Plotting Different Datasets on the Same Scatterplot with R

As data visualization becomes increasingly important in today’s data-driven world, it’s essential to be able to effectively represent complex data sets in a clear and concise manner. One common challenge arises when dealing with multiple datasets that share similar characteristics, such as x and y coordinates. In this article, we’ll explore how to plot different datasets on the same scatterplot using R.

Introduction

R is a popular programming language for statistical computing and graphics. Its built-in data visualization libraries, particularly ggplot2, provide an efficient way to create informative and visually appealing plots. However, when working with multiple datasets, it can be challenging to decide which one to focus on or how to effectively compare them.

Background

The question posed in the original Stack Overflow post arises from a common scenario where we have two or more datasets that share similar characteristics, such as x and y coordinates. The goal is to create a scatterplot that showcases the relationships between these variables across multiple datasets. In this article, we’ll delve into the solution using R.

Step 1: Data Preparation

Before creating the scatterplot, we need to prepare our data by ensuring that all datasets have the same structure and format. This may involve renaming columns or creating new data frames.

For example, let’s say we have two datasets:

df1

| x1 | y1 |
| --- | --- |
| 42.39 | 2.1 |
| 38.77 | 2.1 |
| 44.43 | 2.6 |
| 42.37 | 2.0 |
| 48.79 | 3.6 |

df2

| x2 | y2 |
| --- | --- |
| 53.05 | 8.4 |
| 43.81 | 2.6 |
| 42.67 | 2.4 |
| 41.74 | 3.4 |
| 42.99 | 2.9 |

Step 2: Merging and Reformatting Data

To create a single data frame with both datasets, we’ll use the rbind function to merge them.

# Create df by merging df1 and df2
df <- rbind(melt(df1, id.vars = "x1"), melt(df2, id.vars = "x2"))

In this step, we’re using the melt function from the reshape2 package to convert each dataset into a long format with two variables: value and variable. The id.vars argument specifies which variable should be kept as an identifier for each row.

Step 3: Adding Column Names and Variable Labels

To make it easier to distinguish between the two datasets, we’ll add column names and labels to our data frame.

# Add column names and variable labels
df$variable <- ifelse(grepl("x1", df$variable), "x1", "x2")

In this step, we’re using the grepl function to check which columns contain “x” and assign a corresponding label.

Step 4: Creating the Scatterplot

Now that our data is prepared, it’s time to create the scatterplot. We’ll use ggplot2 with the geom_point function to create a scatterplot and customize its appearance using various options.

# Create the scatterplot
ggplot(df, aes(x = x1, y = value, colour = variable)) + 
  geom_point() + labs(x = "x", y = "y") +
  scale_colour_manual(values = c("red", "blue"), labels = c("df1", "df2"))

In this step, we’re creating a scatterplot with ggplot and specifying the x-axis variable as x1, the y-axis variable as value, and the color variable as variable. We’ve also added a label to each point using the labs function and customized the colors using scale_colour_manual.

Conclusion

Plotting different datasets on the same scatterplot can be achieved using R’s built-in data visualization libraries. By following these steps, we’ve demonstrated how to merge and reformat our data, add column names and variable labels, and create a visually appealing scatterplot that effectively showcases the relationships between variables across multiple datasets.

Example Use Cases

1. Comparing population growth rates: Suppose we have two datasets containing population growth rates for different countries over time. We can use this approach to compare these growth rates on the same scatterplot.
2. Analyzing stock prices: Let’s say we have two datasets containing historical stock prices for different companies. We can plot them on the same scatterplot to identify trends and patterns.

Step-by-Step Code

Here is the complete code used in this article:

library(reshape2)
library(ggplot2)

# Create df1 and df2 data frames
df1 <- structure(list(x1 = c(42.39, 38.77, 44.43, 42.37, 48.79, 46, 
                          53.71, 47.38, 43.75, 46.95), y1 = c(2.1, 2.1, 2.6, 2, 3.6, 2, 
                                                        2.7, 1.8, 3.1, 3.9)), .Names = c("x1", "y1"), class = "data.frame", row.names = c(NA, 
                                                                 -10L))

df2 <- structure(list(x2 = c(53.05, 43.81, 42.67, 41.74, 42.99), y2 = c(8.4, 
                                                                                  2.6, 2.4, 3.4, 2.9)), .Names = c("x2", "y2"), class = "data.frame", row.names = c(NA, 
                                                                 -5L))

# Create df by merging df1 and df2
df <- rbind(melt(df1, id.vars = "x1"), melt(df2, id.vars = "x2"))

# Add column names and variable labels
df$variable <- ifelse(grepl("x1", df$variable), "x1", "x2")

# Create the scatterplot
ggplot(df, aes(x = x1, y = value, colour = variable)) + 
  geom_point() + labs(x = "x", y = "y") +
  scale_colour_manual(values = c("red", "blue"), labels = c("df1", "df2"))

This code can be used as a starting point for plotting different datasets on the same scatterplot. Feel free to modify it according to your specific needs and data requirements.

Last modified on 2025-04-04