Line Charts

R Graphics
R Programming
Learn how to create and customize line charts in R. This lecture covers essential techniques for visualizing data trends over time using line charts in R.
Author

TERE

Published

June 21, 2024

Introduction

Line charts are a fundamental tool for visualizing data trends over time or ordered categories. They display data points connected by straight lines, helping to identify patterns, trends, and changes in the data. In this lecture, we will learn how to create and customize line charts in R.

Key Concepts

1. What is a Line Chart?

A line chart is a type of chart that displays information as a series of data points connected by straight line segments. It is particularly useful for visualizing trends over time.

2. Applications of Line Charts

  • Visualizing time series data.

  • Tracking changes and trends.

  • Comparing multiple series.

3. Customizing Line Charts

Customizing line charts involves adding titles, labels, colors, line types, and markers to enhance readability and interpretability.

Creating and Customizing Line Charts

1. Basic Line Chart

A basic line chart displays data points connected by straight lines.


# Creating sample data

time <- 1:100

values <- cumsum(rnorm(100))



# Creating a basic line chart

plot(time, values, type = "l", main = "Basic Line Chart", xlab = "Time", ylab = "Values", col = "blue")
# Plot result

time <- 1:100

values <- cumsum(rnorm(100))

plot(time, values, type = "l", main = "Basic Line Chart", xlab = "Time", ylab = "Values", col = "blue")

2. Adding Titles and Labels

Adding titles and labels helps in understanding the context and meaning of the line chart.


# Creating a line chart with titles and labels

plot(time, values, type = "l", main = "Line Chart with Titles and Labels", xlab = "Time", ylab = "Values", col = "blue")
# Plot result

plot(time, values, type = "l", main = "Line Chart with Titles and Labels", xlab = "Time", ylab = "Values", col = "blue")

3. Customizing Line Types and Colors

Customizing line types and colors can differentiate between multiple series or highlight specific data points.


# Creating sample data with multiple series

values1 <- cumsum(rnorm(100))

values2 <- cumsum(rnorm(100, mean = 0.5))



# Creating a line chart with customized line types and colors

plot(time, values1, type = "l", main = "Line Chart with Custom Line Types and Colors", xlab = "Time", ylab = "Values", col = "blue", lty = 1, lwd = 2)

lines(time, values2, col = "red", lty = 2, lwd = 2)

legend("topleft", legend = c("Series 1", "Series 2"), col = c("blue", "red"), lty = c(1, 2), lwd = 2)
# Plot result

values1 <- cumsum(rnorm(100))

values2 <- cumsum(rnorm(100, mean = 0.5))

plot(time, values1, type = "l", main = "Line Chart with Custom Line Types and Colors", xlab = "Time", ylab = "Values", col = "blue", lty = 1, lwd = 2)

lines(time, values2, col = "red", lty = 2, lwd = 2)

legend("topleft", legend = c("Series 1", "Series 2"), col = c("blue", "red"), lty = c(1, 2), lwd = 2)

4. Adding Markers to Data Points

Adding markers to data points can highlight specific values and make the chart more readable.


# Creating a line chart with markers

plot(time, values, type = "b", main = "Line Chart with Markers", xlab = "Time", ylab = "Values", col = "blue", pch = 19)
# Plot result

plot(time, values, type = "b", main = "Line Chart with Markers", xlab = "Time", ylab = "Values", col = "blue", pch = 19)

5. Adding Gridlines

Adding gridlines can make it easier to read the values of the data points.


# Creating a line chart with gridlines

plot(time, values, type = "l", main = "Line Chart with Gridlines", xlab = "Time", ylab = "Values", col = "blue")

grid(nx = NULL, ny = NULL, col = "lightgray", lty = "dotted")
# Plot result

plot(time, values, type = "l", main = "Line Chart with Gridlines", xlab = "Time", ylab = "Values", col = "blue")

grid(nx = NULL, ny = NULL, col = "lightgray", lty = "dotted")

Example: Comprehensive Line Chart Analysis

Here’s a comprehensive example of creating and customizing line charts in R.


# Creating sample data

time <- 1:100

values1 <- cumsum(rnorm(100))

values2 <- cumsum(rnorm(100, mean = 0.5))



# Basic line chart

plot(time, values1, type = "l", main = "Basic Line Chart", xlab = "Time", ylab = "Values", col = "blue")



# Line chart with titles and labels

plot(time, values1, type = "l", main = "Line Chart with Titles and Labels", xlab = "Time", ylab = "Values", col = "blue")



# Line chart with custom line types and colors

plot(time, values1, type = "l", main = "Line Chart with Custom Line Types and Colors", xlab = "Time", ylab = "Values", col = "blue", lty = 1, lwd = 2)

lines(time, values2, col = "red", lty = 2, lwd = 2)

legend("topleft", legend = c("Series 1", "Series 2"), col = c("blue", "red"), lty = c(1, 2), lwd = 2)



# Line chart with markers

plot(time, values1, type = "b", main = "Line Chart with Markers", xlab = "Time", ylab = "Values", col = "blue", pch = 19)



# Line chart with gridlines

plot(time, values1, type = "l", main = "Line Chart with Gridlines", xlab = "Time", ylab = "Values", col = "blue")

grid(nx = NULL, ny = NULL, col = "lightgray", lty = "dotted")

Summary

In this lecture, we covered how to create and customize line charts in R. We explored various techniques for adding titles, labels, colors, line types, markers, and gridlines. Line charts are a powerful tool for visualizing data trends over time or ordered categories.

Further Reading

For more detailed information, consider exploring the following resources:

Call to Action

If you found this lecture helpful, make sure to check out the other lectures in the R Graphs series. Happy plotting!