The correct answer is: A. left

The plyr package uses a left join by default. This means that all rows from the left-hand dataset will be included in the output, even if there is no matching row in the right-hand dataset. Any rows in the right-hand dataset that do not have a match in the left-hand dataset will be included in the output with NA values for the columns from the left-hand dataset.

A right join would include all rows from the right-hand dataset, even if there is no matching row in the left-hand dataset. Any rows in the left-hand dataset that do not have a match in the right-hand dataset would be included in the output with NA values for the columns from the right-hand dataset.

A full join would include all rows from both datasets, even if there is no match between the two datasets. Any rows that do not have a match would be included in the output with NA values for the columns from the other dataset.

Here is an example of a left join using the plyr package:

“`r
library(plyr)

Create two data frames

df1 <- data.frame(x = c(1, 2, 3), y = c(4, 5, 6))
df2 <- data.frame(x = c(2, 3, 4), z = c(7, 8, 9))

Join the data frames using a left join

df <- left_join(df1, df2)

Print the results

print(df)
“`

Output

x y z
1 1 4 NA
2 2 5 7
3 3 6 NA

As you can see, the first two rows of df1 were included in the output, even though there was no matching row in df2. The third row of df1 was not included in the output, because there was no matching row in df2. The first two rows of df2 were included in the output, even though there was no matching row in df1. The third row of df2 was included in the output, because there was a matching row in df1.