The create_dataset_summary_table function is a powerful tool for data analysts who need to quickly assess the structure and content of their datasets. This vignette will guide you through the process of using this function and its auxiliary functions to generate a comprehensive summary of any dataframe.

Getting Started

First, let’s load the necessary libraries and our custom package:


Creating The Summary Table

The primary function, create_dataset_summary_table, generates a summary statistics table for a given dataframe. Let’s use the built-in mtcars dataset to see how it works:

summary_table <- create_dataset_summary_table(mtcars)

The resulting table provides detailed information about each column, such as the data type, missing value percentage, range of values, and more.

Helper Functions

The create_dataset_summary_table function relies on several helper functions to perform specific tasks:

Each of these functions plays a crucial role in constructing the final summary table. We will explore each one in more detail in the following sections.


This function helps identify the data type of each column in the dataframe:

## Example usage

get_first_element_class(mtcars$mpg) # "numeric"
#> [1] "numeric"

find_minimum_value and find_maximum_value

These functions assist in determining the range of values for numeric columns:

# Example usage

find_minimum_value(mtcars$hp) # Minimum horsepower 
#> [1] 52
find_maximum_value(mtcars$disp) # Maximum displacement
#> [1] 472


With this function, you can easily check for uniqueness in a particular column:

# Example usage

is_unique_column("cyl", mtcars) # Are cylinder numbers unique?
#> [1] FALSE


This function provides descriptive statistics for numeric columns:

# Example usage

get_distribution_statistics(mtcars$wt) # Distribution statistics for weight
#> [1] "Q1 = 2.58125  | median = 3.325  | Q3 =  3.61  | mean = 3.22  | stdev = 0.978"


Lastly, this function calculates the percentage breakdown of categories in a data vector:

# Example usage

calculate_category_percentages(mtcars$gear) # Category percentages for gears
#> [1] "3: (46.88%) | 4: (37.50%) | 5: (15.62%)"


The create_dataset_summary_table function and its companion functions offer a convenient way to gain immediate insights into the structure and content of your data. By understanding and utilizing these tools, you can expedite your data exploration process and make more informed decisions during your analysis.