How to join (merge) data frames (inner, outer, left, right)

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Given two data frames:

df1 = data.frame(CustomerId = c(1:6), Product = c(rep("Toaster", 3), rep("Radio", 3)))
df2 = data.frame(CustomerId = c(2, 4, 6), State = c(rep("Alabama", 2), rep("Ohio", 1)))

df1
#  CustomerId Product
#           1 Toaster
#           2 Toaster
#           3 Toaster
#           4   Radio
#           5   Radio
#           6   Radio

df2
#  CustomerId   State
#           2 Alabama
#           4 Alabama
#           6    Ohio

How can I do database style, i.e., sql style, joins? That is, how do I get:

  • An inner join of df1 and df2:
    Return only the rows in which the left table have matching keys in the right table.
  • An outer join of df1 and df2:
    Returns all rows from both tables, join records from the left which have matching keys in the right table.
  • A left outer join (or simply left join) of df1 and df2
    Return all rows from the left table, and any rows with matching keys from the right table.
  • A right outer join of df1 and df2
    Return all rows from the right table, and any rows with matching keys from the left table.

Extra credit:

How can I do a SQL style select statement?

r-faq r
13 Answers

There is the data.table approach for an inner join, which is very time and memory efficient (and necessary for some larger data.frames):

library(data.table)

dt1 <- data.table(df1, key = "CustomerId") 
dt2 <- data.table(df2, key = "CustomerId")

joined.dt1.dt.2 <- dt1[dt2]

merge also works on data.tables (as it is generic and calls merge.data.table)

merge(dt1, dt2)

data.table documented on stackoverflow:
How to do a data.table merge operation
Translating SQL joins on foreign keys to R data.table syntax
Efficient alternatives to merge for larger data.frames R
How to do a basic left outer join with data.table in R?

Yet another option is the join function found in the plyr package

library(plyr)

join(df1, df2,
     type = "inner")

#   CustomerId Product   State
# 1          2 Toaster Alabama
# 2          4   Radio Alabama
# 3          6   Radio    Ohio

Options for type: inner, left, right, full.

From ?join: Unlike merge, [join] preserves the order of x no matter what join type is used.

user_7424470

Update join. One other important SQL-style join is an "update join" where columns in one table are updated (or created) using another table.

Modifying the OP's example tables...

sales = data.frame(
  CustomerId = c(1, 1, 1, 3, 4, 6), 
  Year = 2000:2005,
  Product = c(rep("Toaster", 3), rep("Radio", 3))
)
cust = data.frame(
  CustomerId = c(1, 1, 4, 6), 
  Year = c(2001L, 2002L, 2002L, 2002L),
  State = state.name[1:4]
)

sales
# CustomerId Year Product
#          1 2000 Toaster
#          1 2001 Toaster
#          1 2002 Toaster
#          3 2003   Radio
#          4 2004   Radio
#          6 2005   Radio

cust
# CustomerId Year    State
#          1 2001  Alabama
#          1 2002   Alaska
#          4 2002  Arizona
#          6 2002 Arkansas

Suppose we want to add the customer's state from cust to the purchases table, sales, ignoring the year column. With base R, we can identify matching rows and then copy values over:

sales$State <- cust$State[ match(sales$CustomerId, cust$CustomerId) ]

# CustomerId Year Product    State
#          1 2000 Toaster  Alabama
#          1 2001 Toaster  Alabama
#          1 2002 Toaster  Alabama
#          3 2003   Radio     <NA>
#          4 2004   Radio  Arizona
#          6 2005   Radio Arkansas

# cleanup for the next example
sales$State <- NULL

As can be seen here, match selects the first matching row from the customer table.

Update join with multiple columns. The approach above works well when we are joining on only a single column and are satisfied with the first match. Suppose we want the year of measurement in the customer table to match the year of sale.

As @bgoldst's answer mentions, match with interaction might be an option for this case. More straightforwardly, one could use data.table:

library(data.table)
setDT(sales); setDT(cust)

sales[, State := cust[sales, on=.(CustomerId, Year), x.State]]

#    CustomerId Year Product   State
# 1:          1 2000 Toaster    <NA>
# 2:          1 2001 Toaster Alabama
# 3:          1 2002 Toaster  Alaska
# 4:          3 2003   Radio    <NA>
# 5:          4 2004   Radio    <NA>
# 6:          6 2005   Radio    <NA>

# cleanup for next example
sales[, State := NULL]

Rolling update join. Alternately, we may want to take the last state the customer was found in:

sales[, State := cust[sales, on=.(CustomerId, Year), roll=TRUE, x.State]]

#    CustomerId Year Product    State
# 1:          1 2000 Toaster     <NA>
# 2:          1 2001 Toaster  Alabama
# 3:          1 2002 Toaster   Alaska
# 4:          3 2003   Radio     <NA>
# 5:          4 2004   Radio  Arizona
# 6:          6 2005   Radio Arkansas

The three examples above all focus on creating/adding a new column. See the related R FAQ for an example of updating/modifying an existing column.

user_11912590
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