Mark Homework 10 of Xin-Zhao

[zxkathy]

Please visit my HW10:

• Please access my README of HW10.

Last commit: 7d9aa1e My commit list of the homework

@vincenzocoia @gvdr @ksedivyhaley @JoeyBernhardt @mynamedaike @pgonzaleze @derekcho

[margotgunning]

Hi @zxkathy

First off, well done for trying all three tasks, that's a lot of work for the end of the year. It seems as if you ran into some problems with different coding languages with tutorials, and trying to make adjustments for R (I've had the same issues in the past as well!).

You had a lot of good commentary in your report, and it was pretty easy to follow your examples, which is great. You clearly put a lot of work into making the starwars functions/dataframe, and demonstrated a wide knowledge of the different lessons in STAT545/547 ie) joining, mapping ect, and had good coding syntax.

You did a great job cleaning up the Vancouver houses dataframe; the finished project looks great, and theres tons of into to do exploratory analysis with (same goes for the Burnaby data). Your plots were well done, and we can clearly see the differences between Burnaby and Vancouver west.

You added a lot of info to the Gapminder dataset. I would have liked to see some more info about what the different the columns were, and maybe some interpretation about what is going on in the population density/year plot. Other than that, well done using an R package that wraps an API.

Overall, you did a good job trying all three tasks.

Happy Holidays

Margot

[bcahn7]

Hello @zxkathy , Here are some comments on your hw10. Hope it helps!

• You did all three tasks! These were a good review for me. I learned how to use different kinds of method to scrap data from the web.
• In Task 1: Make API queries "by hand" using httr, you customize various functions to combine information from different data-sets: getChar_info, moreInfo, and convertTibble. Functions are quite long but I could easily notice your intention for each function.
• unlist() and map() were well-utilized in the functions.
• In Task 2: Scrape data , the raw data is well-processed and cleaned data is intuitive and neat.
• You provide house information located in the central location of Vancouver such as price, the number of bed rooms and bathrooms, type, address, and so on.
• The plots on house price against the number of rooms are intuitive. Adding combined plot would help for easier comparison.
• In Task 3: Use an R package that wraps an API, you provide combined data between data from gapminder and data from geonames. Detailed information for each country and the plot on population density against time are provided.

Overall, I could see all your efforts for this assignment and the results are all neat and impressive! I learned a lot for each method to scrap data from the web and how to process raw data. You did a great job!

[ksedivyhaley]

Method Chosen: httr, scrape, wrapper Gets data from web: Yes Clean and tidy data frame: Yes Basic Data Exploration: Yes Report/Reflection: Yes

Comments:

• Completed all three versions of the assignment.

Task 1:

• Your omdb call shows the API key – poor security practice.
• While it's good practice, you don't need to define functions that you only use once (ie convertTibble and getChar_info). In this particular case, it would be nice to see at least one intermediate stage of the data in order to see the tidying you are doing on it.

Task 2:

• Would it be possible to shorten the urls in readDat.R? It looks like you have a couple empty query paramenters, eg “search_subtype=&minimum_bedrooms=&maximum_bedrooms=”
• Good job getting the column names for Task 2.
• Nice plots, though free scales make it difficult to compare prices between Vancouver & Burnaby.
• Process is clearer than Task 1.

Task 3:

• Could use some comments. It's reasonably clear if you know what to expect but might cause some confusion if you need to refer to your assignment next year.

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