R Web Books

Spatial Data Science

• Pebesma, Edzer, and Roger Bivand. 2023. Spatial Data Science: With Applications in R. 1st edition. Boca Raton: CRC.

• Lovelace, Robin, Jakub Nowosad, and Jannes Muenchow. 2025. Geocomputation with R. 2nd edition. Boca Raton: CRC.

• Dorman, Michael, Anita Graser, Jakub Nowosad, and Robin Lovelace. 2025. Geocomputation with Python. 1st edition. Chapman; Hall/CRC.

• Wimberly, Michael C. 2023. Geographic Data Science with R: Visualizing and Analyzing Environmental Change. 1st edition. Boca Raton London New York: Chapman; Hall/CRC.

• Anselin, Luc. 2024. An Introduction to Spatial Data Science with GeoDa: Volume 1: Exploring Spatial Data. 1st edition. Boca Raton, FL: Chapman; Hall/CRC.

• Anselin, Luc. 2024. An Introduction to Spatial Data Science with GeoDa: Volume 2: Clustering Spatial Data. 1st edition. Boca Raton, FL: Chapman; Hall/CRC.

• Rey, Sergio, Dani Arribas-Bel, and Levi John Wolf. 2023. Geographic Data Science with Python. 1st edition. Boca Raton: CRC.

• Anselin, Luc, Grant Morrison, Angela Li, and Karina Acosta. 2023. Hands-on Spatial Data Science with R. https://spatialanalysis.github.io/handsonspatialdata/index.html.

• Sabbata, Stefano De. 2024. R for Geographic Data Science. https://sdesabbata.github.io/r-for-geographic-data-science/.

• Comber, Lex. 2024. GeoComputation and Spatial Analysis Practicals. https://bookdown.org/lexcomber/GEOG3195/.

• Comber, Lex. 2025. Big Data & Consumer Analytics - RStudio Practicals. https://bookdown.org/lexcomber/GEOG5917/

• Comber, Lex. 2023. Digital Geographies - RStudio Practicals. https://bookdown.org/lexcomber/GEOG1400/

• Davis, Jerry, and SFSU Institute for Geographic Information Science. 2024. Introduction to Environmental Data Science. https://bookdown.org/igisc/EnvDataSci/.

• Dorman, Michael. 2023. Introduction to Spatial Data Programming with R. https://geobgu.xyz/r/.

• Spatial Data Science with R. https://rspatial.org/raster/.

• Spatial Data Science with R and “terra”. https://rspatial.org/.

Spatial Data Analysis and Spatial Statistics

• Paez, Antonio. 2025. An Introduction to Spatial Data Analysis and Statistics: A Course in R. https://paezha.github.io/spatial-analysis-r/.

• Moraga, Paula. 2023. Spatial Statistics for Data Science: Theory and Practice with R. 1st edition. Boca Raton: CRC.

• Rowe, Francisco, and Dani Arribas-Bel. 2024. Spatial Modelling for Data Scientists. https://gdsl-ul.github.io/san/.

• Geomatics for Environmental Management: An Open Textbook for Students and Practitioners.  https://www.opengeomatics.ca/.

• Arribas-Bel, Francisco Rowe, Dani. 2024. Spatial Modelling for Data Scientists. https://gdsl-ul.github.io/san/.

• Brunsdon, Chris, and Lex Comber. 2022. Code for An Introduction to Spatial Analysis and Mapping in R 2nd edition. https://study.sagepub.com/brunsdon2e

• Lovelace, Robin, and Morgane Dumont. 2017. Spatial Microsimulation with R. 1st edition. Chapman; Hall/CRC.

Cartography and GeoVisualization

• Sievert, Carson. 2020. Interactive Web-Based Data Visualization with R, Plotly, and Shiny. Boca Raton: CRC.

• Tennekes, Martijn, and Jakub Nowosad. 2025. Spatial Data Visualization with tmap: A Practical Guide to Thematic Mapping in R. https://tmap.geocompx.org/

• Dorman, Michael. 2025. Introduction to Web Mapping. https://geobgu.xyz/web-mapping2/index.html.

• Protić, Milan Kilibarda i Dragutin. 2019. Introduction to Geovisualization and Web Cartography. https://osgl.grf.bg.ac.rs/books/gvvk-en/.

• Fidan, Emine. Guide to Creating Interactive Maps in R. https://bookdown.org/eneminef/DRR_Bookdown/.

• Hahn, Nico. 2020. Making Maps with R. https://bookdown.org/nicohahn/making_maps_with_r5/docs/introduction.html.

Geographic Information Systems (GIS)

• Slingsby, Jasper. 2025. A Minimal Introduction to GIS (in R). https://www.ecologi.st/spatial-r/.

• Dorman, Michael. 2025. Introduction to Spatial Data Programming with R. https://geobgu.xyz/r/.

