Basic operator chaining using pipes

Introduction

We have already covered basic operations in tidyverse:

• filter rows
• mutate or add new columns using existing data
• group by and summarize
• rearrange columns
• rename columns
• select columns
• sort rows
• append new column to existing dataframe
• append new row to existing dataframe

We can combine these operations in a chain with the help of the Pipe operation %>%. The pipe operation has takes an input does the processing and gives an output.

The pipe operation does not modify the current input, instead it returns a new dataframe which is a result of the chained operation performed.

Procedure

We will be working with a custom dataframe:

 
# package for creating dataframe
library(tibble) 

# tibble or dataframe 
df <- tibble(col1 = as.integer(c(1,2,3,4,5)), 
             col2 = c(11,12,13,14,15),
             )
View(df)

We will be applying the following operation on the dataframe:

1. add new column col3 with values 10, 20, 30, 40, 50, 60
2. rename the old column col1 to renamed_col1
3. filter to keep rows which have even values for renamed_col1
4. rearrange columns in dataframe to following order renamed_col1, col3, col2

Code

 
# refer procedure for definition of df
library(dplyr)

result <- df %>% 
  # add new column col3
  mutate(col3 = c(10,20,30,40,50)) %>% 
  
  # rename old column col1 to renamed_col1
  rename(renamed_col1 = col1) %>% 
  
  # filter to keep rows which have even values for renamed_col1
  filter(renamed_col1 %% 2 == 0) %>% 
  
  # rearrange columns to order renamed_col1, col3, col2
  select(renamed_col1, col3, col2)

View(result)

The output of above code is:

Conclusion

Thus we have successfully implemented basic chain operation using pipes.

References

• https://r4ds.had.co.nz/