TDM 10100: R Project 9 — 2024

Motivation: Knowing how to merge data frames in R truly opens up a lot of functionality to us, and allows us to design a more comprehensive analysis of our data sets.

Context: The merge function allows us to combine information from multiple data tables.

Scope: merging tables

Learning Objectives:

  • Learn how to merge data frames

Make sure to read about, and use the template found here, and the important information about project submissions here.

Dataset(s)

This project will use the following dataset(s):

  • /anvil/projects/tdm/data/icecream/combined/products.csv (ice cream products)

  • /anvil/projects/tdm/data/icecream/combined/reviews.csv (ice cream reviews)

  • /anvil/projects/tdm/data/flights/subset/airports.csv (information about airports)

  • /anvil/projects/tdm/data/flights/subset/1990.csv (flight data from 1990)

Questions

Question 1 (2 pts)

Read in the ice cream products and reviews files into two separate data frames, as follows:

library(data.table)
productsDF <- fread("/anvil/projects/tdm/data/icecream/combined/products.csv")
reviewsDF <- fread("/anvil/projects/tdm/data/icecream/combined/reviews.csv")

Notice that these two data frames have three columns in common, namely, brand, key, and ingredients. If we try to merge these two data frames by default, R will try to match data on all three columns, BUT the ingredients column has a different role in these two data frames. In the productsDF, the ingredients column has a list of the ice cream ingredients. In the ratingsDF, the ingredients column has values between 1 and 5, or an NA value.

For this reason, when we merge the information from the two tables, we only want to merge the data according to the brand and key values, as follows:

newmergedDF <- merge(productsDF, reviewsDF, by = c("brand", "key") )

Notice that the productsDF has 8 columns, and the reviewsDF has 13 columns, and the newmergedDF has 19 columns, namely, all of the columns from both of the previous two data frames, including two separate ingredients columns, one from each data frame.

In this data frame, there are 978 rows that correspond to the name being "Chocolate Chip Cookie Dough", which has brand == "bj" and key == "16_bj". We can get this subset of the data as follows:

ChocolateChipCookieDoughDF <- subset(newmergedDF, (brand == "bj") & (key == "16_bj"))

Notice that this new data frame called ChocolateChipCookieDoughDF has 978 rows.

What are the average number of stars for the 978 reviews in the data frame called ChocolateChipCookieDoughDF?

Deliverables

  • Print the average number of stars for the 978 reviews in the data frame called ChocolateChipCookieDoughDF.

Question 2 (2 pts)

Starting with the newmergedDF, find the average number of stars for the reviews of ice cream with name == "Half Baked\302\256".

There are two characters that you will not see but they are there. They are encoded as "\302\256".
Deliverables

  • Print the average number of stars for the reviews of ice cream with name == "Half Baked\302\256".

Question 3 (2 pts)

In Project 2, we learned about the grep and grepl functions. Using either of these two functions, you can limit attention to ice cream products that have "Chocolate" in the title. (There are 49 such ice cream products.) Find the average number of stars for all 4831 of the reviews of these products that have "Chocolate" in the product name.

Deliverables

  • Print the average number of stars for all 4831 of the reviews of these products that have "Chocolate" in the product name.

Question 4 (2 pts)

Read in the information about airports, and also the flight data from 1990, into two separate data frames, as follows:

library(data.table)
airportsDF <- fread("/anvil/projects/tdm/data/flights/subset/airports.csv")
flightdataDF <- fread("/anvil/projects/tdm/data/flights/subset/1990.csv")

It is necessary to have 2 cores in your Jupyter Lab session for Question 4 and Question 5.

Do not worry about the warning message from the fread function when you read in the airportsDF data.

These two data frames do not have any columns in common! Nonetheless, the "iata" values from the airportsDF are the three-letter codes corresponding to airports, which are also found in the Origin and Dest columns in the flightdataDF. So when we merge the information from the two tables, if we want to study where the flights depart, then we only want to merge the data according to the iata value (from the airportsDF) merged with the Origin value (from the flightdataDF), as follows:

mybigDF <- merge(airportsDF, flightdataDF, by.x = "iata", by.y = "Origin")

Using this new data frame, find the average departure delay for all flights that have Origin airport in Indiana.

Deliverables

  • Print the average departure delay for all flights that have Origin airport in Indiana.

Question 5 (2 pts)

Using the same data frame from Question 4, find the average departure delay for all flights that have Origin airport in Houston, Texas.

Deliverables

  • Print the average departure delay for all flights that have Origin airport in Houston, Texas.

Submitting your Work

Now you are familiar with the method of merging data from multiple data frames.

Items to submit

  • firstname_lastname_project9.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, comments (in markdown or with hashtags), and code output, even though it may not. Please take the time to double check your work. See the instructions on how to double check your submission.

You will not receive full credit if your .ipynb file submitted in Gradescope does not show all of the information you expect it to, including the output for each question result (i.e., the results of running your code), and also comments about your work on each question. Please ask a TA if you need help with this. Please do not wait until Friday afternoon or evening to complete and submit your work.