Lilly Tech Systems
R for Data Science
Master the tidyverse ecosystem — data wrangling with dplyr, visualization with ggplot2, data cleaning with tidyr, string and date handling, and reproducible reporting with R Markdown.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Overview of R for data science, the tidyverse philosophy, and setting up your DS environment.
2. Tidyverse
The tidyverse ecosystem: core packages, tidy data principles, tibbles, and the pipe operator.
3. Data Wrangling (dplyr)
filter, select, mutate, arrange, summarise, group_by, joins, and window functions.
4. Data Visualization (ggplot2)
Grammar of Graphics, geoms, faceting, themes, colors, annotations, and interactive plots.
5. Data Cleaning (tidyr)
pivot_longer, pivot_wider, separate, unite, handling missing values, and tidy data principles.
6. String & Date Handling
stringr for text manipulation, forcats for factors, and lubridate for dates and times.
7. R Markdown & Reporting
Create reproducible reports, presentations, and dashboards with R Markdown and Quarto.
8. Best Practices
Tidy data workflow, reproducible analysis, large data handling, and package development.
What You'll Learn
By the end of this course, you'll be able to:
Wrangle Data with dplyr
Filter, transform, summarize, and join datasets using the most popular R data manipulation package.
Create Stunning Visualizations
Build publication-quality charts and plots with ggplot2's grammar of graphics.
Clean Messy Data
Reshape, tidy, and handle missing data with tidyr, stringr, and lubridate.
Build Reproducible Reports
Combine code, results, and narrative in R Markdown documents and Quarto.