This article will walk you through a common issue in R's ggplot2 library, where you might want to skip saving a plot if its creation fails or results in an empty plot. We'll demonstrate how to handle these situations gracefully within your code.
The Problem
Often, when generating plots within a loop or a function, you encounter scenarios where the plot creation process may fail due to errors or result in an empty plot. In such cases, attempting to save the plot using ggsave can lead to unexpected behavior or errors.
Let's consider a simple example using the diamonds dataset from the ggplot2 package. We'll loop through different diamond colors, attempting to create a scatter plot for each color. However, in our example, we'll introduce an artificial error condition for the color "Z," which will lead to an empty plot.
library(tidyverse)
data("diamonds")
colors_loop <- c(diamonds$color %>% as.character() %>% unique(), "Z")
for (single_color in colors_loop) {
plot_data <- diamonds %>% filter(color == single_color)
plot_temp <- plot_data %>%
ggplot(aes(x, y)) +
geom_point() +
labs(title = paste0(single_color, "_plot"))
ggsave(paste0(single_color, "_plot.png"))
}
Running this code will result in an error for the color "Z," as no diamonds have that color in the dataset.
Solutions
We can implement a robust error handling approach to prevent these issues. Here's how:
1. Using tryCatch
The tryCatch function allows you to execute a block of code and handle potential errors that might occur.
library(tidyverse)
data("diamonds")
colors_loop <- c(diamonds$color %>% as.character() %>% unique(), "Z")
for (single_color in colors_loop) {
tryCatch({
plot_data <- diamonds %>% filter(color == single_color)
plot_temp <- plot_data %>%
ggplot(aes(x, y)) +
geom_point() +
labs(title = paste0(single_color, "_plot"))
ggsave(paste0(single_color, "_plot.png"))
}, error = function(e) {
print(paste0("Error creating plot for color: ", single_color))
})
}
This code wraps the plot creation and saving steps within a tryCatch block. If an error occurs, the code in the error function will be executed, printing an error message to the console. This allows the loop to continue even when an error is encountered.
2. Checking for Empty Dataframes
Another approach is to check if the filtered plot_data is empty before attempting to create the plot.
library(tidyverse)
data("diamonds")
colors_loop <- c(diamonds$color %>% as.character() %>% unique(), "Z")
for (single_color in colors_loop) {
plot_data <- diamonds %>% filter(color == single_color)
if (nrow(plot_data) > 0) {
plot_temp <- plot_data %>%
ggplot(aes(x, y)) +
geom_point() +
labs(title = paste0(single_color, "_plot"))
ggsave(paste0(single_color, "_plot.png"))
} else {
print(paste0("Empty data for color: ", single_color))
}
}
This code checks the number of rows in plot_data using nrow. If the dataframe is empty, the code skips the plot creation and saving steps, printing a message to the console instead.
3. Combining tryCatch and nrow
For a comprehensive solution, you can combine both tryCatch and the empty dataframe check:
library(tidyverse)
data("diamonds")
colors_loop <- c(diamonds$color %>% as.character() %>% unique(), "Z")
for (single_color in colors_loop) {
tryCatch({
plot_data <- diamonds %>% filter(color == single_color)
if (nrow(plot_data) > 0) {
plot_temp <- plot_data %>%
ggplot(aes(x, y)) +
geom_point() +
labs(title = paste0(single_color, "_plot"))
ggsave(paste0(single_color, "_plot.png"))
} else {
print(paste0("Empty data for color: ", single_color))
}
}, error = function(e) {
print(paste0("Error creating plot for color: ", single_color))
})
}
This solution combines the advantages of both approaches, ensuring that your code handles both errors during plot creation and scenarios where the filtered data is empty.
Conclusion
By implementing error handling techniques like tryCatch and checking for empty dataframes, you can create robust code that gracefully skips saving plots when they fail to generate or result in empty plots. This allows your code to continue running smoothly even in the presence of unexpected situations. Remember to adapt these techniques to your specific code structure and error scenarios to ensure that your plots are saved only when successful.
