• A little bit about myself
• The process of learning at uni (as opposed to high school…)
• Why are we here? What do you expect?
• Why come to lectures?
• What makes good learning?
• Relationship student-teacher, student-TA (make it interactive!)
• Relationship student-student (learning groups!)
• Learning and emotions, creativity (studies show that learning difficult stuff is associated with positive emotions!)

• the twofold difficulty of learning statistics and R
• the twofold reward of learning statistics and R!
• with R, you are standing on the shoulder of giants!
• The difficulty of teaching statistical concepts and R… (what first?)
• This can make the start a bit tricky, but we are here to help!

• COME TO YOUR LECTURES/LABS/TUTORIALS: those who are present tend to do well
• Have the right attitude (YOU are responsible for your learning, we are here to help)
• Be organised, time yourself: Revise your labs every single week!
• Be creative, inquisitive, ask questions, don't miss the train!
• Be realistic: 102 hours self-directed learning (as per paper descriptor) equates to 8.5 hours of homework per week, that's 2 afternoons/week!
• Don't get lost in the ocean of available resources, choose one, stick to it, read!

• Lecture: Wednesdays 09:00 - 11.00 am, WE240
• Be there there on time!
• Labs in WZ337, but double-check all times/rooms
• No labs in week 1

• Semester overview

  Stream	Lab	Tutorial
  1W	WED 3 - 5 pm WZ337	WED 11 am - 12 pm WZ140
  2W	MON 4.30 - 6.30 pm WZ337	MON 12 - 1 pm WZ141
  3W	FRI 10.30 am - 12.30 pm WZ337	TUE 11 am - 12 pm WH415
  4W	WED 1 - 3 pm WZ337	THU 11 am - 12 pm WZ140
  5W	WED 5.30 pm - 7.30 pm WZ337	THU 10 - 11 am WH415

• Need to access blackboard and AUT email address regularly!
• Change lab stream? Please see the Science office staff (WZ level 5)
• Theory: midsemester test/exam (30/70)
• Practical: 2 marks from lab reports (50/50)
• Overall mark: theory/practical (70/30)
• Calculate your overall mark as follows

\[(lab_1 \cdot 0.5 + lab_2 \cdot 0.5) \cdot 0.3 + (test \cdot 0.3 + exam \cdot 0.7) \cdot 0.7 \] - AND you need to pass both the practical and the theory!

• A preview of what is to come at the beginning of a lecture ('learning outcomes')
• A summary of what you should have learnt at the end of a lecture
• A glossary at the end of the lecture
• The lab reports: take them seriously, they are your foundation for passing this paper. Follow the rules, but be creative! Add to them, make them your personal little course manual!
• Practice in them and with them!
• E.g., take notes during the lectures, then put them into your lab reports under the section 'what have I learnt today…'
• Please help me keep lectures and labs interactive. There is nothing better than a seemingly 'dumb question'!

'If you want a stats lecturer, I'll do anything I can for you - I'm your man' (modified from Leonard Cohen)…but:

'Follow me the wise man said, but he walked behind.' (also by Leonard Cohen, unmodified)

I am here for you, but you first need to do your homework, come to lectures, do the labs, read up on things…

• MOST IMPORTANT: Lectures (your presence, your notes) and lab reports, including your own additions, tutorials
• Slides from lectures (accessed via link and pdf sent out shortly before lectures)
• Leuzinger S (2017), An Introduction to R ($20). Required and also used in other papers (SCIE602)

If you want to dig deeper

• M.J. Crawley, The R Book, Springer (approx. $120), also available as free ebook from the AUT library

Slides are NOT enough! Not everything is online, if you are not in the lecture, it's up to you to figure out what's happened!

Dates refer to lecture only!

