Wrap-up

Overview

Questions

• What are the next steps in learning R to further your R coding skills?

Objectives

• Reflect on the key topics covered in this course and identify areas for further study to enhance your R skills.

In this course, we have introduced you to the basics of R and RStudio and demonstrated how these tools can be used for scientific analysis. The concepts and techniques you’ve learned here are essential to getting started with R, but there is much more to explore. Learning R is a journey, and you will find additional topics and tools that are vital for working effectively in your domain.

Some of the topics we touched on briefly, or didn’t have time to explore in depth, will be critical for your continued success with R. Here, we’ll highlight a few areas that you should prioritize as you continue your R journey.

Topics to Explore Further

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While this course covered the essentials, some key topics were only mentioned in passing or were not included. It is important to delve deeper into these areas to fully leverage R for your research.

Factors

• Factors are a specific data type in R used to represent categorical variables with discrete levels. They are essential for performing statistical analysis on categorical data.
• If you plan to analyze survey data, experimental treatments, or any other dataset involving categories, learning how to create and manipulate factors is crucial.
• See Chapter 16 Factors in R for Data Science (2e) by Hadley Wickham.

Missing Data

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• If you will be using R for statistical analysis it is essential that you know how R represents missing values see R for Data Science - missing values

Data Structures: Matrices and Arrays

• Matrices and arrays are foundational data structures in R, used to represent multi-dimensional data.
• These structures are particularly important if you intend to:
  • Work with numerical computations across multi-dimensional datasets.
  • Develop your own statistical R packages or perform advanced simulations.
• Understanding these data structures will give you greater flexibility and power in how you handle and process data.
• See Chapter 13 - Data Types and Structures in the Software Carpentry Course “Programming with R”

Next Steps in Self-Learning

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In order to build on what you’ve learned in this course, we also recommend focusing on the following topics next:

2. Control Flow
   • Mastering control flow (e.g., if, else, for, while) is key to automating repetitive tasks and writing efficient scripts.
   • See Episode 7 - Control Flow in R for Reproducible Scientific Analysis
3. Functions
   • Writing your own functions will allow you to reuse code, making your analyses more modular and less prone to error.
   • See Episode 10 - Functions Explained in R for Reproducible Scientific analysis
4. Data Frame Manipulation with tidyr
   • Learn how to reshape, clean, and organize data efficiently.
   • Tackle more complex data wrangling problems that cannot be solved with dplyr alone.
   • This is especially useful for preprocessing datasets before analysis.
   • See Episode 13 - Data Frame Manipulation with tidyr in R for Reproducible Scientific analysis

Looking Ahead

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The skills you’ve gained here form a strong foundation for using R in scientific research. As you continue to practice and explore R, remember that your growing your coding expertise will help: - Your team, by making your work accessible and reproducible. - Your peers, by sharing scripts and workflows that others can understand and build upon. - The broader scientific community, by contributing to open, transparent, and impactful research.

Key Points

• This course covered the essentials of R and RStudio for reproducible analysis, providing a strong foundation for further learning.
• Topics such as factors, matrices and arrays, and more advanced concepts like data frame manipulation with tidyr, control flow, and functions are critical next steps.
• Continued practice and exploration of R will benefit you, your team, and the wider research community.