Assume that you have created the variable bacon correctly (from the previous question). You want to create a column, called bacon_number, which is equal to 1 for each actor who has ever appeared in a movie with Kevin Bacon in Actors data frame. You want bacon_number to be equal to 0 for Kevin Bacon, and you want it to be equal to NA for any other observations. That means that this question does not ask to generate all possible Bacon numbers. It only focuses on Bacon number 0 (for Kevin Bacon) and Bacon number 1 (for those who have ever appeared in a movie with Kevin Bacon). For all others actors, Bacon number should be NA.

For example, Tom Cruise appeared with Kevin Bacon in the 1992 movie A Few Good Men, making Tom Cruise’s Bacon Number equal to 1. We want bacon_number to be equal to 1 for all observations for Tom Cruise, even in movies where he did not appear together with Kevin Bacon. For example, we would want to make sure that Tom Cruise’s bacon_number is still equal to 1 when he appears in Rain Man in 1988 or Edge of Tomorrow in 2014.

Recall the assumption that you have created the variable bacon successfully before. The following sequence of commands comes right after the previous question. That is, in your Actors data frame you have the 7th column called bacon and it is equal to 1 for every observations of Kevin Bacon and 0 otherwise. Also, recall that Kevin Bacon’s actor_ID is 10000001.

Which of the following series of commands will achieve the above task?

There is AT LEAST ONE correct option, but you MUST SELECT ALL correct option(s). There is no partial credit.

Note that if you put a negative sign (i.e., -) in front of a column name you specified in select(), it means that you want to select all the columns except the one indicated with a negative sign. For example, Actors %% select(-best_actor) is identical to Actors %% select( actor_ID, actor_l, actor_f, title, year, best_actress).

Question

Assume that you have created the variable bacon correctly (from the previous question). You want to create a column, called bacon_number, which is equal to 1 for each actor who has ever appeared in a movie with Kevin Bacon in Actors data frame. You want bacon_number to be equal to 0 for Kevin Bacon, and you want it to be equal to NA for any other observations. That means that this question does not ask to generate all possible Bacon numbers. It only focuses on Bacon number 0 (for Kevin Bacon) and Bacon number 1 (for those who have ever appeared in a movie with Kevin Bacon). For all others actors, Bacon number should be NA.

For example, Tom Cruise appeared with Kevin Bacon in the 1992 movie A Few Good Men, making Tom Cruise’s Bacon Number equal to 1. We want bacon_number to be equal to 1 for all observations for Tom Cruise, even in movies where he did not appear together with Kevin Bacon. For example, we would want to make sure that Tom Cruise’s bacon_number is still equal to 1 when he appears in Rain Man in 1988 or Edge of Tomorrow in 2014.

Recall the assumption that you have created the variable bacon successfully before. The following sequence of commands comes right after the previous question. That is, in your Actors data frame you have the 7th column called bacon and it is equal to 1 for every observations of Kevin Bacon and 0 otherwise. Also, recall that Kevin Bacon’s actor_ID is 10000001.

Which of the following series of commands will achieve the above task?

There is AT LEAST ONE correct option, but you MUST SELECT ALL correct option(s). There is no partial credit.

Note that if you put a negative sign (i.e., -) in front of a column name you specified in select(), it means that you want to select all the columns except the one indicated with a negative sign. For example, Actors %>% select(-best_actor) is identical to Actors %>% select( actor_ID, actor_l, actor_f, title, year, best_actress).

