For details on data used in this assignment, please visit CSSEGISandData

All python requirements are stored in requirements.txt. Run the code below to install the requirements.

pip install -r requirements.txt

In this exercise, Daniel, Ryan, and Justin performed a tutorial for students who want to learn tidy data.

Justin's role was to introduce tidy data and use the example of covid data to perform the melt, pivot, and pivot_table functions.

Ryan's role was to modify and add more into the melt, pivot, and pivot_table functions and assist with autograder.

Daniel's role was to make a bunch of exercises that consisted of autograded answers and questions and arrange the full notebook with autograde cells.

Together, we ran a conclusion about our results and the meaning of all these functions.

For anyone who is attempting to do this assignment, we would highly recommend learning nbGrader.

Knowing how to run a notebook tutorial and then actually test your future students on it would be a huge upgrade to improve your coding skills.

When sending a final tutorial on tidy data, it's the responsibility to make sure that future students not only get the right answers, but end up utilizing it for future projects.

We believe we worked as a team well, even during this virtual environment. We utilized Slack and Zoom to communicate efficienty as we finished the assignment. We splitted the tasks among ourselves and got together to discuss what we were able to do and what we had issues with. We did well especially with implementing the functions; melt, pivot, and pivot_table in the tutorials from two different aspects. We looked this assignment from perspective of students, and teachers so that students could learn the objects easily, and teachers could teach every details about the Tindy data.

In addition, it was an experience using NBgrader for the first time and we could continue learning.

The most challenging part of this assignment by far was using NBgrader overall.

For example, trying to actually grade example answers to assignments successfully on NBgrader or even coming up with successful questions. We went through the NBgrader tutorial, and youtube video

Most of us never used NBgrader before so it's quite a challenge once learning to become accustomed to manage assignments, manage students, combine different notebooks, pushing and pulling the necessary files.

We would need more help in using NBgrader. As for the assignment, the harder challenge would be running the right tests given the examples shown earlier.

Also, we should learn how to send assignments as instructors and have students successfully submit assignments without issues.

Hopefully, there's a faster way using the notebook rather than the idea of JupyterHub.

This assignment was very important in terms of tidy data.

We learned how to modify data in a way that can communicate way easily with the ones who observed it.

For example, with the covid data, there were too many columns.

With the use of pivot_table, we succeeded in explaining the number of contracting cases in just one column.