• Lovelace, Robin, Jakub Nowosad, and Jannes Muenchow. 2025. Geocomputation with R. 2nd edition. Boca Raton: CRC.

• Gimond, Manuel. 2025. Intro to GIS and Spatial Analysis. https://mgimond.github.io/Spatial/index.html.

• Engel, Claudia A. 2025. Using Spatial Data with R. https://cengel.github.io/R-spatial/.

• Roelandt, Nicolas and Jakub Nowosad. 2022. Getting Started with R and R-Spatial. https://bakaniko.github.io/foss4g2022-getting-started-rspatial/.

• Mieno, Taro. R as GIS for Economists. https://tmieno2.github.io/R-as-GIS-for-Economists-Quarto/.

• Branion-Calles, Michael. 2021. A Crash Course in Geographic Information Systems (GIS) Using R. https://bookdown.org/michael_bcalles/gis-crash-course-in-r/.

• Geomatics for Environmental Management: An Open Textbook for Students and Practitioners.  https://www.opengeomatics.ca/.

Remote Sensing

• Camara, Gilberto, Rolf Simoes, Felipe Souza, Felipe Menino, Charlotte Pelletier, Pedro R. Andrade, Karine Ferreira, and Gilberto Queiroz. Satellite Image Time Series Analysis on Earth Observation Data Cubes. https://e-sensing.github.io/sitsbook/.
• Ghosh, Aniruddha, and Robert J. Hijmans. Remote Sensing Image Analysis. https://rspatial.org/raster/rs/index.html

Applied Spatial Data Science

• Walker, Kyle. 2023. Analyzing US Census Data. 1st edition. Boca Raton: Chapman; Hall/CRC.

• Moraga, Paula. 2019. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. CRC Press.

• Solymosi, Reka, and Juanjo Medina. 2025. Crime Mapping in R. https://maczokni.github.io/crime_mapping_textbook/.

• Emily Peterson. 2024. Spatial Epidemiology Workshop. https://bookdown.org/epeterson_2010/spatial_epidemiology_workshop/

• Steif, Ken. 2021. Public Policy Analytics: Code and Context for Data Science in Government. 1st edition. Boca Raton London New York: CRC Press.

• Gannon, J. P. 2024. Hydroinformatics. https://vt-hydroinformatics.github.io/Quarto_Book/.

• Wagner, Helene, ed. 2024. Landscape Genetic Data Analysis with R. https://bookdown.org/hhwagner1/LandGenCourse_book/.

Data Science in General

• Wickham, Hadley, Mine Çetinkaya-Rundel, and Garrett Grolemund, 2023, R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, 2nd edition, Boston: O’Reilly.

• Baumer, Benjamin, Daniel T. Kaplan, and Nicholas J. Horton. 2024. Modern Data Science with R, 3rd edition, Boca Raton: CRC Press.

• Irizarry, Rafael A. 2024. Introduction to Data Science: Data Wrangling and Visualization with R. 2nd edition, Boca Raton: CRC Press.

• Bonnell, Jerry, and Mitsunori Ogihara. 2023. Exploring Data Science with R and the Tidyverse. 1st edition. Chapman; Hall/CRC.

• Timbers, Tiffany, Trevor Campbell, and Melissa Lee. 2024. Data Science: A First Introduction. 1st edition. Boca Raton: Chapman; Hall/CRC.

• Hansjörg Neth. 2025. Introduction to Data Science, University of Konstanz, The Netherlands.

• Hansjörg Neth. 2025. Data Science for Psychologists, University of Konstanz, The Netherlands.

• Estrellado, Ryan A., Emily A. Freer, Joshua M. Rosenberg, and Isabella C. Velásquez. 2024. Data Science in Education Using R. 2nd ed. https://datascienceineducation.com/.

• 김길환, 2024, R 프로그래밍 3판, 웹북

• 한국 R 사용자회, 데이터 과학을 지탱하는 기본기, 웹북

• 김하성, 2021, 데이터 사이언스를 위한 R 프로그래밍, 웹북

• 김하성, 2022, 한국생명공학연구원 연구데이터 분석과정 R, 웹북

Data Analysis and Modeling

• Ismay, Chester, Albert Y. Kim, and Arturo Valdivia. 2025. Statistical Inference via Data Science: A ModernDive into R and the Tidyverse. 2nd edition. Boca Raton: Chapman; Hall/CRC.

• Irizarry, Rafael A. 2024. Introduction to Data Science: Statistics and Prediction Algorithms Through Cast Studies. https://rafalab.dfci.harvard.edu/dsbook-part-2/

• Bischl, Bernd, Raphael Sonabend, Lars Kotthoff, and Michel Lang. 2024. Applied Machine Learning Using Mlr3 in R. Boca Raton: CRC Press.

• Keydana, Sigrid. 2023. Deep Learning and Scientific Computing with R Torch. Boca Raton: CRC Press.

