Explain complete() Function in R with Examples

The complete() function in R's tidyr package is designed to expand a data frame by including all possible combinations of specified columns. By default, it fills missing combinations with NA values. You can manage these missing values in the resulting data frame using the fill parameter.

In this article, I’ll explain the functionality of the complete() function, covering its syntax, parameters, and practical use cases to demonstrate how it can expand datasets with missing combinations, streamlining your data analysis workflow.

Key Points-

• The complete() function is used to fill in missing combinations of data.
• Expand data by specifying one or more columns for completion.
• Handle missing values by using the fill argument.
• Simplifies data preparation for summarization and visualization.
• Works seamlessly with grouped data for advanced operations.

<a id="h-complete-function-in-r"></a>

complete() Function in R

The complete() function is used to define a complete dataset by extending a data frame to include all possible combinations of specified variables. Missing combinations are added with NA or specified default values for other columns.

<a id="h-syntax-of-complete"></a>

Syntax of complete()

The general syntax of the complete() function.

# Syntax of the complete() function
complete(data, ..., fill = list(), explicit = FALSE)

<a id="h-parameters"></a>

Parameters

• explicit: Logical; if TRUE, ensures that all missing combinations are explicitly added.
• data: The input data frame or tibble.
• ...: Columns to consider for completing missing combinations.
• fill: A named list of values to use for filling in missing combinations.

<a id="h-return-value"></a>

Return Value

It returns a data frame or tibble with missing combinations filled in and optionally fills other columns with specified values.

<a id="h-transform-data-with-r-complete-function"></a>

Transform Data with R complete() Function

The complete() function allows you to expand a data frame by generating all possible combinations of specified columns. By default, any missing combinations are filled with NA. To create a complete data frame, pass the specified columns along with the original data frame to this function, and it will include all possible combinations.

# Use complete() to expand data
# Load tidyr package
library(tidyr)

# Example data frame
df <- data.frame(
  Student = c("Geetha", "Ram", "Geetha", "Sai"),
  Subject = c("Math", "Science", "Science", "Math"),
  Score = c(85, 88, 90, 78)
)

print("Original Data Frame:")
print(df)

complete_df <- complete(
  data = df,
  Student,
  Subject
)

print("Data Frame After Using complete():")
print(complete_df)

Yields below output.

The resulting data frame includes all combinations of students and subjects, with NA filled-in for missing scores.

<a id="h-handle-missing-values-using-r-complete"></a>

Handle Missing Values using R complete()

You can handle missing values by using the fill parameter to replace NA values with specified values. Let's set the fill parameter with a specified value to fill the missing combination of specified columns.

# Fill missing values with default value
complete_df <- complete(
  data = df,
  Student,
  Subject,
  fill = list(Score = 0)
)

print("Data Frame with Filled Missing Values:")
print(complete_df)

Yields below output.

<a id="h-grouped-data-with-complete"></a>

Grouped Data with complete()

Alternatively, you can expand grouped data by including all combinations of specified columns using the complete() function. First, apply dplyr's group_by() function to the data frame to create groups, and then use the complete() function to add all possible combinations of the specified values within each group.

# Grouped data frame with complete()
library(dplyr)

# Create data frame
df <- data.frame(
  Class = c("A", "A", "B", "B"),
  Student = c("Geetha", "Ram", "Sai", "Geetha"),
  Subject = c("Math", "Science", "Math", "Science"),
  Score = c(85, 88, 78, NA)
)

# Use complete() with grouping
grouped_df <- df %>%
  group_by(Class) %>%
  complete(Student, Subject, fill = list(Score = 0))

print("Grouped Data Frame After Using complete():")
print(grouped_df)

Yields below output.

# Output:
[1] "Grouped Data Frame After Using complete():"

# A tibble: 8 × 4
# Groups:   Class [2]
  Class Student Subject Score
         
1 A     Geetha  Math       85
2 A     Geetha  Science     0
3 A     Ram     Math        0
4 A     Ram     Science    88
5 B     Geetha  Math        0
6 B     Geetha  Science     8
7 B     Sai     Math       78
8 B     Sai     Science     0

<a id="h-handling-explicit-combinations-with-complete"></a>

Handling Explicit Combinations with complete()

You can use the explicit parameter of complete() to allow all possible combinations of the specified columns that are included in the resulting data frame. This is useful when you want to see every possible combination, even if they were not present in the original dataset.

