How to Fix - Values Not Appearing in ggplot Plot in R

When creating visualizations with ggplot2 in R, you might encounter situations where some values do not appear in the plot. This can be frustrating, but there are several common reasons and straightforward solutions for this issue. This article will guide you through diagnosing and fixing problems related to missing values in ggplot2 plots.

Common Reasons for Values Not Appearing in ggplot Plot

There are the main reasons are available why values do not appear in the plot so we will discuss all of them.

1. Missing Data or NA Values
2. Coordinate Limits
3. Data Transformation Issues
4. Faceting Problems
5. Incorrect Mapping

1. Missing Data or NA Values

If your data contains NA values, ggplot2 will automatically omit these observations. so Check for NA values and decide how to handle them (e.g., removing or imputing them).

data <- data.frame(
  x = c(1, 2, 3, NA, 5),
  y = c(10, 20, 30, 40, 50)
)

# Plotting without handling NA values
library(ggplot2)
ggplot(data, aes(x, y)) +
  geom_point()

# Handling NA values by removing them
data_clean <- na.omit(data)
ggplot(data_clean, aes(x, y)) +
  geom_point()

Output:

Warning message:
Removed 1 row containing missing values or values outside the scale range
(`geom_point()`). 

2. Coordinate Limits

Sometimes data points may fall outside the coordinate limits set by the plot. so Adjust the coordinate limits using xlim() and ylim() or use coord_cartesian().

# Data points outside default limits
data <- data.frame(
  x = c(1, 2, 3, 4, 5),
  y = c(10, 20, 30, 40, 50)
)

# Incorrect limits
ggplot(data, aes(x, y)) +
  geom_point() +
  xlim(0, 3) +  # Limiting x-axis to 0-3
  ylim(0, 25)   # Limiting y-axis to 0-25

# Correct limits
ggplot(data, aes(x, y)) +
  geom_point() +
  xlim(0, 6) +  # Adjusting x-axis
  ylim(0, 60)   # Adjusting y-axis

# Using coord_cartesian to zoom in
ggplot(data, aes(x, y)) +
  geom_point() +
  coord_cartesian(xlim = c(1, 5), ylim = c(10, 50))

Output:

Warning message:
Removed 3 rows containing missing values or values outside the scale range
(`geom_point()`). 

3. Data Transformation Issues

Applying transformations incorrectly may cause data points to disappear. so Ensure transformations are applied correctly within ggplot2.

# Applying log transformation correctly
ggplot(data, aes(x, y)) +
  geom_point() +
  scale_y_log10()

# Applying transformation incorrectly
ggplot(data, aes(x, log(y))) +
  geom_point()  

Output:

4. Faceting Problems

Faceting can sometimes hide data points if they fall outside the limits of individual facets. so Ensure that faceting variables and their levels are correctly specified and within the data range.

# Sample data with faceting
data <- data.frame(
  x = rep(1:5, 2),
  y = c(10, 20, 30, 40, 50, 15, 25, 35, 45, 55),
  group = rep(c("A", "B"), each = 5)
)

# Faceting by 'group'
ggplot(data, aes(x, y)) +
  geom_point() +
  facet_wrap(~ group)

# Ensure all levels are considered
data$group <- factor(data$group, levels = c("A", "B", "C"))

ggplot(data, aes(x, y)) +
  geom_point() +
  facet_wrap(~ group)

Output:

5. Incorrect Mapping

Incorrectly mapping aesthetics can cause some data points to be excluded or misrepresented. so Double-check the mappings and ensure they are correctly specified.

# Sample data
data <- data.frame(
  x = c(1, 2, 3, 4, 5),
  y = c(10, 20, 30, 40, 50),
  category = factor(c("A", "B", "A", "B", "C"))
)

# Incorrect mapping (accidental typo or wrong variable)
ggplot(data, aes(x, y)) +
  geom_point(aes(color = categories))  # 'categories' is incorrect

# Correct mapping
ggplot(data, aes(x, y)) +
  geom_point(aes(color = category))

Output:

! object 'categories' not found

Conclusion

Fixing "values not appearing" in a ggplot2 plot involves:

• Handling missing data or NA values properly.
• Adjusting coordinate limits to ensure all data points are visible.
• Applying data transformations correctly.
• Ensuring faceting variables and levels are correctly specified.
• Checking and correcting aesthetic mappings.

By systematically addressing these common issues, you can ensure that all relevant data points appear in your ggplot2 visualizations, leading to more accurate and insightful plots.