Performing Data Analysis Using R
R is one of the most powerful languages for data analysis, statistical computing, and data visualization. Here’s a structured approach to performing data analysis using R.
1️⃣Importing Data
The first step is loading the dataset. R supports multiple file formats.
(a) Read CSV File
🔹 header = TRUE → Uses the first row as column names
🔹 stringsAsFactors = FALSE → Prevents automatic conversion of strings to factors
(b) Read Excel File (readxl package)
(c) Read Data from a Database (DBI package)
2️⃣ Exploring the Data
Before analysis, check the structure and contents of the dataset.
(a) View the First & Last Few Rows
(b) Check Structure and Summary
(c) Get Column Names
3️⃣ Data Cleaning & Preprocessing
Data often requires cleaning before analysis.
(a) Handling Missing Values
(b) Convert Data Types
(c) Remove Duplicates
4️⃣Data Visualization
R provides powerful visualization libraries like ggplot2 and base R.
(a) Histogram (Distribution)
(b) Scatter Plot
(c) Boxplot (Outlier Detection)
(d) ggplot2 for Advanced Visualization
5️⃣Statistical Analysis
R is widely used for statistical computations.
(a) Mean, Median, and Standard Deviation
(b) Correlation Analysis
(c) T-Test (Compare Two Groups
(d) Linear Regression
6️⃣Machine Learning in R
R supports machine learning using packages like caret, randomForest, and e1071.
(a) Train a Simple Linear Model
(b) Decision Tree
7️⃣Exporting Data
After analysis, you may need to save the results.
(a) Save Processed Data to CSV
(b) Save Model for Future Use
Load it later using:
📌Summary
| Step | Function/Package | Purpose |
|---|---|---|
| Import Data | read.csv(), read_excel(), DBI |
Load data from files/databases |
| Explore Data | head(), summary(), str() |
Check dataset structure |
| Clean Data | na.omit(), as.numeric(), unique() |
Handle missing values, duplicates |
| Visualization | plot(), hist(), ggplot2 |
Graphical analysis |
| Statistical Analysis | mean(), cor(), t.test(), lm() |
Basic statistics & regression |
| Machine Learning | caret, rpart |
Predictive modeling |
| Export Data | write.csv(), saveRDS() |
Save results |