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R (Computer program language)
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The concept ** R (Computer program language)** represents the subject, aboutness, idea or notion of resources found in **Missouri University of Science & Technology Library**.

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R (Computer program language)
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**R (Computer program language)**represents the subject, aboutness, idea or notion of resources found in**Missouri University of Science & Technology Library**.- Label
- R (Computer program language)

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- 25 recipes for getting started with R
- 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 guide to R for social and behavioral science statistics
- A primer in biological data analysis and visualization using R
- Adaptive tests of significance using permutations of residuals with R and SAS
- Advanced Object-Oriented Programming in R : Statistical Programming for Data Science, Analysis and Finance
- Advanced R
- Advanced R 4 data programming and the cloud : using PostgreSQL, AWS, and Shiny
- Advanced R : data programming and the cloud
- Advanced R programming, Part I
- Advanced R statistical programming and data models : analysis, machine learning, and visualization
- Advanced analytics in power BI with R and Python : ingesting, transforming, visualizing
- Advanced analytics with R and Tableau : advanced visual analytical solutions for your business
- Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R
- Advanced statistics with applications in R
- Advanced statistics with applications in R
- An Introduction to Bootstrap Methods with Applications to R
- An introduction to analysis of financial data with R
- An introduction to bootstrap methods with applications to R
- Analyzing baseball data with R
- Applied Probabilistic Calculus for Assets Allocation and Portfolio Optimization in Financial Engineering Using R
- Applied Supervised Learning with R : Use Machine Learning Libraries of R to Build Models That Solve Business Problems and Predict Future Trends
- Applied analytics through case studies using SAS and R : implementing predictive models and machine learning techniques
- Applied data mining for business analytics
- Applied data visualization with R and ggplot2
- Applied data visualization with R and ggplot2 : Create useful, elaborate, and visually appealing plots
- Applied data visualization with R and ggplot2 : Create useful, elaborate, and visually appealing plots
- Applied machine learning and deep learning with R
- Applied machine learning with R
- Applied probabilistic calculus for financial engineering : an introduction using R
- Applied statistics : theory and problem solutions with R
- Applied statistics for environmental science with R
- Applied unsupervised learning with R
- Automated data collection with R : a practical guide to Web scraping and text mining
- Automated trading with R : quantitative research and platform development
- Basic data analysis for time series with R
- Basic statistics : an introduction with R
- Bayesian data analysis in ecology using linear models with R, BUGS, and Stan
- Beginning R 4 : from beginner to pro
- Beginning R : an introduction to statistical programming
- Beginning R : an introduction to statistical programming
- Beginning R : the Statistical Programming Language
- Beginning R : the statistical programming language
- Beyond Spreadsheets with R
- Big Data Analytics with R
- 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
- Big data analytics with R : utilize R to uncover hidden patterns in your big data
- Bioconductor case studies
- Bioinformatics with R Cookbook
- Biostatistical Design and Analysis Using R
- Biostatistics and computer-based analysis of health data using R
- Building a recommendation system with R : learn the art of building robust and powerful recommendation engines using R
- Business analytics using R : a practical approach
- Business case analysis with R : simulation tutorials to support complex business decisions
- CRAN recipes : DPLYR, Stringr, Lubridate, and RegEx in R
- Cleaning data for effective data science : doing the other 80% of the work with Python, R, and command-line tools
- Clinical trial data analysis using R
- Clustering & classification with machine learning in R : harness the power of machine learning for unsupervised & supervised learning in 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
- Computational statistics : an introduction to R
- Computational statistics with R
