How to Create Combination Graph in R Studio | Bar and Line Graph Using Biological Data

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Introduction

Data visualization is one of the most important techniques in biostatistics, bioinformatics, ecology, and biological research. Researchers often work with multiple biological variables that need to be compared simultaneously. In such cases, a simple graph may not clearly represent the relationship between variables. A Combination Graph helps solve this problem by displaying two types of graphical representations together in a single chart.

A Combination Graph combines a Bar Graph and a Line Graph to visualize two different datasets simultaneously. In biological studies, this type of graph is useful for comparing plant growth, chlorophyll content, enzyme activity, species population, biomass production, and many other biological parameters.

In this tutorial, you will learn how to create a Combination Graph in R Studio using biological data with the ggplot2 package. This article explains the concept, dataset creation, complete R script, graph interpretation, and common error solutions step by step.

The graph used in this tutorial is based on biological data comparing leaf length and chlorophyll content among different plant species.

Watch Video Tutoria

What is a Combination Graph?

A Combination Graph is a data visualization method that combines:

  • Bar Graph
  • Line Graph

into a single chart.

The bar graph generally represents one biological variable, while the line graph represents another related variable.

For example:

Plant SpeciesLeaf LengthChlorophyll
Plant A1815
Plant B148
Plant C106
Plant D1612

Here:

  • Bar Graph → Leaf Length
  • Line Graph → Chlorophyll Content

This graph helps compare two biological measurements simultaneously.

Applications of Combination Graph in Biology

Combination graphs are widely used in:

  • Plant Biology
  • Ecology
  • Agriculture
  • Environmental Science
  • Microbiology
  • Genetics
  • Biostatistics

Examples

  • Plant height vs chlorophyll content
  • Species abundance vs temperature
  • Enzyme activity vs pH
  • Biomass vs nutrient concentration
  • Population density vs rainfall

Software Required

Before starting, install the following software:

1. R Software

Download from:
R Project Official Website

2. R Studio

Download from:
RStudio Official Website

Required Package

We use the ggplot2 package for creating professional graphs.

Install package:

install.packages("ggplot2")

Load package:

library(ggplot2)

Biological Dataset Used in This Tutorial

The following biological dataset is used in this example:

SpeciesLeaf LengthChlorophyll
Plant A1815
Plant B148
Plant C106
Plant D1612

Variable Explanation

Species

Represents different plant species.

Leaf Length

Represents average leaf length measurement.

Chlorophyll

Represents chlorophyll content of each species.

Step-by-Step Explanation to Create Combination Graph in R Studio

Step 1: Install ggplot2 Package

Run the following command only once:

install.packages("ggplot2")

This command downloads and installs the package from CRAN.

Step 2: Load ggplot2 Library

library(ggplot2)

This loads the package into the R environment.

Step 3: Create Biological Dataset

biology_data <- data.frame(
  Species = c("Plant A", "Plant B", "Plant C", "Plant D"),
  Leaf_Length = c(18, 14, 10, 16),
  Chlorophyll = c(15, 8, 6, 12)
)

Explanation

data.frame()

Creates a tabular dataset.

Species

Stores plant names.

Leaf_Length

Stores leaf length values.

Chlorophyll

Stores chlorophyll measureme

Step 4: View Dataset

print(biology_data)

This displays the dataset in the R console.

Step 5: Create Combination Graph

Complete script

ggplot(biology_data, aes(x = Species)) +

  geom_bar(aes(y = Leaf_Length, fill = "Leaf Length"),
           stat = "identity",
           width = 0.6) +

  geom_line(aes(y = Chlorophyll,
                color = "Chlorophyll",
                group = 1),
            linewidth = 1.2) +

  geom_point(aes(y = Chlorophyll,
                 color = "Chlorophyll"),
             size = 3) +

  labs(
    title = "Biological Data Combination Graph",
    x = "Plant Species",
    y = "Measurement Values",
    fill = "",
    color = ""
  ) +

  scale_fill_manual(values = c("Leaf Length" = "skyblue")) +

  scale_color_manual(values = c("Chlorophyll" = "darkblue")) +

  theme_minimal(base_size = 14)

Detailed Explanation of Graph Components

ggplot()

ggplot(biology_data, aes(x = Species))

Initializes the graph using the dataset.

geom_bar()

geom_bar(aes(y = Leaf_Length, fill = "Leaf Length"),
         stat = "identity")

Creates the bar graph.

Important Arguments

y = Leaf_Length

Defines bar height.

fill

Adds color legend.

stat = “identity”

Uses actual dataset values.

geom_line()

geom_line(aes(y = Chlorophyll,
              color = "Chlorophyll",
              group = 1))

Creates the line graph.

group = 1

Connects all points using a single line.

geom_point()

geom_point(aes(y = Chlorophyll,
               color = "Chlorophyll"))

Adds points on the line graph.

labs()

Adds graph title and axis labels.

scale_fill_manual()

Defines bar color.

scale_color_manual()

Defines line color.

theme_minimal()

Applies clean professional styling.

Graph Interpretation

The generated graph shows two biological variables together:

  • Leaf Length (Bar Graph)
  • Chlorophyll Content (Line Graph)

Interpretation

Plant A

Shows the highest leaf length and chlorophyll content.

Plant B

Displays moderate leaf length but lower chlorophyll.

Plant C

Shows the lowest values for both variables.

Plant D

Shows increased chlorophyll and moderate leaf length.

The graph indicates a possible positive relationship between leaf length and chlorophyll content.

Common Errors and Solutions

Error 1: Unexpected Symbol

Cause

Missing comma after aes().

Wrong

geom_bar(aes(y = Leaf_Length)
stat = "identity")

Correct

geom_bar(aes(y = Leaf_Length),
         stat = "identity")

Error 2: Object Not Found

Cause

Column name mismatch.

Example

leaf_length

instead of:

Leaf_Length

R is case-sensitive.

Error 3: Scale for Fill Already Present

Cause

Using scale_fill_manual() twice.

Solution

Use:

scale_fill_manual()

for bars and

scale_color_manual()

for lines.

Advantages of Combination Graph

  • Visualizes two variables simultaneously
  • Easy biological comparison
  • Professional scientific visualization
  • Publication-quality graphs
  • Better interpretation of relationships

Download File

Download Full R Script File

2 KB

Conclusion

Combination Graphs are highly effective for visualizing multiple biological variables simultaneously. In this tutorial, we learned how to create a Combination Graph in R Studio using the ggplot2 package and biological data.

The graph successfully compared leaf length and chlorophyll content among different plant species using both bar and line representations. This type of visualization is extremely useful in biostatistics, ecology, plant biology, and environmental research.

By following this step-by-step guide, beginners and researchers can easily create publication-quality biological graphs in R Studio for research papers, reports, and presentations.

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