Summary
The issue at hand is sorting the y-axis of a plot based on values in a different column within a nested data structure and combining multiple plots into one file. The current approach uses ggplot2 and purrr to create individual plots, but the y-axis is not sorting as desired. Additionally, the goal is to save all 117 plots as a single file.
Root Cause
The root cause of the issue is the incorrect use of scale_y_discrete and the lack of sorting in the rec_num column. The current code uses unique((df$rec_num)) to set the limits of the y-axis, which does not account for the sorting order. Furthermore, the rec_num column is not being sorted within each nested group.
Why This Happens in Real Systems
This issue occurs in real systems when working with complex data structures and nested data. The use of tidyr::nest and purrr::map can lead to confusing data structures, making it challenging to perform operations like sorting. Additionally, the lack of understanding of ggplot2 and its various scaling functions can lead to incorrect plot configurations.
Real-World Impact
The impact of this issue is that the plots are not accurately representing the data, making it difficult to draw meaningful conclusions. The y-axis is not sorted as desired, which can lead to misinterpretation of the data. Furthermore, having 117 individual plots instead of a single file can be overwhelming and difficult to manage.
Example or Code
To solve the issue, we need to modify the code to sort the rec_num column within each nested group and use the correct scaling function. Here is an example:
library(dplyr)
library(tidyr)
library(ggplot2)
library(purrr)
# Sort rec_num within each nested group
nested_data %
mutate(data = map(data, ~.x %>% arrange(sort_number)))
# Create plots with correct y-axis sorting
nested_data %
mutate(plot = map(data, ~ ggplot(., aes(x = date_time, y = factor(rec_num, levels = 1:21))) +
geom_point(color = "blue") +
theme_bw() +
scale_x_date(expand = c(0.020, 0), limits = as.Date(c("2022-09-05", "2025-11-04")),
date_breaks = "1 month", date_labels = "%b %Y") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(x = "Date", y = "Receiver Number",
title = paste("Tag ID:", first(tag_id), "Species:", first(.$species),
"Fish Release Date/Time:", first(.$fish_release_datetime)))))
# Save plots as a single file
pdf("all_plots.pdf", width = 24, height = 10 * 117)
walk2(nested_data$plot, nested_data$tag_id, function(plot, tag_id) {
print(plot)
})
dev.off()How Senior Engineers Fix It
Senior engineers would identify the root cause of the issue and modify the code to sort the rec_num column within each nested group. They would also use the correct scaling function, such as factor, to sort the y-axis. Additionally, they would use a loop to save all plots as a single file, such as a PDF.
Why Juniors Miss It
Junior engineers may miss this issue due to a lack of understanding of ggplot2 and its various scaling functions. They may also struggle with working with complex data structures and nested data, leading to incorrect plot configurations. Furthermore, they may not consider the importance of sorting the y-axis, leading to misinterpretation of the data. Key takeaways include:
- Sorting data within nested groups is crucial for accurate plot representation
- Using the correct scaling function is essential for sorting the y-axis
- Saving multiple plots as a single file can be achieved using a loop and a file format like PDF
- Understanding complex data structures and nested data is vital for working with
tidyrandpurrr