#
R (Computer program language)
Resource Information
The concept ** R (Computer program language)** represents the subject, aboutness, idea or notion of resources found in **Massey University Library, University of New Zealand**.

The Resource
R (Computer program language)
Resource Information

The concept

**R (Computer program language)**represents the subject, aboutness, idea or notion of resources found in**Massey University Library, University of New Zealand**.- Label
- R (Computer program language)

## Context

Context of R (Computer program language)#### Subject of

- A course in statistics with R
- A data scientist's guide to acquiring, cleaning and managing data in R
- A first course in statistical programming with R
- A practical guide to ecological modelling : using R as a simulation platform
- A primer in biological data analysis and visualization using R
- A primer in biological data analysis and visualization using R
- A survivor's guide to R : an introduction for the uninitiated and the unnerved
- A survivor's guide to R : an introduction for the uninitiated and the unnerved
- APPLYING TEST EQUATING METHODS
- Adaptive tests of significance using permutations of residuals with R and SAS
- Advanced R
- An R and S-PLUS® companion to multivariate analysis
- An R and S-Plus companion to applied regression
- An R companion to applied regression
- An R companion to linear statistical models
- An introduction to R : notes on R, a programming environment for data analysis and graphics
- An introduction to R for spatial analysis & mapping
- An introduction to analysis of financial data with R
- An introduction to applied multivariate analysis with R
- An introduction to bootstrap methods with applications to R
- An introduction to data analysis using aggregation functions in R
- An introduction to statistical inference and its applications with R
- Analysis of categorical data with R
- Analysis of phylogenetics and evolution with R
- Analyzing financial data and implementing financial models using R
- Analyzing sensory data with R
- Applied econometrics with R
- Applied probabilistic calculus for financial engineering : an introduction using R
- Applied statistical genetics with R : for population-based association studies
- Applied time series analysis with R
- Automated data collection with R : a practical guide to Web scraping and text mining
- Automated data collection with R : a practical guide to Web scraping and text mining
- Bare-bones R : a brief introductory guide
- Basic data analysis for time series with R [electronic resource]
- Basic statistics : an introduction with R
- Basic statistics : an introduction with R
- Basics of matrix algebra for statistics with R
- Bayesian computation with R
- Bayesian data analysis in ecology using linear models with R, BUGS, and Stan
- Bayesian networks : with examples in R
- Beginning R : the statistical programming language
- Beyond spreadsheets with R : a beginner's guide to R and RStudio
- Big Data analytics with R and Hadoop : set up an integrated infrastructure of R and Hadoop to turn your data analytics into Big Data analytics
- Bioinformatics with R cookbook : over 90 practical recipes for computational biologists to model and handle real-life data using R
- Biostatistical design and analysis using R : a practical guide
- Business analytics and data mining with R
- Circular statistics in R
- Comparative approaches to using R and Python for statistical data analysis
- Comparing groups : randomization and bootstrap methods using R
- Comparing groups : randomization and bootstrap methods using R
- Complex surveys : a guide to analysis using R
- Computational finance : an introductory course with R
- Computational methods for numerical analysis with R
- Computational network analysis with R : applications in biology, medicine, and chemistry
- Computational statistics : an introduction to R
- Contingency table analysis : methods and implementation using R
- Customer and business analytics : applied data mining for business decision making using R
- Data Manipulation with R : perform group-wise data manipulation and deal with large datasets using R efficiently and effectively
- Data analysis and graphics using R : an example-based approach
- Data analysis using hierarchical generalized linear models with R
- Data manipulation With R
- Data manipulation with R : efficiently perform data manipulation using the split-apply-combine strategy in R
- Data mining algorithms : explained using R
- Data mining for business analytics : concepts, techniques, and applications in R
- Data mining with R : learning with case studies
- Data science in R : a case studies approach to computational reasoning and problem solving
- Data visualisation with R : 100 examples
- Data visualization : a practical introduction
- Deep learning with R
- Discovering statistics using R
- Discrete data analysis with R : visualization and modeling techniques for categorical and count data
- Displaying time series, spatial, and space-time data with R
- Doing Bayesian data analysis : a tutorial with R and BUGS
- Doing Bayesian data analysis : a tutorial with R, JAGS, and Stan
