Understanding the Problem with ggplot2’s Y-Axis Range in Data Visualization

Understanding the Problem with ggplot2’s Y-Axis Range

As a data visualization enthusiast, I have encountered numerous challenges while working with popular libraries like R and Python. In this article, we will delve into the world of ggplot2, a powerful data visualization library for R, to explore a common issue that can be frustrating: displaying correct y-axis range.

The Problem with the Data Frame

The problem statement begins with an attempt to plot random test score data in ggplot2. However, the scores are displayed on an unordered y-axis instead of the expected low-to-high order with fixed intervals. The provided code snippet demonstrates this issue:

grade <- rep(c(5,6,7,8,9),times=6)
years <- rep(c(2008,2009,2010), each=10)
tests <- rep(c("English","Math"),times=3,each=5)
scores <- c(3.3,7.6,10.8,4.8,3.0,-2.8,14.8,12.4,0.3,6.0,7.0,3.1,3.7,-0.5,0.6,6.2,9.6,5.3,1.9,1.1,0.0,5.5,6.2,0.3,-0.4,2.2,4.9,4.7,2.6)

data2 <- data.frame(cbind(years,grade,tests,scores))

graph_2 <- ggplot(data=data2, aes(x=years, y=scores)) +
         geom_point(aes(color=factor(interaction(grade,tests)),size=1)) +
         geom_line(aes(group=interaction(tests,grade), color=factor(interaction(grade,tests)))) +
         facet_grid(. ~ grade)

graph_2

Understanding the Cause

The issue lies in how the data frame is constructed using the cbind() function. This function combines the input vectors into a matrix with all elements of the same type, which results in character columns being converted to factors.

data2 <- data.frame(cbind(years,grade,tests,scores))
str(data2)
'data.frame':   30 obs. of  4 variables:
 $ years : Factor w/ 3 levels "2008","2009",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ grade : Factor w/ 5 levels "5","6","7","8",..: 1 2 3 4 5 1 2 3 4 5 ...
 $ tests : Factor w/ 2 levels "English","Math": 1 1 1 1 1 2 2 2 2 2 ...
 $ scores: Factor w/ 28 levels "-0.4","-0.5",..: 17 27 10 20 15 3 12 11 5 24 ...

The Solution

To resolve the issue, remove the cbind() function and create the data frame using only the relevant columns:

data2 <- data.frame(years,grade,tests,scores)
str(data2)
'data.frame':   30 obs. of  4 variables:
 $ years : num  2008 2008 2008 2008 2008 ...
 $ grade : num  5 6 7 8 9 5 6 7 8 9 ...
 $ tests : Factor w/ 2 levels "English","Math": 1 1 1 1 1 2 2 2 2 2 ...
 $ scores: num  3.3 7.6 10.8 4.8 3 -2.8 14.8 12.4 0.3 6 ...

With this change, numeric columns are treated as such, and the plot displays on the expected low-to-high order with fixed intervals.

Conclusion

In conclusion, when working with ggplot2, it’s crucial to understand how data frames are constructed using functions like cbind(). By removing unnecessary columns and creating data frames from relevant ones, we can resolve common issues like displaying correct y-axis range.

Last modified on 2023-06-30