Getting Started with R

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.

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 with R {.unnumbered} [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. 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/)