https://dacss.github.io/DACSS_601_Summer2023_Sec1/posts/Homework_3_Zhongyue_Lin.html

[giscus[bot]]

https://dacss.github.io/DACSS_601_Summer2023_Sec1/posts/Homework_3_Zhongyue_Lin.html

Summarize statistics and visualize project data sets - World Happiness Data

https://dacss.github.io/DACSS_601_Summer2023_Sec1/posts/Homework_3_Zhongyue_Lin.html

[sp-conway]

Nice job Zhongyue. I think you've made solid progress on this project.

I took some notes as I read, which are included below. In general, I think you can make it clearer exactly what you are showing to the reader (e.g., which variables you're getting descriptive stats for). I also think you should make some visualizations highlighting a few countries with high, low, and medium values to identify the types of countries that score poorly on these measure. In addition, you do show some pretty solid differences by continent. Africa seems to do worse than the americas, which tend to do worse than Europe. I might explore that further.

Please let me know if you have any questions.

• Great description of your dataset!
• GDP per capita - minor typo, should be “Capita” not “Capital”
• Good job reading in the dataset
  • A fun exercise (maybe for challenge 10?) would be to try and write a function that reads in a dataset and use purrr::map() to read in the datasets from all year
    • Don’t use head(), either. Show the whole data frame
• Could you please show the data frame to the reader? That is, include a line of code that is just the name of the data frame. This will be helpful for allowing the reader to see how the changes you’re make via code manifest themselves in the actual data.
• Data set visualization test
  • What is this figure?
• Great research questions.
• Descriptive statistics
  • Rather than compute separate descriptive statistic tables, can you just compute one big one? And include a column that displays the variable name. Because right now the reader needs to open each code block to figure out the variable you’re getting statistics of.
    • Same comment for the stats grouped by country. The reader needs to figure out which variable you’re getting stats for, rather than seeing it in the table
  • Nice job using enquo()!
• It could be very limiting to only focus on countries that always have data. I would strongly caution against doing this, or at least if you do so, please also do it with ALL countries included.
• Nice scatterplots
  • I’m not sure that having developing vs. developed be a different shape is adding anything. There’s too much going on in the data for me to adequately interpret it.
• Rank over Time in the us
  • Is this the us’s rank compared to all other countries? I would clarify it.