- Customer and business analytics : applied data mining for business decision making using R
- DATA SCIENCE FOR BUSINESS WITH R
- Data Manipulation with R : perform group-wise data manipulation and deal with large datasets using R efficiently and effectively
- Data Science Programming All-In-One for Dummies
- Data analysis using hierarchical generalized linear models with R
- Data analysis with R : a comprehensive guide to manipulating, analyzing, and visualizing data in R
- Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language
- Data manipulation with R : efficiently perform data manipulation using the split-apply-combine strategy in R
- Data manipulation with R and SQL : building effective, coherent, and streamlined data structures
- Data mashups in R
- Data mashups in R
- Data mining and business analytics with R
- Data mining applications with R
- Data science in R : a case studies approach to computational reasoning and problem solving
- Data science in the cloud with Microsoft Azure machine learning and R : 2015 update
- Data science programming all-in-one
- Data science with Microsoft Azure and R
- Data science with Python and R
- Data visualization in R with ggplot2 : creating effective and attractive data visualizations
- Deep learning mit R und Keras : Das Praxis-Handbuch : von Entwicklern von Keras und RStudio
- Deep learning with R
- Deep learning with R cookbook : over 45 unique recipes to delve into neural network techniques using R 3.5x
- Deep learning with R for beginners : design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
- Deep learning with R in motion
- Differential equation analysis in biomedical science and engineering, Ordinary differential equation applications with R
- Displaying time series, spatial, and space-time data with R
- Doing Bayesian Data Analysis : a Tutorial Introduction with R and BUGS
- Doing Bayesian Data Analysis, 2nd Edition
- Doing Bayesian data analysis : a tutorial introduction with R
- Doing data science in R : an introduction for social scientists
- Domain-specific languages in R : advanced statistical programming
- Dynamic documents with R and knitr
- Easy R : access, prepare, visualize, explore data, and write papers
- Easy Statistics for Food Science with R
- Easy, reproducible report with R
- Efficient R optimization
- Efficient R programming : a practical guide to smarter programming
- Efficient data processing with R
- Ensemble classification methods with applications in R
- Event history analysis with R
- Expert data wrangling with R : streamline your work with tidyr, dplyr, and ggvis
- Exploratory Data Analysis with R
- Exploratory multivariate analysis by example using R
- Exploring everyday things with R and Ruby
- Financial risk modelling and portfolio optimization with R
- Financial risk modelling and portfolio optimization with R
- Foundations and applications of statistics : an introduction using R
- Functional data structures in R : advanced statistical programming in R
- Functional programming in R : advanced statistical programming for data science, analysis and finance
- Fundamentals of statistical modeling and machine learning techniques
- Generalized additive models : an introduction with R
- 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 for data science
- Getting started with RStudio
- Getting started with Shiny
- Getting started with machine learning in R
- Ggplot2 essentials : explore the full range of ggplot2 plotting capabilities to create meaningful and spectacular graphs
- Graphical data analysis with R
- Graphing data with R : an introduction
- Great R, Level 1
- Guidebook to R graphics using Microsoft Windows
- Handbook of educational measurement and psychometrics using R
- Handbook of fitting statistical distributions with R
- Handbook of regression analysis with applications in R
- Hands-on data analytics with R
- Hands-on data science for marketing : improve your marketing strategies with machine learning using Python and R
- Hands-on data science with R : techniques to perform data manipulation and mining to build smart analytical models using R
- Hands-on ensemble learning with R : a beginner's guide to combining the power of machine learning algorithms using ensemble techniques
- Hands-on geospatial analysis with R and QGIS : a beginner's guide to manipulating, managing, and analyzing spatial data using R and QGIS 3.2.2
- Hands-on programming with R
- Hands-on reinforcement learning with R : get up to speed with building self-learning systems using R 3.x
- Hurricane climatology : a modern statistical guide using R
- Instant R starter : jump into the R programming language and go beyound "Hell World!"
