Suppose you have a subset of our usual North American stock market data loaded into comp data frame with 80,200 observations with all 50 columns. You executed: comp %% select(emp) %% summarise (ave_emp = mean(emp, na.rm = TRUE), number = sum(is.na(emp))) R gave the following output as a result: ave_emp number 8.291884 10,200 Then, you run the following code. comp %% select(emp) %% mutate(num = n()) %% summarise(min_num = min(num)) The following output was displayed. min_num _{______________ What is the redacted value above?

Question

Suppose you have a subset of our usual North American stock market data loaded into comp data frame with 80,200 observations with all 50 columns. You executed: comp %>% select(emp) %>% summarise (ave_emp = mean(emp, na.rm = TRUE), number = sum(is.na(emp))) R gave the following output as a result: ave_emp number 8.291884 10,200 Then, you run the following code. comp %>% select(emp) %>% mutate(num = n()) %>% summarise(min_num = min(num)) The following output was displayed. min_num _{______________ What is the redacted value above?

BlackTom AI · Accepted Answer

n() returns the number of rows in the current data frame (or group). In this pipeline, that means each row gets a value equal to the total number of rows after the select(), so min(num) will be the total observation count.

1) Relevant knowledge points and concepts
- dplyr::select() keeps the specified column(s) but preserves the number of rows.
- dplyr::mutate(num = n()) sets num to n(), where n() is the count of rows in the current data frame (or group); it does not count non-missing values, it counts rows.
- dplyr::summarise(min_num = min(num)) computes the minimum of the column num across all rows.

2) Complete reasoning process step by step
- The problem statement explicitly says comp has 80,200 observations (rows) and 50 columns.
- comp %>% select(emp) produces a data frame with 80,200 rows and one column emp.
- mutate(num = n()) computes n() = 80,200 and writes that same value into the new column num for every row.
- summarise(min_num = min(num)) computes the minimum value of the num column. Since every row contains 80,200, the minimum is 80,200.

3) Intermediate steps / calculations shown
- Start: n_rows(comp) = 80,200.
- After select(emp): rows = 80,200.
- mutate(num = n()): num_i = 80,200 for i = 1..80,200.
- summarise(min_num = min(num)) = min(80,200, 80,200, ..., 80,200) = 80,200.

4) Why the correct answer is correct
- n() counts rows (80,200), independent of how many NA values emp has. The earlier summarise that showed number = 10,200 was counting NA values via sum(is.na(emp)), which is a different quantity. That NA count does not affect n().

5) Why other options are incorrect
- 232362 and 100000 are not supported by any provided row counts and contradict the stated 80,200 observations.
- 10200 is the number of NA emp values (sum(is.na(emp))), not the total row count; n() does not compute NA counts.
- "Not enough information provided" is false because the prompt explicitly gives the total number of observations (80,200).

6) Conclusion
Therefore the redacted value is 80200, which matches the provided correct answer.

类似问题

na is a vector of numeric elements. You do not know how many elements na has. When you run the following, you are returned 1. sum(is.na(na)) If you execute the following, what would be the output? sum(is.na(na[which(is.na(na))] + na)) ==length(na)

Dharma has vector w in her active R session. There are no missing values in w. That is, there is no NA’s in w. She executed the following code: sum(w) == sum(w[which(w)]) R returned (outputted) TRUE on her R console. TRUE/FALSE: w is not a logical vector.

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