Stack Bars in Plot without Preserving Label Order

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When working with bar plots using the ggplot2 package in R, it’s common to want to stack bars on top of each other. However, when dealing with categorical data where labels are not numerical values, preserving the original label order can become a challenge. In this article, we’ll explore how to create stacked bar plots without preserving the label order and discuss potential solutions using alternative packages.

Understanding ggplot2’s Behavior

The question provided by the user highlights the behavior of ggplot2 when creating stacked bar plots with categorical data. The issue is that ggplot2 preserves the original label order according to the levels of the categorical variable used in the aes() function. This means that if you have a variable with multiple levels, and you want to stack bars on top of each other, ggplot2 will maintain the original order of these levels.

For example, consider the following code:

d <- read.table(text='Day Location Length Amount
            1 2 3 1
            1 1 4 2
            3 3 3 2
            3 2 5 1',header=T)

d$Amount <- as.factor(d$Amount) # in real world is not numeric

ggplot(d, aes(x = Day, y = Length)) + 
  geom_bar(aes(fill = Amount), stat = "identity")

In this example, the user wants to create a stacked bar plot where the greater bars are always on top. However, since the Amount variable has multiple levels (in this case, only two unique values: 1 and 2), ggplot2 maintains the original order of these labels.

Alternative Approach Using Data Frames

One potential solution to this issue is to sort the data frame by the column of values in decreasing order before creating the stacked bar plot. Here’s how you can achieve this using a data frame:

d <- read.table(text='Day Length Amount
                1 3 1
                1 4 2
                3 3 2
                3 5 1',header=T)

# Sort the data by column of values in decreasing order
d <- d[order(d$Length, decreasing = TRUE),]

# Duplicate column of values as a factor
d$LengthFactor <- factor(d$Length, levels= unique(d$Length) )

# Create the stacked bar plot
ggplot(d)+
  geom_bar(aes(x=Day, y=Length, group=LengthFactor, fill=Amount), # (1)
           stat="identity", color="white")

In this example, we first sort the data frame d by the column of values (Length) in decreasing order. We then duplicate the column of values as a factor and use it to create the stacked bar plot.

Alternative Approach Using Data Tables

Another potential solution is to use the data.table package, which provides an efficient way to sort and manipulate data frames. Here’s how you can achieve this using data tables:

library(data.table)

sam <- data.frame(population=c(rep("PRO",8),rep("SOM",4)),
                allele=c("alele1","alele2","alele3","alele4",rep("alele5",2),
                            rep("alele3",2),"alele2","alele3","alele3","alele2"), 
                frequency=rep(c(10,5,4,6,7,16),2) #,rep(1,6)))

# Sort the data table by column of values in decreasing order
sam <- sam[order(sam$frequency, decreasing = TRUE),]

# Convert the column of values to a factor
sam$frequency <- factor(sam$frequency, levels = unique(sam$frequency) )

# Create the stacked bar plot
library(ggplot2)
ggplot(sam)+
  geom_bar(aes(x=population, y=frequencySum, group=frequency, fill=allele), # (1)
           stat="identity", color="white")

In this example, we first sort the data table sam by the column of values (frequency) in decreasing order. We then convert the column of values to a factor and use it to create the stacked bar plot.

Conclusion

When working with bar plots using ggplot2, preserving the original label order according to the levels of the categorical variable can become a challenge. However, by sorting the data frame or data table by the column of values in decreasing order and duplicating this column as a factor, we can create stacked bar plots without preserving the label order. This approach may not be suitable for all scenarios, but it provides an alternative solution to this common issue.

References

ggplot2 documentation
data.table documentation
[R documentation](https:// cran.r-project.org/)

Last modified on 2023-11-02