WEEK 01
Getting started with R
Arithmetic, objects, vectors, data frames, the $ operator, head and max.
Practice task
Inspect USArrests, find two variable maxima, and explain one result in a sentence.
Teacher’s Kit — Weeks 1–6: Line-by-line R explanations, expected outputs, instructor prompts and session plans. Open the Instructor Kit →
New: supplemental Weeks 5–6 · Original fictional-data exercises; the Week 5–6 Moodle files have not yet been inspected. Explore the proposed Weeks 5–6 →
Download the original four-week companion pack: Fictional CSV datasets, 4 student R worksheets, 4 worked instructor solutions and bilingual README. Browse the package / Download ZIP →. Original UCL course files are not redistributed.
Open instructor resources: Preparation guide, 90-minute session plan, original worked R examples and mock assessment at the UCL PALS Open Instructor Desk. No private sign-in required.
4 November · Today’s 90-minute lesson · A first-session timeline, questions, student practice and homework options. Open the lesson plan →
New to programming? Before Week 1, use our original What Is Programming? · Lesson 0 with a five-person synthetic example and starter R exercises.
Step-by-step R code guide · Explore step-by-step explanations, fictional examples and source mappings for the four weeks. Step-by-step R code guide →
Four practicals, one coherent route
WEEK 02
Data wrangling
Read CSV, join on two keys, select, filter, arrange, mutate, group_by and summarise.
Practice task
Join participant information to repeated measurements; identify the actual pre/post groups.
WEEK 03
Descriptive statistics and AQ-10 scoring
Wide/long data, pivot_longer, scoring lookups, mean, SD, range and histogram.
Practice task
Score all ten items with lookup tables, then describe participant totals and their distribution.
WEEK 04
Data visualisation
ggplot2 histograms, scatterplots, box/violin plots, mean ± SD, facets and themes.
Practice task
Show one distribution and one relationship in two charts, including what the charts cannot show.
Source status and academic scope
The student reports studying in UCL Psychology and Language Sciences (PALS). A supplied lecture handout is headed “Introduction to Statistical Methods”. The exact enrolled UCL module code is not yet confirmed, so this is not presented as an official UCL syllabus.
The original UCL PDFs, R worksheets and associated CSV files were supplied privately by the student. They are not reposted here. Use the originals provided in your course together with the supplementary practice ZIP shared privately by your tutor.
Check the supplied codebook before analysing
- Measurement occasion: 0 = pretest; 1 = posttest; 2 = one-week follow-up. The Week 2 sheet labels 1 and 2 as pre/post, which conflicts with the dataset codebook.
- Sex coding in the source data: 1 = female, 2 = male. Some practical-sheet hints reverse those labels.
- A boxplot ordinarily shows the median and quartiles, not mean ± standard deviation. Error bars of ±1 SD are not 95% confidence intervals.
- AQ-10 screening scores must not be treated as a clinical diagnosis; summary plots and pre/post means alone do not establish causality.