R Road Map

Author

Vestin Hategekimana

Published

October 7, 2023

R Learning Roadmap for Budding Data Scientists and Statisticians

Hey there, future statistician and data scientist! Welcome to the wonderful world of R, a programming language specifically designed for data analysis, statistics, and graphical representation. Whether you’re a statistician looking to expand your toolkit or an aspiring data scientist eager to dive into data manipulation and visualization, this roadmap is your guiding star.

Note: This blog will probably evolve with the new content we are providing on YouTube.

Why R?

R is a flexible and powerful tool. Developed by statisticians, for statisticians, it boasts an incredible ecosystem of packages, making it one of the most popular choices for data-driven tasks. Plus, it’s free and open source!

R Learning Roadmap

This roadmap is divided into four main categories:

  1. Foundation
  2. Intermediate Techniques
  3. Advanced Analysis
  4. Specialized Areas

Let’s break these down!

1. Foundation

Before you can master the intricate techniques of data science and statistics in R, you need to get the basics right.

Base: Learn how to install R and Rstudio, the basics of synthaxis, the first types and how to create functions.

Base R playlist

Managment: Learn how to use the language and its dedicated editor to their full potential, with tips and basics.

Statistic with R playlist

Statistic: Learn the basics of statistical analysis and conduct your first study from A to Z using all the tools available in R.

2. Intermediate Techniques

With the basics in hand, let’s delve deeper!

Data workflow: Learn the basics of the Tidyverse and master data manipulation. You can learn it at any level.

Tidyverse playlist

  • Data Manipulation: Master the dplyr package for tasks like filtering, arranging, and summarizing data. You’ll love the pipe (%>%) operator!
  • Advanced Visualization: Get to know ggplot2, the most popular visualization package in R. The Grammar of Graphics will revolutionize how you think about plotting.

ggplot2 playlist

  • Data Cleaning: tidyr is your friend here. Learn techniques like pivot, separate, and unite.

3. Advanced Analysis

Now, let’s dive deep!

  • Advanced Statistical Modeling: Explore more advanced techniques like multiple regression, logistic regression, and ANOVA.

  • Machine Learning: With packages from the Tidymodel, dive into classification, clustering, and regression models.

  • Time Series Analysis: Use packages like forecast for time series decomposition and forecasting.

  • Reporting: Learn how to knit your R Markdown documents into interactive HTML, PDFs, and slideshows to share your findings.

4. Specialized Areas

Depending on your interest, there’s always more to explore:

  • Text Mining: With quanteda and tidytext, dive into the world of NLP.

  • Geospatial Analysis: sf and leaflet will help you work with spatial data.

  • Bioinformatics: If you’re into biology, Bioconductor provides tools for bioinformatics.

  • Shiny Apps: Turn your analyses into interactive web applications with shiny.

Final Words

Remember, the journey of learning R, like all things, is best taken one step at a time. You might feel overwhelmed initially, but trust the process. With each line of code, each plot, and each model, you’re getting better.

Happy coding, and here’s to your data-driven adventures with R!