ANU Statistics Courses: Recommended Learning Resources for Getting Started with R

5 March 2026 12 min read

In ANU’s statistics courses, R is the core computing tool throughout both undergraduate and postgraduate study. According to the 2024 QS World University Rankings by Subject, ANU’s Statistics and Operational Research program ranks 36th globally, and its curriculum requires students to master basic R programming and data analysis skills in their first year. The Data Science Skills Demand Report published by the Australian Bureau of Statistics (ABS) in 2023 notes that R usage among Australian government and research institutions has exceeded 68%, making it a high-demand skill in the job market. This article systematically reviews the official textbooks, online platforms, on-campus support and learning pathways for getting started with R in ANU statistics courses, helping new students cross the programming threshold efficiently.

The Role of R in the Course Structure

ANU’s core statistics courses — STAT1008 Quantitative Data Analysis and STAT2001 Introduction to Statistical Inference — both use R as the default programming environment. According to the 2024 ANU course outline, STAT1008 runs for 12 teaching weeks: the first 4 weeks focus on basic R operations (vectors, data frames, basic plotting), and the remaining 8 weeks progressively introduce R implementations of linear regression and hypothesis testing. The course includes a 2-hour computer lab each week, where students complete set exercises in the RStudio environment.

STAT2001 moves on to R function writing and simulation techniques, requiring students to write Monte Carlo simulation code on their own. An internal 2023 teaching report from the ANU Mathematical Sciences Institute (MSI) shows that about 72% of students can independently complete basic data analysis tasks after finishing STAT1008, while the remaining 28% still need extra tutoring. R proficiency is directly tied to course grades — lab assignments account for 30%-40% of the final mark.

Recommended Textbooks and Official Guides

ANU statistics courses do not prescribe a single textbook, but most lecturers recommend R for Data Science (by Hadley Wickham) as the entry-level read. O’Reilly published the second edition in 2023, covering the core usage of the tidyverse package family (dplyr, ggplot2, tidyr). ANU Library’s electronic resources provide online reading access to the book, which students can use free of charge with their ANU account.

Another frequently used textbook is Using R for Introductory Statistics (by John Verzani), whose content closely matches the teaching pace of STAT1008 and ends each chapter with exercises in the style of past ANU exams. The second edition (2014) has consistently ranked in the top three most borrowed statistics titles among the physical copies at ANU Library.

On-Campus R Workshops

The ANU Academic Skills Centre runs 6 free R workshops each semester, covering three modules: data import, visualisation basics and statistical analysis. Data from Semester 1, 2024 show an average of 45 registrations per workshop, with an actual attendance rate of about 78%. The workshops combine lectures with hands-on practice: each 90-minute session spends the first 30 minutes explaining syntax and the remaining 60 minutes completing exercises with teaching assistants on hand.

In addition, the ANU Mathematical Sciences Institute Postgraduate Students’ Association (MSI-PSA) runs a weekly “R Clinic” where senior PhD students provide one-on-one help. In 2023 the service handled 320 consultations, resolving an average of 2.3 technical questions per session.

Online Learning Platforms and Resources

Beyond on-campus resources, several online platforms offer introductory R courses compatible with the ANU curriculum. DataCamp is the online practice platform officially recommended by the ANU Statistics Department; its “Introduction to R” course includes 6 hours of interactive programming exercises covering vector operations, data frame subsetting and basic statistical functions. ANU students can obtain a discounted DataCamp subscription code through the university IT service (about AUD 89 per year, down from the regular AUD 149).

The “R Programming” course from Johns Hopkins University on Coursera (taught by Roger Peng) is listed as a supplementary resource in several ANU course outlines. The course runs for 4 weeks at 3-4 hours per week, covering control flow, loops and function definitions, and closely matches the R programming requirements of STAT2001. As of 2024, the course has more than 2.8 million enrolments worldwide and a rating of 4.5/5.

YouTube Channels and Video Tutorials

The R Programming 101 channel (320,000 subscribers) offers more than 150 free videos, and its “R in 10 Minutes” series has been recommended multiple times on ANU student forums. Videos are 5-15 minutes long and focus on a single topic (e.g. “how to draw a boxplot with ggplot2”), making them ideal for learning in short bursts.

The StatQuest with Josh Starmer channel (1.2 million subscribers) explains statistical concepts alongside R code in its R-themed videos; for example, the video on implementing the t-test in R has 470,000 views. In ANU’s 2023 course feedback survey, about 34% of students said they had used this channel to help understand class material.

Official Documentation and Community Forums

The official R documentation (cran.r-project.org) provides the most authoritative package descriptions and function references. ANU teaching emphasises the habit of consulting documentation — about 15% of the marks in STAT1008 lab assignments come from choosing function arguments appropriately. The R tag on Stack Overflow has more than 430,000 solved questions, and ANU students can access its advanced search features from the university IP range.

