Progress Bar Not Showing With Map On Nested Dataframes A Comprehensive Guide

Introduction

In this article, we will delve into a peculiar issue encountered while using the map function from the purrr package in R, specifically when dealing with nested dataframes. The problem arises when a progress bar, intended to provide visual feedback on the progress of the map function, fails to appear. This article aims to provide a comprehensive understanding of the issue, its cause, and potential solutions, illustrated with a reproducible example.

Understanding the Issue

The purrr package in R is a powerful tool for functional programming, offering a suite of functions for iterating over lists and vectors. The map function, in particular, is widely used for applying a function to each element of a list or vector. When working with large datasets or computationally intensive functions, the map function can take a significant amount of time to complete. To provide users with feedback on the progress of the operation, purrr offers a .progress argument, which, when set to TRUE, displays a progress bar.

However, a problem arises when applying map to a nested dataframe. A nested dataframe is a dataframe where one or more columns contain other dataframes. This structure is often used when dealing with hierarchical data or when applying operations to subsets of data. In such cases, the progress bar may not be displayed, even when the .progress argument is set to TRUE. This can be frustrating for users, as it makes it difficult to track the progress of the operation and estimate the time remaining.

Reproducible Example

To illustrate the issue, consider the following reproducible example, which demonstrates the problem using the tidyverse and purrr packages:

library(tidyverse)
library(purrr)

packageVersion("purrr")
#> [1] '1.0.4'

df <- tibble::tibble(
  a = c(1,1,2,2),
  b = c(2,3,4,5)
  )

simple_func <- function(x) {
  Sys.sleep(2)
  return(x+1)
  }

# This works
df %>%
  mutate(c = map(b, simple_func, .progress=TRUE))
#>  ■■■■■■■■■■■■■■■■                  50% |  ETA:  4s
#>  ■■■■■■■■■■■■■■■■■■■■■■■           75% |  ETA:  2s
#> # A tibble: 4 × 3
#> #      a     b c        
#> #  <dbl> <dbl> <list>   
#> #1     1     2 <dbl [1]>
#> #2     1     3 <dbl [1]>
#> #3     2     4 <dbl [1]>
#> #4     2     5 <dbl [1]>

# This doesn't show progress bar
df %>%
  group_by(a) %>%
  nest() %>%
  mutate(c = map(data, simple_func, .progress=TRUE))
#> # A tibble: 2 × 3
#> # Groups:   a [2]
#> #      a data             c           
#> #  <dbl> <list>           <list>      
#> #1     1 <tibble [2 × 1]> <df [2 × 1]>
#> #2     2 <tibble [2 × 1]> <df [2 × 1]>

In this example, we first create a simple dataframe df with two columns, a and b. We then define a function simple_func that takes a number as input, waits for 2 seconds, and returns the number plus 1. Next, we use the mutate and map functions to apply simple_func to each element of column b. The .progress argument is set to TRUE, and the progress bar is displayed as expected.

However, when we group the dataframe by column a, nest the data, and then apply map to the nested dataframes, the progress bar does not appear. This demonstrates the issue we are investigating.

Root Cause Analysis

The reason for the progress bar not showing in the nested dataframe scenario is related to how purrr handles progress updates within grouped operations. When you use group_by() followed by nest(), you're essentially creating a list of dataframes, each representing a group. The map() function then iterates over this list, applying the specified function to each dataframe.

The issue arises because the progress bar mechanism in purrr is designed to work within a single map() call. When you nest dataframes and then use map(), the progress bar is not properly propagated or updated across the different groups. This is because the .progress = TRUE argument is interpreted within the context of each individual map() call on the nested dataframes, rather than across the entire grouped operation.

In essence, purrr doesn't have a built-in mechanism to track the overall progress when map() is applied to a list of dataframes resulting from nest(). The progress bar is initialized and updated for each nested dataframe independently, but there's no aggregation or display of the progress across all groups.

This behavior is a known limitation of purrr and has been discussed in various forums and issue trackers related to the tidyverse ecosystem. While it might seem counterintuitive, it's a consequence of the design choices made in implementing the progress bar functionality within purrr.