# Handling Explicit Combinations
# Load tidyr package
library(tidyr)

# Create data frame
df <- data.frame(
  Student = c("Geetha", "Ram"),
  Subject = c("Math", "Science"),
  Score = c(85, 90)
)

# Using complete() with explicit = TRUE
explicit_df <- complete(
  data = df,
  Student = c("Geetha", "Ram", "Sai"),  # Adding an additional student
  Subject = c("Math", "Science", "History"),  # Adding an additional subject
  explicit = TRUE
)

print("Data Frame with Explicit Combinations:")
print(explicit_df)

Yields below output.

# Output:
[1] "Data Frame with Explicit Combinations:"

> print(explicit_df)
# A tibble: 9 × 3
  Student Subject Score
        
1 Geetha  History    NA
2 Geetha  Math       85
3 Geetha  Science    NA
4 Ram     History    NA
5 Ram     Math       NA
6 Ram     Science    90
7 Sai     History    NA
8 Sai     Math       NA
9 Sai     Science    NA

<a id="h-combining-complete-with-expand-grid"></a>

Combining complete() with expand_grid()

You can also combine complete() and expand_grid() to create all possible combinations explicitly. This approach offers fine-grained control over the expansion process.

Combining complete() with expand_grid()
# Load tidyr package
library(tidyr)

# Create data frame
df <- data.frame(
  Student = c("Geetha", "Ram"),
  Subject = c("Math", "Science"),
  Score = c(85, 90)
)

print("Original Data Frame:")
print(df)

# Generate all possible combinations with expand_grid()
all_combinations <- expand_grid(
  Student = c("Geetha", "Ram", "Sai"),
  Subject = c("Math", "Science", "History")
)

print("All Possible Combinations Using expand_grid():")
print(all_combinations)

# Use complete() to merge and fill missing combinations
expanded_df <- complete(
  data = df,
  Student = all_combinations$Student,
  Subject = all_combinations$Subject,
  fill = list(Score = 0)  # Filling missing values with 0
)

print("Data Frame After Combining complete() with expand_grid():")
print(expanded_df)

Yields below output.

[1] "Data Frame After Combining complete() with expand_grid():"

> print(expanded_df)
# A tibble: 9 × 3
  Student Subject Score
        
1 Geetha  History     0
2 Geetha  Math       85
3 Geetha  Science     0
4 Ram     History     0
5 Ram     Math        0
6 Ram     Science    90
7 Sai     History     0
8 Sai     Math        0
9 Sai     Science     0

<a id="h-frequently-asked-questions"></a>

Frequently Asked Questions

The complete() function is used to create a complete dataset by expanding a data frame to include all possible combinations of specified variables. Missing combinations are added with NA or specified default values for other columns.

When combinations of the specified variables are missing in the dataset, complete() add rows for those combinations and fill the other columns NA by default. You can customize this behavior by using the fill argument.
For example: complete(data, var1, var2, fill = list(var3 = 0))

complete(): Fills in missing combinations in an existing dataset.
expand_grid(): Creates all possible combinations of variables from scratch without referencing an existing dataset. expand_grid() is often used with complete() for greater control.

Use the fill argument to specify the default values for missing cells in the expanded dataset.
For example: complete(data, var1, var2, fill = list(var3 = 0, var4 = "unknown"))

complete() function handles grouped data when used with the dplyr package. Simply group the data first with group_by() and then apply complete()
For example: library(dplyr)grouped_data <- df %>%group_by(group_var) %>%complete(var1, var2)

The explicit argument ensures that all specified combinations are explicitly represented in the dataset, even if they are not present in the original data.
For Example: complete(data, var1, var2, explicit = TRUE)

<a id="h-conclusion"></a>

Conclusion

In this article, I have explained into the complete() function from the tidyr package, a versatile and powerful tool for expanding datasets by including all possible combinations of specified variables. I also demonstrated how to fill missing combinations with default or specified values. Its flexibility in handling grouped data, integrating seamlessly with functions like expand_grid(), and managing explicit combinations underscores its value for data analysts and scientists.

Happy Learning!!

<a id="h-related-articles"></a>

Related Articles

• Explain the tidyr gather() function with examples.
• Explain the R tidyr spread() function.
• Explain the pivot_longer() function in R.
• Explain the pivot_wider() function in R.
• Explain the fill() function in R.
• Explain the drop_na() function in R.
• Explain the replace_na() function in R.
• Explain the separate() function in R.
• Explain the unite() function in R.
• Explain separate_rows() function in R.

<a id="h-references"></a>

References

https://www.rdocumentation.org/packages/tidyr/versions/0.6.0/topics/complete