- Instant heat maps in R how-to : learn how to design heat maps in R to enhance your data analysis
- Introduction to R for Quantitative Finance
- Introduction to R for business intelligence : learn how to leverage the power of R for business intelligence
- 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 R programming
- Introduction to Shiny : learn how to build interactive web apps with R, Shiny, and reactive programming
- Introduction to data analysis with R for forensic scientists
- Introduction to data science with R : manipulating, visualizing, and modeling data with the R language
- Introduction to deep learning using R : a step-by-step guide to learning and implementing deep learning models using R
- Introduction to machine learning with R : rigorous mathematical analysis
- Introduction to renewable power systems and the environment with R
- Introduction to scientific programming and simulation using R
- Introduction to stochastic processes with R
- Joint models for longitudinal and time-to-event data : with applications in R
- Jupyter cookbook : over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more
- Jupyter for data science : exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter
- Learn R programming
- Learn by example : statistics and data science in R
- Learning Bayesian models with R : become an expert in Bayesian machine learning methods using R and apply them to solve real-world big data problems
- Learning R
- Learning R programming : become an efficient data scientist with R
- Learning RStudio for R Statistical Computing
- Learning data mining with R : develop key skills and techniques with R to create and customize data mining algorithms
- Learning probabilistic graphical models in R : familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R
- Learning quantitative finance with R : implement machine learning, time-series analysis, algorithmic trading and more
- Learning shiny : make the most of R's dynamic capabilities and create web applications with Shiny
- Learning social media analytics with R : transform data from social media platforms into actionable insights
- Learning to program with R
- Linear regression analysis with JMP and R
- MOVING FROM IBM SPSS TO R AND RSTUDIO : a statistics companion
- Machine Learning with R, the tidyverse, and mlr
- Machine Learning with R, the tidyverse, and mlr
- Machine learning avec R
- Machine learning in R : automated algorithms for business analysis : applying K-Means clustering, decision trees, random forests, and neural networks
- Machine learning using R : a comprehensive guide to machine learning
- Machine learning using R : with time series and industry-based uses in R
- Machine learning with R : discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R
- Machine learning with R : expert techniques for predictive modeling
- Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications
- Machine learning with R cookbook : analyze data and build predictive models
- Machine learning with R cookbook : explore over 110 recipes to analyze data and build predictive models with the simple and easy-to-use R code
- Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5
- Making your case : using R for program evaluation
- Marketing data science : modeling techniques in predictive analytics with R and Python
- Mastering R for quantitative finance : use R to optimize your trading strategy and build up your own risk management system
- Mastering R programming
- Mastering RStudio : develop, communicate, and collaborate with R : harness the power of RStudio to create web applications, R packages, markdown reports and pretty data visualizations
- Mastering Spark with R : the complete guide to large-scale analysis and modeling
- Mastering data analysis with R
- Mastering data analysis with R : gain clear insights into your data and solve real-world data science problems with R--from data munging to modeling and visualization
- Mastering machine learning with R : advanced machine learning techniques for building smart applications with R 3.5
- Mastering machine learning with R : advanced prediction, algorithms, and learning methods with R 3.x
- Mastering predictive analytics with R : machine learning techniques for advanced models
- Mastering predictive analytics with R : master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts
- Mastering scientific computing with R
- Mastering shiny : build interactive apps, reports, and dashboards powered by R
- Mastering text mining with R : master text-taming techniques and build effective text-processing applications with R
- Mathematical Statistics with Applications in R
- Mathematical statistics with resampling and R
- Mathematics for data science and machine learning using R
- Metaprogramming in R : advanced statistical programming for data science, analysis and finance
- Modeling techniques in predictive analytics : business problems and solutions with R
- Modeling techniques in predictive analytics with Python and R : a guide to data science
- Modern Industrial Statistics : with applications in R, MINITAB and JMP
- Modern R programming cookbook : recipes to simplify your statistical applications
- Modern Regression Techniques Using R : a Practical Guide
- Modern data science with R
- Multilevel modeling : applications in STATA, IBM SPSS, SAS, R, & HLM
- Multiple comparisons using R
- Multivariate time series analysis : with R and financial applications
- Multivariate time series analysis and applications
- Neural networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles
- Nonlinear parameter optimization using R tools
- Nonparametric hypothesis testing : rank and permutation methods with applications in R
- Nonparametric statistical methods using R
- Nonparametric statistical methods using R
- Numerical analysis using R : solutions to ODEs and PDEs
- Option pricing and estimation of financial models with R
- Oracle R enterprise : harnessing the power of R in Oracle database
- Panel data econometrics with R
- Parallel R
- Practical Data Science with R, Second Edition
- Practical Machine Learning in R
- Practical Predictive Analytics
- Practical R 4 : applying R to data manipulation, processing and integration
- Practical R for biologists : an introduction
- Practical data science cookbook : 89 hands-on recipes to help you complete real-world data science projects in R and Python
- Practical data science cookbook : practical recipes on data pre-processing, analysis and visualization using R and Python
- Practical data science with R
- Practical data science with R : video edition
- Practical machine learning cookbook : resolving and offering solutions to your machine learning problems with R
- Practical predictive analytics : back to the future with R, Spark, and more!
