and Writing zoo Series With Date-time Index100 xpReading and Writing zoo Series From csv Files100 xpPassing Arguments to the save() Function100 xpReading and Writing Lists of Objects100 xpSaving Output of R to a File100 xp - 13
Numbers and Dates
GratuitoA bit more details about numeric variables and date-time objects.
Floating Point Numbers100 xpFloating Point Calculations100 xpModular Arithmetic Operators100 xpDetermining the Memory Usage of R Objects100 xpDate Object100 xpPOSIXct Date-time Objects100 xpOperations on POSIXct Objects100 xpMoment of Time and Clock Time100 xpMethods for Manipulating POSIXct Objects100 xpPOSIXlt Date-time Objects100 xpTime Zones and Date-time Conversion100 xpManipulating Date-time Objects Using lubridate100 xpTime Zones Using lubridate100 xplubridate Time Span Objects100 xpEnd-of-month Dates100 xpRQuantLib Calendar Functions100 xpReview of Date-time Classes in R100 xpEuStockMarkets Data100 xpBoxplots100 xpManaging NA Values100 xp - 14
Time Series
GratuitoPresent and investigate time series data in R.
xts Time Series Objects100 xpCoercing zoo Time Series Into Class xts100 xpPlotting Multiple xts Using Packages xts and quantmod100 xpPlotting xts Using Package ggplot2100 xpPlotting Multiple xts Using Package ggplot2100 xpInteractive Time Series Plots Using Package dygraphs100 xpInteractive Time Series Plots Using Package plotly100 xpSubsetting xts Time Series100 xpSubsetting Recurring xts Time Intervals100 xpProperties of xts Time Series100 xplag() and diff() Operations on xts Time Series100 xpConverting xts to Lower Periodicity100 xpPlotting OHLC Time Series Using xts100 xpTime Series Classes in R100 xp
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Crea Tu Cuenta Gratuita
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Al continuar, acepta nuestros Términos de uso, nuestra Política de privacidad y que sus datos se almacenan en los EE. UU.