Data merging is a process of combining two or more datasets into a single dataset based on one or more common variables. In R, there are several functions that can be used for data merging, such as merge(), jo

in(), and rbind().

As researched by  R Programming Assignment Help team Let’s consider an example where we have two datasets: “customer_data” and “order_data”. The “customer_data” dataset contains information about customers such as their ID, name, and email address. The “order_data” dataset contains information about orders made by customers such as order ID, order date, and customer ID.

Here’s how we can merge the two datasets using the merge() function in R:


# create customer_data and order_data datasets

customer_data <- data.frame(

  customer_id = c(1, 2, 3, 4),

  name = c(“John”, “Jane”, “Bob”, “Alice”),

  email = c(“”, “”, “”, “”)


order_data <- data.frame(

  order_id = c(101, 102, 103, 104),

  order_date = c(“2022-01-01”, “2022-01-02”, “2022-01-03”, “2022-01-04”),

  customer_id = c(1, 2, 3, 4)


# merge the two datasets

merged_data <- merge(customer_data, order_data, by = “customer_id”)

# print the merged dataset


In the above code, we first create the two datasets “customer_data” and “order_data” using the data.frame() function. We then use the merge() function to merge the two datasets based on the “customer_id” variable which is common to both datasets. The resulting merged dataset contains columns from both datasets with the common variable “customer_id” used as the key to merge the two datasets.

We can also merge datasets using other common variables, for example, we can merge the two datasets based on the “order_id” variable as follows:


# merge the two datasets based on the order_id variable

merged_data2 <- merge(customer_data, order_data, by.x = “customer_id”, by.y = “customer_id”)

# print the merged dataset


In this case, we specify the by.x and by.y arguments to merge the two datasets based on the “customer_id” variable in both datasets.

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Another way to merge datasets is by using the join() function from the dplyr package. As observed by Statistics Assignment Help team of experts, Here’s an example:



# merge the two datasets based on the customer_id variable

merged_data3 <- customer_data %>%

  inner_join(order_data, by = “customer_id”)

# print the merged dataset


In this case, we use the inner_join() function to merge the two datasets based on the “customer_id” variable.

Finally, we can also merge datasets by row using the rbind() function. Here’s an example:


# create a new order_data2 dataset with additional rows

order_data2 <- data.frame(

  order_id = c(105, 106),

  order_date = c(“2022-01-05”, “2022-01-06”),

  customer_id = c(1, 2)


# merge the two datasets by row

merged_data4 <- rbind(order_data, order_data2)

# print the merged dataset


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