hw04 ready for grading

[yidie]

Last commit: b537826

[sdhanani]

You do a good job reshaping the data and using the spread function and plotting the life expectancy and you do a good job showing what the new data shape can plot and you explain why you think one format is better over the other for what you would be interested in plotting. It would have been nice for you to show a bit of what the regular format of the data was before using spread so that it would be easier to compare the two tables.

You do a good job producing a second gapmider data.frame that purposefully has missing information in order to better demonstrate the different joins. You describe each type of join you look at well but it would have been nice if you had kept using the kable function for the table outputs as it would make the assingment much easier to read.

Your code is concise and easy to read and follow showing steps broken down into logical steps to create a solution to the task at hand. You show competency in both presentation of graphs through modifying themes and using kable (although you might've wanted to use it more) but show competency of the material learned in class.

Good job!

[zxkathy]

Hi @yidie, this is Kathy who visited your hw4 and here are some of my comments:

Dataset Reshaping:

• Successfully completed Activity 3 and turning your data frame into the wide format using the functions of spread() and other tidyverse functions (such as piping, group_by() and summarise().
• Two examples of why the wide format data frame is useful. The second example is an even stronger example than the first one in that the second one is even harder to plot using the long format data frame.
• Your code is neat and easy to read. Your structure of the homework is clear. I can easily follow you when reading your homework.

Joining Datasets:

• Successfully completed Activity 1. You can explore all the functions of _join() using the the subset of gapminder data frame and one created by yourself.
• Both of your data frames are not big, and easy to locate. Additionally, after each join example, you have a short paragraph of what does this _join() function do and an explanation of the result. Readers can follow your logic easily and quickly understand the difference of various join functions.
• In addition to what you have explored, you may want to flip the positions of gapminder1, gapminder2 for each join types. i.e. left_join(gapminder2, gapminder1) and see whether you will get a different result.

Overall:

Well done! Clear logic and structures, proper comments and neat code!

[derekcho]

Hi @yidie, here are some comments about your hw04:

• You can remove the warnings and messages when loading packages by adding the arguments warning=FALSE, message=FALSE to the header of the R statement in RMarkdown.
• For task 1, I am not sure if you meant to show the usual shape or not, but it did not show. However, you correctly applied the spread function to convert the data into wide format!
• Your scatterplots were generally well done, although I would suggest that you color by year in the second plot so that we could tell which year the life expectancies correspond to (plots should be self-contained)
• In general, it is considered bad practice to select rows by specifying their row names (what if an extra row gets inserted at the top). Since year does not matter in your example, this would be a better way to code it:

  list_of_countries = c(“Afghanistan”,”Costa Rica”,”Greece”,”Mali”,”New Zealand”,”Chile”)
  gapminder1 <- gapminder %>%
  group_by(country) %>%
  select(country %in% list_of_countries)

• You explained and demonstrated the different types of joins well! Your explanations were concise, but pretty clear. I would also suggest that you knit these tables with the kable function for added visual appeal!
• Good work overall. Your assignment was well done!

Your grade will be emailed to you at a later date.