Making Scatterplots and Line Plots in Base R

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

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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()

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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

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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

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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.

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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.

head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

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, there are numbers for different shapes col → color cex → size, changes the size of the plots

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$hp,
  mtcars$mpg,
  pch = 18,
  col = "purple",
  cex = 1.2,
  xlab = "Horsepower",
  ylab = "Miles per Gallon",
  main = "Fuel Efficiency vs Car Horsepower"
  )

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 = "l")

#type = "l" means line, "b"= both line and dots, no letter is dots 
plot(year, population, type = "b")

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

## integer(0)

Adding Labeling and Styling

plot(
  year,
  population,
  type = "l",
  lwd = 2, #line width
  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:

1. Comparing two variables
2. Each point is independent

Use line plots when:

3. Data are ordered
4. 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:

1. Uses week on the x-axis
2. Uses score on the y-axis
3. Shows BOTH points and lines
4. Has labeled axes
5. Includes a title
6. Changes the color and line width

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

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

Line Plus Points

plot(
   week,
   score,
   type = "b",
  lwd = 2, 
  col = "darkgreen",
  xlab = "Week",
  ylab = "Score",
  main = "Week vs. Score"
)

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())

head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

plot(
  mtcars$wt,
  mtcars$hp,
  xlab = "Car Weight",
  ylab = "Horsepower",
  main = "Car Weight Compared to Horsepower",
  col = "blue",
  pch = 2,
  cex = 1
  )
grid( col = "pink",lwd = 3)

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”)

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

duration = c(10, 20, 30, 40, 50, 60)
heart_rate = c(95, 110, 125, 140, 150, 160)

plot(
  duration,
  heart_rate,
  xlab = "Minutes Excersized",
  ylab = "Avg. Heart Rate (bpm)",
  main = "Workout Duration vs. Heart Rate",
  type = "b",
  lwd = 3,
  lty = "dashed"
  )

legend("topleft",
       legend = "heart_rate",
       lty = "dashed",
       lwd = 3,
       pch = 16)