Learning Pathway and Time Planning

For complete beginners, the ANU Statistics Department recommends the “4-4-4 study method”: the first 4 weeks to master basic syntax (variables, vectors, data frames), the middle 4 weeks to learn data visualisation and basic statistical tests, and the last 4 weeks to practise function writing and simulation. Each phase suggests 3-5 hours per week, including lab sessions and self-study.

According to data from the ANU Student Success Centre 2023, students who followed this pathway averaged 72.3 points in the STAT1008 final exam, higher than the 63.8 points of those without a systematic plan. When paying tuition across borders, some study-abroad families use dedicated channels such as Flywire tuition payments to complete the currency exchange, so they can set aside budget in advance for buying textbooks or subscribing to online platforms.

Common Mistakes and How to Avoid Them

The most common beginner mistake is ignoring data type conversion. Confusing factors with character strings in R can cause statistical functions to produce wrong output. Analysis of past STAT1008 exam papers shows that about 22% of code errors stem from this kind of issue. The second most frequent mistake is not setting the working directory (setwd), which causes file-reading failures. The default path on ANU computer lab machines differs from personal computers, so students should confirm the current path before each lab session.

R vs. Python: Which to Choose

In ANU statistics courses, R is compulsory, but some electives (such as DATA1001 Introduction to Data Science) also use Python. According to the 2024 ANU course handbook, R has clear advantages in statistical analysis and visualisation (e.g. the syntactic consistency of ggplot2), while Python is stronger in machine learning and deep learning. Statistics students are advised to master R first, then add Python depending on their research direction.

Setting Up the Lab Environment

ANU provides two R environments: campus computer labs with R 4.3.2 and RStudio 2023.12 installed, including more than 120 pre-installed packages. Students can also access the same environment remotely through the ANU virtual desktop (VDI) without any local installation. In Semester 1, 2024, the VDI averaged about 1,200 logins per day, with response times kept under 3 seconds during peak hours (2-4 pm).

For personal computers, ANU IT Services recommends installing the latest version of R (4.4.0 or later) and RStudio Desktop (the free version is fine). Key packages include: tidyverse (data manipulation), ggplot2 (visualisation), dplyr (data cleaning) and rmarkdown (report generation). Installation takes about 15 minutes, and the university provides detailed illustrated guides.

Package Management and Version Control

R package dependencies can be complex, and ANU recommends using the renv package for project-level environment management. It locks the package versions used by each project, preventing code from breaking after package updates. STAT2001 requires students to submit a renv.lock file with their final project, worth 5% of the total lab assignment mark.

Using R in Exams and Assignments

Exams in ANU statistics courses come in two forms: closed-book written tests and computer-based lab exams. The STAT1008 midterm is a closed-book written test that does not involve writing R code but requires interpreting R output (such as the p-values produced by the summary() function). The final exam, by contrast, includes 40% hands-on computer questions, where students must complete data import, cleaning, analysis and visualisation within 2 hours.

Lab assignments usually require submitting R Markdown files (.Rmd format), which combine code, output and written explanations into HTML or PDF reports. Across the 8 STAT1008 lab assignments in 2023, each averaged 80-120 lines of code, checked after submission by the automatic grading system (Gradescope) for code correctness and completeness of comments.

R Implementations of Common Statistical Methods

In ANU courses, the t-test uses the t.test() function, linear regression uses lm(), and analysis of variance uses aov(). Parameter settings for these functions are a frequent exam point in lab assignments. For example, the paired argument in t.test() defaults to FALSE, so it must be changed manually for a paired test. In the 2023 STAT2001 final exam, about 18% of students lost marks for failing to set this parameter.

FAQ

Q1: Can a complete R beginner keep up in ANU statistics courses?

Yes. STAT1008 assumes no prior knowledge, with the first 4 weeks devoted to basic R operations. According to an ANU 2023 student survey, about 65% of students had no programming experience before enrolment, yet after 12 weeks of study the average final computer exam score was 71.4 out of 100. Extra tip: completing DataCamp’s free “Introduction to R” course (about 4 hours) in the 2 weeks before the semester starts can shorten the adjustment period by about 30%.

Q2: Which matters more in ANU statistics courses, R or Python?

R is the compulsory tool, covering the STAT1008 to STAT3000 course series and accounting for about 80% of the curriculum. Python is used only in DATA1001 and some electives. According to the 2024 ANU course handbook, statistics students must complete at least 4 core courses built around R, while only 1 Python-related course is compulsory.

Q3: How can I get R learning resources for free?

ANU students can access the ebook edition of R for Data Science free through the library (requires an ANU account login). DataCamp offers a 7-day free trial covering the full introductory course. The R Programming 101 and StatQuest channels on YouTube are completely free. In addition, the ANU Academic Skills Centre runs 6 free workshops per semester at no extra cost.

References

QS 2024, QS World University Rankings by Subject: Statistics & Operational Research
Australian Bureau of Statistics 2023, Data Science Skills Demand Report
ANU Mathematical Sciences Institute 2023, Teaching Report: STAT1008 Course Outcomes
ANU Academic Skills Centre 2024, Workshop Attendance Statistics, Semester 1
Unilink Education 2024, ANU Student Resource Database