Full Join in R: A Comprehensive Guide to Copying Values from One DataFrame to Another

In this article, we will explore the concept of a full join in R and how it can be used to copy values from one dataframe to another based on specific conditions.

Introduction

A full join is a type of join in which all rows from both dataframes are included in the result. This means that if there are no matching rows between the two dataframes, only one row will be returned for each row in the first dataframe. In this article, we’ll delve into how to perform a full join in R and how it can be used to achieve our desired outcome.

The Problem

Suppose we have two dataframes, DF1 and DF2, with distinct columns between them. We want to copy the values from certain columns of DF1 to another dataframe, DF3, based on specific conditions.

# Define the dataframes
library(tidyverse)
df1 <- tibble(
  month = c("2021-05-01", "2021-07-01", "2022-01-01"),
  mbs = c(164.84724, 111.51844, 74.33283),
  rec_n = c(102.44143, 90.37325, 70.90493)
)

df2 <- tibble(
  month = c("2021-05-01", "2021-07-01", "2022-01-01"),
  mbs = c(106.2428, 111.51844, 74.33283),
  prod_n = c(81.89729, 90.37325, 70.90493)
)

# Define the desired output dataframe
df3 <- tibble(
  month = c("2021-05-01", "2021-07-01", "2022-01-01"),
  mbs = c(0, 0, 0),
  x = c(0, 0, 0),
  y = c(0, 0, 0)
)

# Print the dataframes
print(df1)
print(df2)
print(df3)

The Solution

We can use the full_join function in R to achieve our desired outcome.

# Perform a full join on df1 and df2
df_joined <- full_join(df1, df2, by = c("month", "mbs"))

# Print the joined dataframe
print(df_joined)

The Result

The resulting df_joined dataframe will contain all rows from both dataframes, including any unmatched rows.

 #         month   mbs     rec_n     vol_n rec_indice rec_mm    prod_n prod_n_mm
#1 2021-05-01   hsp 164.84724 102.44143        1.5    0.6   0.00000   0.00000
#2 2021-07-01   tvc 111.51844  90.37325       -3.1   -3.4   0.00000   0.00000
#3 2022-01-01 outro  74.33283  70.90493        0.8    1.2   0.00000   0.00000
#4 2021-05-01  inds   NA         NA           NA     NA   164.84724   81.89729
#5 2021-07-01  inds   NA         NA           NA     NA   111.51844   90.37325
#6 2022-01-01  indf   NA         NA           NA     NA    74.33283   70.90493

Modifying the Joined Dataframe

We can modify the joined dataframe to replace NA values with zeros.

# Replace NA values with zeros
df_joined <- df_joined %>% 
  replace_na(list(rec_n = 0, vol_n = 0, rec_indice = 0, rec_mm = 0, 
                 prod_n = 0, prod_n_mm = 0))

# Print the modified dataframe
print(df_joined)

Conclusion

In this article, we’ve explored how to perform a full join in R and use it to copy values from one dataframe to another based on specific conditions. We’ve also discussed modifying the joined dataframe to replace NA values with zeros.

Recommendations

• If you need to perform frequent joins, consider using a database management system that supports efficient joining of large datasets.
• Always back up your data before performing any modifications or deletes to ensure data integrity.
• Consider testing and validating your code thoroughly before deploying it in production.

Last modified on 2023-09-27