# The Difference Between merge() vs. join() in R

In R, combining and reshaping data are indispensable operations. Two prevalent methods for combining data frames are the merge() function from base R and the join() functions from the dplyr package. While both are designed to combine datasets, they have distinct differences in syntax, functionality, and use cases.

### 1. Introduction

The merge() function in base R and the suite of join() functions in the dplyr package are crucial for integrating datasets. They each serve to combine two data frames based on a common variable (key), but they offer different options, syntax, and are part of different ecosystems within R.

### 2. Overview of merge( ) in Base R

#### Syntax:

merge(x, y, by.x = "ID", by.y = "ID", all = FALSE)
• x and y: The data frames to be merged.
• by.x and by.y: The column names in the data frames x and y to merge on.
• all: Logical. If FALSE, it performs an inner join, if TRUE, it performs an outer join, returning all rows.

#### Usage:

data1 <- data.frame(ID = c(1, 2, 3), Value = c("A", "B", "C"))
data2 <- data.frame(ID = c(2, 3, 4), Score = c(80, 90, 70))
merged_data <- merge(data1, data2, by = "ID")
print(merged_data)

#### Output:

  ID Value Score
1  2     B    80
2  3     C    90

### 3. Overview of join( ) in dplyr

dplyr offers a suite of join() functions, each serving a different purpose.

#### Syntax:

left_join(x, y, by = "ID")
• x and y: The data frames to be joined.
• by: The column name to join by.

#### Usage:

library(dplyr)
joined_data <- left_join(data1, data2, by = "ID")
print(joined_data)

#### Output:

  ID Value Score
1  1     A    NA
2  2     B    80
3  3     C    90

### 4. Different join( ) Functions in dplyr

• inner_join(): Returns only the rows with matching keys in both data frames.
• left_join(): Returns all rows from the left data frame and the matched rows from the right data frame.
• right_join(): Returns all rows from the right data frame and the matched rows from the left data frame.
• full_join(): Returns all rows when there is a match in either the left or the right data frames.
• semi_join(): Returns all rows from the left data frame where there are matching values in the right data frame.
• anti_join(): Returns all rows from the left data frame where there are not matching values in the right data frame.

### 5. Detailed Comparison

#### 5.1 Ecosystem:

• merge(): Part of base R, does not require additional libraries.
• join(): Part of the dplyr package, requiring the installation and loading of the package.

#### 5.2 Syntax and Functionality:

• merge() uses a more general and flexible syntax, which can be more verbose.
• join() functions have more specialized syntax, making them more concise and readable.

#### 5.3 Performance:

• dplyr‘s join() functions are typically more efficient and faster on larger datasets compared to the merge() function, due to dplyr‘s optimized C++ backend.

#### 5.4 Default Behavior:

• merge() performs an inner join by default.
• left_join() is typically the default go-to in the dplyr package, preserving all rows of the left data frame.

### 6. Choosing the Right Function

The choice between merge() and join() depends on the user’s specific needs, familiarity, and the complexity of the task at hand.

• Use merge() when:
• You are working in an environment with only base R available.
• You need a versatile function and do not mind a more verbose syntax.
• Use join() functions when:
• You are already working with the tidyverse ecosystem.
• You need more specialized, concise, and readable syntax.
• You are working with large datasets and need optimized performance.

### 7. Practical Examples

#### Using merge():

data1 <- data.frame(ID = c(1, 2, 3), Value = c("A", "B", "C"))
data2 <- data.frame(ID = c(2, 3, 4), Score = c(80, 90, 70))

merged_data <- merge(data1, data2, by = "ID", all = TRUE)

#### Using join():

library(dplyr)

joined_data <- full_join(data1, data2, by = "ID")
While merge() and join() functions serve to combine data frames in R, their applications are quite diverse. The merge() function is a versatile tool in base R, suitable for various joining tasks with a generalized syntax. In contrast, the join() functions from the dplyr package offer a more specialized, concise approach with optimized performance, especially advantageous in the tidyverse ecosystem.
Understanding the differences and the appropriate application of each is crucial for effective data manipulation and analysis in R. Whether you choose merge() or one of the join() functions will depend on your specific use case, the libraries you are working with, and your personal preference or comfort with the syntax.