Join Special Cases

Joining data frames is a common task in data analysis and manipulation, but it can also present some special cases that we need to be mindful of and handle as appropriate for the purpose at hand.

In this section, we will discuss some of these special cases, including:

This section is complemented by:

  • Join Types: where we cover how to specify the join type.
  • Join Columns: where we cover how to specify join columns.

Duplicate Column Names

We wish to rename columns that have the same name in both data frames being joined.

Suffix

We wish to add a suffix per data frame to columns with the same name in both data frames.

In this example, we wish to rename col_2 coming from df_1 to col_2_1 and rename col_2 coming from df_2 to col_2_2.

df = df_1%>% inner_join(df_2, by="col_1", suffix=c("_1", "_2"))

Here is how this works:

  • We use the inner_join() function from the dplyr package to combine df_1 and df_2 based on the common column col_1.
  • By default, inner_join() function will use .x and .y as suffixes to resolve duplicate column names. We can change these suffixes by passing a vector of suffixes ("_1", "_2") in this example.
  • The same applies to other join types. See Join Types.
  • The output data frame df will contain the rows that have the same value for col_1 in both df_1 and df_2 with three columns col_1, col_2_1, and col_2_2.

Rename

We wish to rename columns with the same name in both data frames.

In this example, we wish to rename col_3 coming from df_1 to col_2 and rename col_4 coming from df_2 to col_3 and col_5 to col_4.

df = df_1%>%
  rename(col_2 = col_3)%>%
  inner_join(df_2%>% rename(col_3 = col_4, col_4 = col_5), by = "col_1")

Here is how this works:

  • We use the inner_join() function from the dplyr package to combine df_1 and df_2 based on the common columns col_1 and col_2.
  • We use the function rename() from dplyr to change column names from their current values to the desired values.
  • To rename columns, rename() expects a mapping between the current column names and the desired column names of the form desired_name = current_name which in this case is col_3 = col_4 for df_1 and col_3 = col_4, col_4 = col_5 for df_2.
  • We only need to include mapping for the columns that we wish to rename. Columns that are not mentioned in the call to rename() will retain their current names.
  • See Renaming for coverage of different renaming scenarios.
  • The same applies to other join types. See Join Types.

Alternative: Rename After Join

df = df_1 %>%
  inner_join(df_2, by = "col_1") %>%
  rename(col_2 = col_3.x, col_3 = col_3.y, col_4 = col_5)

Here is how this works:

  • This works similarly to the primary solution except we rename the columns after performing the join operation.
  • The dplyr join functions automatically add .x for columns coming from df_1 and .y suffix for columns coming from df2 if they have the same name.
  • The same applies to other join types. See Join Types.

Duplicate Join Values

We wish to join two data frames and handle duplicate values in the join columns by removing them. If the desired output is a Cartesian product, then there is no need to remove duplicate values.

In this example, we wish to drop rows with duplicate values in col_1 in df_1 and then perform the join operation.

df = df_1%>%
  distinct(col_1, .keep_all = TRUE)%>%
  inner_join(df_2)

Here is how this works:

  • We use the distinct() function to drop rows with duplicate values from the column col_1 of the data frame df_1. See Uniqueness.
  • We set .keep_all = TRUE to keep all columns from df_1 after selecting distinct rows based on col_1.
  • We pass the subset of columns we wish to use to check for duplicates, col_1 in this example.
  • The output data frame df will contain the rows that have the same value for col_1 in both df_1 and df_2 (except the second row from df_1) and all columns in both data frames with only one col_1.

Missing Join Values

We wish to join two data frames with missing values in the join columns. NA values should not be matched because NA values are not comparable. That said, we should be mindful that ignoring NA values will have different outcomes based on the join type.

  • Inner Join: Any rows with missing values in the join column will be excluded.
  • Left Join: The resulting data frame will include all rows from the left data frame, along with any matching rows from the right data frame. Rows with missing values in the join column will be included in the resulting data frame and will have NA values for the columns from the right data frame.
  • Right Join: Similar to left join but rows with missing values in the join column will be included in the resulting data frame and will have NA values for the columns from the left data frame.
  • Full Join: The resulting data frame will include all rows from both data frames. Rows with no matching or missing values in the join column will be included in the resulting data frame and will have NA values for the columns from the other data frame.

In this example, we wish to ignore rows with NA values in the join column col_1.

df = df_1%>% inner_join(df_2, na_matches = "never")

Here is how this works:

  • We use the inner_join() function from the dplyr package to combine df_1 and df_2 based on the common column col_1.
  • By default, inner_join() treats two NA or NaN values as equal. We set na_matches=”never” to specify that two NA or NAN values should be treated as different.
  • The output data frame df will contain the rows that have the same value for col_1 in both df_1 and df_2 excluding any rows with NA values in col_1 and all columns in both data frames with only one col_1.
  • The same applies to other join types. See Join Types.

Different Join Data Types

We wish to join two data frames on a common column with a different data type.

In this example, we wish to join df_1 and df_2 on col_1 which is of type integer in df_1 and of type string in df_2.

df = df_1%>% inner_join(df_2%>% mutate(col_1 = as.integer(col_1)))

Here is how this works:

We cast col_1 to integer before joining using as.integer(col_1) function. See Type Casting.

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I/O