This function provides a comprehensive overview of a data frame, including its dimensions, variable types, missing values count and a preview of the first few rows.
Value
A list containing the following components:
- dimensions
A vector of two elements: the number of rows and columns in the data.
- variable_types
A named vector with the class of each variable (column) in the data.
- missing_values
A named vector with the count of missing values (NA) for each variable.
- preview
A data frame showing the first
preview_rowsrows of the dataset.
Details
The function is useful for quickly inspecting the structure of a data frame and identifying any missing values or general characteristics of the data. It also allows users to customize how many rows they want to preview from the dataset.
Examples
# Example usage with a simple data frame
data <- data.frame(
Age = c(25, 30, NA, 22, 35),
Height = c(175, 160, 180, NA, 165),
Gender = c("Male", "Female", "Female", "Male", "Male")
)
overview <- data_overview(data, preview_rows = 4)
print(overview)
#> $dimensions
#> [1] 5 3
#>
#> $variable_types
#> Age Height Gender
#> "numeric" "numeric" "character"
#>
#> $missing_values
#> Age Height Gender
#> 1 1 0
#>
#> $preview
#> Age Height Gender
#> 1 25 175 Male
#> 2 30 160 Female
#> 3 NA 180 Female
#> 4 22 NA Male
#>
# Example usage with the default preview size (6 rows)
overview_default <- data_overview(data)
print(overview_default)
#> $dimensions
#> [1] 5 3
#>
#> $variable_types
#> Age Height Gender
#> "numeric" "numeric" "character"
#>
#> $missing_values
#> Age Height Gender
#> 1 1 0
#>
#> $preview
#> Age Height Gender
#> 1 25 175 Male
#> 2 30 160 Female
#> 3 NA 180 Female
#> 4 22 NA Male
#> 5 35 165 Male
#>