Suppose you have a data frame called df with 10 observations. One of the columns is a character column called ID. The first value of ID is "01", the second is "02", and so forth. The 10th observation for ID is "10".

You then run the following code:

df %%
mutate(ID_num = as.numeric(ID), ID_new = as.character(ID_num)) %%
filter(ID != ID_new)

How many rows are displayed (outputted) in the result?

Question

Suppose you have a data frame called df with 10 observations. One of the columns is a character column called ID. The first value of ID is "01", the second is "02", and so forth. The 10th observation for ID is "10".

You then run the following code:

df %>%
  mutate(ID_num = as.numeric(ID), ID_new = as.character(ID_num)) %>%
  filter(ID != ID_new)

How many rows are displayed (outputted) in the result?

BlackTom AI · Accepted Answer

The scenario involves a character column ID with values '01','02', ..., '10', and a dplyr pipeline that creates two new columns before filtering.

Option 1: '1' is used when considering the numeric conversion of '01' to 1 and then back to a character '1'. Since '01' becomes '1' after the numeric round-trip, the comparison ID != ID_new yields '01' != '1', which is TRUE, so that row remains. Therefore this option would not correspond to the final row count by itself.

Option 2: '2' follows the same logic as the first two rows: converting '02' to 2 and back to '2' gives a mismatch with the original '02'. Each such row remains, so this option underestimates or overestimates only if miscounted across all rows, but individually each non-'10' entry passes the filter.

Option 3: '5' would imply a misinterpretation of how many of the original IDs fail the filter; however, for IDs '03' through '09', numeric conversion yields '3' through '9', which do not match their original two-character forms, so those rows pass the filter. There is no single row that would single-handedly produce a count of exactly five unless multiple identical disagreements were miscounted, which is not how the filter behaves.

Option 4: '9' aligns with the actual filtering outcome. The only ID for which ID_num equals ID_new is the one that remains identical after conversion, and that occurs when ID is '10' because as.numeric('10') is 10 and as.character(10) is '10'. Therefore, the row with ID '10' is removed, while all other rows ('01' through '09') remain, yielding a total of nine rows after filtering.

Option 5: '10' would suggest that no rows were removed by the filter, but as explained, the row with ID '10' is eliminated because ID != ID_new evaluates to FALSE for that row. Hence this would be an incorrect expectation for the final count.

In summary, the filtering step eliminates exactly one observation—the one where the string ID equals its own numeric-then-string representation ('10'). All other IDs do not equal their numeric-then-string forms, so they pass through. The resulting number of displayed rows is nine, corresponding to the remaining observations after the filter.

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