Data Visualization with Scatter Plots in R

Introduction

Scatter plots are a fundamental tool in data visualization, used to display the relationship between two numerical variables. By plotting data points on a Cartesian plane, scatter plots help identify patterns, correlations, and outliers within a dataset. This article explores the importance of scatter plots, demonstrates how to create them using R, and showcases several variations to highlight their versatility.

Importance of Scatter Plots

Identifying Relationships: They reveal relationships or correlations between two numerical variables.

Detecting Patterns: They help detect patterns, trends, and clusters in the data.

Spotting Outliers: They make it easy to identify outliers that deviate significantly from the overall trend.

Visualizing Distribution: They provide insights into the distribution and spread of data points.

Creating Scatter Plots in R

Basic Scatter Plot

A basic scatter plot displays the relationship between two numerical variables.

Code

# Create a sample dataset
library(tidyverse)
set.seed(123)
data <- data.frame(
  X = rnorm(100, mean = 50, sd = 10),
  Y = rnorm(100, mean = 50, sd = 10)
)

# Create a basic scatter plot
ggplot(data, aes(x = X, y = Y)) +
  geom_point() +
  labs(title = "Basic Scatter Plot",
       x = "X Value",
       y = "Y Value") +
  theme_classic()

Scatter Plot with Color and Size

Adding color and size to data points can provide additional information about other variables.

Code

# Create a sample dataset with additional variables
data2 <- data.frame(
  X = rnorm(100, mean = 50, sd = 10),
  Y = rnorm(100, mean = 50, sd = 10),
  Category = sample(c("A", "B", "C"), 100, replace = TRUE),
  Size = rnorm(100, mean = 5, sd = 2)
)

# Create a scatter plot with color and size
ggplot(data2, aes(x = X, y = Y, color = Category, size = Size)) +
  geom_point() +
  labs(title = "Scatter Plot with Color and Size",
       x = "X Value",
       y = "Y Value",
       color = "Category",
       size = "Size") +
  theme_minimal()

Scatter Plot with Smoothing Line

Adding a smoothing line to a scatter plot helps visualize the overall trend in the data.

Code

# Create a scatter plot with a smoothing line
ggplot(data, aes(x = X, y = Y)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(title = "Scatter Plot with Smoothing Line",
       x = "X Value",
       y = "Y Value") +
  theme_minimal()

Faceted Scatter Plot

Faceting allows the creation of multiple panels of scatter plots based on the values of a categorical variable, making it easier to compare different subsets of the data.

Code

# Create a faceted scatter plot
ggplot(data2, aes(x = X, y = Y, color = Category)) +
  geom_point() +
  facet_wrap(~ Category) +
  labs(title = "Faceted Scatter Plot",
       x = "X Value",
       y = "Y Value",
       color = "Category") +
  theme_classic()

Scatter Plot with Marginal Histograms

Adding marginal histograms to a scatter plot provides additional information about the distribution of each variable.

Code

library(ggExtra)

# Create a scatter plot with marginal histograms
p <- ggplot(data, aes(x = X, y = Y)) +
  geom_point() +
  labs(title = "Scatter Plot with Marginal Histograms",
       x = "X Value",
       y = "Y Value") +
  theme_minimal()

ggMarginal(p, type = "histogram")

Interactive Scatter Plots

Code

library(plotly)

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

Code

# Create an interactive scatter plot
p <- ggplot(data2, aes(x = X, y = Y, color = Category, size = Size)) +
  geom_point() +
  labs(title = "Interactive Scatter Plot",
       x = "X Value",
       y = "Y Value",
       color = "Category",
       size = "Size") +
  theme_minimal()

ggplotly(p)