FreeCodingCourses
Roadmap
4–8 months
4 phases
~32 weeks

R Programming Roadmap

R is a language built for statistics and data analysis, and it stays the default in academia, biotech, and research data science. This roadmap runs in four phases: the core R syntax and data structures, then statistics and modeling, then visualization and reporting, then the SQL and Python skills industry data roles expect alongside R. It is for statistics students, researchers, and analysts who want a clear order to learn in. Phase 4 adds Python on purpose, because most data science jobs list both languages.
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  1. Phase 1:R Fundamentals

    beginner
    2 weeks

    R Syntax

    Vectors, lists, data frames, factors, and basic control flow: the building blocks of R code.

    beginner
    2 weeks

    Data Import & Tidyverse

    Read CSV, Excel, and database data. The Tidyverse (dplyr, tidyr, readr) is the modern way to work with data in R.

    beginner
    2 weeks

    Functions & Control

    Write R functions, handle loops, use the apply family of functions, and understand R's vectorized approach to computation.

  2. Phase 2:Statistics & Analysis

    beginner
    2 weeks

    Descriptive Statistics

    Summarize and explore data: means, medians, distributions, and correlation. R makes these one-liners.

    intermediate
    3 weeks

    Statistical Inference

    Hypothesis testing, confidence intervals, t-tests, chi-square, and ANOVA: the core of statistical reasoning.

    intermediate
    3 weeks

    Regression

    Linear regression, logistic regression, model diagnostics, and interpreting coefficients: the foundational modeling skills.

  3. Phase 3:Visualization & Communication

    intermediate
    2 weeks

    ggplot2

    The grammar of graphics: build publication-quality charts by layering data, aesthetics, and geometries. One of the best data visualization tools anywhere.

    beginner
    1 week

    R Markdown

    Combine code, output, and narrative in one document. R Markdown reports reproduce automatically when the data changes.

    intermediate
    3 weeks

    Shiny (optional)

    Build interactive web apps and dashboards from R code. Shiny apps can be published free on shinyapps.io and make strong portfolio pieces.

  4. Phase 4:Industry-Ready Skills

    beginner
    2 weeks

    SQL Basics

    Most data comes from databases. SQL lets you pull exactly the data your R analysis needs rather than loading whole tables.

    intermediate
    4 weeks

    Python for Data

    Industry data science roles commonly ask for R and Python. Pandas and NumPy cover the Python side of the data analysis stack.

    intermediate
    6 weeks

    Portfolio Projects

    Two end-to-end analysis projects: a statistical report in R Markdown and an interactive Shiny dashboard, both published and linkable.

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Languages in this roadmap

RPythonSQLMachine Learning

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The same plan step by step, with a specific free course for each stage.

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