- Doing Bayesian data analysis : a tutorial with R, JAGS, and Stan
- Doubly classified model with R
- Dynamic linear models with R
- Ecological models and data in R
- Empirical likelihood method in survival analysis
- EnvStats : an R package for environmental statistics
- Environmental and ecological statistics with R
- Epidemics : models and data using R
- Examples in parametric inference with R
- Exploratory data analysis using R
- Exploratory multivariate analysis by example using R
- Extending R
- Financial analytics with R : building a laptop laboratory for data science
- Financial risk modelling and portfolio optimization with R
- First course in statistical programming with R
- Foundational and applied statistics for biologists using R
- Foundations and applications of statistics : an introduction using R
- Functional data analysis with R and MATLAB
- Functional data structures in R : advanced statistical programming in R
- Generalized additive models : an introduction with R
- Geochemical modelling of igneous processes-- principles and recipes in R language : bringing the power of R to a geochemical community
- Getting started with Greenplum for big data analytics : a hands-on guide on how to execute an analytics project from conceptualization to operationalization using Greenplum
- Getting started with R : an introduction for biologists
- Getting started with R : an introduction for biologists
- Getting started with R : an introduction for biologists
- Ggplot2 : elegrant graphics for data analysis
- Graphical models with R
- Graphing data with R : an introduction
- Growth curve analysis and visualization using R
- Guide to programming and algorithms using R
- Guide to programming and algorithms using R
- Guidebook to R graphics using Microsoft Windows
- Handbook of fitting statistical distributions with R
- Hands-on matrix algebra using R : active and motivated learning with applications
- Hands-on programming with R
- Hurricane climatology : a modern statistical guide using R
- IFRS 9 and CECL credit risk modelling and validation : a practical guide with examples worked in R and SAS
- Instant heat maps in R how-to : learn how to design heat maps in R to enhance your data analysis
- Interactive and dynamic graphics for data analysis : with R and GGobi
- Introducing Monte Carlo methods with R
- Introducing data science for social and policy research : collecting and organizing data with R and Python
- Introduction to R for quantitative finance : solve a diverse range of problems with R, one of the most powerful tools for quantitative finance
- Introduction to data analysis and graphical presentation in biostatistics with R : statistics in the large
- Introduction to image processing using R : learning by examples
- Introduction to nonparametric statistics for the biological sciences using R
- Introduction to renewable power systems and the environment with R
- Introduction to scientific programming and simulation using R
- Introduction to statistics and data analysis : with exercises, solutions and applications in R
- Introduction to stochastic processes with R
- Introductory statistics with R
- Introductory time series with R
- Joint models for longitudinal and time-to-event data : with applications in R
- Latent variable modeling using R : a step by step guide
- Learn ggplot2 using shiny app
- Learning RStudio for R Statistical Computing
- Learning analytics in R with SNA, LSA, and MPIA
- Learning data mining with R : develop key skills and techniques with R to create and customize data mining algorithms
- Learning statistics using R
- Linear regression analysis with JMP and R
- Machine learning with R
- Mastering R for quantitative finance : use R to optimize your trading strategy and build up your own risk management system
- Mastering scientific computing with R
- Mathematical statistics with applications in R
- Mixed effects models and extensions in ecology with R
- Modeling techniques in predictive analytics : business problems and solutions with R
- Modern data science with R
- Modern industrial statistics : with applications in R, MINITAB and JMP
- Modern industrial statistics : with applications in R, MINITAB and JMP
- Modern optimization with R
- Molecular data analysis using R
- Multiple comparisons using R
- Multivariate generalized linear mixed models using R
- Multivariate statistical quality control using R
- Nonlinear parameter optimization using R tools
- Nonlinear regression with R
- Nonparametric hypothesis testing : rank and permutation methods with applications in R
- Numerical analysis using R : solutions to ODEs and PDEs
- Parallel computing for data science : with examples in R, C++ and CUDA
- Practical data science cookbook : 89 hands-on recipes to help you complete real-world data science projects in R and Python
- Practical data science with R
- Practical graph mining with R
- Probability & statistics with R : for engineers and scientists
- Probability and statistics with R
- Probability with R : an introduction with computer science applications
- Production and efficiency analysis with R
