R data frame objects can be joined together with the dplyr function inner_join(). Corresponding rows with a matching column value in each data frame are combined into one row of a new data frame, and non-matching rows are dropped.

dplyr‘s inner_join() takes two data frames as arguments and returns a new data frame with the corresponding rows merged together. Non-matching rows from each data frame are dropped in the resulting data frame.

For example, consider the sales and targets data frames of a t-shirt company. sales contains the monthly revenue for the company and has two columns: month and revenue. targets contains the goals for monthly revenue for each month and has two columns: month and target. To perform an inner join on the two data frames using dplyr:

sales_vs_targets <- sales %>%
  inner_join(targets)

inner_join() will use the month column as the column to match on, as both the sales and target data frames have a month column. The resultant data frame will only contain the matching rows from sales and targets.

Multiple data frames can be merged together at once by stringing multiple calls to inner_join with the pipe %>%.

For example, consider the same sales and targets data frames of a t-shirt company. An additional data frame small_medium_large contains the number of small, medium and large t-shirts sold per month and has four columns: month, small, medium, and large. To perform an inner join on the three data frames using dplyr:

sales_vs_targets <- sales %>%
  inner_join(targets) %>%
  inner_join(small_medium_large)

inner_join() will use the month column as the column to match on, as the sales, target, and small_medium_large data frames have a month column. The resultant data frame will only contain the matching rows from sales, targets, and small_medium_large.