BlackTom AI · Accepted Answer

To tackle this question, we need to determine which sequences of dplyr commands correctly construct bacon_number such that: - bacon_number is 0 for Kevin Bacon (actor_ID 10000001), - bacon_number is 1 for actors who have appeared in a movie with Kevin Bacon, and - bacon_number is NA for all other actors. The approach in each option uses a two-step strategy: (a) compute a per-movie indicator of whether Kevin Bacon is in that movie by taking the max of bacon within each (title, year) group, and (b) propagate that signal to each actor by taking the max across that actor’s rows, then recode 0 to NA and set Kevin Bacon’s Bacon number to 0. Below I analyze each option in turn. Option 1: Actors <- Actors %>% group_by(title, year) %>% mutate(bacon_number_temp = max(bacon)) Actors <- Actors %>% group_by(actor_ID) %>% mutate(bacon_number = max(bacon_number_temp)) Actors$bacon_number[Actors$bacon_number == 0] <- NA Actors$bacon_number[Actors$actor_ID == 10000001] <- 0 Actors <- Actors %>% select(-bacon_number_temp) This works because: (1) bacon_number_temp is 1 for movies that include Kevin Bacon and 0 otherwise, calculated within each (title, year). (2) Taking max over each actor_id aggregates across all their movies, yielding 1 if they share any movie with Kevin Bacon, and 0 otherwise. (3) Replacing 0 with NA meets the requirement for non-connected actors, while explicitly setting Kevin Bacon’s row to 0 preserves the correct Bacon number for him. Finally, removing the temporary column cleans up the data. Overall, this option correctly implements the intended logic. Option 2: bacon_temp <- Actors %>% group_by(title, year) %>% summarize(bacon_number_temp = max(bacon)) data_temp <- left_join(Actors, bacon_temp) data_temp2 <- data_temp %>% group_by(actor_ID) %>% summarize(bacon_number = max(bacon_number_temp)) Actors <- left_join(data_temp, data_temp2) Actors$bacon_number[Actors$bacon_number == 0] <- NA Actors$bacon_number[Actors$actor_ID == 10000001] <- 0 Actors <- Actors %>% select(-bacon_number_temp) This option is also correct. It mirrors the first approach but uses a left_join workflow to bring the per-movie bacon flag into the main dataset, then aggregates per actor_ID to compute bacon_number. The final steps of replacing 0 with NA and assigning 0 to Kevin Bacon reproduce the required semantics, and the temporary per-movie column is removed. The logic is equivalent to Option 1, just implemented with joins instead of pure grouped mutate, so it yields the same correct result. Option 3: bacon_temp <- Actors %>% group_by(title, year) %>% mutate(bacon_number_temp = max(bacon)) data_temp <- left_join(Actors, bacon_temp) data_temp2 <- data_temp %>% group_by(actor_ID) %>% mutate(bacon_number = max(bacon_number_temp)) Actors <- left_join(data_temp, data_temp2) Actors$bacon_number[Actors$bacon_number == 0] <- NA Actors$bacon_number[Actors$actor_ID == 10000001] <- 0 Actors <- Actors %>% select(-bacon_number_temp) This variant also yields the correct outcome. Here, bacon_number_temp is computed per (title, year) and carried along via a left_join. Then, for each actor_ID, bacon_number is determined by taking the max across that actor’s rows. The subsequent left_join restores the bacon_number to the main data frame, and the final adjustments (0 -> NA and Kevin Bacon -> 0) are applied. The only potential concern could be column naming during the join, but since bacon_number exists in the joined result and bacon_number_temp is removed at the end, the pipeline remains coherent and correct. Option 4: Actors <- Actors %>% group_by(title, year) %>% mutate(bacon_number_temp = max(bacon)) Actors <- Actors %>% group_by(actor_ID) %>% mutate(bacon_number = max(bacon_number_temp)) Actors$bacon_number[Actors$bacon_number == 0] <- NA Actors$bacon_number[Actors$actor_ID == 10000001] <- 0 Actors <- Actors %>% select(-bacon_number_temp) This option is correct as well. It uses mutate in both grouping steps (first to create bacon_number_temp per movie, then to compute bacon_number per actor by taking the maximum across their rows). The post-processing steps are identical to the other options, yielding 0 for Kevin Bacon, 1 for anyone who shared a movie with him, and NA for others. The approach is a straightforward, consistent variant of the two-tier aggregation strategy. Option 5: All of the options are incorrect. This statement is not accurate, because Options 1, 2, 3, and 4 each implement a valid sequence of operations that achieves the described Bacon-number transformation. Therefore, this option is incorrect.

COMM_V 205 101 102 103 2025W1 Assignment B2 (R)

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