Newcomers to R are often intimidated by the command-line interface, the vast number of functions and packages, or the processes of importing data and performing a simple statistical analysis. The R Primer provides a collection of concise examples and solutions to R problems frequently encountered by new users of this statistical software. This new edition adds coverage of R Studio and reproducible research.
Newcomers to R are often intimidated by the command-line interface, the vast number of functions and packages, or the processes of importing data and performing a simple statistical analysis. The R Primer provides a collection of concise examples and solutions to R problems frequently encountered by new users of this statistical software. This new edition adds coverage of R Studio and reproducible research.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Claus Thorn Ekstrøm is an associate professor of statistics in the Department of Basic Sciences and Environment and leader of the Center for Applied Bioinformatics at the University of Copenhagen. His research interests include genetic marker error detection, simulation-based inference, image analysis, and the analysis of microarray DNA chips, metabolic profiles, and quantitative traits for complex human families.
Inhaltsangabe
Preface. Importing data. Reading spreadsheets. Importing data from other statistical software programs. Exporting data. Manipulating data. Working with data frames. Factors. Transforming variables. Statistical analyses. Descriptive statistics. Linear models. Generalized linear models. Methods for analysis of repeated measurements. Specific methods. Model validation. Contingency tables. Agreement. Multivariate methods. Resampling statistics and bootstrapping. Robust statistics. Non-parametric methods. Survival analysis. Graphics. High-level plots. More advanced graphics. Working with graphics. Getting information. R packages. The R workspace. R Studio. Getting information. Using R Studio for reproducible research. Large datasets. Bibliography. Index.
Preface. Importing data. Reading spreadsheets. Importing data from other statistical software programs. Exporting data. Manipulating data. Working with data frames. Factors. Transforming variables. Statistical analyses. Descriptive statistics. Linear models. Generalized linear models. Methods for analysis of repeated measurements. Specific methods. Model validation. Contingency tables. Agreement. Multivariate methods. Resampling statistics and bootstrapping. Robust statistics. Non-parametric methods. Survival analysis. Graphics. High-level plots. More advanced graphics. Working with graphics. Getting information. R packages. The R workspace. R Studio. Getting information. Using R Studio for reproducible research. Large datasets. Bibliography. Index.
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