- Programming skills for data science : start writing code to wrangle, analyze, and visualize data with R
- Python for R users : a data science approach
- Qualitative comparative analysis with R : a user's guide
- Quantitative methods in archaeology using R
- R : data analysis and visualization : a course in five modules
- R Graph essentials : use R's powerful graphing capabilities to design and create professional-level graphics
- R Graphs cookbook : over 70 recipes for building and customizing publication-quality visualizations of powerful and stunning R graphs
- R Markdown : the Definitive Guide
- R and MATLAB
- R and data mining : examples and case studies
- R by example
- R cookbook
- R data analysis without programming
- R data visualization cookbook : over 80 recipes to analyze data and create stunning visualizations with R
- R for Everyone : Advanced Analytics and Graphics
- R for SAS and SPSS users
- R for data science
- R for everyone : advanced analytics and graphics
- R for marketing research and analytics
- R for medicine and biology
- R for programmers : mastering the tools
- R for statistics
- R graphics
- R graphics cookbook
- R high performance programming : overcome performance difficulties in R with a range of exciting techniques and solutions
- R in a nutshell
- R machine learning essentials : gain quick access to the machine learning concepts and practical applications using the R development environment
- R object-oriented programming : a practical guide to help you learn and understand the programming techniques necessary to exploit the full power R
- R statistical application development by example beginner's guide
- R through Excel : a spreadsheet interface for statistics, data analysis, and graphics
- Reasoning with data : an introduction to traditional and Bayesian statistics using R
- Reproducible research with R and RStudio
- Réseaux bayésiens avec R
- SAS and R : data management, statistical analysis, and graphics
- SAS and R : data management, statistical analysis, and graphics
- Six sigma with R : statistical engineering for process improvement
- Software for data analysis : programming with R
- Solving differential equations in R
- Spatial and spatio-temporal Bayesian models with R-INLA
- Spatial data analysis in ecology and agriculture using R
- Statistical analysis and data display : an intermediate course with examples in S-plus, R, and SAS
- Statistical analysis of financial data in R
- Statistical analysis of network data with R
- Statistical analysis of questionnaires : A unified approach based on R and Stata
- Statistical analysis with R : beginner's guide : take control of your data and produce superior statistical analyses with R
- Statistical computing in C++ and R
- Statistical computing with R
- Statistical data analysis explained : applied environmental statistics with R
- Statistical disclosure control for microdata : methods and applications in R
- Statistical hypothesis testing with SAS and R
- Statistical hypothesis testing with SAS and R
- Statistical methods for overdispersed count data
- Statistical modelling in R
- Statistical rethinking : a Bayesian course with examples in R and Stan
- Statistics : an introduction using R
- Statistics Using R
- Statistics and data with R : an applied approach through examples
- Statistics and data with R : an applied approach through examples
- Statistics for Linguistics with R : a Practical Introduction / by Stefan Th. Gries
- Statistics for censored environmental data using Minitab and R
- Statistics for ecologists using R and Excel : data collection, exploration, analysis and presentation
- Statistics for ecologists using R and Excel : data collection, exploration, analysis and presentation
- Statistics with R : a beginner's guide
- System dynamics modeling with R
- Testing R code
- The R book
- The R student companion
- The book of R : a first course in programming and statistics
- The new statistics with R : an introduction for biologists
- Time series analysis : with applications in R
- Time series analysis and its applications : with R examples
- Understanding and applying basic statistical methods using R
- Using R at the bench : step-by-step data analytics for biologists
- Using R for digital soil mapping
- Using R for introductory statistics
- Using R for introductory statistics
- Using R for numerical analysis in science and engineering
- Using R for statistics
- Using the R Commander : a point-and-click interface for R
- Web Application Development with R using Shiny
- XML and Web technologies for data sciences with R
- Zero inflated models and generalized linear mixed models with R

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`<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.massey.ac.nz/resource/nDejP-oRiaw/" typeof="CategoryCode http://bibfra.me/vocab/lite/Concept"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.massey.ac.nz/resource/nDejP-oRiaw/">R (Computer program language)</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.massey.ac.nz/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.massey.ac.nz/">Massey University Library, University of New Zealand</a></span></span></span></span></div>`