Скачать или смотреть Enhance Your Scatter Plot: How to Color Data Points by Country in R

Discover how to differentiate points on your scatter plot in R using ggplot2, making data visualization clearer and more insightful.
---
This video is based on the question https://stackoverflow.com/q/69773765/ asked by the user 'LF123' ( https://stackoverflow.com/u/17280939/ ) and on the answer https://stackoverflow.com/a/69773851/ provided by the user 'TarJae' ( https://stackoverflow.com/u/13321647/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Colour line on scatter plot - R

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Introduction

Creating informative visualizations is crucial when analyzing data. Scatter plots are a great way to display relationships between two quantitative variables. However, when working with multiple categories, like country data, showing distinct colors for each category can greatly enhance clarity. In this guide, we’ll explore a common scenario where you might want to differentiate data points in a scatter plot by color and provide a clear, step-by-step solution using R's ggplot2 package.

The Problem

Lois, a data analyst, had plotted CO2 emissions for Argentina and Brazil in a scatter plot. However, she faced a challenge: both countries were represented in the same color (green), making it difficult to distinguish between the two. The goal was to assign different colors to each country's data points to improve the visualization’s effectiveness.

Solution Overview

To solve this issue, we will modify the existing R code by adjusting the plotting aesthetics. The essential part of our solution involves mapping the colour aesthetic to a factor that represents the different countries. Here are the steps to achieve this:

Step 1: Load Required Libraries

We need to ensure that we have the required libraries loaded. ggplot2 is used for creating the scatter plot, and dplyr allows us to manipulate the data efficiently.

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Filter Your Data

Next, filter your dataset to include only the relevant data for Argentina and Brazil during the specified years (1950 to 2019). This is done using the filter function from dplyr.

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Create the Scatter Plot with Color Mapping

In this step, we will modify the ggplot function to change the colour aesthetic parameter. Instead of defaulting to a single color, we will set it to colour = factor(Country). This way, each country will have a unique color assigned automatically by ggplot2.

[[See Video to Reveal this Text or Code Snippet]]

Explanation of Code Changes

factor(Country): This converts the country variable into a factor, allowing ggplot2 to assign different colors to each category.

geom_point(): This function is responsible for plotting the points.

na.rm = TRUE: This argument removes NA values, preventing them from affecting the plot.

labs(): This function helps label the axes for clarity.

Conclusion

By following the steps outlined above, Lois can successfully color-code her scatter plot to distinguish between CO2 emissions from Argentina and Brazil. This enhancement not only improves the aesthetics of the plot but also provides clearer insights into the data.

Now you can apply these techniques to any scatter plot in R using ggplot2, making your visualizations not only more informative but also more engaging! Happy plotting!