hw02 ready for grading

[HScheiber]

@vincenzocoia @gvdr @ksedivyhaley @joeybernhardt @mynamedaike @pgonzaleze @derekcho Link to the main page of my homework repo.

Link to the homework 2 Section.

Link to the homework 2 gapminder-dpylr exploration markdown file.

Last SHA: 1f8f96c117aa75f3fe2a33c501eacd36eaa51305

[Alexander-Morin]

Alexander's review:

Hayden's repo is cleanly organized, with subfolder structure and a READme that clearly lists the purpose and destination of the relevant material. Organizing the homework assignments in a table is a very nice touch. Clicking on HW02 leads to the READme of HW02, with links to the gapminder exploration markdowns.

At every step, Hayden's repo is well annotated and includes links to other pages of interest.

The gapminder exploration goes well above what is required. Each question has a header, and an explanation of the result always follows the code. Numerous functions are presented to explain the dimension of the gapminder data set.

Numerous variables and their summaries are explored, again with excellent annotation. kable() is appropriately for some of these summaries.

Most noticeable is the breadth of plotting that Hayden accomplished. Scatter plots, strip plots (with summary statistics), histograms, density plots... you want 'em, he's gott'em.

Finally, Hayden accurately describes what was wrong with the initial analysts code, offering 2 alternative corrections as well as a simplified explanation of why the original method did not work.

If this isn't a check-plus effort, then we are all doomed.

[sdhanani]

Your assignment was easy to find and you explain your thought process and guide the reader really well through your your assignment. You demonstrate the ability to plot multiple types of plots very well and you layer your plots very well using the piping and boolean functions we learned in class. After going over your assignment, I realize that there is a lot more depth to ggplot2 than what I have been using and also a cleaner way to incorporate piping and the dplyr functions that we have learned into ggplot.

I will definitely be checking out the links that you reference that you used during your assignment to work on improving my own plots.

I also like how you used kable with your tables and I learned a new way to use kable with my own assignment through reading yours.

Great job with the assignment!

[mynamedaike]

Hello @HScheiber ,

1. Smell test the data You answered all the questions and got the number of variables and observations in more than one way. You explained how these functions are different in application contexts specifically.

2. Explore individual variables You explored both categorical variables and quantitative variable using functions like range() and summary() and visualized them using tables and plots.

3. Explore various plot types You performed a deep and comprehensive exploration on various kinds of plots such as scatter plot, histogram, density plot, box plot and bar plot.

4. Use filter(), select() and %>% You used filter() and %>%. It seems that select()was not used.

5. Bonus

• You gave two solutions to the incorrect code and explained why the failure occurs in detail.

• You used knitr::kable() to present tables in many places.

• You also used other dplyr functions such as group_by() and summarize().

6. Report your process You described how you finished this assignment and helpful resources.

Overall, You did a very good job. Your repository is well organized. The markdown file is easy to find and comfortable to read as it is well commented. You also demonstrated very solid skills in using different kinds of plots to visualize the data. Keep up good work!