- Practical time series analysis : prediction with statistics and machine learning
- Predictive analytics : parametric models for regression and classification using R
- Pro data visualization using R and JavaScript
- Pro machine learning algorithms : a hands-on approach to implementing algorithms in Python and R
- Probability and mathematical statistics : theory, applications, and practice in R
- Probability with R : an introduction with computer science applications
- Probability with applications in R
- Programming graphical user interfaces in R
- Programming skills for data science : start writing code to wrangle, analyze, and visualize data with R
- Psychologie statistique avec R
- Python for R users : a data science approach
- Python vs. R for data science
- Quantitative finance with R
- R : data analysis and visualization : a course in five modules
- R : mining spatial, text, web, and social media data : create and customize data mioning algorithms : a course in three modules
- R : predictive analysis : master the art of predictive modeling
- R : recipes for analysis, visualization and machine learning : get savvy with R language and actualize projects aimed at analysis, visualization and machine learning
- R : unleash machine learning techniques : find out how to build smarter machine learning systems with R : follow this three module course to become a more fluent machine learning practitioner : a course in three modules
- R Data Science Quick Reference : a Pocket Guide to APIs, Libraries, and Packages
- R Deep learning essentials : build automatic classification and prediction models using unsupervised learning
- R Graph Cookbook
- R Graphs cookbook : over 70 recipes for building and customizing publication-quality visualizations of powerful and stunning R graphs
- R Statistical Application Development by Example Beginner's Guide
- R and MATLAB
- R and Python for oceanographers : a practical guide with applications
- R and data mining : examples and case studies
- R bioinformatics cookbook : use R and Bioconductor to perform RNAseq, genomics, data visualization, and bioinformatic analysis
- R cookbook
- R cookbook : proven recipes for data analysis, statistics, and graphics
- R data analysis cookbook : a journey from data computation to data-driven insights
- R data analysis cookbook : over 80 recipes to help you breeze through your data analysis projects using R
- R data analysis projects : build end to end analytics systems to get deeper insights from your data
- R data mining : implement data mining techniques through practical use cases and real-world datasets
- R data science essentials : learn the essence of data science and visualization using R in no time at all
- R data structures and algorithms : increase speed and performance of your applications with efficient data structures and algorithms
- R data visualization recipes : a cookbook with 65+ data visualization recipes for smarter decision-making
- R deep learning cooking : solve complex neural net problems with TensorFlow, H2O and MXNet
- R deep learning essentials : a step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet
- R deep learning projects : master the techniques to design and develop neural network models in R
- R for Microsoft Excel users : making the transition for statistical analysis
- R for data science
- R for data science : import, tidy, transform, visualize, and model data
- R for data science cookbook : over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques
- R for dummies
- R for dummies
- R for everyone : advanced analytics and graphics
- R for everyone : advanced analytics and graphics
- R for programmers : mastering the tools
- R für Data Science : Daten importieren, bereinigen, umformen, modellieren und visualisieren
- R graphics
- R graphics cookbook
- R graphics cookbook : practical recipes for visualizing data
- R graphs cookbook : detailed hands-on recipes for creating the most useful types of graphs in R-starting from the simplest versions to more advanced applications
- R in a nutshell
- R in a nutshell
- R in a nutshell
- R in action : data analysis and graphics with R
- R in action : data analysis and graphics with R
- R machine learning by example : understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully
- R machine learning projects : implement supervised, unsupervised, and reinforcement learning techniques using R 3.5
- R machine learning solution
- R object-oriented programming : a practical guide to help you learn and understand the programming techniques necessary to exploit the full power R
- R packages
- R programming
- R programming LiveLessons : fundamentals to advanced
- R programming by example : practical, hands-on projects to help you get started with R
- R programming for bioinformatics
- R programming for statistics and data science
- R programming fundamentals
