📊 Data Visualization & Graphics in R – Create Line, Pie, Bar & Distribution Charts
📊 Transform your data into clear, compelling visual insights using R’s built-in graphics and advanced plotting tools.
🧲 Introduction – Visualize Data Trends, Categories & Distributions with R Graphics
Visualization is the key to unlocking the meaning behind numbers. R provides powerful tools to represent trends, patterns, comparisons, and distributions through a wide array of chart types. With simple syntax in base R or the elegant layering system of ggplot2, you can turn datasets into impactful visual stories.
From business reports to academic analysis, mastering R visualization helps convey data-driven insights quickly and effectively.
🎯 What You’ll Learn:
- How to use base R functions for simple plots
- How to visualize trends and relationships with line and scatter plots
- How to represent categories with pie and bar charts
- How to explore distributions using boxplots and histograms
📘 Topics Covered
| 📈 Topic | 📖 Description |
|---|---|
| 🖼️ R – Charts & Graphs Overview | Introduction to R’s graphics capabilities and plotting ecosystem. |
| 📊 R – Plot / Line / Scatterplot | Use base R to plot trends, relationships, and numeric comparisons. |
| 🥧 R – Pie Charts / Bar Charts | Visualize categorical data using pie charts, vertical and horizontal bar plots. |
| 📦 R – Boxplots / Histograms | Represent data distribution and spread using box-and-whisker and histogram plots. |
🖼️ R – Charts & Graphs Overview
R supports multiple systems for charting:
- Base R plotting: Quick, minimal syntax for standard visualizations
ggplot2: Grammar of graphics for layered, aesthetic-rich plots- Other packages:
lattice,plotly,highcharterfor interactive and specialized charts
Start with base plots, then level up with ggplot2 for customization.
📊 R – Plot / Line / Scatterplot
➤ Line Chart Example:
x <- 1:10
y <- x^2
plot(x, y, type = "l", col = "blue", main = "Line Plot", xlab = "X", ylab = "Y")
type = "l"for line,"p"for points,"b"for both- Use
plot()to create custom axes, titles, and point styles
➤ Scatterplot:
plot(mtcars$wt, mtcars$mpg, main="MPG vs Weight", xlab="Weight", ylab="MPG", col="red")
Great for identifying correlations and clustering.
🥧 R – Pie Charts / Bar Charts
➤ Pie Chart:
slices <- c(10, 20, 30, 40)
labels <- c("Q1", "Q2", "Q3", "Q4")
pie(slices, labels = labels, main = "Quarterly Share")
Use for simple, single-variable category breakdowns.
➤ Bar Chart:
counts <- table(mtcars$cyl)
barplot(counts, main = "Car Cylinders", xlab = "Cylinders", ylab = "Frequency", col = "green")
barplot()for verticalbarplot(..., horiz = TRUE)for horizontal
📦 R – Boxplots / Histograms
➤ Boxplot:
boxplot(mtcars$mpg, main="MPG Boxplot", ylab="Miles Per Gallon")
Helps show medians, IQR, and outliers at a glance.
➤ Histogram:
hist(mtcars$mpg, main="MPG Histogram", xlab="Miles Per Gallon", col="lightblue")
Use for understanding value distribution and skewness.
📌 Summary – Recap & Next Steps
📊 R’s plotting tools allow you to visually analyze trends, categories, and statistical properties in just a few lines of code. Whether you’re building reports or dashboards, mastering charts and graphs helps you present findings with impact and clarity.
Visualization not only supports data exploration but also enhances communication of insights to stakeholders and teams.
🔍 Key Takeaways:
- Use base R for quick plots, and ggplot2 for advanced designs
- Line and scatter plots visualize relationships and trends
- Pie and bar charts are ideal for categorical breakdowns
- Boxplots and histograms analyze distributions and outliers
⚙️ Real-World Relevance:
From academic papers to business dashboards, data visualization is a core tool in analytics, reporting, and machine learning pipelines.
🎓 Next Steps:
Experiment with ggplot2‘s layered syntax, explore color palettes with RColorBrewer, and make your charts interactive using plotly.
❓ Frequently Asked Questions (FAQs)
Q1: What’s the difference between base R and ggplot2 for plots?
✅ Base R is simpler and quick for exploratory charts. ggplot2 allows complex, layered, and customizable graphics using the grammar of graphics.
Q2: How do I add labels and legends to plots in R?
✅ Use main, xlab, ylab, legend(), and text() in base R, or labs(), theme(), scale_* functions in ggplot2.
Q3: How can I save plots as images?
✅ Use png(), jpeg(), or pdf() before plotting, then dev.off() to save. In RStudio, use the Export button in the Plots pane.
Q4: Can I combine multiple plots in one window?
✅ Yes. Use par(mfrow = c(nrows, ncols)) or layout functions. With ggplot2, use gridExtra::grid.arrange() or patchwork.
Q5: Are R plots interactive?
✅ Base R plots are static. Use plotly, highcharter, or shiny for interactivity.
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