1. Demonstrate an understanding of biological variability and how it is measured and reported.

• Explain systematic vs. unsystematic biological variation, explain accuracy and precision
• Understand what a frequency distribution is
• Be able to plot and interpret histograms
• Be familiar with the most common distributions that are relevant for biological data sets (e.g. normal, uniform, poisson), be able to calculate probabilities and quantiles

2. Demonstrate an understanding of sampling.

• Explain the difference between a population and a sample
• Know when to use the standard deviation vs. the standard error
• Be able to design a simple biological experiment or observational study

3. Demonstrate competence in basic biological and ecological data analyses

• Be able to organise and process biological data sets
• Use a series of simple tests like the t-test, correlation test, regression, Chi-squared, and their non-parametric alternatives where applicable.
• Interpret the results in a biological context

4. Demonstrate competence in reporting experimental results in a written format suitable for publication

• Be able to draw scientifically accurate and correct figures and tables
• Understand the basic structure of a scientific report
• Appropriately report statistical results (scientific writing and layout, proper use of units etc.)

5. Demonstrate an understanding of “significance”

• Understand the difference between the word ‘significance’ in common language vs. statistics
• Understand the concept of type I and type II errors
• Understand the concept of statistical power

6. Present work at an appropriate academic standard.

• A free statistics package, created by Ross Ihaka (Prof. at UoA) and Robert Gentleman in the early nineties
• Now covering everything from data processing to graphing
• Based on a command line interface
• R has become the data processing, analysis, plotting and programming tool in science
• Your future employer may well ask for your R skills!
• Commercial users include Google, Pfizer, Shell, Bank of America… but also DOC, crown research institutes, councils…
• Enormous free online resources (helplists, code)
• If this is the first and last time you do stats, look at R as an important learning experience!

• The short answer is: there is no way around R if you are doing a Bachelor of Science at AUT or at any top uni
• You will use it in Research Techniques (BIOL602), Terrestrial Ecology (ECOL602), Biogeography (ENVS622), Graphs R Us SCIE605 Pacific Islands Coastal Ecology (ECOL732), Research Project (SCIE701), Quantitative Analysis (SCIE805, postgrad), Advanced Ecological Data Analysis (SCIE807) and many more!
• You will need it for your Honours, Masters and PhD theses
• Our recommendation to use R is not commercially motivated (R is free)
• AUT is a top ranking uni and we are simply keeping up with the best universities

…you will receive an ‘R Markdown’ file for every lab:

• the file is ready to be compiled or 'knitted', but you still need to do work… (fill things in, eventually edit and write short pieces of R code)
• you need to do ALL lab reports and follow instructions therein, 2 random reports will be selected and marked as per earlier slide and paper booklet

Step-by-step guide NOW…

• Yes, you MUST use R markdown
• We will help you with the technicalities, it may appear messy at first but you will see the benefits soon and get used to the process
• Some flexibility is expected (trouble shooting, using google)
• You may delete the instructions if you want to, the lab reports are living, interactive, and personalised documents!
• You are encouraged to add extra stuff, but keep it concise and well organised. Messy and partly wrong additions will lead to a lower mark!
• Use '>' before you type, it makes it easier for us to see your answers

• You MUST submit every single lab report on time (Thursday 4 pm the following week after the lab, through turnitin)!
• I will randomly select 2 out of the 10 reports and mark them, one before, and one after the break
• Make sure you save both your code (the R Markdown file) and your (compiled) word file!
• You must submit your own report. If you plagiarise, there will be drastic consequences

• Download and install R (google 'R software' > Download R for Windows/mac)
• Close R
• Download and install RStudio (google 'Rstudio')
• Open RStudio
• Click on Packages (one of the tabs in the lower right-hand panel) > Install, select 'knitr', 'markdown', and 'rmarkdown'
• Go to file > new file > R Markdown
• Start playing :)

• How to study for this paper
• What resources to use
• How/when to hand in lab reports (this is most important and has caused good students to fail this paper in the past!)
• How to use RMarkdown for lab reports

• R
• RStudio
• RMarkdown
• to 'knit' (compile) an Rmarkdown file
• Console
• Editor
• R function, arguments