Chapter 55: R Study Plan

study plan — the realistic, step-by-step roadmap that actually works for someone who wants to become comfortable & employable with R in 2026.

I’m going to give you the most practical, up-to-date self-study R plan that reflects what really works for beginners in India right now — not a 6-month university syllabus, but a focused, flexible 12–20 week path that balances theory, coding practice, mini-projects, and portfolio building.

I’ll explain it like your personal offline mentor who has guided many students from zero to doing real internships / freelance / thesis work.

──────────────────────────── Realistic 2026 R Self-Study Plan – 12 to 20 weeks ────────────────────────────

Goal Setting (before day 1 – 30 minutes)

Answer these 3 questions honestly (write them down):

1. Main goal in next 6–12 months? a) College assignments / MSc / PhD thesis b) Data analyst / business intelligence job c) Machine learning / AI side interest d) Personal projects / hobby / Kaggle
2. Weekly time available? → Realistic answer: 6–12 hours/week (most working students)
3. Do you already have R + RStudio installed? → If no → do it today (takes 20–30 min)

Phase 0 – Setup & Mindset (Days 1–3)

Must-do before writing any code

1. Install R (latest stable) + RStudio Desktop Free → https://posit.co/download/rstudio-desktop/
2. Install these 5 packages once (run in console):

3. Create folder: C:\Users\YourName\R_Learning_2026 Inside it create sub-folders:
   • scripts
   • data
   • projects
   • reports
4. Mindset agreement (write & stick somewhere): “I will type every line myself — no copy-paste learning” “I will do one small project every 7–10 days” “I will not try to memorize — I will understand patterns”

Phase 1 – Core Language & Data Handling (Weeks 1–4)

Goal: Write 20–50 line scripts comfortably, manipulate tables without panic

Week 1–2 (Basics)

• RStudio layout + script vs console
• Assignment (<-), basic types (numeric, character, logical)
• Vectors (c(), :, seq(), rep())
• Indexing ([ ], $)
• Logical subsetting (df[df$age > 18, ])
• Missing values & na.rm = TRUE
• Installing/loading packages
• cat(), print(), paste(), paste0(), sprintf()

Week 3–4 (Data Frames + dplyr)

• Create data frame manually & read CSV/Excel
• Inspect (glimpse, skim, str, summary, head/tail)
• Clean names (janitor::clean_names)
• Core dplyr: filter, select, mutate, arrange, group_by + summarise, count, slice_max
• Pipe |>
• if_else(), case_when()
• Add calculated columns (profit margin, age group, etc.)

Mini-projects (do at least 2)

1. Import your own marks/exam CSV → calculate average, median per subject
2. Create fake monthly expense table → find highest/lowest month, average by category
3. Use mtcars or iris → filter cars with mpg > 20, summarise mean mpg by cyl

Phase 2 – Visualization & Exploratory Analysis (Weeks 5–9)

Goal: Make plots people actually want to look at

Week 5–6 (ggplot2 foundation)

• Grammar: data + aes + geom + labs + theme
• Main geoms: point, line, col, bar, histogram, boxplot, smooth
• Aesthetic mappings: color, fill, size, alpha, shape
• Scales (color_brewer, log10, date)
• Themes (minimal, light, bw)
• Save with ggsave()

Week 7–8 (Advanced ggplot)

• Facets (wrap & grid)
• Annotations (text, label, segment)
• Combine plots (patchwork package)
• Marginal plots (ggExtra::ggMarginal)
• Statistical layers (ggstatsplot, stat_summary)

Week 9 (Exploratory phase)

• GGally::ggpairs() for correlation matrix
• DataExplorer::create_report() instant EDA report
• Boxplot + violin + jitter for group comparison

Mini-projects

1. Scatter + smooth + facet — any two numeric variables from iris/mtcars
2. Bar chart of top 10 categories (use slice_max)
3. Time series line plot (use economics dataset or your own dates)
4. Boxplot comparison (mpg by cyl, or your own group variable)

Phase 3 – Statistics & Modelling Basics (Weeks 10–16)

Goal: Answer real questions with data

Week 10–11 (Descriptive + basic inference)

• Summary stats (mean, median, sd, IQR, quantile)
• Table summaries (table, prop.table, dplyr::count)
• Correlation (cor, cor.test)
• t-test / wilcox (t.test, rstatix::t_test)
• Chi-square (chisq.test)

Week 12–14 (Regression)

• Linear model (lm) + interpretation
• Logistic (glm(family = binomial))
• Model diagnostics (performance::check_model)
• Tidy output (broom::tidy, glance)
• Model comparison (modelsummary)

Week 15–16 (Reporting)

• Quarto documents (.qmd)
• Code chunks, inline R, tables (flextable, gt, kableExtra)
• Render to HTML / PDF
• Add plots + statistical tables

Mini-projects

1. t-test: compare two groups (e.g. mpg by am in mtcars)
2. Correlation + scatter matrix
3. Linear regression: predict mpg from wt + hp
4. Create one-page Quarto report with data summary + 2 plots + 1 model

Phase 4 – Portfolio & Next Level (Week 17+)

Goal: Have something to show employers / professors

1. Build 3–5 small portfolio projects (GitHub repo):
   • Sales dashboard (CSV → clean → summarise → 4 plots → report)
   • Student performance analysis
   • COVID / weather / IPL dataset exploration
   • Simple predictive model + interpretation
2. Choose one specialization path:
   • Data analyst → deep tidyverse + SQL bridge + Power BI/Tableau export
   • Research / thesis → advanced stats (lme4, survival, lavaan) + Quarto
   • Machine learning → tidymodels
   • Shiny apps → shiny & bslib

Realistic Timeline & Weekly Rhythm

• 6–10 hours/week → 4–6 months to junior-level comfort
• Daily rhythm (when possible):
  • 30–60 min theory / video
  • 60–90 min typing exercises / mini-project
  • 15 min reviewing mistakes

Resources I Personally Recommend in 2026 (Free + High Quality)

• Primary book (free online): R for Data Science (2nd ed) – Hadley Wickham
• Interactive (free): Posit Primers + DataCamp first chapters
• Video (free): “R Programming” by freeCodeCamp (4 hours), “dplyr & ggplot2” by Data School
• Cheat sheets: Posit (dplyr, ggplot2, data import)
• Practice datasets: palmerpenguins, gapminder, nycflights13, your own CSV files

Where are you right now?

Tell me:

• Which phase / week feels closest to your current level?
• What is your main goal (exam, job, thesis, hobby)?
• How many hours/week can you really give?
• Do you want weekly plan (e.g. “Week 1: do these 5 exercises + watch this video”)?
• Or pick any phase and I give you 10 targeted exercises for it?

I’m ready to customize the next step exactly for you — just say the word! 🚀📚