R for Data Analysis: From Zero to Intermediate

Hands-on wrangling, visualization, and reporting in R

Live cohort Beginner to Intermediate 6 weeks 4 to 6 hours per week Starts 13 Oct 2026 1200 Taka

Apply

By the end of this course you will be able to

Import data from CSV, Excel and SPSS files and get it into a tidy, analysis-ready shape.
Wrangle, filter, group and summarise datasets fluently with dplyr.
Build clear, publication-quality charts with ggplot2 and know which chart to reach for.
Write reproducible analyses in R Markdown or Quarto that rerun end to end without manual steps.
Use RStudio Projects and Git so your work is organised and recoverable.
Take a raw dataset through to a finished report on your own.

Prerequisites

No prior programming experience needed.
Comfort with basic spreadsheet work (sorting, filtering, simple formulas).
A laptop able to run R and RStudio.

Tools you will use

R and RStudio (both free)
tidyverse: dplyr, tidyr, readr, ggplot2, stringr, forcats
R Markdown or Quarto
Git and GitHub

Week by week

Week 1

Getting comfortable in R

Installing R and RStudio; touring the four panes.
Why RStudio Projects matter, and why setwd() does not.
Vectors, data frames, and the main data types.
Calling functions, reading the help pages, and decoding error messages.
Installing and loading packages.

Deliverable: A working project folder with a script that loads a dataset and prints its structure.

Week 2

Data wrangling with dplyr

The pipe, and why it makes code readable.
The core verbs: filter(), select(), arrange(), mutate(), summarise().
Grouped operations with group_by().
Combining datasets: left_join() and friends.
Common beginner traps: factors, NA propagation, silent recycling.

Deliverable: A summary table answering three questions about a supplied dataset.

Week 3

Importing and tidying real data

Reading CSV, Excel and SPSS files with readr, readxl and haven.
What tidy data means, and reshaping with pivot_longer() and pivot_wider().
Handling missing values deliberately rather than by accident.
Working with text (stringr), categories (forcats) and dates (lubridate).
Building a small cleaning script you can rerun on new data.

Deliverable: A cleaning script that turns a messy raw file into a tidy dataset.

Week 4

Visualization with ggplot2

The grammar of graphics: data, aesthetics, geoms.
Scatter, bar, line, box and histogram, and when each is the right choice.
Scales, colour, facets and annotation.
Themes and making a chart presentation-ready.
Saving charts at the right size and resolution with ggsave().

Deliverable: Three finished charts with captions, exported as image files.

Week 5

Reproducible reporting

R Markdown and Quarto: code, output and prose in one document.
Chunk options, caching, and controlling what the reader sees.
Rendering to HTML, Word and PDF.
Parameterised reports: one template, many outputs.
Git and GitHub basics: commit, push, and recovering earlier versions.

Deliverable: A rendered report, tracked in a Git repository.

Week 6

Capstone project

Writing your own functions to stop repeating yourself.
Iterating over groups and files.
Structuring an analysis others can follow.
Exploratory analysis on a dataset of your choosing.
Presenting findings and taking questions.

Deliverable: An end to end analysis: raw data in, rendered report out, presented to the cohort.

Assessment

Weekly deliverable with written feedback.

A final capstone project, presented in the last session.

Certificate of completion for participants who submit the capstone.

How the cohort runs

Sessions are live, with recordings available afterwards. Each week pairs a working session with a short practice task, and every task gets individual written feedback before the next session.

Questions

If you are unsure whether the level is right for you, get in touch before enrolling and describe what you have done in R so far.