Getting Started Digital Soil Mapping with R

Digital Soil Mapping (DSM) is the process of creating maps of soil properties or classes using statistical, geostatistical, or machine learning techniques based on field observations, laboratory measurements, and environmental covariates.

Ris a popular open-source programming language for statistical computing and graphics that can be used for DSM. It was developed in 1980 based on the S-language, and an open-source community regularly updates the software for a robust, programmable, portable, and open-source computing environment. We can use it to solve complex and sophisticated problems and “routine” analysis without restrictions on access or use.

Here are some steps to get started with DSM in R:

Data collection and pre-processing: Collect soil samples and environmental covariates such as topography, climate, geology, and vegetation. Clean and transform the data into a format suitable for analysis in R.
Exploratory data analysis: Explore the data using summary statistics, histograms, boxplots, scatterplots, and correlation matrices. Identify outliers, missing values, and spatial autocorrelation.
Spatial data analysis: Use R packages such as raster, sp, and sf to handle spatial data. Create maps of the study area and overlay the soil and environmental data. Compute spatial statistics such as variograms, semivariograms, and covariance functions to quantify the spatial dependence of the data.
Statistical modeling: Fit statistical models to predict soil properties or classes based on environmental covariates. Use packages such as h20, gstat, randomForest, caret, and mlr to fit models such as linear regression, kriging, geographically weighted regression, random forests, support vector machines, and neural networks.
Model assessment and validation: Use cross-validation, bootstrapping, or independent validation data to assess the accuracy and reliability of the models. Compare the performance of different models and choose the best one.
Map production: Use R packages such as ggplot2, raster, spplot, and leaflet to create maps of the predicted soil properties or classes. Customize the maps by adding legends, titles, labels, and basemaps.

DSM with R requires some knowledge of statistics, spatial analysis, and programming. There are many resources available online, such as tutorials, books, and forums, to help you get started and improve your skills.

R is a programming language and software environment for statistical computing and graphics. It is widely used for data analysis and statistical modeling, and it has a large and active community of users and developers. If you are interested in learning R, here are some steps to get started:

Learning R can be a challenging but rewarding experience.

Here are some steps to get started with learning R:

Install R: You can download R for free from the official R website (https://www.r-project.org/). Choose the version of R that is appropriate for your operating system.

Install an IDE: An Integrated Development Environment (IDE) can help make coding in R easier. RStudio (https://rstudio.com/) is a popular and user-friendly IDE for R.

Learn the basics: Start by learning the basic syntax and data types in R. You can find many online tutorials and resources to help you get started. The official R documentation (https://cran.r-project.org/manuals.html) is also a great resource.

Practice: Practice coding in R by working on small projects and exercises. Kaggle (https://www.kaggle.com/) and DataCamp (https://www.datacamp.com/) offer many R courses and projects to help you improve your skills.

Join the R community: Joining the R community can help you learn from other R users and get answers to your questions. You can find R user groups in many cities, and there are also many online communities such as the RStudio Community (https://community.rstudio.com/).

Some popular resources for learning Data Science with R include:

R for Data Science by Hadley Wickham and Garrett Grolemund
Data Science in R by Roger D. Peng
Hands-On Machine Learning with R by Bradley Boehmke & Brandon Greenwell
Kaggle Learn
Geographic Data Science with R
Spatial Data Science with R and “terra”
R for Geographic Data Science
Geospatial Data Science With R

# Getting Started Digital Soil Mapping with R {.unnumbered} Digital Soil Mapping (DSM) is the process of creating maps of soil properties or classes using statistical, geostatistical, or machine learning techniques based on field observations, laboratory measurements, and environmental covariates. [R](https://www.r-project.org/)is a popular open-source programming language for statistical computing and graphics that can be used for DSM. It was developed in 1980 based on the [S-language](https://en.wikipedia.org/wiki/S_(programming_language)), and an open-source community regularly updates the software for a robust, programmable, portable, and open-source computing environment. We can use it to solve complex and sophisticated problems and "routine" analysis without restrictions on access or use. Here are some steps to get started with DSM in R: 1. Data collection and pre-processing: Collect soil samples and environmental covariates such as topography, climate, geology, and vegetation. Clean and transform the data into a format suitable for analysis in R. 2. Exploratory data analysis: Explore the data using summary statistics, histograms, boxplots, scatterplots, and correlation matrices. Identify outliers, missing values, and spatial autocorrelation. 3. Spatial data analysis: Use R packages such as raster, sp, and sf to handle spatial data. Create maps of the study area and overlay the soil and environmental data. Compute spatial statistics such as variograms, semivariograms, and covariance functions to quantify the spatial dependence of the data. 4. Statistical modeling: Fit statistical models to predict soil properties or classes based on environmental covariates. Use packages such as h20, gstat, randomForest, caret, and mlr to fit models such as linear regression, kriging, geographically weighted regression, random forests, support vector machines, and neural networks. 5. Model assessment and validation: Use cross-validation, bootstrapping, or independent validation data to assess the accuracy and reliability of the models. Compare the performance of different models and choose the best one. 6. Map production: Use R packages such as ggplot2, raster, spplot, and leaflet to create maps of the predicted soil properties or classes. Customize the maps by adding legends, titles, labels, and basemaps. DSM with R requires some knowledge of statistics, spatial analysis, and programming. There are many resources available online, such as tutorials, books, and forums, to help you get started and improve your skills. [R](https://www.r-project.org/) is a programming language and software environment for statistical computing and graphics. It is widely used for data analysis and statistical modeling, and it has a large and active community of users and developers. If you are interested in learning R, here are some steps to get started: Learning R can be a challenging but rewarding experience. Here are some steps to get started with learning R: **Install R**: You can download R for free from the official R website (https://www.r-project.org/). Choose the version of R that is appropriate for your operating system. **Install an IDE**: An Integrated Development Environment (IDE) can help make coding in R easier. RStudio (https://rstudio.com/) is a popular and user-friendly IDE for R. **Learn the basics**: Start by learning the basic syntax and data types in R. You can find many online tutorials and resources to help you get started. The official R documentation (https://cran.r-project.org/manuals.html) is also a great resource. **Practice**: Practice coding in R by working on small projects and exercises. Kaggle (https://www.kaggle.com/) and DataCamp (https://www.datacamp.com/) offer many R courses and projects to help you improve your skills. **Join the R community**: Joining the R community can help you learn from other R users and get answers to your questions. You can find R user groups in many cities, and there are also many online communities such as the RStudio Community (https://community.rstudio.com/). Some popular resources for learning Data Science with R include: 1. [R for Data Science](https://r4ds.had.co.nz/) by Hadley Wickham and Garrett Grolemund 2. [Data Science in R](https://bookdown.org/rdpeng/rprogdatascience/) by Roger D. Peng 3. [Hands-On Machine Learning with R](https://bradleyboehmke.github.io/HOML) by Bradley Boehmke & Brandon Greenwell 4. [Kaggle Learn](https://www.kaggle.com/code/rtatman/welcome-to-data-science-in-r) 5. [Geographic Data Science with R](https://bookdown.org/mcwimberly/gdswr-book/) 6. [Spatial Data Science with R and “terra”](https://rspatial.org/) 7. [R for Geographic Data Science](https://sdesabbata.github.io/r-for-geographic-data-science/index.html) 8. [Geospatial Data Science With R](https://zia207.github.io/geospatial-r-github.io/)