stat545ubc-2021 / guanyu_chen Goto Github PK

• Hi Guanyu, Great work on milestone 1. One thing, please knit the rmd file to a "github_document" or an md file as html files cannot be rendered in GitHub in a readable format.
  - Task 1.1 - besides choosing the dataset names and just listing them as it is - it is nice to add a short description about those datasets too. It adds more detail to the data analysis report.
  - 1.4 - You should come up with sample questions for both of the datasets (steam_games and flow_sample) - you only added potential research questions for your final choice dataset i.e, flow_sample
  - Great set of research questions to pursue the next milestones
  - Well done overall. All the best for your next milestone!
  Please feel free to reach out if you have any further questions. The grades will be posted later today in Canvas.

Hi Guanyu, Congrats on finishing your mini data analysis
- Fantastic repo organization and detailed README. Good job!
- Overall well done and everything looks good! Great work
Please feel free to reach out if you have any further questions. The grades will be posted later today in Canvas.

Hi Guanyu, Great work on milestone 2.
- Good job on repo organization - putting each milestone files in their own subfolder; Detailed README as well!
- You have not mentioned which dataset you are using at the beginning of your analysis
- It would make sense to adjust the x-axis of the minimum flow histogram (or log transform it) as it is being skewed to the left.
- How does the 1.2.2 plotting help answer your question?
- I'd actually say your data by itself is untidy! Because for a given station ID and a given year there are two rows for each extreme_type (max and min) - this violates the first rule of tidy data.
- Although I see that you have demonstrated how to transform the data between two type in your next exercise - for which you will get the marks
- 2.3, first data you have created is the actual tidy format for this data - removing station_id is not a good choice as it serves as a mark for each observation even though you wont be using it in any analysis.
- Adding comments to your code explaining what each line does is also highly encouraged!
- Well done overall. All the best for your next milestone!
Please feel free to reach out if you have any further questions. The grades will be posted later this week in Canvas.