Call Number (LC) Title Results
QA276.45.P9 Modern statistics : a computer-based approach with Python / 1
QA276.45.P98 Statistics and data visualisation with Python / 1
QA276.45.P98 D46 2021 Applied univariate, bivariate, and multivariate statistics using Python / 1
QA276.45.P98 D465 2021 Applied Univariate, Bivariate, and Multivariate Statistics Using Python A Beginner's Guide to Advanced Data Analysis. 1
QA276.45.R3 Multiple factor analysis by example using R /
R BOOK
Advanced statistics with applications in R /
A course in statistics with R /
Advanced R statistical programming and data models : analysis, machine learning, and visualization /
Mastering text mining with R : master text-taming techniques and build effective text-processing applications with R /
Gráficos estadísticos y mapas con R
Beginning data science in R 4 : data analysis, visualization, and modelling for the data scientist /
R for data science : import, tidy, transform, visualize, and model data /
Advanced R 4 data programming and the cloud : using PostgreSQL, AWS, and Shiny /
Statistical data cleaning with applications in R /
Statistik mit R Schnelleinstieg : R einfach lernen in 14 Tagen /
Da gui mo shu ju fen xi he jian mo : ji yu Spark yu R = Mastering Spark with R /
Statistical application development with R and Python : power of statistics using R and Python /
Efficient R optimization /
Using R for big data with Spark : hands-on data analytics in the Cloud using Spark, AWS, SparkR, and more /
R recipes : a problem-solution approach /
Statistik mit R : eine praxisorientierte Einführung in R /
Practical data science with R : video edition /
Applied unsupervised learning with R /
R for Microsoft Excel users : making the transition for statistical analysis /
Functional data structures in R : advanced statistical programming in R /
Sams teach yourself R in 24 hours /
Simulation for data science with R : harness actionable insights from your data with computational statistics and simulations using R /
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 /
Data manipulation with R and SQL : building effective, coherent, and streamlined data structures /
R deep learning essentials : a step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet /
Machine learning with R : expert techniques for predictive modeling to solve all your data analysis problems /
Beginning R : an introduction to statistical programming /
R data analysis projects : build end to end analytics systems to get deeper insights from your data /
R PROGRAMMING FOR ACTUARIAL SCIENCE
Hands-on ensemble learning with R : a beginner's guide to combining the power of machine learning algorithms using ensemble techniques /
Functional programming in R : advanced statistical programming for data science, analysis and finance /
R programming for statistics and data science /
R programming /
Efficient data processing with R /
R : kurz & gut /
R 4 quick syntax reference : a pocket guide to the language, API's and library /
R kukku bukku /
Practical R 4 : applying R to data manipulation, processing and integration /
Efficient R programming : a practical guide to smarter programming /
Data visualization in R with ggplot2 : creating effective and attractive data visualizations /
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 /
Mastering R for quantitative finance : use R to optimize your trading strategy and build up your own risk management system /
Learning shiny : make the most of R's dynamic capabilities and create web applications with Shiny /
Gai lü tu mo xing : ji yu R yu yan = Learning probabilistic graphical models in R /
Mastering predictive analytics with R : machine learning techniques for advanced models /
Using R to unlock the value of big data : big data analytics with Oracle R Enterprise and Oracle R Connector for Hadoop /
Text mining with R : a tidy approach /
The R book /
Easy, reproducible report with R /
Business case analysis with R : simulation tutorials to support complex business decisions /
R für Data Science : Daten importieren, bereinigen, umformen und visualisieren /
Learn R programming /
R programming by example : practical, hands-on projects to help you get started with R /
R ultimate 2023 : R for data science and machine learning.
R data analysis cookbook : a journey from data computation to data-driven insights /
Machine learning in R : automated algorithms for business analysis : applying K-Means clustering, decision trees, random forests, and neural networks /
Advanced R /
Understanding and applying basic statistical methods using R /
R web scraping quick start guide : techniques and tools to crawl and scrape data from websites /
R : recipes for analysis, visualization and machine learning : get savvy with R language and actualize projects aimed at analysis, visualization and machine learning /
Hands-on data science with R : techniques to perform data manipulation and mining to build smart analytical models using R /
The essential R reference /
R programming fundamentals /
Introduction to Shiny : learn how to build interactive web apps with R, Shiny, and reactive programming /
Learning quantitative finance with R : implement machine learning, time-series analysis, algorithmic trading and more /
Advanced Object-Oriented Programming in R : Statistical Programming for Data Science, Analysis and Finance /
R Data Science Quick Reference : a Pocket Guide to APIs, Libraries, and Packages /
Advanced R : data programming and the cloud /
Shiny R : LiveLessons /
Big data analytics with R : utilize R to uncover hidden patterns in your big data /
Mastering predictive analytics with R : master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts /
R projects for dummies /
R-powered Excel for analytics /
25 recipes for getting started with R /
Automated trading with R : quantitative research and platform development /
Liang hua jin rong R yu yan chu ji jiao cheng = Introduction to R for quantitative finance /
Advanced R programming.