• Hvitfeldt, Emil. 2023. An Introduction to Statistical Learning with Applications in R: Tidymodels Labs. https://emilhvitfeldt.github.io/ISLR-tidymodels-labs/.

• Tipton, Elizabeth, Arend M. Kuyper, and Kaitlyn G. Fitzgerald. 2022. Introduction to Statistics and Data Science. https://nustat.github.io/intro-stat-ds/.

• Kuhn, Max, and Julia Silge. 2023. Tidy Modeling with R: A Framework for Modeling in the Tidyverse. 1st edition. Sebastopol, CA: O’Reilly Media.

• Boehmke, Brad, and Brandon M. Greenwell. 2019. Hands-on Machine Learning with R. 1st edition. Boca Raton London New York: Chapman; Hall/CRC.

• Hvitfeldt, Emil, and Julia Sigle, 2022, Supervised Machine Learning for Text Analysis in R, Boca Raton: CRC Press.

• Introduction to Bayesian Inference and Statistical Learning. https://bookdown.org/lehre_rdusl/BayesLearn/

Data Visualization

• Wickham, Hadley. 2016. ggplot2: Elegant Graphics for Data Analysis. 2nd edition, Switzerland: Springer.

• Rennie, Nicola, 2026, The Art of Data Visualization with ggplot2: The TidyTuesday Cookbook, Boca Raton: CRC Press.

• Kabacoff, Robert, 2024, Modern Data Visualization with R, Boca Raton: CRC Press.

• Dougherty, Jack, and Ilya Ilyankou, 2025, Hands-on Data Visualization: Interactive Storytelling from Spreadsheets to Code, Boston: O’Reilly.

• Alexander, Rohan. 2023. Telling Stories with Data With A;;ications in R. Boca Raton: CRC Press.

• Healy, Kieran. 2019. Data Visualization: A Practical Introduction, Princeton: Princeton University Press.

• Sievert, C., 2020, Interactive Web-Based Data Visualization with R, plotly, and shiny, Boca Raton: CRC Press.

• Wilke, C., 2019, Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures, Sebastopol, CA: O’Reilly.

• Dougherty, Jack, and Ilya Ilyankou, 2025, Hands-on Data Visualization: Interactive Storytelling from Spreadsheets to Code, Boston: O’Reilly. (김태현 옮김, 2022, 핸즈온 데이터 시각화, 서울: 한빛미디어.)

• Yau, Nathan. 2024. Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. 2nd edition, Hoboken: Wiley. (한상택 옮김, 2025, 데이터리터러시를 높이는 데이터 시각화: 숫자를 메시지로 바꾸는 스토리텔링과 그래프 작성, 서울: 인사이트.)

• Allchin, Carl. 2021. Communicating with Data: Making Your Case with Data. O’Reilly Media. (이한호 옮김, 2022, 데이터로 전문가처럼 말하기: 효율적 의사 전달을 위한 데이터 시각화와 비즈니스 스토리텔링의 기술, 서울: 한빛미디어.)

• Wilke, Claus. 2019. Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. O’Reilly Media. (권혜정 옮김, 2020, 데이터 시각화 교과서: 데이터 분석의 본질을 살리는 그래프와 차트 제작의 기본 원리와 응용, 안양: 책만.)

• Cairo, Alberto. 2016. The Truthful Art: Data, Charts, and Maps for Communication. New Riders. (이제원 옮김, 2019, 진실을 드러내는 데이터 시각화의 과학과 예술, 서울: 인사이트.)

• 한국R사용자회, 2022, 데이터 시각화, 오픈전자책.

Data Communication

• Wickham, Hadley, 2021, Mastering Shiny: Build Interactive Apps, Reports, and Dashboards Powered by R, Sebastopol, CA: O’REILLY. (이영록 옮김, 2022, R 사용자를 위한 Shiny 마스터 가이드, 서울: 인사이트.)

• Granjon, David, 2022, Outstanding User Interfaces with Shiny, Boaca Raton: CRC Press.

• Fay, Colin, Vincent Guyader, Sebastien Rochette, and Cervan Girard. 2022, Engineering Production-Grade Shiny Apps, Boca Raton: CRC Press.

• Robinson, Loan, R Shiny Applications in Finance, Medicine Pharma and Education Industry

Text Mining and Natural Language Processing

• Francom, Jerid. 2024. An Introduction to Quantitative Text Analysis for Linguistics: Reproducible Research Using R. Routledge.

• Silge, Julia, and David Robinson. 2017. Text Mining with R: A Tidy Approach. 1st edition. Beijing Boston Farnham Sebastopol Tokyo: O’Reilly Media.

• 한국R사용자회ㆍ안도현, 2022, R 텍스트마이닝, 오픈전자책.

• 김영우, 2021, 쉽게 배우는 R 텍스트 마이닝, 서울: 이지스퍼블리싱.

Data Collection

• Pittard, Steve. 2022. Web Scraping with r. https://steviep42.github.io/webscraping/book/.