bulkreadr (version 1.2.0)

fill_missing_values: Fill missing values in a data frame

Description

fill_missing_values() is an efficient function that addresses missing values in a data frame. It uses imputation by function, also known as column-based imputation, to impute the missing values. For continuous variables, it supports various methods of imputation, including minimum, maximum, mean, median, harmonic mean, and geometric mean. For categorical variables, missing values are replaced with the mode of the column. This approach ensures accurate and consistent replacements derived from individual columns, resulting in a complete and reliable dataset for improved analysis and decision-making.

Usage

fill_missing_values(
  df,
  selected_variables = NULL,
  method = c("mean", "min", "max", "median", "harmonic", "geometric")
)

Value

A data frame with missing values imputed according to the specified method.

Arguments

df

A dataframe to process for missing value imputation.

selected_variables

An optional vector of variable names within df for which missing values should be imputed. If NULL (default), imputation is applied to all variables in the data frame. Variables must be quoted.

method

A character string specifying the imputation method for continuous variables. Supported methods are "min", "max", "mean", "median", "harmonic", and "geometric". The default method is "mean". For categorical variables, the mode is always used.

Examples

library(dplyr)

# Assuming 'df' is the dataframe you want to process

df <- tibble::tibble(
Sepal_Length = c(5.2, 5, 5.7, NA, 6.2, 6.7, 5.5),
Petal_Length = c(1.5, 1.4, 4.2, 1.4, NA, 5.8, 3.7),
Petal_Width = c(NA, 0.2, 1.2, 0.2, 1.3, 1.8, NA),
Species = c("setosa", NA, "versicolor", "setosa",
           NA, "virginica", "setosa")
)

# Impute using the mean method for continuous variables

result_df_mean <- fill_missing_values(df, method = "mean")

result_df_mean

# Impute using the geometric mean for continuous variables and specify
# variables `Petal_Length` and `Petal_Width`.

result_df_geomean <- fill_missing_values(df, selected_variables = c
("Petal_Length", "Petal_Width"), method = "geometric")

result_df_geomean

# Impute missing values (NAs) in a grouped data frame

# You can do that by using the following:

sample_iris <- tibble::tibble(
Sepal_Length = c(5.2, 5, 5.7, NA, 6.2, 6.7, 5.5),
Petal_Length = c(1.5, 1.4, 4.2, 1.4, NA, 5.8, 3.7),
Petal_Width = c(0.3, 0.2, 1.2, 0.2, 1.3, 1.8, NA),
Species = c("setosa", "setosa", "versicolor", "setosa",
           "virginica", "virginica", "setosa")
)

sample_iris %>%
group_by(Species) %>%
group_split() %>%
map_df(fill_missing_values, method = "median")