Homework 04 is ready for grading

[HScheiber]

Link to the main page of my homework repo.

Link to the homework 4 Section.

Link to the homework 4 - Tidy data and joins markdown file.

Last SHA: 3c6010f1f88ff7ffa6ec74ccceb6c0346fafead5

[marbazua]

Hi Hayden.

• I think your repository is very well organized and I could easily find the homework 4.
• Your md file was well structured and everything was clearly commented. That was really helpful when reading your homework.
• About the first part (General data reshaping and relationship to aggregation):Good job reshaping the data frame. I would have liked to see the code for the scatterplot using the wide format because I’m not sure on how to do it for more than two countries. Did you try? You used a function that I wasn’t aware of (the transpose function t) so I learned that when reading your assignment
• About the second part (Join, merge, look up) I think it’s very important that you performed a data cleaning before doing anything else. Also, good job with reshaping the World Bank data before merging. Finally, you try joining it with gapminder using the various join functions’ and included useful comments. It was very complete and well done.

Mar.

[teilhard1976]

The student addressed the challenges of Homework 04 comprehensively. His repo was structured simply and clearly so it was very easy navigate.

He did three out of four activities under the first prompt. He showed his skills in reshaping and aggregating data. He is not afraid to try new things and to accept that his solutions could be further improved. Moreover, his reflections of his workflows gave me a sense of his priorities. He wanted to ensure that his tables and graphs are readable to human readers. And for me I find them easy to follow and understand. I would have liked to know why the student chose to feature Canada, China, and Rwanda as opposed to other countries. Is there a narrative point that the student wishes to make? It would have added to my appreciation of these visualizations.

He did one activity under the second prompt. His work on this challenge offered a usable, even elegant, guide to the various "joins". And I think he should be also be commended for trying to use the world bank data.

-- TP

[ksedivyhaley]

Reshaping activity: Yes (several) Join activity: Yes Reflect on process: Yes Bonus (merge/match): No

Comments:

• For the reshaping segment, the plot was supposed to represent (for example) Canada on the x-axis and China on the y-axis rather than plotting year vs life expectancy for multiple countries. The intent is to demonstrate that some types of plot are easier to make with wide data – though you are correct that usually long data is preferred (and good exploration of working with either for your “life expectancy by continent” data).
• Try limiting the number of decimal places displayed in kable.
• In the second activity, what does unique() do?
• Nice wrangling of the external dataset before joining – good observation about the data types.
• You should also be able convert your factors to character vectors using mutate().
• Overall very solid work, excellent handling of reshaping.

Your mark will be distributed later. If you would like more feedback, please feel free to message me on slack.