- R programming fundamentals : deal with data using various modeling techniques
- R programming fundamentals : deal with data using various modeling techniques
- R projects for dummies
- R quick syntax reference
- R quick syntax reference : a pocket guide to the language, APIs and library
- R recipes : a problem-solution approach
- R statistical application development by example beginner's guide
- R statistics cookbook : over 100 recipes for performing complex statistical operations with R 3.5
- R troubleshooting solutions
- R web scraping quick start guide : techniques and tools to crawl and scrape data from websites
- RStudio for R statistical computing cookbook : over 50 practical and useful recipes to help you perform data analysis with R by unleashing every native RStudio feature
- Regression analysis with R : design and develop statistical nodes to identify unique relationships within data at scale
- Remote sensing data analysis in R
- Reproducible research and reports with R Markdown : how to streamline your reporting workflow in R
- Robust nonlinear regression : with applications using R
- SAS for R users : a book for budding data scientists
- Sams teach yourself R in 24 hours
- Self-affine scaling sets in R2
- Shiny R : LiveLessons
- Simulation for data science with R : harness actionable insights from your data with computational statistics and simulations using R
- Spatial Analytics with ArcGIS
- Spatial and spatio-temporal Bayesian models with R-INLA
- Spatial data analysis in ecology and agriculture using R
- Spatial modeling in GIS and R for earth and environmental sciences
- Speaking 'R' : the language of data science
- Statistical Analysis with R
- Statistical analysis in simple steps using R
- Statistical analysis of climate series : analyzing, plotting, modeling, and predicting with R
- Statistical analysis with R
- Statistical application development with R and Python : power of statistics using R and Python
- Statistical computing in C++ and R
- Statistical computing with R
- Statistical data cleaning with applications in R
- Statistical hypothesis testing with SAS and R
- Statistical methods for overdispersed count data
- Statistical programming With SAS/IML software
- Statistical rethinking : a Bayesian course with examples in R and Stan
- Statistical rethinking : a Bayesian course with examples in R and Stan
- Statistics and data analysis for microarrays using R and Bioconductor
- Statistics for Linguistics with R : a Practical Introduction
- Statistics for machine learning : build supervised, unsupervised, and reinforcement learning models using both Python and R
- Statistics in toxicology using R
- Statistics with R : a beginner's guide
- Statistik mit R : eine praxisorientierte Einführung in R
- Testing R code
- Text mining with R : a tidy approach
- The R book
- The R book
- The art of R programming : tour of statistical software design
- The book of R : a first course in programming and statistics
- The essential R reference
- The essentials of data science : knowledge discovery using R
- Understanding SQL and R : learn how to do data analysis and visualization with SQL and R / with Casimir Saternos
- Understanding and applying basic statistical methods using R
- Understanding and applying basic statistical methods using R
- Understanding complexity by clustering data with maching learning and R
- Unsupervised learning with R : work with over 40 packages to draw inferences from complex datasets and find hidden patterns in raw unstructured data
- Unsupervised machine learning projects with R
- Using R and Hadoop for statistical computation at scale
- Using R for big data with Spark : hands-on data analytics in the Cloud using Spark, AWS, SparkR, and more
- Using R for numerical analysis in science and engineering
- Using R for statistics
- Using R to unlock the value of big data : big data analytics with Oracle R Enterprise and Oracle R Connector for Hadoop
- Using Shiny to teach econometric models
- Web Application Development with R using Shiny
- Web application development with R using Shiny : build stunning graphics and interactive data visualizations to deliver cutting-edge analytics
- Web application development with R using Shiny : integrate the power of R with the simplicity of Shiny to deliver cutting-edge analytics over the Web
- Writing code for R packages
- Writing great R code

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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.library.mst.edu/resource/jWtk33yXvEA/" typeof="CategoryCode http://bibfra.me/vocab/lite/Concept"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.mst.edu/resource/jWtk33yXvEA/">R (Computer program language)</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.mst.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.mst.edu/">Missouri University of Science & Technology Library</a></span></span></span></span></div>`