Visualising Success Rates for Multiple Metrics

Visualizing success rates for multiple metrics can be achieved using a Venn diagram. In this article, we will explore how to create a Venn diagram from a dataframe in R and customize it to show the desired information.

Setting Up the Problem

We have a dataframe mydata with four columns: trial, metricA, metricB, metricC, and metricD. Each column represents whether a trial was successful or not for each metric. The goal is to visualize how many trials were successful or failed for each metric and across metrics.

Creating the Initial Venn Diagram

The original code provided by the user attempts to create a Venn diagram using the ggVennDiagram function from the ggplot2 package.

mydata <- read.csv("trials-metrics.csv")

mA<-mydata$metricA 
mB<-mydata$metricB 
mC<-mydata$metricC 
mD<-mydata$metricD 

x <- list(
  A = mA, 
  B = mB, 
  C = mC,
  D = mD
)

ggVennDiagram(x, category.names = c("A","B","C","D"))

However, this code only shows the overlap between each pair of metrics and does not provide the desired information.

Understanding the Issue

The issue with the original code is that it treats each metric as a separate entity, which means it only compares shared values between groups. To create a Venn diagram that shows success rates for multiple metrics, we need to find unique combinations of metric outcomes for each trial.

Solution 1: Filtering Successes per Metric

One approach is to filter the dataframe to include only trials where each metric has a specific outcome (e.g., “success”). We can use the trial column and filter for successes in each metric.

n <- 1000
set.seed(123)

mydata <- data.frame(
  trial = seq_len(n), 
  metricA = sample(c("success", "failed"), n, replace = TRUE),
  metricB = sample(c("success", "failed"), n, replace = TRUE),
  metricC = sample(c("success", "failed"), n, replace = TRUE),
  metricD = sample(c("success", "failed"), n, replace = TRUE)
)

library(ggVennDiagram)

x <- list(
  A = mydata$trial[mydata$metricA == "success"],
  B = mydata$trial[mydata$metricB == "success"],
  C = mydata$trial[mydata$metricC == "success"],
  D = mydata$trial[mydata$metricD == "success"]
)

ggVennDiagram(x, category.names = LETTERS[1:4])

This code creates a Venn diagram that shows success rates for each metric and across metrics.

Solution 2: Using lapply to Filter Successes

Alternatively, we can use the lapply function to filter successes in each metric. This approach is more concise and flexible.

xx <- lapply(mydata[-1], \(x) mydata$trial[x == "success"])
ggVennDiagram(xx, category.names = LETTERS[seq_along(xx)])

This code creates a Venn diagram that shows success rates for each metric and across metrics.

Customizing the Venn Diagram

To customize the Venn diagram, we can adjust the category.names argument in the ggVennDiagram function. For example, we can add labels to each region or change the colors used.

ggVennDiagram(xx, category.names = LETTERS[seq_along(xx)], 
             label.size = 3, 
             label.color = "blue")

This code adds labels to each region of the Venn diagram in blue.

Conclusion

In this article, we explored how to create a Venn diagram from a dataframe in R and visualize success rates for multiple metrics. We discussed two approaches: filtering successes per metric and using lapply to filter successes. We also provided tips on customizing the Venn diagram to better suit your needs.

Additional Tips

• To create more complex Venn diagrams, consider using the ggVenn package instead of ggVennDiagram.
• When working with large datasets, consider using the dplyr package for data manipulation and filtering.
• To customize the appearance of your Venn diagram, explore the various options available in the ggplot2 package.

Last modified on 2024-09-26