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R Academy · Lesson

Data Summaries and Visualization

Combine summary statistics and visual plots to gain initial insights into data structure.

Data Summaries and Visualization is a free R Academy lesson on CoddyKit — lesson 1 of 3. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the R Academy learning path, one of 3 lessons in the course, and your progress syncs across the web and the CoddyKit app.

1

Introduction to Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA) helps summarize datasets and uncover patterns, trends, and outliers.

Data Summaries and Visualization — illustration 1

2

Viewing Data Structure

Use str() to check the structure of a dataset.

str(mtcars)

3

Summarizing Data

Use summary() to get key statistics for each variable.

summary(mtcars)

4

Checking Missing Values

Use is.na() to detect missing values in a dataset.

sum(is.na(mtcars))

5

Visualizing Distributions

Use hist() to plot histograms for numerical variables.

hist(mtcars$mpg, col='blue', main='Histogram of MPG')

6

Creating Boxplots

Use boxplot() to detect outliers in a dataset.

boxplot(mtcars$mpg, main='Boxplot of MPG', col='red')

7

Scatter Plots

Use plot() to explore relationships between two numerical variables.

plot(mtcars$mpg, mtcars$hp, main='MPG vs Horsepower', xlab='MPG', ylab='Horsepower', col='green')

8

9

Detecting Correlations

Use cor() to calculate correlation coefficients between numerical variables.

cor(mtcars$mpg, mtcars$hp)

10

Summary

In this lesson, you learned:

  • How to summarize datasets using summary() and str().
  • How to visualize distributions and detect outliers.
  • How to explore relationships between variables using correlation and scatter plots.
Data Summaries and Visualization — illustration 10

Frequently asked questions

Is the “Data Summaries and Visualization” lesson free?

Yes — the full text of “Data Summaries and Visualization” is free to read here on the web, and the R Academy course includes 3 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the R Academy course, upgrade to CoddyKit PRO.

What will I learn in “Data Summaries and Visualization”?

Combine summary statistics and visual plots to gain initial insights into data structure. 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; this is — lesson 1 of 3, so you can start here or from the beginning and move at your own pace.

How long does the “Data Summaries and Visualization” 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

  1. Data Summaries and Visualization
  2. Identifying Patterns and Outliers
  3. EDA Best Practices
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