hw02 ready for grading

[abishekarun]

@vincenzocoia @gvdr @ksedivyhaley @joeybernhardt @mynamedaike @pgonzaleze @derekcho 2nd assignment Rendered markdown file

SHA included in the link as it was suggested last time..

[mylinhthibodeau]

Dear Rajendran Arun,

Here is my peer review process:

Excellent homework

• Well organized
• Interesting
• Shows in depth understanding of R coding and its underlying processes and functions
• Fulfill all the objectives layed out in the stat545 hw2 description
• Clear explanations and descriptions of the process at the beginning, perhaps a bit less in the second half
• I have learned a lot of things from reviewing your homework, thank you for that!

Smell test the data

• You have provided 2 or 3 orthogonal methods to obtain the diverse characteristics of gapminder dataset (e.g. class, variables, column).
• I really like your explanation of the difference between a vector and a matrix and how this affects diverse R functions to query the data.

Explore individual variables

• Use of summary function combined to kable to render a visually pleasing table !
• Use of base R plot to illustrate individual variables explore (continous and categorical).

I did not understand that the x axis (index) represents in this plot function (plot(type='p',gapminder$gdpPercap) # scatter plot), so if you have enough time, don't hesitate to let me know what I missed (but if not, that's totally fine too)

Explore various plot types

• Clear explanation on the type of variables used for plots
• If any, the only suggestion I have would be to add more explanation regarding what the plots represent and how they help in understanding the data.

Use filter(), select() and %>%

• Good understanding of how to use these functions to organize data for tables and plots
• You made a country list and then you use if to illustrate some plots, but there is not way to decipher which data points belong to which country. Perhaps an idea could be to add an aesthetic (e.g. colour) to do so.

Optional - Section: But I want to do more!

• You have identified very well the mistake in the command and solved how to obtain the data requested by the user, well done !!

Things I have learned reading your homework

Thank you so much for teaching me the following items:

• kableExtra packages and how to better use kable
• how to better use the base R plot function
• some additional subtle differences about the variable characteristics

Again, excellent job !!

Regards, My Linh

[teilhard1976]

Created a Homework 02 Folder. Files are well organized in this folder. So it was easy for me to navigate. The Readme file for Homework is quite helpful. I appreciate particularly the section on the resources that helped the student. Now I am inspired to use them as well. Links to the R markdown document and Rendered Markdown File were a nice touch.

The exploration of the data and the demonstration of functions are quite comprehensive. Dividing the work flow into clear sections and subsections would have made reviewing the R markdown document easier. The reflection of the process needs to be elaborated as well. For instance, why did the student install kableExtra? What does it do? The purpose might be quite evident for him, but not for the beginners of R such as myself.

All in all, a very solid effort.

[mynamedaike]

Hello @abishekarun ,

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 two categorical variables and two quantitative variable and visualized them using tables and different kinds of 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 pie chart.

4. Use filter(), select() and %>% Yes. You used all of these.

5. Bonus

• You explained how the code should be changed to get the correct result.

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

• You also used other dplyr functions including mutate(), group_by(), summarize(), etc.

6. Report your process You described the easy and difficult part of this assignment for you and helpful resources.

You did a very good job. Your repository is well organized. The markdown file is easy to find. You demonstrated deep understanding in using different kinds of plots to visualize the data.