we have data from multiple sources. To perform an analysis, we need to merge two dataframes together with one or more common key variables.
A full match returns values that have a counterpart in the destination table. The values that are not match won't be return in the new data frame. The partial match, however, return the missing values as NA.
We will see a simple inner join. The inner join keyword selects records that have matching values in both tables. To join two datasets, we can use merge() function. We will use three arguments :
merge(x, y, by.x = x, by.y = y)
Arguments: -x: The origin data frame -y: The data frame to merge -by.x: The column used for merging in x data frame. Column x to merge on -by.y: The column used for merging in y data frame. Column y to merge on
It is not surprising that two dataframes do not have the same common key variables. In the full matching, the dataframe returns only rows found in both x and y data frame. With partial merging, it is possible to keep the rows with no matching rows in the other data frame. These rows will have NA in those columns that are usually filled with values from y. We can do that by setting all.x= TRUE.
For instance, we can add a new producer, Lucas, in the producer data frame without the movie references in movies data frame. If we set all.x= FALSE, R will join only the matching values in both data set. In our case, the producer Lucas will not be join to the merge because it is missing from one dataset.
Let's see the dimension of each output when we specify all.x= TRUE and when we don't.
# Create a new producer
add_producer <- c('Lucas', 'US')
# Append it to the ` producer` dataframe
producers <- rbind(producers, add_producer)
# Use a partial merge
m3 <-merge(producers, movies, by.x = "surname", by.y = "name", all.x = TRUE)