TDM 10100: Project 10 — Fall 2026

  • Temporal Data

Project Objectives

Motivation: Temporal data (i.e., data about dates and times) are pervasive in data analysis.

Context: We continue to explore time series and temporal aspects of data

Scope: R, Python, dates/times

Learning Objectives

  • Continue to learn about date/time stamps in data

Please use 4 Cores for this project.

Please use the template found here, and the important information about project submissions here, when you are ready to submit your work.

Datasets

  • /anvil/projects/tdm/data/icecream/combined/reviews.csv

  • /anvil/projects/tdm/data/craigslist/vehicles.csv

  • /anvil/projects/tdm/data/consumer_complaints/complaints.csv

  • /anvil/projects/tdm/data/zillow/State_time_series.csv

  • /anvil/projects/tdm/data/taxi/green/green_tripdata_2019-06.csv

Questions

Question 1 (2 points)

Re-consider the ice cream reviews data (which we have studied in Projects 1, 3, 4, 6, and 8): /anvil/projects/tdm/data/icecream/combined/reviews.csv

There was one week in which 90 or more reviews were given each day. Use R to find that week, and then use Python to again find that week.

It is OK if you use Generative AI to help you, but (as always) please document how you use Generative AI for such a purpose.

Deliverables

  • Use R to find the one week in which 90 or more reviews were given each day

  • Then use Python to find the one week in which 90 or more reviews were given each day

Question 2 (2 points)

In the Craigslist data, which we first studied in Project 5: /anvil/projects/tdm/data/craigslist/vehicles.csv

there are many columns. To see them all, remember that we need to run a command like this:

options(repr.matrix.max.cols = Inf)
head(myDF)

The first 27 entries in the posting_date column are NA (altogether there are 68 values of NA in the posting_date column), but most of the posting_date column entries are not NA values.

Use R to explore the year and month (first ignore the days) in which the posting_date occurs.

Once you recognize that all of the data comes from one year, you can switch perspectives and find the months and dates in which the posting_date occurs (and now ignore the years).

What is the date range in which the posting_date values occur, i.e., from what starting date through what ending date?

Now solve these questions again with Python.

Deliverables

  • Use R to explore the year and month (first ignore the days) in which the posting_date occurs.

  • Then find the months and dates in which the posting_date occurs (and now ignore the years).

  • What is the date range in which the posting_date values occur, i.e., from what starting date through what ending date?

  • Now solve these 3 questions again with Python.

Question 3 (2 points)

In the project6examples1, you learned how to read only a few columns from a data set in R. You learned a similar skill in project6examples4 in Python.

Using that knowledge, read in only the Date Received column from the data set /anvil/projects/tdm/data/consumer_complaints/complaints.csv. We studied this data during Project 2 and Project 6.

In R, plot the number of complaints per day.

The dates are steadily increasing over time, but there was one day in early 2025 with a ton of complaints. Which date was that?

Now solve the same two questions again, using Python.

Deliverables

  • In R, plot the number of complaints per day.

  • The dates are steadily increasing over time, but there was one day in early 2025 with a ton of complaints. Which date was that?

  • Now solve the same two questions again, using Python.

Question 4 (2 points)

We studied the Zillow state time series data /anvil/projects/tdm/data/zillow/State_time_series.csv in Projects 5 and 6.

The number of rows per year has remained relatively constant over time (not exactly constant, but relatively constant). What is the reason for this? Please give two or three sentences of explanation in English.

You can solve this question in R or Python; you do not need to solve it with both tools.

Deliverables

  • Why are the number of rows per year remaining relatively constant over time?

Question 5 (2 points)

Do more people go out during the week or on the weekends?

Use the Green Taxi Cab data, which we also studied in Project 5: /anvil/projects/tdm/data/taxi/green/green_tripdata_2019-06.csv and either R or Python to justify your claim. Please give two or three sentences of explanation in English.

You can solve this question in R or Python; you do not need to solve it with both tools.

The wday function from lubridate might help you, if you solve this question in R. The .dt.dayofweek might help you, if you solve this question in Python.

Deliverables

  • Do more people go out during the week or on the weekends?

Submitting your Work

Once you have completed the questions, save your Jupyter notebook. You can then download the notebook and submit it to Gradescope.

Items to submit

  • firstname_lastname_project10.ipynb

You must double check your .ipynb after submitting it in gradescope. A very common mistake is to assume that your .ipynb file has been rendered properly and contains your code, markdown, and code output even though it may not. Please take the time to double check your work. See here for instructions on how to double check this.

You will not receive full credit if your .ipynb file does not contain all of the information you expect it to, or if it does not render properly in Gradescope. Please ask a TA if you need help with this.