#
R (Computer program language)
Resource Information
The concept ** R (Computer program language)** represents the subject, aboutness, idea or notion of resources found in **Merrimack Valley Library Consortium**.

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

## Context

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

- A data scientist's guide to acquiring, cleaning, and managing data in R
- 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 object-oriented programming in R : statistical programming for data science, analysis and finance
- Applied analytics through case studies using SAS and R : implementing predictive models and machine learning techniques
- Applied data visualization with R and ggplot2 : Create useful, elaborate, and visually appealing plots
- Applied probabilistic calculus for financial engineering : an introduction using R
- Applied unsupervised learning with R
- Beginning data science in R : data analysis, visualization, and modelling for the data scientist
- Business case analysis with R : simulation tutorials to support complex business decisions
- Data analysis with R : a comprehensive guide to manipulating, analyzing, and visualizing data in R
- Deep learning with R
- Domain-specific languages in R : advanced statistical programming
- Dynamic documents with R and knitr
- Functional data structures in R : advanced statistical programming in R
- Functional programming in R : advanced statistical programming for data science, analysis and finance
- Graphical data analysis with 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
- Introduction to deep learning using R : a step-by-step guide to learning and implementing deep learning models using 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 for applied statistics : with data visualizations, regressions, and statistics
- Learning quantitative finance with R : implement machine learning, time-series analysis, algorithmic trading and more
- Learning social media analytics with R : transform data from social media platforms into actionable insights
- Machine learning using R : with time series and industry-based uses in R
- Machine learning with R cookbook : analyze data and build predictive models
- Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5
- 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
- Metaprogramming in R : advanced statistical programming for data science, analysis and finance
- Modern R programming cookbook : recipes to simplify your statistical applications
- Neural networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles
- Practical data science cookbook : practical recipes on data pre-processing, analysis and visualization using R and Python
- 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!
- Pro machine learning algorithms : a hands-on approach to implementing algorithms in 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
- 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 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 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 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 data science : import, tidy, transform, visualize, and model data
- R for everyone : advanced analytics and graphics
- R graphics
- R graphics cookbook : practical recipes for visualizing data
- R machine learning projects : implement supervised, unsupervised, and reinforcement learning techniques using R 3.5
- R programming by example : practical, hands-on projects to help you get started with R
- 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 statistics cookbook : over 100 recipes for performing complex statistical operations with R 3.5
- R web scraping quick start guide : techniques and tools to crawl and scrape data from websites
- Regression analysis with R : design and develop statistical nodes to identify unique relationships within data at scale
- Robust nonlinear regression : with applications using R
- Spatial analytics with ArcGIS : use the spatial statistics tools provided by ArcGIS and build your own to perform complex geographic analysis
- Statistical analysis with R
- Statistical application development with R and Python : power of statistics using R and Python
- Statistical rethinking : a Bayesian course with examples in R and Stan
- Statistics for machine learning : build supervised, unsupervised, and reinforcement learning models using both Python and R
- Text mining with R : a tidy approach
- Web application development with R using Shiny : build stunning graphics and interactive data visualizations to deliver cutting-edge analytics

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