Last updated: 2024-12-27

Checks: 2 0

Knit directory: R_tutorial/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Repository version: 0ce17c3

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 0ce17c3. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  analysis/Basic_Operations_with_Raster_Data.Rmd
    Untracked:  analysis/Resources.Rmd

Unstaged changes:
    Modified:   analysis/Introduction.Rmd
    Modified:   analysis/Loading_Geospatial_Data_in_RStudio.Rmd
    Modified:   analysis/Understanding_Geospatial_Data.Rmd
    Modified:   analysis/_site.yml
    Deleted:    analysis/about.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/How_To.Rmd) and HTML (docs/How_To.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 0ce17c3 Ohm-Np 2024-12-27 wflow_publish("analysis/How_To.Rmd")

Welcome to the R Tutorial! This guide is designed to help you learn R programming in a structured and interactive way. To get the most out of this tutorial, follow these tips:

  1. Start from the Beginning:
    If you’re new to R or programming in general, it’s recommended to start with the first chapter and work through each section in order. Each chapter builds on the previous one, so following the sequence will give you a solid foundation.

  2. Download the Data Files:
    You will need access to geospatial data files for many exercises in this tutorial. Download the datasets (available in Geopackage for vector and GeoTIFF for raster formats) from the provided links to follow along with the examples.

  3. Use the Code Examples:
    Each chapter includes R code examples for you to run and experiment with. Copy the code into your own R environment and modify it to see how it works. Practicing hands-on is the best way to learn.

  4. Explore the Interactive Exercises:
    Some chapters include interactive challenges to test your understanding. Try to complete these exercises before checking the solutions. If you get stuck, refer back to the examples and explanations for guidance.

  5. Refer to the Links and Resources:
    Throughout the tutorial, I’ve included helpful links for additional reading or external resources. Don’t hesitate to follow these links if you need more information on specific topics or tools.

  6. Take Your Time:
    Learning programming takes practice, so don’t rush. Feel free to revisit previous sections, experiment with different commands, and take your time to master the concepts. The tutorial is meant to be a resource you can return to whenever needed.

By following these tips, you’ll be able to progress through the tutorial at your own pace, building a strong understanding of R and its applications in geospatial analysis.