EDA Best Practices
Learn a systematic approach to exploring new datasets and communicating findings.
EDA Best Practices is a free R Academy lesson on CoddyKit. This is lesson 3 of 3. Any 3 lessons of this learning path are free to read in full — after that, CoddyKit PRO unlocks every lesson, plus hands-on practice with a built-in code editor and a 24/7 AI tutor. It is part of the R Academy learning path, and your progress syncs across the web and the CoddyKit app. The R Academy course includes 3 lessons in total.
Exploratory Data Analysis (EDA) Best Practices
EDA is crucial for understanding data quality, distributions, and potential issues before analysis.
Understand Your Data
Start by loading and inspecting the dataset using head() and str().
head(mtcars)
str(mtcars)Check for Missing Values
Detect and handle missing data using is.na() and sum().
sum(is.na(mtcars))Visualizing Distributions
Use histograms and density plots to check data distributions.
hist(mtcars$mpg, col='blue', main='Histogram of MPG')Detecting Outliers
Boxplots and IQR can help identify outliers in numerical data.
boxplot(mtcars$mpg, main='Boxplot of MPG', col='red')Exploring Relationships
Scatter plots reveal potential relationships between variables.
plot(mtcars$mpg, mtcars$hp, main='MPG vs Horsepower', xlab='MPG', ylab='Horsepower', col='green')Calculating Correlations
Use cor() to measure relationships between numerical variables.
cor(mtcars$mpg, mtcars$hp)Standardizing and Normalizing Data
Data scaling ensures fair comparisons between variables.
scaled_data <- scale(mtcars[, c('mpg', 'hp')])Summary
In this lesson, you learned:
- How to check data structure and missing values.
- How to visualize distributions and detect outliers.
- How to analyze relationships and correlations.
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Write and run real code in your browser, get instant help from a 24/7 AI tutor, and pick up where you left off on the web or in the app.
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Frequently Asked Questions
Is the “EDA Best Practices” lesson free?
Yes — any 3 lessons of the R Academy learning path, including “EDA Best Practices”, are free to read in full here on the web. After that, CoddyKit PRO unlocks every lesson, plus interactive practice with a built-in code editor and a 24/7 AI tutor. The R Academy course includes 3 lessons in total.
What will I learn in “EDA Best Practices”?
Learn a systematic approach to exploring new datasets and communicating findings. You practise R Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start R Academy?
No prior experience is required. R Academy on CoddyKit is structured for beginners through advanced learners, so you can start here or from the beginning and move at your own pace. This is lesson 3 of 3.
How long does the “EDA Best Practices” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this R Academy lesson?
Yes. Every R Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Data Summaries and Visualization
- Identifying Patterns and Outliers
- EDA Best Practices