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")
)Arguments
- df
A dataframe to process for missing value imputation.
- selected_variables
An optional vector of variable names within
dffor which missing values should be imputed. IfNULL(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, themodeis always used.
Examples
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
# 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
#> # A tibble: 7 × 4
#> Sepal_Length Petal_Length Petal_Width Species
#> <dbl> <dbl> <dbl> <chr>
#> 1 5.2 1.5 0.94 setosa
#> 2 5 1.4 0.2 setosa
#> 3 5.7 4.2 1.2 versicolor
#> 4 5.72 1.4 0.2 setosa
#> 5 6.2 3 1.3 setosa
#> 6 6.7 5.8 1.8 virginica
#> 7 5.5 3.7 0.94 setosa
# 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
#> # A tibble: 7 × 4
#> Sepal_Length Petal_Length Petal_Width Species
#> <dbl> <dbl> <dbl> <chr>
#> 1 5.2 1.5 0.732 setosa
#> 2 5 1.4 0.2 NA
#> 3 5.7 4.2 1.2 versicolor
#> 4 NA 1.4 0.2 setosa
#> 5 6.2 2.22 1.3 NA
#> 6 6.7 5.8 1.8 virginica
#> 7 5.5 3.7 0.732 setosa
# 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")
#> # A tibble: 7 × 4
#> Sepal_Length Petal_Length Petal_Width Species
#> <dbl> <dbl> <dbl> <chr>
#> 1 5.2 1.5 0.3 setosa
#> 2 5 1.4 0.2 setosa
#> 3 5.2 1.4 0.2 setosa
#> 4 5.5 3.7 0.2 setosa
#> 5 5.7 4.2 1.2 versicolor
#> 6 6.2 5.8 1.3 virginica
#> 7 6.7 5.8 1.8 virginica