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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 **Merrimack Valley Library Consortium**.

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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**Merrimack Valley Library Consortium**.- Label
- R (Computer program language)

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- A beginners guide to R programming
- A data scientist's guide to acquiring, cleaning, and managing data in R
- Adaptive tests of significance using permutations of residuals with R and SAS
- Advanced R
- 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 with R and Tableau : advanced visual analytical solutions for your business
- Advanced data mining projects with R
- Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R
- Advanced machine learning with R
- Advanced object-oriented programming in R : statistical programming for data science, analysis and finance
- Advanced practical reinforcement learning : agents and environments
- Advanced statistics with applications in R
- Advanced tools and techniques beyond base R
- An introduction to bootstrap methods with applications to R
- 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 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 unsupervised learning with R
- Automated trading with R : quantitative research and platform development
- Basic data analysis for time series with R
- Beginning R : an introduction to statistical programming
- Beginning R : the statistical programming language
- Beginning data science in R : data analysis, visualization, and modelling for the data scientist
- Big data analytics with R : utilize R to uncover hidden patterns in your big data
- Big data analytics with R and Hadoop
- 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
- Classifying and clustering data with R
- Clustering & classification with machine learning in R : harness the power of machine learning for unsupervised & supervised learning in R
- Computational actuarial science with R
- Data analysis with R : a comprehensive guide to manipulating, analyzing, and visualizing data in R
- Data analytics with R
- Data analytics with R Shiny
- Data manipulation with R and SQL : building effective, coherent, and streamlined data structures
- Data mashups in 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 with Microsoft Azure and R
- Data science with Python and R
- Data science with R master class
- Data visualization in R with ggplot2 : creating effective and attractive data visualizations
- Deep learning with R
- Deep learning with R
- Deep learning with R in motion
- Developing financial analysis tools
- Displaying time series, spatial, and space-time data with R
- Doing Bayesian data analysis : a tutorial with R and BUGS
- Domain-specific languages in R : advanced statistical programming
- Dynamic documents with R and knitr
- Easy, reproducible report with R
- Efficient R optimization
- Efficient R programming : a practical guide to smarter programming
- Efficient data processing with R
- Event history analysis with R
- Expert data wrangling with R : streamline your work with tidyr, dplyr, and ggvis
- Exploratory data analysis using R
- Extending machine learning algorithms
- Financial risk modelling and portfolio optimization with 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 R programming and statistical analysis
- Fundamentals of statistical modeling and machine learning techniques
- 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 Shiny
- Getting started with machine learning in R
- Getting started with machine learning with R
- Getting started with neural nets in R
- Graphical data analysis with R
- Graphing data with R : an introduction
- Hands-on data analytics with R
- Hands-on data exploration 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 3.4
- 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 reinforcement learning with R : get up to speed with building self-learning systems using R 3.x
- Introduction to R
- 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 science using R programming
- 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 scientific programming and simulation using R
- Jupyter cookbook : over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more
- Jupyter for data science
- Jupyter for data science : exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter
- Learn R for applied statistics : with data visualizations, regressions, and statistics
- 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 for geospatial analysis : leverage the power of R to elegantly manage crucial geospatial analysis tasks
- Learning R programming : become an efficient data scientist with R
- Learning data analysis with R
- Learning path : intermediate data science with R
- Learning path : introduction to data science with R
- Learning predictive analytics with Python : gain practical insights into predictive modelling by implementing predictive analytics algorithms on public datasets with Python
- 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
- Machine learning and data science : an introduction to statistical learning methods with 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 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
- Machine learning with R series : K nearest neighbor (KNN), linear regression, and test mining
- 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 ggplot2
- 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 text mining with R : master text-taming techniques and build effective text-processing applications with 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 analystics with Python and R : a guide to data science
- Modeling techniques in predictive analytics : business problems and solutions with R
- Modern R programming cookbook : recipes to simplify your statistical applications
- Modern analysis of customer surveys : with applications using R
- Multiple factor analysis by example using R
- Neural networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles
- Nonparametric hypothesis testing : rank and permutation methods with applications in R
- Nonparametric statistical methods using R
- Parallel computing for data science : with examples in R, C++ and CUDA
- 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
- 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 reinforcement learning : agents and environments
- Practical time series analysis : prediction with statistics and machine learning
- Pro machine learning algorithms : a hands-on approach to implementing algorithms in Python and R
- Programming for data science with R
- Programming languages for data science (Julia, Scala, Python, and 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
- Python vs. R for data science
- Quantitative finance with R
- R : data analysis and visualization
- 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 Deep learning essentials : build automatic classification and prediction models using unsupervised learning
- R Shiny : more advanced functionality
- R Shiny beyond the basics
- R and MATLAB
- 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
- R data analysis projects : build end to end analytics systems to get deeper insights from your data
- R data analytics projects
- R data mining : implement data mining techniques through practical use cases and real-world datasets
- R data mining projects
- 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 : import, tidy, transform, visualize, and model data
- 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 everyone : advanced analytics and graphics
- R for medicine and biology
- R graphics
- 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 action : data analysis and graphics with R
- R in action : data analysis and graphics with R
- R in action : video edition
- 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 packages
- R programming
- R programming LiveLessons
- R programming LiveLessons : fundamentals to advanced
- R programming by example : practical, hands-on projects to help you get started with R
- R programming for statistics and data science
- R programming fundamentals
- R programming fundamentals : deal with data using various modeling techniques
- R projects for dummies
- R quick syntax reference : a pocket guide to the language, APIs and library
- R recipes : a problem-solution approach
- 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
- 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 data scientists
- Sams teach yourself R in 24 hours
- Shiny R : LiveLessons
- Simulation for data science with R : harness actionable insights from your data with computational statistics and simulations using R
- Speaking 'R' : the language of data science
- Species distribution models with GIS and machine learning in R
- Statistical analysis with R
- Statistical application development with R and Python : power of statistics using R and Python
- Statistical computing with R
- Statistical data cleaning with applications in R
- Statistical hypothesis testing with SAS and R
- Statistical programming With SAS/IML software
- Statistical rethinking : a Bayesian course with examples in R and Stan
- Statistics for data science
- Statistics for machine learning : build supervised, unsupervised, and reinforcement learning models using both Python and R
- Text mining with R : a tidy approach
- The R book
- The art of R programming : tour of statistical software design
- The book of R : a first course in programming and statistics
- Understanding SQL and R : learn how to do data analysis and visualization with SQL and 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 statistics
- Using R to unlock the value of big data : big data analytics with Oracle R Enterprise and Oracle R Connector for Hadoop
- Web analytics with hands on projects in R
- 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
- ggplot2 essentials : explore the full range of ggplot2 plotting capabilities to create meaningful and spectacular graphs

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