Learning Objectives

By the end of this lesson, you will be able to:

Create scatterplots using base R
Create line plots using base R
Customize plots with titles, axis labels, colors, and point styles
Explain when to use scatterplots versus line plots
Create basic visualizations for scientific data

Introduction

R has powerful plotting tools built directly into the base language.
Today we will learn how to create two very common types of plots:

Scatterplots – used to explore relationships between two variables
Line plots – used to show change over time or ordered data

These skills form the foundation for data visualization in R.

Key Components of Base R Plotting

High-level functions

These functions initiate a new graph in the plotting window. The specific function called depends on the class of the input data.

Examples include:

plot()
hist()
boxplot()
barplot()

Customization parameters (graphical parameters)

Arguments can be passed to high-level functions to control appearance.

Common options include:

xlab, ylab – Specify labels for the x and y axes
main – Sets the main title of the plot
type – Character indicating the plot type
- "p" for points
- "l" for lines
- "b" for both
col – Specifies colors
pch – Defines the plotting symbol (e.g., 19 for filled circles)
lwd – Controls line width
lty – Specifies line type (e.g., 1 = solid, 2 = dashed)
xlim, ylim – Define the limits for the x and y axes

Low-level functions

These functions add features to the current active plot without starting a new one.

points() – Adds points
lines() – Adds connected line segments
abline() – Adds a straight line
- abline(v = value) for vertical lines
- abline(h = value) for horizontal lines
legend() – Adds a legend
title() – Adds main, subtitle, xlab, and ylab after the plot has been created

Global parameters with `par()`

The par() function is used to set or query global graphical parameters that affect all subsequent plots during an R session.

Part 1: Exploring a Dataset

We will begin using a built-in dataset called mtcars.

Each row represents a car model.
Each column represents a variable such as miles per gallon, horsepower, or weight.

df1 = read.csv("data-2_13_2.csv")
head(df1)

##   first_name age gpa favorite_pet voted birthdate
## 1    Zachary  20  NA      hamster     1 4/17/2004
## 2      Diane  18 2.1          dog     1 9/17/2006
## 3    Natasha  22  NA          cat     0 9/13/2002
## 4      Wendy  19 2.9       parrot     0  7/1/2005
## 5        Ray  22 3.8       rabbit     0  5/6/2002
## 6       Kari  20 3.9       rabbit     0 9/20/2004

head(df1)

##   first_name age gpa favorite_pet voted birthdate
## 1    Zachary  20  NA      hamster     1 4/17/2004
## 2      Diane  18 2.1          dog     1 9/17/2006
## 3    Natasha  22  NA          cat     0 9/13/2002
## 4      Wendy  19 2.9       parrot     0  7/1/2005
## 5        Ray  22 3.8       rabbit     0  5/6/2002
## 6       Kari  20 3.9       rabbit     0 9/20/2004

plot(df1$age, df1$voted)

Part 2: Scatterplots

In base R:

First variable → x-axis Second variable → y-axis

plot(mtcars$wt, mtcars$mpg)

Adding Labels and Title

plot(
  mtcars$wt,
  mtcars$mpg,
  xlab = "Weight (1000 lbs)",
  ylab = "Miles per Gallon",
  main = "Fuel Efficiency vs Car Weight"
)

Customizing Points

Useful options:

pch → shape col → color cex → size

plot(
  mtcars$wt,
  mtcars$mpg,
  pch = 19,
  col = "blue",
  cex = 1.2,
  xlab = "Weight (1000 lbs)",
  ylab = "Miles per Gallon",
  main = "Fuel Efficiency vs Car Weight"
)

Practice Project: Scatterplots

Make a scatterplot with:

1.Horsepower (hp) on x-axis 2.Miles per gallon (mpg) on y-axis 3.Change color and point shape.

plot(
  mtcars$wt,
  mtcars$mpg,
  pch = 11,
  col = "cyan",
  cex = 1.2,
  xlab = "Horsepower",
  ylab = "Miles per Gallon  (mpg) ",
  main = "Fuel Efficiency vs Car Weight"
)

Part 3: Line Plots

Line plots connect points in order.

