hw 04 peer review

[Jenncscampbell]

HI @navysealtf9k

I couldn't see an open issue for this hw so I created one here.

Here is my feedback:

• I found your graph based on Sri Lanka and India really interesting. It is cool to know how to separate our variables so that a plot like this is possible. That is a really cool legend for size- it’s quite easy for the reader.
• It looks like you went through the main joining functions and seem to have mastery of this concept.
• I really liked your review of the joining and how it compares to other functions like merge
• Also found the match function completely different than the joining functions they are obviously used for very different things. But I too had trouble interpreting the match functions.

Overall great job! Check plus!

Jenn

[navysealtf9k]

Hi Jenn,

I completely forgot to open an issue for it. Thank you for doing so and for your review.

[peterwhitman]

Hi Abdullah,

Nice job on the homework! I'm impressed by the organization of submission and the aesthetics of your .md file.

Reshaping prompt:

• Strengths:

  • Successively reshaped dataset using spread function
  • Employed the kable function to output an aesthetically appealing table
  • Good documentation of thoughts/process

• Weakness: You have essentially plotted the same variable on both the x and y axis. I would recommend using the melt function, which would allow you to output a dataframe the country columns consolidated into one column. This would allow you to put time on the x-axis and life expectancy on the y-axis. Feel free to take a gander at my .md file -- you will see what I'm talking about.

Join prompt:

• Strengths:

  • Good exploration of the various join function we have discussed in class
  • Good documentation and discussion of join functions
  • Successively employs join functions to join two data frames for further analysis

• Weakness: You are getting warnings after each join. Try specifying the columns which you are basing the joins on.

In addition to the two prescribed prompts, you went above and beyond and played around with the merge function, which is pretty neat.

Well done!

Peter

[derekcho]

Hi @navysealtf9k, here are some comments about your hw04:

• For your first task, your gapminder_wide_form table was a bit confusing to read at first with all of the extra zeros. This is fine for analysis and generating plots, but not ideal for looking at. In general, we convert data to wide format with spread to make tables that are easier to look at and long format for analytic purposes. However, I do understand your goal of adding population as an addition variable in your plot!
• If you are trying to compare life expectancy over time for these two countries, I think it would be better to have them side by side rather than perpendicular to each other for easier comparisons. It isn’t easy to see which country has a higher life expectancy for each year just by looking at the plot.
• That is a pretty cool dataset that you have for task 2! You demonstrated the uses of full and inner joins well! However, since your alternative dataset does not change over time, I think it would have been better if you had grouped gapminder by year so that we could see what happened with other countries other than Afghanistan and Yemen.
• Coding style was neat and efficient. Repository is well organized and easy to navigate.
• Overall, your selected tasks were well done! I appreciate that you tried to do things a little differently and it is always encouraged! However, I think that trying to add population as an additional variable in your plot may have caused some problems as well. Remember, we do not need our plots to show too many different things at once! Good job!

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