The Greatest Crossover Event in the History of STA199 Labs

This data was originally collected for a FiveThirtyEight article. The version of the avengers data we will work with here can be uploaded from the avengers.csv file.

This dataset includes information about characters across the entire Marvel Cinematic Universe (MCU), so some of the names will be familiar if you are a fan of the films or comics. Don’t worry if you aren’t a Marvel fan; no background knowledge is needed to successfully complete this lab!

We will focus on the following variables in this lab:

Header	Definition
name	The full name or alias of the character
appearances	The number of comic books that character appeared in as of April 30
current	Is the member currently active on an avengers affiliated team?
gender	The recorded gender of the character
probationary	Sometimes the character was given probationary status as an Avenger, this is the date that happened
full_reserve	The month and year the character was introduced as a full or reserve member of the Avengers
year	The year the character was introduced as a full or reserve member of the Avengers
years_since_joining	2015 minus the year
death1	Yes if the Avenger died, No if not.
return1	Yes if the Avenger returned from their first death, No if they did not, blank if not applicable

See FiveThirtyEight’s GitHub repo for the full codebook.

1. You are interested in creating a data frame with the most “classic” Avengers. Use filter to make a new data frame that only includes Avengers that were 1) created in 1970 or earlier and 2) were not given probationary status. Assign the new data frame as classic_avengers. Confirm that once you have filtered, you are left with a data frame with 27 observations.

2. Who are the newest classic Avengers? Using a single pipeline: Create a new variable called years_served that represents the number of years served as of 2021.. (Hint: you can use either the year variable or years_since_joining variables to do this.) Then, arrange the dataset in ascending order of years_served. Lastly, select the name and years_served and display the first three rows.
   • Report the names of the three newest classic Avengers and how long they have served in your narrative.
3. Has the percentage of female Avengers changed over time? To explore this question, find the percentage of female Avengers in the classic_avengers dataset and compare it to the percentage of females among all Avengers. What do you conclude based on these results?

4. Sort the full avengers dataset in descending order of appearances and display only the columns name, appearances, death1, and return1 for the top five observations.
   • What do you observe about these Avengers in terms of deaths and returns?
5. Use the full avengers dataset to examine the mean and median number of appearances for Avengers who have died at least once compared to those who have not died at least once.
   • What do you learn about Marvel movies from your results?
   • What do the mean and median tell you about the distribution of appearances for these two groups?

6. Who’s more popular - the new or classic Avengers? Calculate the mean number of appearances for each introduction year (year). Then create a scatterplot of the mean appearances by introduction year. Do not include observations with the introduction year 1900, as “1900” indicates the value of full_reserve is missing.
   • What are 2 - 3 observations you have about the mean appearances based on introduction year?