R Language TutorialReadDiscussCoursesPracticeImprove Article ImproveSave Article SaveLike Article LikeR Programming Tutorial is designed for beginners and experts. This R Tutorial gives you knowledge of all concepts of R programming language.This R Language Tutorial covers all the basic and advanced concepts of R, including introduction, features, installation, variables, data types, operators, if statements, vectors, data handling, graphics, and statistical modeling.What is R Programming?R is a programming language and also a software environment for statistical computing and data analysis. R was developed by Ross Ihaka and Robert Gentleman at the university of Auckland, New Zealand. R is an open-source programming language and it is available on widely used platforms e.g. Windows, Linux, and Mac. It generally comes with a command-line interface and provides a vast list of packages for performing tasks. R is an interpreted language that supports both procedural programming and object-oriented programming.How to Install RGo to https://cloud.r-project.org/ and download the latest version of R for Windows, Mac or Linux.When you have downloaded and installed R, you can run R on your Command prompt or any IDE.Recent Articles on R!R Tutorial – Table of Content BasicsFundamentals of RVariablesInput and OutputDecision MakingControl FlowFunctionsData StructuresStringsVectorsListsArrays MatricesFactorsDataFramesObject Oriented ProgrammingError HandlingFile HandlingPackages in RData InterfacesData VisualizationStatisticsMachine Learning with RBasicsIntroduction to R Programming LanguageInteresting Facts about R Programming LanguageR vs PythonEnvironments in R ProgrammingIntroduction to R StudioHow to Install R Studio on Windows and Linux?Creation and Execution of R File in R StudioClear the Console and the Environment in R StudioHello World in R ProgrammingFundamentals of RBasic SyntaxCommentsOperatorsKeywordsData TypesVariablesIntroduction to VariablesScope of VariableDynamic ScopingLexical ScopingLexical Scoping vs Dynamic ScopingInput and OutputTaking Input from UserPrinting Output of R ProgramPrint the Argument to the Screen – print() FunctionDecision MakingDecision Making – if, if-else, if-else-if ladder, nested if-else, and switchif statementif-else statementSwitch caseControl FlowIntroduction to Control StatementsLoops (for, while, repeat)For loopwhile loopRepeat loopgoto statementBreak and Next statementsNext StatementFunctionsIntroduction to FunctionsFunction ArgumentsTypes of FunctionsRecursive FunctionsConversion FunctionsData StructuresIntroduction to Data StructuresStringsIntroduction to StringsWorking with TextString ManipulationConcatenate Two StringsString MatchingHow to find a SubString?Finding the length of string – nchar() methodAdding elements in a vector – append() methodConvert string from Lowercase to Uppercase – toupper() functionConvert String from Uppercase to Lowercase – tolower() methodSplitting Strings – strsplit() methodPrint a Formatted string – sprintf() Function>>> More Functions on StringsVectorsIntroduction to VectorsOperations on VectorsAppend Operation on VectorsDot Product of VectorsTypes of VectorsAssigning Vectors Getting and Setting Length of the Vectors – length() FunctionCreating a Vector of sequenced elements – seq() FunctionGet the Minimum and Maximum element of a Vector – range() FunctionFormatting Numbers and Strings – format() FunctionReplace the Elements of a Vector – replace() FunctionSorting of a Vector – sort() FunctionConvert elements of a Vector to Strings – toString() FunctionExtracting Substrings from a Character Vector – substring() Function>>> More Functions on VectorsListsIntroduction to ListsTwo Dimensional ListOperations on ListsList of VectorsList of DataframesNamed ListCheck if the Object is a List – is.list() FunctionConvert an Object to List – as.list() FunctionCheck if an Object of the Specified Name is Defined or not – exists() FunctionApply a Function over a List of elements – lapply() FunctionPerforming Operations on Multiple Lists simultaneously – mapply() Function>>> More Functions on ListsArraysIntroduction to ArraysMultidimensional ArrayArray OperationsSorting of ArraysConvert values of an Object to Logical Vector – as.logical() FunctionPerforming different Operations on Two Arrays – outer() FunctionIntersection of Two Objects – intersect() FunctionGet Exclusive Elements between Two Objects – setdiff() Function>>> More Functions on ArraysMatricesIntroduction to MatricesCreate Matrix from VectorsOperations on MatricesMatrix MultiplicationAlgebraic Operations on a MatrixCombining MatricesMatrix TransposeInverse of MatrixWorking with Sparse MatricesCheck if the Object is a Matrix – is.matrix() FunctionConvert an Object into a Matrix – as.matrix() FunctionGet or Set Dimensions of a Matrix – dim() FunctionCalculate Cumulative Sum of a Numeric Object – cumsum() FunctionCompute the Sum of Rows of a Matrix or Array – rowSums Function>>> More Functions on MatricesFactorsIntroduction to FactorsLevel Ordering of FactorsConvert Factor to Numeric and Numeric to FactorCheck if a Factor is an Ordered Factor – is.ordered() FunctionConvert an Unordered Factor to an Ordered Factor – as.ordered() FunctionChecking if the Object is a Factor – is.factor() FunctionConvert a Vector into Factor – as.factor() Function>>> More Functions on FactorsDataFramesIntroduction to Data FramesMatrix vs DataframeDataFrame OperationsDataFrame ManipulationJoining of DataframesThe Factor Issue in a DataFrameData ReshapingCreating a Data Frame from VectorsData Wrangling – Data TransformationData Wrangling – Working with TibblesMelting and CastingSubsetting of DataFramesHandling