Suppose df has 30 rows with 3 numeric columns named d, e, and f. When you executed length(which(df$d = 5)) the output returned is 20. Then, you executed df % mutate(d = if_else(d = 5, 5, 0)) TRUE/FALSE: When you execute the following line of code, the result returned can be less than 10. length(which(df$d == 0))

Question

Suppose df has 30 rows with 3 numeric columns named d, e, and f. When you executed

length(which(df$d >= 5))

the output returned is 20.

Then, you executed

df <- df %>%
   mutate(d = if_else(d >= 5, 5, 0))

TRUE/FALSE: When you execute the following line of code, the result returned can be less than 10.

length(which(df$d == 0))

BlackTom AI · Accepted Answer

We start by restating the setup to ensure it's clear: - There are 30 rows and three numeric columns, with d being the column of interest. - Initially, length(which(df$d >= 5)) returned 20, so there are 20 rows where d >= 5 and 10 rows where d < 5. - Then we mutate d to be 5 when d >= 5, and 0 otherwise: df <- df %>% mutate(d = if_else(d >= 5, 5, 0)). Now, analyze each answer option: Option 1: True. The statement asks whether the result of length(which(df$d == 0)) can be less than 10 after the mutation. - After the mutation, every original d >= 5 becomes 5, and every original d < 5 becomes 0. Therefore, exactly the rows that previously had d < 5 will now have d == 0. - Since there were 10 rows with d < 5 before the mutation (because 20 had d >= 5), there will be exactly 10 rows with d == 0 afterward, assuming no missing values alter the count. - Consequently, length(which(df$d == 0)) will be 10, not less than 10. This makes the statement "can be less than 10" false under the common assumption of no NAs. Option 2: False. Under standard circumstances with no missing values in d, the result cannot be less than 10 after the mutation. - Since 20 observations satisfied d >= 5 originally, 10 did not. The mutation maps all d >= 5 to 5 and all others to 0, so the count of zeros should mirror the original count of values < 5, which is 10. - Therefore, the length is exactly 10, not something smaller, assuming d had no NAs to interfere with the which() counting. Consider potential edge cases that could affect the conclusion: - If there were NA values in df$d before or after the mutation, the behavior of if_else and which() could alter counts. with NA values, some results might be NA or excluded depending on the exact handling and the version of dplyr. If you have NA values in d, the count of zeros could be less predictable and potentially not equal to 10. - If the data frame contained non-numeric or unexpected values, the comparison d >= 5 could yield different truth values for those rows, though the provided initial condition (20 such rows) constrains the typical outcome. In the typical, clean scenario (no NAs, numeric d, and standard if_else behavior), the mutation yields exactly 10 zeros, so the statement that the length can be less than 10 is not correct.

COMM_V 205 101 102 103 2025W1 Exam B2 (R) 2025W1 Final - Requires Respondus LockDown Browser

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