Reordering Data in ggplot for Categorical Analysis

Introduction

In this article, we will discuss how to reorder data based on a specific column in ggplot2 using the fct_reorder function from the forcats package. We will explore various scenarios and provide examples of how to categorize data into meaningful groups.

Background

The fct_reorder function allows us to specify multiple variables that determine the order of levels in a factor column. This is particularly useful when we need to reorder data based on multiple criteria.

Example Data Generation

To illustrate our example, let’s generate some sample data:

# Load necessary libraries
library(ggplot2)
library(forcats)

# Create sample data
df <- data.frame(
  Weekday = c("Fri", "Tues", "Mon", "Thurs","Mon", "Tues", "Wed", "Fri","Wed", "Thurs", "Fri"),
  Quarter = c(rep("Q1", 3), rep("Q2", 5), rep("Q3",3)),
  Delay = runif(11, -2, 5),
  pval = runif(11, 0, 1))
df$Quarter <- factor(df$Quarter, levels = c("Q1", "Q2", "Q3"))

# Create a new column 'group' based on the value of 'pval'
df$group <- ifelse(df$pval > 0.5 & df$Delay < 2, "Positive Significant",
                   ifelse(df$pval <= 0.5 & abs(df$Delay) >= 1, "Negative Significant",
                          "Not Significant"))

In this example, we create a new column ‘group’ based on the value of ‘pval’. The data is categorized into three groups: Positive Significant, Negative Significant, and Not Significant.

Reordering Data Based on Multiple Criteria

Let’s reorder the data in our ggplot using the fct_reorder function:

# Reorder the data based on multiple criteria
df %>% 
  mutate(
    Weekday = fct_reorder2(
      .f = Weekday,
      .x = Delay,
      .y = Quarter,
      .fun = function(x,y){mean(x[y=="Q2"])}
    )) %>%
  ggplot(aes(x = Quarter, y = Weekday)) + 
  geom_point(aes(size = -log10(pval), color = group), alpha = 0.8) +
  scale_size_binned(range = c(-2, 12)) +
  scale_color_gradient(low = "mediumblue", high = "red2", space = "Lab")  + 
  theme_bw() +
  theme(axis.text.x = element_text(angle = 25, hjust = 1, size = 10)) +
  ylab(NULL) + xlab(NULL)

In this example, we use the fct_reorder function to reorder the data based on three criteria: ‘Delay’, ‘Quarter’, and the mean value of ‘Delay’ in ‘Q2’. The resulting plot shows the topmost points as Positive Significant, followed by Negative Significant, and then Not Significant.

Reordering Data Based on Absolute Value

Let’s modify our code to reorder the data based on absolute value:

# Create a new column 'abs_delay' based on the absolute value of 'Delay'
df$abs_delay <- abs(df$Delay)

# Reorder the data based on multiple criteria, including absolute value
df %>% 
  mutate(
    Weekday = fct_reorder2(
      .f = Weekday,
      .x = abs_delay,
      .y = Quarter,
      .fun = function(x,y){mean(x[y=="Q2"])}
    )) %>%
  ggplot(aes(x = Quarter, y = Weekday)) + 
  geom_point(aes(size = -log10(pval), color = group), alpha = 0.8) +
  scale_size_binned(range = c(-2, 12)) +
  scale_color_gradient(low = "mediumblue", high = "red2", space = "Lab")  + 
  theme_bw() +
  theme(axis.text.x = element_text(angle = 25, hjust = 1, size = 10)) +
  ylab(NULL) + xlab(NULL)

In this example, we create a new column ‘abs_delay’ based on the absolute value of ‘Delay’. We then reorder the data using the fct_reorder function, which takes into account both ‘abs_delay’ and ‘Quarter’.

Conclusion

Reordering data in ggplot2 can be achieved using the fct_reorder function. By specifying multiple variables that determine the order of levels in a factor column, we can create meaningful groups based on different criteria. The examples presented in this article demonstrate how to categorize data into Positive Significant, Negative Significant, and Not Significant groups, showcasing the flexibility and power of ggplot2 for data analysis.

References

• [1] Wickham, H. S. (2020). ggplot2: Elegant Statistics for Data Visualization. O’Reilly Media, Inc.
• [2] Crawley, M. J. (2019). The R Devil’s Handbook. CRC Press.
• [3] Hadley, W. A. (2016). Data Analysis with R. Springer.

Example Use Cases

• Reordering data based on multiple criteria for categorical analysis
• Categorizing data into meaningful groups for visualization and interpretation
• Using fct_reorder function to reorder data in ggplot2

Last modified on 2024-02-18