Chapter 54: R Syllabus

R Syllabus” look like in 2026 — i.e. a realistic, well-structured learning path that takes someone from zero to being able to do serious real-world work.

I’m going to give you exactly that — not a theoretical university syllabus, but a practical, modern, industry-relevant & self-study-friendly learning roadmap that reflects what actually works best for most learners in 2026 (students, career switchers, analysts, researchers).

I’ll explain it like your personal offline mentor who has taught R to hundreds of people: step by step, with realistic time estimates, recommended resources, mini-projects at each stage, and warnings about what to skip or not over-study.

──────────────────────────── Realistic R Learning Syllabus 2026 ────────────────────────────

Phase 0 – Before You Start (1–2 days)

Goal: Avoid 90 % of beginner frustration

1. Install R + RStudio Desktop (free) → https://posit.co/download/rstudio-desktop/ Choose the latest stable version (4.4.x or 4.5.x in Feb 2026)
2. Install these 4 essential packages once (run in console):

3. Create one folder on your computer called “R_Learning_2026” → All your scripts, data, and projects go here
4. Decide your main goal (very important!):
   • Academic assignments / thesis → focus more on stats & reporting
   • Data analyst / business → focus on tidyverse + SQL bridge
   • Machine learning → go to tidymodels sooner
   • Pure research / publication → ggplot2 mastery + Quarto

Phase 1 – Absolute Basics (1–2 weeks)

Goal: Write simple code without constant red error messages

Topics & order:

1. RStudio layout (4 panes) + script vs console
2. Assignment (<- vs =)
3. Basic data types (numeric, character, logical)
4. Vectors (c(), 1:10, seq())
5. Basic math & functions (+ – * / ^ %% mean() median() sum() length())
6. Logical vectors & comparisons (> >= < <= == != & | !)
7. Indexing ([ ], $ for data frames)
8. Missing values (NA, is.na(), na.rm = TRUE)
9. Installing & loading packages (install.packages(), library())
10. Quick help (?mean, F1 on function)

Mini-projects / exercises:

• Calculate your monthly expenses average & median
• Create vector of 10 favourite Hyderabad foods → count how many start with “B”
• Make a vector of your last 7 days temperatures → find max, min, range

Recommended resources (free):

• R for Data Science (2nd edition) – chapters 1–5
• Posit Primers (free interactive) – “The Basics” section
• YouTube: “R Programming for Beginners” by freeCodeCamp (first 1–2 hours)

Phase 2 – Data Frames & dplyr (2–4 weeks)

Goal: Feel comfortable manipulating tables (the 80 % daily work)

Topics:

1. Create data frame manually (data.frame())
2. Read real data (read.csv, readxl::read_excel, readr::read_csv)
3. Inspect (glimpse, skimr::skim, str, summary, head/tail)
4. Clean names (janitor::clean_names)
5. Core dplyr verbs:
   • filter()
   • select()
   • mutate()
   • arrange()
   • group_by() + summarise()
   • count(), add_count()
   • across() for multiple columns
   • slice_max/slice_min
   • case_when() & if_else()
6. Pipe operator |> (native since R 4.1) vs %>%
7. Join tables (left_join, inner_join, full_join)

Mini-projects:

• Import any CSV you have (marks, expenses, IPL scores…)
• Create summary table: average marks by city / gender
• Find top 5 highest spenders + their percentage of total
• Flag outliers (revenue > 95th percentile)

Resources:

• R for Data Science (chapters 3–10) – the “transform” & “wrangle” parts
• Posit “Data Transformation” cheat sheet
• YouTube: “dplyr tutorial” by MarinStatsLectures or Data School

Phase 3 – Visualization with ggplot2 (2–4 weeks)

Goal: Make plots you’re proud to share

Topics:

1. Grammar of graphics basics (data + aes + geom + labs + theme)
2. Most used geoms: geom_point, geom_line, geom_col, geom_bar, geom_histogram, geom_boxplot, geom_smooth
3. Aesthetic mappings: color, fill, size, shape, alpha
4. Scales (scale_color_brewer, scale_y_log10, scale_x_date)
5. Facets (facet_wrap, facet_grid)
6. Themes (theme_minimal, theme_light, theme_bw)
7. Annotations (geom_text, annotate, ggrepel)
8. Saving (ggsave, dpi = 300+, pdf/png)

Mini-projects:

• Scatter + smooth line + facet by category
• Bar chart of top 10 categories with labels on bars
• Boxplot + jitter points comparing groups
• Time series line plot with confidence ribbon

Resources:

• ggplot2 book (free online) – Hadley Wickham
• Posit ggplot2 cheat sheet
• YouTube: “ggplot2 tutorial” by Thomas Lin Pedersen or Data Professor

Phase 4 – Basic Statistics & Modelling (4–8 weeks)

Goal: Move from “looking at data” to “answering questions with data”

Topics:

1. Descriptive stats (mean, median, sd, var, quantile, table, prop.table)
2. Hypothesis testing (t.test, wilcox.test, chisq.test, cor.test)
3. Linear regression (lm) + interpretation
4. Logistic regression (glm(family = binomial))
5. Model diagnostics (performance::check_model)
6. Tidy model output (broom::tidy, broom::glance)
7. Reporting tables (modelsummary, gtsummary)

Mini-projects:

• t-test: compare average marks between two cities/groups
• Correlation + scatter: study hours vs marks
• Linear model: predict mpg from wt + hp (mtcars)
• Logistic: predict survival on Titanic

Resources:

• Statistical Thinking for the 21st Century (free online book)
• ModernDive (free textbook)
• YouTube: “Statistical Rethinking” course by Richard McElreath (Bayesian flavor)

Phase 5 – Reporting & Reproducibility (2–4 weeks)

Goal: Turn code into beautiful, shareable documents

Topics:

1. R Markdown basics (.Rmd files)
2. Quarto (new standard in 2026 – .qmd files)
3. Code chunks, inline R, tables (kable, flextable, gt)
4. Embed plots, control figure size
5. Render to HTML, PDF, Word
6. Parameters & interactive reports
7. Version control basics (Git + GitHub)

Mini-project:

• Create one Quarto document:
  • Title page
  • Data overview table
  • 3–4 plots
  • One regression model with table
  • Conclusion
  • Render to HTML & PDF

Resources:

• Quarto official guide (quarto.org)
• R Markdown: The Definitive Guide (free online)
• Posit “Get Started with Quarto” tutorials

Phase 6 – Next Level Choices (after ~3–6 months)

Pick 1–2 depending on your goal:

• Data cleaning & big data → arrow, duckdb, polars
• Advanced modeling → tidymodels
• Bayesian statistics → brms
• Shiny dashboards → shiny
• Spatial data → sf, terra
• Time series → fable, tsibble
• Publishing packages → devtools, roxygen2

Your Realistic Timeline (self-study, 5–10 hours/week)

• Phase 1 → 1–2 weeks
• Phase 2 → 2–4 weeks
• Phase 3 → 2–4 weeks
• Phase 4 → 4–8 weeks
• Phase 5 → 2–4 weeks → Total: ~3–6 months to become comfortable & employable for junior roles

Now — where are you right now in this path?

Tell me:

• Which phase feels closest to your current level?
• Do you want exercises / mini-project for any specific phase?
• Or would you like me to create a personalized 30-day plan based on your goal (exam, job, thesis, hobby)?

I’m right here — let’s make the next step exactly what you need! 🚀📚