🌿 Correlation Analysis in R: Exploring Plant Height Step-by-Step

Whether you're a student, researcher, or just a curious plant lover, understanding the relationships between plant height and other variables (like sunlight, water, or soil type) is crucial. One powerful statistical tool for this is correlation analysis—and R makes it easy.

In this blog post, we’ll walk through how to perform a correlation analysis in R using plant height data. Let's dive in!

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📌 What is Correlation?

Correlation measures the strength and direction of a relationship between two numerical variables. It ranges from -1 (perfect negative relationship) to 1 (perfect positive relationship), with 0 meaning no correlation.

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🛠 Step 1: Set Up Your Environment

First, open R or RStudio and load the required libraries. For this analysis, we’ll use tidyverse for data handling and ggpubr for visualization.

# Install packages if not already installed
install.packages("tidyverse")
install.packages("ggpubr")
# Load libraries
library(tidyverse)
library(ggpubr)

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🌱 Step 2: Load or Create Your Data

Let’s assume you have a dataset with plant height and other variables like sunlight, water, and fertilizer.

Here’s a sample dataset:

# Sample dataset
plant_data <- data.frame(
  height_cm = c(45, 50, 55, 60, 65, 70, 75, 80),
  sunlight_hours = c(5, 6, 6, 7, 8, 9, 10, 11),
  water_ml = c(300, 350, 400, 450, 500, 550, 600, 650),
  fertilizer_score = c(2, 3, 3, 4, 5, 5, 6, 7)
)

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📊 Step 3: Explore the Data

Before running a correlation analysis, it’s good practice to look at the data:

head(plant_data)
summary(plant_data)

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📈 Step 4: Compute the Correlation Matrix

Use the cor() function to calculate the correlation coefficients:

cor_matrix <- cor(plant_data)
print(cor_matrix)

This will give you a matrix showing the correlation between all pairs of variables.

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🔍 Step 5: Visualize the Correlations

Let’s visualize the correlation between height_cm and other variables using scatter plots:

# Scatter plot with regression line
ggscatter(plant_data, x = "sunlight_hours", y = "height_cm",
          add = "reg.line", conf.int = TRUE,
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "Sunlight (hours)", ylab = "Plant Height (cm)")

Repeat for other variables (water_ml, fertilizer_score) by changing the x argument.

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✅ Step 6: Interpret the Results

• A positive correlation means that as one variable increases, the other tends to increase.
• A negative correlation means that as one increases, the other decreases.
• A value close to 0 means no clear relationship.

Example interpretation:

The correlation between plant height and sunlight is 0.98, indicating a strong positive relationship. As sunlight increases, plant height increases significantly.

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📌 Bonus: Significance Testing

You can test whether a correlation is statistically significant using cor.test():

cor.test(plant_data$height_cm, plant_data$sunlight_hours)

This provides a p-value. If p < 0.05, the correlation is statistically significant.

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🌟 Conclusion

Correlation analysis is a quick and effective way to uncover patterns in your plant growth data. With just a few lines of R code, you can:

• Quantify relationships
• Visualize patterns
• Support your plant biology research with real stats!

So next time you're wondering whether watering more really makes your plants taller—R has your back!