R Programming Roadmap 2026
6 phases · 12 weeks · build one project in every phase.
Step-by-step plan
- RStudio and projects
- Data types and vectors
- Missing values and factors
- Packages and help
- Reading CSV and Excel files
Project: Import a public dataset, clean column types and summarise key variables with base R
- Lists and data frames
- dplyr verbs
- Joins
- tidyr pivoting
- Strings and dates
Project: Combine monthly sales files, clean them and build a tidy analysis-ready table
- ggplot2 grammar
- Facets and themes
- Chart selection
- Quarto reports
- Interactive charts
Project: Create a parameterized Quarto report with charts for each sales region
- Descriptive statistics
- Distributions
- t-test and chi-square
- ANOVA
- A/B test analysis
- Power analysis
Project: Analyse an A/B test dataset and write a recommendation with confidence intervals
- Linear and logistic regression
- tidymodels workflow
- Random forests
- Clustering
- Model evaluation
Project: Build and evaluate a customer churn model and explain its key drivers
- Functions and purrr
- Shiny apps
- renv and Git
- testthat
- Scheduling and databases
Project: Deploy a Shiny dashboard on a churn model with filters and downloadable reports
Practise each topic
Frequently asked questions
How long does it take to learn R Programming?
Following this roadmap takes about 12 weeks with regular practice. Working professionals usually need a little longer.
What should I learn first in R Programming?
Start with phase 1: RStudio and projects, Data types and vectors, Missing values and factors. Build the phase project before moving on.
How do I know I am job-ready in R Programming?
When you can finish all phase projects on your own, solve the medium questions in the topic sheets and clear the mock interview comfortably.