Data manipulation and transformation require the use of data manipulation verbs and the dplyr package in R is crucial. One of its functions is filter(), which allows the row to be selected based on imposed conditions. However, one of the activities that frequently occur in data analysis processing is data filtering based on a variable with changing values. In this article, we will explore how to use variables within the dplyr in the R Programming Language.

Understanding dplyr::filter()

The filter() function is part of the dplyr package and is used to select rows from a data frame based on specified conditions. Here’s the basic syntax:

filter(.data, …)

Where:

data: A data frame or tibble.

…: Logical conditions to filter the rows. Multiple conditions can be combined using logical operators.

Basics of dplyr::filter()

Before proceeding to work with variables, it is critical to recall how the filter() function is used at a basic level. The filter() function is also taken from the dplyr package which is highly popular in data manipulation processes and it helps to select rows in the data frame based on the defined conditions.

library(dplyr)

# Sample data frame
df <- data.frame(
  id = 1:5,
  value = c(10, 20, 30, 40, 50)
)
df
# Basic usage of filter()
filtered_df <- df %>% filter(value > 20)
print(filtered_df)

Output:

  id value
1  1    10
2  2    20
3  3    30
4  4    40
5  5    50

  id value
1  3    30
2  4    40
3  5    50

• df: Creates a sample data frame.
• df %>% filter(value > 20): Uses the filter() function to select rows where the value column is greater than 20. The %>% operator (pipe) is used to pass the data frame df to the filter() function.

Using Variables in filter()

To filter data based on certain variables one has to know how to evaluate this particular variable in a filter() function. The !! (bang-bang) operator from the rlang package helps in unquoting the variable so that it can be evaluated.

library(dplyr)
library(rlang)

# Sample data frame
df <- data.frame(
  id = 1:5,
  value = c(10, 20, 30, 40, 50)
)
df
# Variable to filter on
threshold <- 20

# Using the variable in filter()
filtered_df <- df %>% filter(value > !!threshold)
print(filtered_df)

Output:

  id value
1  1    10
2  2    20
3  3    30
4  4    40
5  5    50

  id value
1  3    30
2  4    40
3  5    50

• threshold <- 20: Defines a variable threshold with a value of 20.
• filter(value > !!threshold): Uses the !! operator to unquote the threshold variable, allowing filter() to evaluate value > 20.

Example with Multiple Variables

You can also use multiple variables to create complex filtering conditions. Here’s an example:

library(dplyr)
library(rlang)

# Sample data frame
df <- data.frame(
  id = 1:5,
  value = c(10, 20, 30, 40, 50),
  category = c("A", "B", "A", "B", "A")
)
df
# Variables to filter on
threshold <- 20
category_filter <- "A"

# Using multiple variables in filter()
filtered_df <- df %>% filter(value > !!threshold, category == !!category_filter)
print(filtered_df)

Output:

  id value category
1  1    10        A
2  2    20        B
3  3    30        A
4  4    40        B
5  5    50        A

  id value category
1  3    30        A
2  5    50        A

• category_filter <- “A”: Defines a variable category_filter with a value of “A”.
• filter(value > !!threshold, category == !!category_filter): Uses both threshold and category_filter variables to filter rows where value is greater than 20 and category is “A”.

Using Variables with Different Data Types

Filtering based on different data types (e.g., strings, numeric) requires ensuring the variable is evaluated correctly. Here’s an example with a string variable:

library(dplyr)
library(rlang)

# Sample data frame
df <- data.frame(
  id = 1:5,
  value = c(10, 20, 30, 40, 50),
  category = c("A", "B", "A", "B", "A")
)
df
# String variable to filter on
category_filter <- "B"

# Using the string variable in filter()
filtered_df <- df %>% filter(category == !!category_filter)
print(filtered_df)

Output:

  id value category
1  1    10        A
2  2    20        B
3  3    30        A
4  4    40        B
5  5    50        A

  id value category
1  2    20        B
2  4    40        B

• category_filter <- “B”: Defines a variable category_filter with a value of “B”.
• filter(category == !!category_filter): Uses the !! operator to unquote the category_filter variable, allowing filter() to evaluate category == “B”.

Conclusion

Using variables within the dplyr::filter() function is one of the most versatile and convenient tools to make your data processing code easily transformable and reusable. By leveraging the !! If you use the operator from the rlang package, you can easily turn variables into your filter conditions. This technique provides to maximize and minimize the dataset manipulating flexibility to make your code look better and able to apply for other datasets and analysis.