R Syllabus



Introduction to Programming in R Week 1Introduction to RRMarkdownWeek 2Data Manipulation with dplyrCreating VisualizationsWeek 3Reading DataIterate Over Lists with purrrReshaping DataWeek 4Linear ModelsGeneralized Linear ModelsAssessing Model QualityWeek 5Cross-ValidationPenalized RegressionBoosted TreesWeek 6Shiny BasicsShiny DashboardInstructor BioTextbookSyllabusThis class is an intensive introduction to R. It starts with the very basics of assigning variables and reading data. It then progresses to using RMarkdown for document and presentation creation.Week 1Introduction to RThe RStudio InterfaceBasic MathAssigning VariablesWorking DirectoriesRelative PathsReading Data Read from text files with readrRead from Excel files with readxlWriting FunctionsRMarkdownRMarkdown Primer SectionsText FormattingListsLinksIntegrating R into Markdown Code ChunksChunk OptionsIncluding FiguresOutput Formats HTMLPDFWordPresentationsWeek 2Data Manipulation with dplyrUnderstanding a tblUse pipes for cleaner codeSelect columns with selectFilter rows with filterChange and create columns with mutateCalculate summary statistics with summarizeGroup data for calculations with group_byJoins with left_joinCreating Visualizationsggplot2 paradigmAestheticsScatter plots Color CodingSizeShapeOpacitySmall multiple plotsHistogramsDensity PlotsCombining LayersViolin PlotsThemesWeek 3Reading DataCSVs with readrDatabases with DBIJSON with jsonliteWeb pages with rvestIterate Over Lists with purrrBasics of functional programmingMapping over a listDifference from lapplyConsistent Data TypesMapping to different data types chacracternumericdata.frameMapping functions with multiple argumentsReshaping DataConvert from wide to long with gatherConvert from long to wide with spreadWeek 4Linear ModelsSimple Linear Model with lmThe Formula InterfaceMultiple RegressionTidying models with broomVisualizing models with coefplotGeneralized Linear ModelsLogistic Regression for Binary DataPoisson Regression for Count DataQuasipoisson Regression for Overdispersed Count DataAssessing Model QualityAICBICWeek 5Cross-ValidationUse Cross-Validation for Model AssessmentPenalized RegressionL1 Penalty (Lasso)L2 Penalty (Ridge)Implement via the Elastic Net with glmnetTuning HyperparametersBoosted TreesDecision TreesBoosted TreesFit Model with xgboostWeek 6Shiny BasicsInputsOutputsReactive ExpressionsHTML Widgets Interactive PlotsInteractive MapsInteractive TablesShiny DashboardServer CodeUI CodeDashboard LayoutInstructor BioJared P. Lander is the Chief Data Scientist of Lander Analytics, a data science and artificial intelligence consulting and training firm based in New York City; the organizer of the New York Open Statistical Programming Meetup—the world’s largest R meetup—–and the New York R Conference); author of R for Everyone and an adjunct professor at Columbia University. With an M.A.?from Columbia University in statistics and a B.S. from Muhlenberg College in mathematics, he has experience in both academic research and industry. Very active in the data community, Jared is a frequent speaker at conferences, universities and meetups around the world. His writings on statistics can be found at and his work has been featured in publications such as Forbes and the Wall Street Journal.TextbookR for Everyone, Second Edition ................
................

In order to avoid copyright disputes, this page is only a partial summary.

Google Online Preview   Download