RStudio Course

Lesson 1 • Installing R and RStudio

Covers downloading and configuring R and RStudio on major operating systems. Establishes a working environment required for all subsequent exercises.

Lesson 2 • Navigating the RStudio Interface

Introduces the four main panes and their functions within RStudio. Enables efficient workflow by mastering layout customisation and pane usage.

Lesson 3 • Writing and Running R Scripts

Teaches creating, saving, and executing R script files. Connects scripting habits to reproducible and organised data analysis workflows.

Lesson 4 • Getting Help in RStudio

Demonstrates built-in help tools, documentation lookup, and community resources. Empowers students to troubleshoot independently throughout the course.

Lesson 1 • Working with Vectors

Teaches creating, indexing, and operating on atomic vectors. Vectors are the fundamental building block of all R data structures.

Lesson 2 • Lists and Nested Structures

Explains lists as flexible containers for mixed data types. Prepares students for handling complex, hierarchical data common in real projects.

Lesson 3 • Scalar Data Types in R

Covers numeric, integer, character, logical, and complex types. Understanding types prevents coercion errors in later data manipulation tasks.

Lesson 4 • Matrices and Arrays

Introduces two-dimensional matrices and multi-dimensional arrays. Builds skills for numerical computing and structured data representation.

Lesson 1 • Importing JSON and Web Data

Demonstrates jsonlite and httr for reading JSON and API responses. Expands data access to modern web-based and semi-structured sources.

Lesson 2 • Exporting Data and Results

Covers write.csv(), writexl, and saveRDS for saving outputs. Ensures students can deliver clean, reproducible data products to stakeholders.

Lesson 3 • Reading CSV and Text Files

Teaches read.csv(), read.table(), and readr functions for flat files. Establishes the most common data ingestion workflow used in practice.

Lesson 4 • Connecting to Databases

Introduces DBI and odbc packages for querying relational databases. Enables students to access enterprise data sources directly from RStudio.

Lesson 1 • Handling Missing Data

Covers detection, removal, and imputation of missing values in data frames. Prevents downstream errors caused by NA values in analysis pipelines.

Lesson 2 • Joining and Merging Data Frames

Explains left, right, inner, and full joins using dplyr join functions. Builds the ability to combine data from multiple sources into one dataset.

Lesson 3 • Reshaping Data with tidyr

Demonstrates pivot_longer() and pivot_wider() for tidy data transformation. Tidy structure is required for most visualisation and modelling functions.

Lesson 4 • Core dplyr Verbs

Teaches filter(), select(), mutate(), arrange(), and summarize(). These five verbs handle the majority of real-world data transformation tasks.

Lesson 1 • Common Chart Types

Covers scatter plots, bar charts, histograms, box plots, and line charts. Matching chart type to data type is a core data communication skill.

Lesson 2 • Saving and Exporting Plots

Covers ggsave() with format, resolution, and size options for export. Ensures plots meet quality standards for reports, presentations, and publications.

Lesson 3 • Faceting and Multi-Panel Plots

Demonstrates facet_wrap() and facet_grid() for small-multiple visualisations. Faceting reveals group-level patterns without cluttering a single chart.

Lesson 4 • Customising Themes and Labels

Teaches axis labels, titles, legends, and built-in themes for polished output. Customisation transforms raw plots into professional, audience-ready visuals.

Lesson 1 • Hypothesis Testing

Covers t-tests, chi-square tests, and ANOVA for comparing groups. Students select the appropriate test based on data type and research question.

Lesson 2 • Correlation Analysis

Teaches Pearson and Spearman correlation coefficients and significance tests. Correlation quantifies linear and monotonic relationships between variables.

Lesson 3 • Descriptive Statistics

Computes measures of central tendency, spread, and distribution shape. Descriptive summaries are the starting point for any quantitative analysis.

Lesson 4 • Multiple Regression and Model Selection

Extends regression to multiple predictors and introduces model comparison criteria. Students build parsimonious models that balance fit and complexity.

Lesson 1 • Formatting Tables in Reports

Demonstrates knitr::kable() and kableExtra for styled, publication-ready tables. Well-formatted tables communicate data summaries more effectively than raw output.

Lesson 2 • Parameterised Reports

Teaches YAML parameters and render() for generating multiple report variants. Parameterisation automates report production across different subgroups or time periods.

Lesson 3 • Outputting to PDF and Word

Configures LaTeX-based PDF output and Word document templates. Matching output format to organisational standards ensures stakeholder acceptance.

Lesson 4 • Introduction to R Markdown

Explains the R Markdown file structure, YAML header, and knitting process. Reproducible reporting eliminates manual copy-paste errors in analytical deliverables.

Lesson 1 • Functional Programming with purrr

Teaches map(), map_df(), and walk() for applying functions over lists. Functional iteration replaces error-prone for-loops with readable, predictable code.

Lesson 2 • Debugging and Error Handling

Introduces tryCatch(), browser(), and traceback() for diagnosing code failures. Robust error handling prevents silent failures in production analysis pipelines.

Lesson 3 • Control Flow and Conditionals

Explains if/else, switch(), for-loops, and while-loops in R. Control flow structures enable dynamic, condition-dependent programme behaviour.

Lesson 4 • Writing Custom Functions

Covers function syntax, arguments, default values, and return values. Custom functions eliminate code duplication and enforce consistent logic across scripts.