Descriptive and Visual Missing Value Assessment — missing

Provides descriptive statistics and visualizations of missing values in a dataframe.

Usage

missing_values(data, color = "#79E1BE", all = FALSE)

# S3 method for class 'missing_values'
print(x, ...)

# S3 method for class 'outliers'
print(x, ...)

Arguments

data: Dataframe containing the variables to be analyzed.
color: Character string indicating the color for missing values. Default: "#79E1BE"
all: Logical parameter that shows all variables including those without missing values. Default: FALSE.
x: An object of class "outliers".
...: Further arguments passed to or from other methods.

Value

An object of class "missing_values" containing descriptive statistics and ggplot objects.

Methods (by generic)

print(missing_values): Print method for objects of class "missing_values".

Functions

print(outliers): Print method for objects of class "outliers".

Examples

# Clinical dataset with missing values
clinical_df <- clinical_data(dropout = 0.1, missing = 0.05)

# Missing value analysis of only variables with missing values
missing_values(clinical_df)
#> 
#> Missing Value Analysis
#> 
#> Complete rows: 240 (80.0%)
#> Missing cells: 97 (4.0%)
#> 
#>           n_missing pct_missing
#> response         44       14.67
#> weight           29        9.67
#> biomarker        24        8.00
#> 


# Show all variables including those without missing values
missing_values(clinical_df, all = TRUE)
#> 
#> Missing Value Analysis
#> 
#> Complete rows: 240 (80.0%)
#> Missing cells: 97 (4.0%)
#> 
#>                n_missing pct_missing
#> response              44       14.67
#> weight                29        9.67
#> biomarker             24        8.00
#> participant_id         0        0.00
#> visit                  0        0.00
#> sex                    0        0.00
#> treatment              0        0.00
#> age                    0        0.00
#>