Helper functions in select

Introduction

We have already covered basic select operation here. There are certain helper functions which will help select columns conveniently.

Some of the helper functions are:

• starts_with(…)
• ends_with(…)
• contains(…)
• matches(…)
• num_range(…)

Procedure

We will be working with the following custom dataframe.

 
# package for creating dataframe
library(tibble) 

df <- tibble(col_end1 = as.integer(c(1,2,3)), 
             col_end2 = as.integer(c(4,5,6)), 
             col2 = c(5.0,6.3,9.7), 
             col3 = c("string1", "string2","string3"),
             col4 = c(FALSE, TRUE, FALSE),
             col5 = as.factor(c("A","B","A")),
             start_col1 = as.integer(c(11,22,33)), 
             start_col2 = as.integer(c(44,53,64)), 
             col_contain_1 = as.integer(c(101,228,303)), 
             col_contain_2 = as.integer(c(443,335,664)), 
             )
View(df)

The first few rows are as follows:

We will explore the following helper functions:

• starts_with(<pattern>): It will select the columns that start with the pattern specified.
• ends_with(<pattern>): It will select the columns that end with the pattern specified.
• contains(<pattern>): It will select the columns that contain the pattern specified.
• matches(<regex>): It will select the columns that match the specified regex.
• num_range(<column_name>, <integer_range>): It will select the columns that satisfy the column_name+integer_range combinations.

Code

1. starts_with:

 
# refer procedure for definition of df
# select all columns that start with start
View(select(df, starts_with("start")))

The output of above code is:

Thus we have selected the columns that start with the pattern “start”.

2. ends_with:

# refer procedure for definition of df
# select all columns that end with end1, end2
View(select(df, ends_with("end1"),ends_with("end2"))) 

The output of above code is:

Thus we have selected the columns that end with the pattern “end1” or “end2”.

3. contains:

 
# refer procedure for definition of df
# select all columns that contain "contain"
View(select(df, contains("contain")))

The output of above code is:

Thus we have selected the columns that contain the pattern “contain”.

4. matches:

 
# refer procedure for definition of df
# select columns that match the regex "^col"
View(select(df, matches("^col")))

The output of above code is:

Thus we have selected the columns that match the regex pattern “^col”, which means starts with col.

5. num_range:

 
# refer procedure for definition of df
# select columns col2, col3
View(select(df, num_range("col", 2:3)))

The output of above code is:

Thus we have selected the columns that form combinations of col with numbers 2 and 3 i.e. col2 and col3.

Conclusion

Thus we have successfully explored helper function for select operation.

References

• https://r4ds.had.co.nz/