Data Imputation with Row Means in R: A Step-by-Step Guide

Introduction

Missing data is a common problem in statistical analysis, where some observations are not available or have been lost due to various reasons such as non-response, errors, or data recording issues. When dealing with questionnaire items, missing values can significantly impact the accuracy of analysis and conclusions. One effective method for imputing missing data is by replacing it with the row mean of the observable values for each question. In this article, we will explore how to implement this technique in R.

What are Row Means?

The row mean of a dataset is calculated by summing up all the values in a particular row and then dividing by the number of values in that row. For example, if we have a dataset with multiple questionnaires (rows) and each questionnaire has multiple items (columns), the row mean for each item can be used to replace missing values.

Why Use Row Means for Data Imputation?

Using row means as imputation methods has several advantages:

Simple Implementation: The row mean method is straightforward to implement, especially when dealing with datasets where there are only a few rows and columns.
Efficient Computation: The method involves minimal computations compared to other imputation techniques like regression-based models or machine learning algorithms.

However, it also has some limitations:

Sensitivity to Outliers: Row means can be affected by outliers in the dataset, which may lead to inaccurate imputations.
Assumes Missing Completely At Random (MCAR): The method assumes that missing data are MCAR, meaning they are randomly distributed and do not depend on any other variable.

Step-by-Step Guide: Imputing Missing Data with Row Means in R

Here’s a step-by-step guide to impute missing values using row means in R:

Step 1: Load Required Libraries

Before you start, make sure to load the necessary libraries. In this case, we will use base R functions for data manipulation.

# Install and load required library
install.packages("dplyr")
library(dplyr)

Step 2: Create a Sample Dataset with Missing Values

Create a sample dataset df that includes multiple questionnaires (rows) and items (columns), with some missing values:

# Create the dataset
df <- data.frame(
    A1 = c(6, 8, NA, 1, 5),
    A2 = c(NA, NA, 9, 3, 6),
    A3 = c(9, NA, 1, NA, 4),
    B1 = c(NA, NA, 9, 3, 6),
    B2 = c(9, NA, 1, NA, 4),
    B3 = c(NA, NA, 9, 3, 6)
)

# Print the dataset
print(df)

Step 3: Select Rows for Specific Questionnaires

To impute missing values for each item separately, we need to select rows that correspond to each questionnaire. We can use the select function from the dplyr library to achieve this.

# Select rows for A and B questionnaires
df1 <- df %>% select(starts_with("A"))
df2 <- df %>% select(starts_with("B"))

print(df1)
print(df2)

Step 4: Identify Missing Values

We need to identify the positions of missing values in both df1 and df2.

# Find NA values (missing data) in df1
x1 <- which(is.na(df1), arr.ind = TRUE)

print(x1)

Step 5: Calculate Row Means

We will calculate the row means for each questionnaire using the rowMeans function. The na.rm argument is used to ignore missing values when calculating mean.

# Calculate row mean of A items
x1_mean_A <- rowMeans(df1, na.rm = TRUE)
print(x1_mean_A)

# Replace NA values in df1 with calculated mean
df1[x1[,1]] <- x1_mean_A[x1[,1]]
print(df1)

Step 6: Repeat the Process for B Questionnaires

Repeat steps 4 and 5 for the B questionnaire.

# Find NA values (missing data) in df2
x2 <- which(is.na(df2), arr.ind = TRUE)

print(x2)

# Calculate row mean of B items
x2_mean_B <- rowMeans(df2, na.rm = TRUE)
print(x2_mean_B)

# Replace NA values in df2 with calculated mean
df2[x2[,1]] <- x2_mean_B[x2[,1]]
print(df2)

Step 7: Combine the Dataframes

Finally, combine the imputed dataframes df1 and df2.

# Combine the dataframes
df_imputed <- cbind(df1, df2)

print(df_imputed)

Conclusion

In this article, we explored how to replace missing data with row means in R. The technique is simple yet effective for imputing missing values when dealing with datasets where there are only a few rows and columns. However, it assumes that the data follow the Missing Completely At Random (MCAR) assumption, which may not be always true.

We also discussed some limitations of this method, including its sensitivity to outliers and assuming MCAR. Despite these limitations, row means can be an efficient approach for imputing missing values when a more complex analysis is not necessary.

By following the steps outlined in this article, you can easily implement row mean imputation in your R workflow.

Last modified on 2023-05-21