Getting started with R LanguageData framesReading and writing tabular data in plain-text files (CSV, TSV, etc.)Pipe operators (%>% and others)Linear Models (Regression)data.tableboxplotFormulaSplit functionCreating vectorsFactorsPattern Matching and ReplacementRun-length encodingDate and TimeSpeeding up tough-to-vectorize codeggplot2ListsIntroduction to Geographical MapsBase PlottingSet operationstidyverseRcppRandom Numbers GeneratorString manipulation with stringi packageParallel processingSubsettingDebuggingInstalling packagesArima ModelsDistribution FunctionsShinyspatial analysissqldfCode profilingControl flow structuresColumn wise operationJSONRODBClubridateTime Series and Forecastingstrsplit functionWeb scraping and parsingGeneralized linear modelsReshaping data between long and wide formsRMarkdown and knitr presentationScope of variablesPerforming a Permutation TestxgboostR code vectorization best practicesMissing valuesHierarchical Linear ModelingClassesIntrospection*apply family of functions (functionals)Text miningANOVARaster and Image AnalysisSurvival analysisFault-tolerant/resilient codeReproducible RUpdating R and the package libraryFourier Series and Transformations.RprofiledplyrcaretExtracting and Listing Files in Compressed ArchivesProbability Distributions with RR in LaTeX with knitrWeb Crawling in RArithmetic OperatorsCreating reports with RMarkdownGPU-accelerated computingheatmap and heatmap.2Network analysis with the igraph packageFunctional programmingGet user inputroxygen2HashmapsSpark API (SparkR)Meta: Documentation GuidelinesI/O for foreign tables (Excel, SAS, SPSS, Stata)I/O for database tablesI/O for geographic data (shapefiles, etc.)I/O for raster imagesI/O for R's binary formatReading and writing stringsInput and outputRecyclingExpression: parse + evalRegular Expressions (regex)CombinatoricsPivot and unpivot with data.tableInspecting packagesSolving ODEs in RFeature Selection in R -- Removing Extraneous FeaturesBibliography in RMDWriting functions in RColor schemes for graphicsHierarchical clustering with hclustRandom Forest AlgorithmBar ChartCleaning dataRESTful R ServicesMachine learningVariablesThe Date classThe logical classThe character classNumeric classes and storage modesMatricesDate-time classes (POSIXct and POSIXlt)Using texreg to export models in a paper-ready wayPublishingImplement State Machine Pattern using S4 ClassReshape using tidyrModifying strings by substitutionNon-standard evaluation and standard evaluationRandomizationObject-Oriented Programming in RRegular Expression Syntax in RCoercionStandardize analyses by writing standalone R scriptsAnalyze tweets with RNatural language processingUsing pipe assignment in your own package %<>%: How to ?R Markdown Notebooks (from RStudio)Updating R versionAggregating data framesData acquisitionR memento by examplesCreating packages with devtools

Get user input

Other topics

User input in R

Sometimes it can be interesting to have a cross-talk between the user and the program, one example being the swirl package that had been designed to teach R in R.

One can ask for user input using the readline command:

name <- readline(prompt = "What is your name?")

The user can then give any answer, such as a number, a character, vectors, and scanning the result is here to make sure that the user has given a proper answer. For example:

result <- readline(prompt = "What is the result of 1+1?")
while(result!=2){
    readline(prompt = "Wrong answer. What is the result of 1+1?")
}

However, it is to be noted that this code be stuck in a never-ending loop, as user input is saved as a character.

We have to coerce it to a number, using as.numeric:

result <- as.numeric(readline(prompt = "What is the result of 1+1?"))
while(result!=2){
    readline(prompt = "Wrong answer. What is the result of 1+1?")
}

Syntax:

  • variable <- readline(prompt = "Any message for user")

  • name <- readline(prompt = "What's your name")

Contributors

Topic Id: 5098

Example Ids: 17993

This site is not affiliated with any of the contributors.