Missing ValuesConvert an Object to Data Frame – as.data.frame() FunctionGet the number of columns of an Object – ncol() FunctionGet the number of rows of an Object – nrow() FunctionGet Addition of the Objects passed as Arguments – sum() FunctionCreate Subsets of a Data frame – subset() Function>>> More Functions on DataFramesObject Oriented ProgrammingIntroduction to Object-Oriented ProgrammingClassesObjectsEncapsulationPolymorphismInheritanceAbstractionLooping over ObjectsCreating, Listing, and Deleting Objects in MemoryS3 classExplicit CoercionR6 ClassesGetting attributes of Objects – attributes() and attr() FunctionGet or Set names of Elements of an Object – names() FunctionGet the Minimum element of an Object – min() FunctionGet the Maximum element of an Object – max() Function>>> More Functions on R ObjectsError HandlingIntroduction to Error HandlingCondition HandlingDebugging in R ProgrammingFile HandlingIntroduction to File HandlingReading FilesWriting to FilesRead Lines from a File – readLines() FunctionWorking with Binary FilesPackages in RIntroduction to Packagesdplyr Packageggplot2 packageGrid and Lattice PackagesShiny Packagetidyr PackageWhat Are the Tidyverse Packages?Data MungingData InterfacesData HandlingImporting Data in R ScriptHow To Import Data from a File?Exporting Data from scripts Working with CSV files Working with XML FilesWorking with Excel FilesWorking with JSON Files Reading Tabular Data from filesWorking with DatabasesDatabase ConnectivityManipulate Data Frames Using SQLData VisualizationGraph PlottingGraphical ModelsPlotting Graphs using Two Dimensional ListData VisualizationCharts and GraphsAdd Titles to a GraphAdding Colors to ChartsAdding Text to PlotsAdding axis to a PlotSet or View the Graphics PalettePlotting of Data using Generic plotsBar ChartsLine GraphsAdding Straight Lines to a PlotAddition of Lines to a PlotHistogramsPie ChartsScatter plotsCreate One Dimensional ScatterplotsCreate a Plot Matrix of ScatterplotsCreate Dot ChartsBoxplots in R LanguageStratified BoxplotCreate a HeatmapPareto ChartWaffle ChartDraw a Quantile-Quantile PlotCreating 3D PlotsDescribe Parts of a Chart in Graphical FormPrincipal Component AnalysisSocial Network AnalysisStatisticsIntroduction to StatisticsCalculate the Mean, Median, and ModeCalculate the Average, Variance, and Standard DeviationHomogeneity of Variance TestCovariance and CorrelationCorrelation MatrixVisualize correlation matrix using correlogramDistance Matrix by GPUDescriptive AnalysisNormal DistributionBinomial DistributionCompute the Negative Binomial DensityPoisson FunctionsANOVA TestMANOVA TestNaive Bayes ClassifierK-NN ClassifierCentral TendencyVariabilitySkewness and KurtosisAbsolute and Relative FrequencyPermutation Hypothesis TestAB TestingCompletely Randomized DesignRandomized Block DesignBartlett’s TestTree EntropyTukey’s Five-number SummaryCompute Summary Statistics of SubsetsHypothesis TestingBootstrappingTime Series AnalysisT-Test ApproachMachine Learning with RIntroduction to Machine LearningSetting up Environment for Machine LearningSupervised and Unsupervised LearningClassificationRegression and its TypesRegression AnalysisDecision TreeRandom Forest ApproachRoot-Mean-Square ErrorClusteringHierarchical ClusteringDBScan ClusteringDeep LearningBuilding a Simple Neural NetworkHow Neural Networks are used for Regression?Multi Layered Neural NetworksSurvival AnalysisStem and Leaf PlotsWhy Use R Programming Language?R programming language is a best resource for data science, data analysis, data visualization and machine learning. R provides various statistical techniques like statistical tests, clustering and data reduction. Graph making is easy eg. pie chart, histogram, box, plot, etc. R is totally free and open-source Programming language. The community support with the R language is very large and it works on all OS. R programming comes with many packages (libraries of functions) to solve various problems.Applications of R Programming LanguageSome of the important applications of R Programming Language are listed below:R is used in wide range of industries for example academics, government, insurance, retail, energy, media, technology, and electronics.R helps in importing and cleaning data and data analysis.R is used in data science. R language provides us many libraries for data science e.g. Dplyr, Ggplot2, shiny, Lubridate, Knitr, Caret, Janitor.FAQs on R TutorialQ.1 What is Rstudio ?Answer:Rstudio is the IDE for programming in R. It is used to write scripts, access files, and make graphics. It is widely used in data science, machine learning, and research.Q.2 What are some popular packages in R?Answer:R include ggplot2 the packages for data visualization, dplyr for data manipulation, tplyr for data cleaning.Q.3 What’s the difference between R and Python?Answer:R programmingPython programmingData visualization libraries and tools are good in R language.R has poor data visualization than python.Production is poor than python.Production is better than R.Model Interpretability is good in R programming language.Model Interpretability is not good in python.R has relative complex syntax and learning.Syntax is simple in python.R is used when the data analysis process requires analysis and processing.Python is used when the data analysis process require integrated with web applications.Model creation is similar to Python.Model creation is similar to R.Q.4 Which is more demanding language? Python or R?Answer:R has more demand than Python in Data science. Specific skills are needed in compare to Python which is a multi-purpose language. Last Updated : 04 Sep, 2023Like Article Save Article Please Login to comment...