"Applied Spatial Data Analysis with R" by Roger S. Bivand, Edzer J. Pebesma, and Virgilio Gómez-Rubio is a comprehensive textbook that addresses the challenges of teaching spatial data analysis to social science students. The book is structured into two parts: Part I covers the basics of using R for spatial data analysis, including data handling, visualization, and programming; Part II focuses on spatial statistical analysis, including point data, geostatistics, and areal data analysis. The book is written in an accessible style, making it suitable for readers with little or no background in spatial analysis or statistics. It provides concise explanations of spatial methods, along with R code and outputs, allowing readers to apply the methods directly. The book's strengths include its comprehensive coverage of spatial analysis and statistics in under 300 pages, its clear and easy-to-follow style, and its seamless integration of methods and R programming. The authors also provide references to additional resources for more detailed information. The book is recommended for students and instructors looking for a single textbook on spatial data analysis and R programming. However, the author suggests that future editions could include more detailed coverage of spatial econometric models and a comparison of R with other software packages. Overall, the book is an excellent resource for those seeking to quickly learn spatial analysis and statistics using R."Applied Spatial Data Analysis with R" by Roger S. Bivand, Edzer J. Pebesma, and Virgilio Gómez-Rubio is a comprehensive textbook that addresses the challenges of teaching spatial data analysis to social science students. The book is structured into two parts: Part I covers the basics of using R for spatial data analysis, including data handling, visualization, and programming; Part II focuses on spatial statistical analysis, including point data, geostatistics, and areal data analysis. The book is written in an accessible style, making it suitable for readers with little or no background in spatial analysis or statistics. It provides concise explanations of spatial methods, along with R code and outputs, allowing readers to apply the methods directly. The book's strengths include its comprehensive coverage of spatial analysis and statistics in under 300 pages, its clear and easy-to-follow style, and its seamless integration of methods and R programming. The authors also provide references to additional resources for more detailed information. The book is recommended for students and instructors looking for a single textbook on spatial data analysis and R programming. However, the author suggests that future editions could include more detailed coverage of spatial econometric models and a comparison of R with other software packages. Overall, the book is an excellent resource for those seeking to quickly learn spatial analysis and statistics using R.