The merge() function in base R and the various join() functions from the dplyr package can both be used to join two data frames together.
There are two main differences between these two functions:
1. The join() functions from dplyr tend to be much faster than merge() on extremely large data frames.
2. The join() functions from dplyr preserve the original order of rows in the data frames while the merge() function automatically sorts the rows alphabetically based on the column you used to perform the join.
The following example illustrates difference #2 in practice.
Example: The Difference Between merge() and join()
Suppose we have the following two data frames in R:
#define first data frame df1 <- data.frame(team=c('Mavs', 'Hawks', 'Spurs', 'Nets'), points=c(99, 93, 96, 104)) df1 team points 1 Mavs 99 2 Hawks 93 3 Spurs 96 4 Nets 104 #define second data frame df2 <- data.frame(team=c('Mavs', 'Hawks', 'Spurs', 'Nets'), assists=c(19, 18, 22, 25)) df2 team assists 1 Mavs 19 2 Hawks 18 3 Spurs 22 4 Nets 25
Suppose we use the merge() function in base R to perform a left join, using the ‘team’ column as the column to join on:
#perform left join using base R merge(df1, df2, by='team', all.x=TRUE) team points assists 1 Hawks 93 18 2 Mavs 99 19 3 Nets 104 25 4 Spurs 96 22
Notice that the rows are sorted in alphabetical order based on the values in the ‘team’ column.
Now suppose we use the left_join() function from dplyr to perform a left join, again using the ‘team’ column as the column to join on:
library(dplyr) #perform left join using dplyr left_join(df1, df2, by='team') team points assists 1 Mavs 99 19 2 Hawks 93 18 3 Spurs 96 22 4 Nets 104 25
Notice that the order of the rows match the original order of the rows in the data frame before performing the left join.
The following tutorials explain how to perform other common operations in R: