Rubric - STIN300 final assignment

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1	Rubric (vurderingsmatrise) - STIN300 final assignment				Self-assessment: Useful for you as a presubmission checklist. Feel free to attach your self-assessment to your Final assignment submission, it is a good starting point for our assessment too. Format: A sentence or three about where and how you meet your stated level of achievement.
2	Aspect	Good	Acceptable	Incomplete	If you wish to include a self-assessment: Copy this spreadsheet into either google docs (make it world-readable and include a link as a comment to your submission) or download as Excel and attach it to your submission. Let me know if you have problems copying/downloading.

3	File upload	You have uploaded both an .html report, the .Rmd it was generated from, and any data files required.		You have uploaded .Rmd or .html, but not both. Not all required data files are available.
4	Knitting	Your Rmd file can be knitted without changes provided its data files are in the same folder as the Rmd.	The Rmd can be knitted with some adjustments to file paths that only exist on your system.	Your Rmd file cannot be knitted. (Hence the html file cannot be from the same Rmd.)
5	Document structure	Your document has clear, meaningful headings. We recommend the IMRaD structure, in which case you should separate Materials and methods (computation to read in, wrangle and analyse data, storing them as R objects) from Results (simply printing/plotting the results). Chunk options adjust figures to a suitable size. Routine startup messages from tidyverse etc are suppressed. R code is collapsed with the code_folding setting.	Some headings are misformatted so they display as regular text. Figures are excessively big. Tidyverse startup messages clutter the report. R code and/or unnecessary output is shown in the document.	Your document is not structured with headings.
6	Logical structure of writing	You outline the real-world phenomena that you study, formulate one or two research questions, and explain clearly how your data relates to the phenomena. The reader is guided through the data processing that sheds light on your chosen questions. The resulting data graphics and statistical results are interpreted back to real-world terms, in both Results (what the data says, that everyone would agree on) and Discussion (your interpretation and conclusions, which might be open to discussion).	You outline your research question and how your data and computing answers it, but in a less engaging and readable manner, or it may be hard to follow your line of argument. The results or implications of them are not described in real-world terms.	You fail to define a research question or how the data processing relates to the phenomena that you study, and the results are hard to understand for anyone outside your field of expertise.
7	Figure design	You have identified variables whose relationship expresses one of the phenomena or hypotheses you focus on, possibly devised suitable aggregate measures, and map the resulting variables to geometrical units and aesthetics, in such a way that it facilitates thinking about your research question, and/or answers a relevant question.	You have devised a figure that describes your data so as to give a helpful descriptive overview.	The figure fails to make an understandable point, or illustrates something else than the main aim of the text.
8	Figure execution	Your figures meet Good requirements in most respects and Fair otherwise, as described in the Rubric for data graphics.	Most aspects are Fair, none are Poor.	Some aspects are Poor.
9	R code	Your code achieves the desired results and is commented at a suitable level of detail (i.e. helpful to your future self a couple months from now). It is self-explanatory as far as practicable, through meaningful variable names, indexing using names rather than numbers, and mostly follows a style guide (e.g. using the styler add-in for RStudio).	Your code gets the job done and can be understood by a teaching assistant, but is cluttered or poorly explained.	Your code is difficult to understand or provides little evidence that you understand your data processing.
10	Statistics	You have chosen a statistical model or procedure that adequately reflects your chosen hypothesis or phenomenon of study, and explain this clearly. The procedure is correctly applied, results are translated back to real-world terms, and you draw conclusions that are warranted by the analysis.	You apply an appropriate model or procedure to your data, but there are minor errors in its application, or you do not clearly justify the approach, or the interpretation of results misses some important points.	The choice of model or procedure is not appropriate to the problem, or you make major errors in its application, or you fail to demonstrate a clear understanding of what the model results mean.
11	Flow of prose	You begin every paragraph with a topic sentence so the reader knows where you're going with this. The following sentences elaborate, exemplify or explain. The closing sentence leads on to the next paragraph where applicable. Every sentence says something, has proper grammar and punctuation.	The text is understandable to a coworker or fellow student who haven't taken STIN300.	The text is only comprehensible to someone already familiar with the dataset.
12	General impression	Your report feels solid, trustworthy, clear and neat.	Your report provides the necessary information, but appears cluttered or untidily formatted, or the prose is unclear and riddled with errors of spelling or grammar.	Vital information is missing or incorrectly presented.
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