Scatter Plot with ggplot2 and predict() in R: A Deep Dive into the Model and Regression Line

In this article, we will delve into the world of scatter plots created with ggplot2 in R, focusing on the relationship between a model’s predict function and the regression line. We’ll explore the differences between geom_abline() and geom_line(), and provide a comprehensive guide to creating a well-formatted scatter plot.

Introduction to Scatter Plots with ggplot2

A scatter plot is a graphical representation that shows the relationship between two variables. In this case, we are using ggplot2 to create a scatter plot of bacteria (bac) against total amount (Total). The goal of the analysis is to model the Total amount based on the CDS sequence.

Setting Up the Environment

To begin, we need to set up our R environment with the necessary libraries. We’ll be working with ggplot2 for creating the scatter plot and lm() for linear modeling.

# Install and load necessary libraries
install.packages("ggplot2")
library(ggplot2)

Loading the Data

Next, we need to load our dataset into R. In this example, we’re using a sample dataset (data_total) that contains information about bacteria, their Phylum, Domain, CDS sequence, and Total amount.

# Load the data
data_total <- data.frame(bac = c("bac1", "bac2", "bac3"), 
                         Phylum = c("Phylum1", "Phylum2", "Phylum3"),
                         Domain = c("Domain1", "Domain2", "Domain3"),
                         CDS = c(1, 2, 3),
                         Total = c(10, 20, 30))

Creating the Scatter Plot

Now that we have our data loaded, we can create the scatter plot using ggplot(). We’ll use geom_point() to display each point on the plot and theme_bw() for a black-and-white theme.

# Create the scatter plot
ggplot(data_total, aes(x = CDS, y = Total, colour = Domain)) +
  geom_point(size = 3.2, alpha = 0.4) +
  theme_bw() +
  theme(axis.line = element_line(colour = "black"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_blank(),
        panel.background = element_blank(),
        legend.title = element_blank(),
        legend.key = element_blank())

Model and Regression Line

Now that we have our scatter plot, let’s create a linear model using lm() to predict the Total amount based on the CDS sequence.

# Create a linear model
modlinear <- lm(Total ~ CDS)

To visualize this relationship, we’ll use predict() to generate values for the Total amount at different points in the CDS sequence. We can create a new data frame (predicted) with these values and then plot them using geom_line().

# Create a data frame with predicted values
predicted <- data.frame(logCds2 = seq(min(modlinear$coefficients[2]), max(modlinear$coefficients[2]), length.out = length(modlinear$coefficients[1])))
predicted$Total <- predict(modlinear, newdata = predicted)

# Add the regression line to the scatter plot
ggplot(data_total, aes(x = CDS, y = Total, colour = Domain)) +
  geom_point(size = 3.2, alpha = 0.4) +
  geom_line(data = predicted, colour = "blue")

The Role of geom_abline() vs. geom_line()

In R, there are two main options for creating a regression line in ggplot2: geom_abline() and geom_line(). The key difference between these two options lies in what they represent.

geom_abline() represents an abline (a straight line) where the equation is y = ax + b. It’s used to specify the slope (a) and intercept (b) of a linear model, but it doesn’t automatically calculate the values at specific points along the x-axis.

On the other hand, geom_line() plots a line that fits the data, assuming a linear relationship between the variables. When plotting predicted values using predict(), we need to use geom_line(data=predicted) and specify additional arguments like colour and linetype.

In our example code, we used both functions. The blue line on the scatter plot represents the regression line for the model created with lm().

Conclusion

Creating a scatter plot with ggplot2 in R involves more than just plotting points; it requires creating a linear model to understand the relationship between two variables. In this article, we covered the basics of using geom_point(), theme_bw(), and geom_line() to create an informative scatter plot.

When modeling data, remember that you can use both geom_abline() and geom_line() for different purposes in your plots, such as plotting a specific linear model or fitting a line to the data.

We hope this article has provided a comprehensive guide on how to work with ggplot2 and create well-formatted scatter plots in R.

Last modified on 2024-11-09