Potential Solutions and Workarounds

While there isn't a direct, built-in solution to display a progress bar when using map() on nested dataframes, several workarounds and alternative approaches can help you track the progress of your operations:

1. Using future_map with future Package

The future package provides a way to perform parallel processing in R, and the future_map function from the furrr package (which extends purrr) can be used to apply a function in parallel while also displaying a progress bar. This approach can be particularly useful for computationally intensive tasks.

First, you need to install and load the future and furrr packages:

# install.packages(c("future", "furrr"))
library(future)
library(furrr)

Then, you can use future_map with a specified plan (e.g., multisession for parallel processing):

plan(multisession)

df %>%
  group_by(a) %>%
  nest() %>%
  mutate(c = future_map(data, simple_func, .progress = TRUE))

This approach leverages parallel processing to speed up the operation and displays a progress bar that reflects the overall progress across all groups.

2. Implementing a Custom Progress Bar

If you need a more tailored solution, you can implement a custom progress bar using packages like progress or tqdm. This involves manually updating the progress bar within your function or loop.

Here's an example using the progress package:

library(progress)

custom_map <- function(data, func) {
  n <- length(data)
  pb <- progress_bar$new(total = n)
  result <- list()
  for (i in seq_along(data)) {
    result[[i]] <- func(data[[i]])
    pb$tick()
  }
  result
}

df %>%
  group_by(a) %>%
  nest() %>%
  mutate(c = custom_map(data, simple_func))

In this example, we define a custom custom_map function that creates a progress bar using the progress package and manually updates it within the loop.

3. Using a Loop with print Statements

For simpler cases, you can use a loop with print statements to provide basic progress feedback. This approach doesn't offer a visual progress bar, but it can still be helpful for tracking the progress of your operation.

df_nested <- df %>%
  group_by(a) %>%
  nest()

result <- list()
n_groups <- nrow(df_nested)
for (i in 1:n_groups) {
  print(paste("Processing group", i, "of", n_groups))
  result[[i]] <- simple_func(df_nested$data[[i]])
}

df_nested$c <- result

This approach provides textual feedback on the progress of the operation, which can be sufficient for some use cases.

4. Deblurring the Dataframe

Another workaround involves "deblurring" the dataframe by unnesting it before applying the function and then re-nesting it afterward. This can sometimes allow the progress bar to function correctly, but it might not be suitable for all scenarios, especially if the nesting structure is crucial for your analysis.

df %>%
  group_by(a) %>%
  nest() %>%
  unnest(cols = c(data)) %>%
  group_by(a) %>%
  mutate(c = map(b, simple_func, .progress = TRUE)) %>%
  nest(data = c(b, c))

5. Optimizing the Function

If the primary concern is the time taken by the map operation, consider optimizing the function being applied. Reducing the computational complexity or using more efficient algorithms can significantly reduce the processing time, making the absence of a progress bar less critical.

Best Practices and Recommendations

When working with nested dataframes and applying functions using map, it's essential to be aware of the limitations of the progress bar functionality. Here are some best practices and recommendations to keep in mind:

• Understand the limitations: Be aware that the .progress = TRUE argument in map might not work as expected when dealing with nested dataframes or grouped operations.
• Choose the right approach: Select the most appropriate workaround or alternative based on your specific needs and the complexity of your operation. future_map is a good option for computationally intensive tasks, while custom progress bars offer more flexibility.
• Provide feedback: Even if a visual progress bar isn't available, consider providing some form of feedback to the user, such as print statements or logging messages.
• Optimize your code: Whenever possible, optimize the function being applied to reduce processing time.
• Consider alternative data structures: If the nesting structure is not essential, consider using a flat dataframe or other data structures that might be more amenable to progress tracking.

Conclusion

The issue of the progress bar not showing when using map on nested dataframes is a known limitation in purrr. While there isn't a direct solution, several workarounds and alternative approaches can help you track the progress of your operations. By understanding the cause of the issue and the available solutions, you can effectively manage this challenge and ensure that your data processing tasks are completed efficiently.

This article has provided a comprehensive overview of the problem, its cause, and potential solutions, illustrated with a reproducible example. By following the best practices and recommendations outlined in this article, you can confidently work with nested dataframes and apply functions using map while maintaining visibility into the progress of your operations.