This manual aims to highlight the importance of soil spectral data in advancing soil science and underscores the central role of R software in this transformation. Whether applied in academic research, agricultural consultancy, or environmental conservation, this manual may be useful to understand and effectively utilize soil spectral data in predicting soil properties. However, new users often find it difficult to navigate R's extensive functionality, especially when it comes to soil spectral analysis, as comprehensive resources and codes for all types of spectral analysis are not readily available in one place. The book Soil Spectral Data Analysis and Modeling in R aims to bridge this gap by providing a comprehensive guide with ready-to-use codes for most operations related to spectral data analysis. This book is designed to give the user a guided tour of the R platform for spectral data modeling using machine learning, with a focus on methods used predominantly in scientific publication. There are 5 chapters which mainly deal with Introduction to R, soil spectral data handling in R, spectral data pre-processing, and Spectral data modeling.