R Programming for Data Analysis
tidyverse, ggplot2 and R Markdown reports from zero.
Taught by Bibek Sharma · Data Scientist
What you'll learn
- Set up R, RStudio and the tidyverse in a project and write pipelines with the native pipe
- Import CSV, Excel and multi-file data with readr and readxl, then inspect and fix column types
- Answer analytical questions with dplyr verbs: filter, select, mutate, arrange, group_by, summarise and joins
- Reshape wide and long data with tidyr and handle missing values explicitly
- Build publication-quality charts with ggplot2 using aesthetics, geoms, facets, scales and themes
- Produce parameterised, reproducible reports with Quarto or R Markdown and lock packages with renv
About this course
R is the language behind a huge share of published statistics, the Google Data Analytics certificate and countless analyst teams, and the tidyverse makes it one of the friendliest ways to analyse data. This beginner course takes you from installing R and RStudio to a complete, reproducible report. You will import CSV and Excel files with readr and readxl, transform data with dplyr verbs such as filter, mutate, group_by and summarise, reshape it with tidyr, and build clear charts with ggplot2. Later lessons handle strings, dates and factors with stringr, lubridate and forcats, then wrap the analysis into an R Markdown or Quarto document that renders to HTML or PDF with one click. No programming background is needed; every concept is introduced with small examples you run yourself. It suits analysts, students, researchers and Excel users who want reproducible analysis and publication-quality visualisation. You finish with a portfolio-ready report built entirely from code.
Course content
7 lessons- 1R, RStudio and the TidyverseInstall R and RStudio, learn vectors and data frames, and write your first pipeline with the native pipe.Preview
- 2Importing and Inspecting DataRead CSV and Excel files with readr and readxl, then inspect structure, types and summaries before analysing.
- 3Transforming Data with dplyrfilter, select, mutate, arrange, group_by and summarise: the six verbs behind almost every analysis.
- 4Tidy Data with tidyrReshape between wide and long, split and combine columns, and handle missing values so every dataset fits the tidyverse.
- 5Visualising with ggplot2Build charts layer by layer with the grammar of graphics: aesthetics, geoms, facets, scales and themes.
- 6Strings, Dates and FactorsClean text with stringr, handle dates with lubridate, and control category order with forcats.
- 7Reproducible Reports with R Markdown and QuartoCombine code, results and prose in one document that renders to HTML, PDF or Word with a single click.