How To Create A Dataframe With Different Number Of Rows

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Merge Two Data Frames With Different Number of Rows in R

Merge Two Data Frames With Different Number of Rows in R

Created: May-16, 2021

Use the full_join Function to Merge Two R Data Frames With Different Number of Rows
Use the left_join Function to Merge Two R Data Frames With Different Number of Rows
Use the right_join Function to Merge Two R Data Frames With Different Number of Rows

This article will demonstrate multiple methods of merging two data frames with a different number of rows in R.

Use the `full_join` Function to Merge Two R Data Frames With Different Number of Rows

full_join is part of the dplyr package, and it can be used to merge two data frames with a different number of rows. The function takes data frames to be merged as the first two arguments and returns the same type of object as the first argument. This function can operate on data frame extension types like tibble or lazy data frames. full_join extracts all rows and columns from both data frame arguments. It fills elements with NA-s when there are no matching values.

              library(dplyr)  v1 <- c(1.1, 1.2, 1.3, 2.1, 2.2, 2.3) v2 <- c(11, 12, 13, 21, 22, 23)  df1 <- data.frame(v1, v2)  v1 <- c(9.1, 9.2, 9.3, 9.1, 9.2, 9.3, 9.3, 9.2, 9.1) v2 <- c(101, 102, 103, 201, 202, 203, 204, 403, 404) wday <- factor(c("Wed", "Thu", "Mon", "Wed", "Thu", "Fri", "Mon", "Tue", "Wed"))  df2 <- data.frame(v1, v2, wday)  dff <- df1 %>% full_join(df2) dff

                              v1  v2 wday 1  1.1  11 <NA> 2  1.2  12 <NA> 3  1.3  13 <NA> 4  2.1  21 <NA> 5  2.2  22 <NA> 6  2.3  23 <NA> 7  9.1 101  Wed 8  9.2 102  Thu 9  9.3 103  Mon 10 9.1 201  Wed 11 9.2 202  Thu 12 9.3 203  Fri 13 9.3 204  Mon 14 9.2 403  Tue 15 9.1 404  Wed

Use the `left_join` Function to Merge Two R Data Frames With Different Number of Rows

left_join is another method from the dplyr package. It takes arguments similar to the full_join function, but left_join extracts all rows from the first data frame and all columns from both of them.

              library(dplyr)  v1 <- c(1.1, 1.2, 1.3, 2.1, 2.2, 2.3) v2 <- c(11, 12, 13, 21, 22, 23)  df1 <- data.frame(v1, v2)  v1 <- c(9.1, 9.2, 9.3, 9.1, 9.2, 9.3, 9.3, 9.2, 9.1) v2 <- c(101, 102, 103, 201, 202, 203, 204, 403, 404) wday <- factor(c("Wed", "Thu", "Mon", "Wed", "Thu", "Fri", "Mon", "Tue", "Wed"))  df2 <- data.frame(v1, v2, wday)  dfl <- df1 %>% left_join(df2) dfl

Output:

                              v1 v2 wday 1 1.1 11 <NA> 2 1.2 12 <NA> 3 1.3 13 <NA> 4 2.1 21 <NA> 5 2.2 22 <NA> 6 2.3 23 <NA>

Use the `right_join` Function to Merge Two R Data Frames With Different Number of Rows

right_join works similar to the left_join function except extracting all rows from the second data frame argument rather than the first. The function also copies all columns from both data frames to a newly constructed object.

              library(dplyr)  v1 <- c(1.1, 1.2, 1.3, 2.1, 2.2, 2.3) v2 <- c(11, 12, 13, 21, 22, 23)  df1 <- data.frame(v1, v2)  v1 <- c(9.1, 9.2, 9.3, 9.1, 9.2, 9.3, 9.3, 9.2, 9.1) v2 <- c(101, 102, 103, 201, 202, 203, 204, 403, 404) wday <- factor(c("Wed", "Thu", "Mon", "Wed", "Thu", "Fri", "Mon", "Tue", "Wed"))  df2 <- data.frame(v1, v2, wday)  dfr <- df1 %>% right_join(df2) dfr

Output:

                              v1  v2 wday 1 9.1 101  Wed 2 9.2 102  Thu 3 9.3 103  Mon 4 9.1 201  Wed 5 9.2 202  Thu 6 9.3 203  Fri 7 9.3 204  Mon 8 9.2 403  Tue 9 9.1 404  Wed

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