R data structures and algorithms : increase speed and performance of your applications with efficient data structures and algorithms /
R yu yan bian cheng zhi nan = Learning R programming /
Metaprogramming in R : advanced statistical programming for data science, analysis and finance /
Practical predictive analytics : back to the future with R, Spark, and more! /
R für Data Science : Daten importieren, bereinigen, umformen, modellieren und visualisieren /
Statistical analysis with R essentials /
Machine learning with R cookbook : analyze data and build predictive models /
R da shu ju fen xi shi yong zhi nan = Big data analytics with R /
Introduction to R for quantitative finance : solve a diverse range of problems with R, one of the most powerful tools for quantitative finance /
R programming LiveLessons : fundamentals to advanced /
Expert data wrangling with R : streamline your work with tidyr, dplyr, and ggvis /
Reproducible research and reports with R Markdown : how to streamline your reporting workflow in R /
R All-in-One
Shu ju ke xue zhi bian cheng ji shu : shi yong R jin xing shu ju qing li, fen xi yu ke shi hua /
R jin nang miao ji = R cookbook /
R graphics cookbook : practical recipes for visualizing data /
R statistics cookbook : over 100 recipes for performing complex statistical operations with R 3.5 /
Data analysis with R : a comprehensive guide to manipulating, analyzing, and visualizing data in R /
Graphing data with R : an introduction /
R : predictive analysis : master the art of predictive modeling /
Writing great R code /
Shen du xue xi shi zhan shou ce : R yu yan ban = R deep learning cookbook /
Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R /
CRAN recipes : DPLYR, Stringr, Lubridate, and RegEx in R /
Mastering Spark with R : the complete guide to large-scale analysis and modeling /
R quick syntax reference /
Learning probabilistic graphical models in R : familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R /
Introduction to data science with R : manipulating, visualizing, and modeling data with the R language /
Programming skills for data science : start writing code to wrangle, analyze, and visualize data with R /
Deep learning with R in motion /
Using the R commander : a point-and-click interface for R /
Statistical inference via data science : a ModernDive into R and the Tidyverse /
R FOR STATISTICS.
R for data science /
R data analysis without programming : explanation and interpretation /
Hands-on data science for librarians /
Introduction to R for terrestrial ecology : basics of numerical analysis, mapping, statistical tests and advanced application of R /
PROBABILITY with applications and r.
An introduction to data analysis using aggregation functions in R /
An introduction to R for quantitative economics : graphing, simulating and computing /
Guide to programming and algorithms using R /
The R software : fundamentals of programming and statistical analysis /
Multistate analysis of life histories with R /
Data wrangling with R /
Modern optimization with R /
Practical R 4 applying R to data manipulation, processing and integration /
CRAN recipes DPLYR, Stringr, Lubridate, and RegEx in R /
Learn R for applied statistics : with data visualizations, regressions, and statistics /
R for marketing research and analytics /
QCA with R : a comprehensive resource /
Advanced R 4 data programming and the cloud using PostgreSQL, AWS, and Shiny /
Functional programming in R 4 : advanced statistical programming for data science, analysis, and finance /
Random forests with R
Beginning data science with R /
The R book
Data science : a first introduction /
Bookdown an enhanced version of R Markdown /
VISUALIZING SURVEYS IN R
MODERN STATISTICS WITH R from wrangling and exploring data to inference and predictive modelling.
R FOR DATA SCIENCE import, tidy, transform, visualize, and model data.
A Primer in Biological Data Analysis and Visualization Using R.
Data Analysis Explained.
DATA SCIENCE a first introduction with python.
Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language /
Advanced statistics with applications in R 		189
QA276.45.R3 A34 2014 R Graphs cookbook over 70 recipes for building and customizing publication-quality visualizations of powerful and stunning R graphs /
R Graphs cookbook : over 70 recipes for building and customizing publication-quality visualizations of powerful and stunning R graphs / 		2
QA276.45.R3 A344 2010 R in a nutshell / 1
QA276.45.R3 A35 2010 R in a nutshell / 1
QA276.45.R3 A35 2012 R in a nutshell / 1
QA276.45.R3 A43 2012 R by example / 1
QA276.45.R3 A45 2011 A tiny handbook of R 1
QA276.45.R3 A555 2019eb Introduction to statistics using R / 1
QA276.45.R3 A76 2024 Humanities data in R : Exploring Networks, Geospatial Data, Images, and Text / 1
QA276.45.R3 B43 2017 Getting started with R : an introduction for biologists. 1
QA276.45.R3 .B533 2014 R object-oriented programming : a practical guide to help you learn and understand the programming techniques necessary to exploit the full power R / 1
QA276.45.R3 B66 2013 Analyzing compositional data with R / 1
QA276.45.R3 B73 2008eb A first course in statistical programming with R / 1
QA276.45.R3 B73 2016eb A first course in statistical programming with R / 1
QA276.45.R3 B76 2018 Business case analysis with R : simulation tutorials to support complex business decisions /
Advanced statistics for the behavioral sciences : a computational approach with R / 		2
QA276.45.R3 B78 2022 Spatial sampling with R / 1