They are best for:

Time series Ordered data Showing trends

year <- c(2000, 2005, 2010, 2015, 2020)
population <- c(282, 295, 309, 321, 331)

Basic Line Plot

plot(year, population, type = "b")

plot(year, population, type = "l")
points(year, population)

#type = "l" means line.

Adding Labeling and Styling

plot(
  year,
  population,
  type = "l",
  lwd = 2,
  col = "darkgreen",
  xlab = "Year",
  ylab = "Population (millions)",
  main = "Population Growth Over Time"
)

Line Plus Points

plot(
  year,
  population,
  type = "b",
  pch = 19,
  col = "purple",
  lwd = 2,
  xlab = "Year",
  ylab = "Population (millions)",
  main = "Population Growth Over Time"
)

Scatterplots vs Line Plots

Use scatterplots when:

Comparing two variables
Each point is independent

Use line plots when:

Data are ordered
Showing change over time

Practice: Line Plots

Create your own line plot using the example data below.

These data represent test scores over five weeks.

Make a line plot that:

Uses week on the x-axis
Uses score on the y-axis
Shows BOTH points and lines
Has labeled axes
Includes a title
Changes the color and line width

week <- c(1, 2, 3, 4, 5)

score <- c(70, 75, 78, 85, 90)

plot(
  week,
  score,
  type = "b",
  pch = 19,
  col = "blue",
  lwd = 2,
  xlab = "week",
  ylab = "score",
  main = "Grades Over Time"
)
abline(a = 71, b = 2, col = "red", lty = 2, lwd = 2)

Homework:

For this homework, you will create:

One scatterplot
One line plot

You will also use at least ONE new base R plotting feature that we did not practice in class.

Choose at least ONE of the following new features (you can also find a different one if you’d like):

abline() → adds a trend line
grid() → adds background grid lines
legend() → adds a legend to your plot

(You only need to choose ONE, but you may use more if you’d like.)

Part A: Scatterplot

Use the mtcars dataset.

Create a scatterplot that:

Uses two numeric variables of your choice
Includes axis labels
Includes a title
Customizes point color or shape
Includes ONE new plotting feature (abline(), grid(), or legend())

plot(
  mtcars$wt,
  mtcars$mpg,
  pch = 12,
  col = "red",
  cex = 1.2,
  xlab = "Horsepower",
  ylab = "Miles per Gallon  (mpg) ",
  main = "Fuel Efficiency vs Car Weight"
)
grid()

Part B: Line Plot

Create your own small dataset (at least 5 points). Create a line plot that:

Shows both points and lines (type = “b”)

Includes axis labels 2.Includes a title
Customizes line appearance
Includes ONE new plotting feature (abline(), grid(), or legend())

places <- c(1, 2, 3, 4, 5)
miles <- c(50, 55, 78, 82, 90)

plot(
  places,
  miles,
  type = "b",
  pch = 19,
  col = "blue",
  lwd = 2,
  xlab = "Places",
  ylab = "Miles",
  main = "Distances By Routes"
)
abline(a = 71, b = 2, col = "red", lty = 2, lwd = 2)

Making Scatterplots and Line Plots in Base R

Your Name

2026-02-17

Learning Objectives

Introduction

Key Components of Base R Plotting

High-level functions

Customization parameters (graphical parameters)

Low-level functions

Global parameters with `par()`

Part 1: Exploring a Dataset

Part 2: Scatterplots

Adding Labels and Title

Customizing Points

Practice Project: Scatterplots

Part 3: Line Plots

Basic Line Plot

Adding Labeling and Styling

Line Plus Points

Scatterplots vs Line Plots

Practice: Line Plots

Homework:

Part A: Scatterplot

Part B: Line Plot

Making Scatterplots and Line Plots in Base R

Your Name

2026-02-17

Learning Objectives

Introduction

Key Components of Base R Plotting

High-level functions

Customization parameters (graphical parameters)

Low-level functions

Global parameters with par()

Part 1: Exploring a Dataset

Part 2: Scatterplots

Adding Labels and Title

Customizing Points

Practice Project: Scatterplots

Part 3: Line Plots

Basic Line Plot

Adding Labeling and Styling

Line Plus Points

Scatterplots vs Line Plots

Practice: Line Plots

Homework:

Part A: Scatterplot

Part B: Line Plot

Global parameters with `par()`