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

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Top 5 Answer for r - How to join (merge) data frames (inner, outer, left, right)

vote vote

98

By using the merge function and its optional parameters:

Inner join: merge(df1, df2) will work for these examples because R automatically joins the frames by common variable names, but you would most likely want to specify merge(df1, df2, by = "CustomerId") to make sure that you were matching on only the fields you desired. You can also use the by.x and by.y parameters if the matching variables have different names in the different data frames.

Outer join: merge(x = df1, y = df2, by = "CustomerId", all = TRUE)

Left outer: merge(x = df1, y = df2, by = "CustomerId", all.x = TRUE)

Right outer: merge(x = df1, y = df2, by = "CustomerId", all.y = TRUE)

Cross join: merge(x = df1, y = df2, by = NULL)

Just as with the inner join, you would probably want to explicitly pass "CustomerId" to R as the matching variable. I think it's almost always best to explicitly state the identifiers on which you want to merge; it's safer if the input data.frames change unexpectedly and easier to read later on.

You can merge on multiple columns by giving by a vector, e.g., by = c("CustomerId", "OrderId").

If the column names to merge on are not the same, you can specify, e.g., by.x = "CustomerId_in_df1", by.y = "CustomerId_in_df2" where CustomerId_in_df1 is the name of the column in the first data frame and CustomerId_in_df2 is the name of the column in the second data frame. (These can also be vectors if you need to merge on multiple columns.)

vote vote

85

I would recommend checking out Gabor Grothendieck's sqldf package, which allows you to express these operations in SQL.

library(sqldf)  ## inner join df3 <- sqldf("SELECT CustomerId, Product, State                FROM df1               JOIN df2 USING(CustomerID)")  ## left join (substitute 'right' for right join) df4 <- sqldf("SELECT CustomerId, Product, State                FROM df1               LEFT JOIN df2 USING(CustomerID)") 

I find the SQL syntax to be simpler and more natural than its R equivalent (but this may just reflect my RDBMS bias).

See Gabor's sqldf GitHub for more information on joins.

vote vote

72

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.

vote vote

64

You can do joins as well using Hadley Wickham's awesome dplyr package.

library(dplyr)  #make sure that CustomerId cols are both type numeric #they ARE not using the provided code in question and dplyr will complain df1$CustomerId <- as.numeric(df1$CustomerId) df2$CustomerId <- as.numeric(df2$CustomerId) 

Mutating joins: add columns to df1 using matches in df2

#inner inner_join(df1, df2)  #left outer left_join(df1, df2)  #right outer right_join(df1, df2)  #alternate right outer left_join(df2, df1)  #full join full_join(df1, df2) 

Filtering joins: filter out rows in df1, don't modify columns

semi_join(df1, df2) #keep only observations in df1 that match in df2. anti_join(df1, df2) #drops all observations in df1 that match in df2. 
vote vote

56

There are some good examples of doing this over at the R Wiki. I'll steal a couple here:

Merge Method

Since your keys are named the same the short way to do an inner join is merge():

merge(df1,df2) 

a full inner join (all records from both tables) can be created with the "all" keyword:

merge(df1,df2, all=TRUE) 

a left outer join of df1 and df2:

merge(df1,df2, all.x=TRUE) 

a right outer join of df1 and df2:

merge(df1,df2, all.y=TRUE) 

you can flip 'em, slap 'em and rub 'em down to get the other two outer joins you asked about :)

Subscript Method

A left outer join with df1 on the left using a subscript method would be:

df1[,"State"]<-df2[df1[ ,"Product"], "State"] 

The other combination of outer joins can be created by mungling the left outer join subscript example. (yeah, I know that's the equivalent of saying "I'll leave it as an exercise for the reader...")

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