See the documentation on ?merge, which states:
By default the data frames are merged on the columns with names they both have,
but separate specifications of the columns can be given by by.x and by.y.
This clearly implies that merge will merge data frames based on more than one column. From the final example given in the documentation:
x <- data.frame(k1=c(NA,NA,3,4,5), k2=c(1,NA,NA,4,5), data=1:5)
y <- data.frame(k1=c(NA,2,NA,4,5), k2=c(NA,NA,3,4,5), data=1:5)
merge(x, y, by=c("k1","k2")) # NA's match
This example was meant to demonstrate the use of incomparables, but it illustrates merging using multiple columns as well. You can also specify separate columns in each of x and y using by.x and by.y.
Hope this helps;
df1 = data.frame(CustomerId=c(1:10),
Hobby = c(rep("sing", 4), rep("pingpong", 3), rep("hiking", 3)),
Product=c(rep("Toaster",3),rep("Phone", 2), rep("Radio",3), rep("Stereo", 2)))
df2 = data.frame(CustomerId=c(2,4,6, 8, 10),State=c(rep("Alabama",2),rep("Ohio",1), rep("Cal", 2)),
like=c("sing", 'hiking', "pingpong", 'hiking', "sing"))
df3 = merge(df1, df2, by.x=c("CustomerId", "Hobby"), by.y=c("CustomerId", "like"))
Assuming df1$Hobby and df2$like mean the same thing.
You can also use the join command (dplyr).
For example:
new_dataset <- dataset1 %>% right_join(dataset2, by=c("column1","column2"))
Source: Stackoverflow.com