Imputing Missing Values in R: Data Science with R Programming Guide (2026)

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📑 Table of Contents

Introduction to Data Science with R

1 Introduction to Data Science with R
2 Data Science Lifecycle in R
3 Applications of Data Science
4 Data Science Tools for R
5 R vs Python for Data Science
6 Data Science Careers with R
7 Future of Data Science
8 Data Science Workflow using R
9 Statistics for Data Science
10 Why Learn R Programming for Data Science

R Programming Fundamentals

11 Introduction to R Programming
12 R Variables
13 R Data Types
14 R Operators
15 Conditional Statements in R
16 Loops in R Programming
17 Functions in R
18 R Packages
19 Data Frames in R
20 R Programming Best Practices

Setting Up R Data Science Environment

21 Installing R Programming Language
22 Installing RStudio IDE
23 RStudio Interface Overview
24 Working Directory in R
25 Installing R Packages
26 Loading R Packages
27 CRAN Repository
28 Working with R Script Files
29 Using RStudio Projects
30 RStudio Productivity Tips

Data Structures in R

31 Introduction to Data Structures in R
32 Vectors in R
33 Matrices in R
34 Arrays in R
35 Lists in R
36 Data Frames in R
37 Factors in R
38 Subsetting Data in R
39 Data Structure Conversion in R
40 Built-in Datasets in R

Data Manipulation with dplyr

41 Introduction to dplyr for Data Manipulation
42 Selecting Columns using select()
43 Filtering Rows using filter()
44 Creating New Columns with mutate()
45 Sorting Data with arrange()
46 Grouping Data with group_by()
47 Summarizing Data using summarise()
48 Joining Datasets with dplyr
49 Using the Pipe Operator %>%
50 dplyr Best Practices for Data Analysis

Data Cleaning and Preprocessing in R

51 Introduction to Data Cleaning in R
52 Handling Missing Values in R
53 Removing Missing Values
54 Imputing Missing Values in R
55 Removing Duplicate Records in R
56 Outlier Detection in R
57 Data Normalization in R
58 Data Transformation Techniques
59 Encoding Categorical Data in R
60 Data Validation and Preprocessing Best Practices

Exploratory Data Analysis in R

61 Introduction to Exploratory Data Analysis in R
62 Summary Statistics in R
63 Understanding Data Distribution in R
64 Correlation Analysis in R
65 Scatter Plots in R
66 Histograms in R
67 Box Plots in R
68 Feature Relationship Analysis
69 Extracting Data Insights in R
70 EDA Best Practices in R

Data Visualization with ggplot2

71 Introduction to Data Visualization with ggplot2
72 Scatter Plots with ggplot2
73 Bar Charts with ggplot2
74 Line Charts with ggplot2
75 Histograms with ggplot2
76 Box Plots with ggplot2
77 Faceting in ggplot2
78 Customizing Themes in ggplot2
79 Interactive Visualization in R
80 Data Visualization Best Practices in R

Feature Engineering in R

81 Introduction to Feature Engineering in R
82 Feature Scaling in R
83 Data Normalization in R
84 Feature Transformation Techniques
85 Encoding Categorical Variables in R
86 Polynomial Features in R
87 Feature Selection in R
88 Feature Importance in R
89 Dimensionality Reduction in R
90 Feature Engineering Pipelines in R

Machine Learning with R

91 Introduction to Machine Learning with R
92 Types of Machine Learning in R
93 Machine Learning Workflow in R
94 Training and Testing Data in R
95 Regression Models in R
96 Classification Models in R
97 Making Predictions using ML Models
98 Bias Variance Tradeoff in R
99 Machine Learning Pipelines in R
100 Machine Learning Best Practices in R

Supervised Machine Learning in R

101 Introduction to Supervised Machine Learning in R
102 Linear Regression in R
103 Logistic Regression in R
104 Decision Trees in R
105 Random Forest in R
106 K Nearest Neighbors Algorithm in R
107 Support Vector Machine in R
108 Naive Bayes Algorithm in R
109 Ensemble Learning in R
110 Regression vs Classification in R

Unsupervised Learning in R

111 Introduction to Unsupervised Learning in R
112 Clustering Concepts in R
113 K Means Clustering in R
114 Hierarchical Clustering in R
115 DBSCAN Clustering in R
116 Dimensionality Reduction in R
117 Principal Component Analysis in R
118 Cluster Evaluation Techniques
119 Applications of Clustering in Data Science
120 Unsupervised Learning Best Practices

Model Evaluation and Optimization in R

121 Introduction to Model Evaluation in R
122 Confusion Matrix in R
123 Precision and Recall in R
124 F1 Score in R
125 ROC Curve and AUC in R
126 Cross Validation in R
127 Hyperparameter Tuning in R
128 Grid Search in R
129 Overfitting vs Underfitting in R
130 Model Optimization Techniques in R

Real World Data Science Projects using R

131 Introduction to Real World Data Science Projects in R
132 Sales Forecasting Project using R
133 Customer Segmentation using R
134 Fraud Detection System using R
135 Recommendation System Project in R
136 Stock Price Prediction using R
137 Customer Churn Prediction Project
138 Sentiment Analysis Project in R
139 Image Classification Project in R
140 Data Science Case Studies using R

Data Science Career and Interview Preparation (R)

141 Introduction to Data Science Careers with R
142 Essential Data Science Skills for R Professionals
143 Building a Data Science Portfolio using R
144 Data Science Resume Preparation for R Professionals
145 Top Data Science Interview Questions with R
146 Statistics Interview Questions for Data Science
147 Machine Learning Interview Questions using R
148 Data Science Case Study Interview Preparation
149 Kaggle Practice Roadmap for Data Scientists
150 Freelancing and Job Opportunities in Data Science

Imputing Missing Values in R

Intermediate Topic 4 of 10

Imputation

Instead of removing missing values, we can replace them with mean or median values.

r data$age[is.na(data$age)] <- mean(data$age, na.rm=TRUE)

Next Tutorial: Removing Duplicates

Removing Missing Values Removing Duplicate Records in R

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