Loading and Plotting Large Matrices in R: A “By Parts” Approach

When working with large datasets in R, it’s not uncommon to encounter memory errors or performance issues. One approach to mitigating these problems is to load the data in smaller chunks, process each chunk separately, and then combine the results. In this article, we’ll explore how to plot a matrix “by parts” using the readr package and the dplyr and ggplot2 libraries.

Overview of the Problem

The problem at hand is loading a large 50k x 50k matrix into R memory and plotting its distribution. However, due to memory constraints, attempting to load the entire matrix using read.table() results in an error. Instead, we want to load smaller submatrices one at a time and plot their distributions without overwhelming the system’s memory.

Solution: Using `read_csv_chunked` from `readr`

One way to accomplish this is by utilizing the read_csv_chunked function from the readr package. This function allows us to read data in chunks, apply processing functions to each chunk, and then combine the results.

Required Libraries

Before we begin, make sure you have the necessary libraries installed:

library(dplyr)
library(ggplot2)
library(readr)

These libraries provide the core functionality for data manipulation, visualization, and reading.

Generating Sample Data

To demonstrate our approach, let’s generate some sample fake data:

temp <- tempfile(fileext = ".csv")

fake_dat <- as.data.frame(matrix(rnorm(1000*100), ncol = 100))

write_csv(fake_dat, temp)

In this example, we create a temporary file temp.csv and write a sample dataset containing 100,000 rows and 100 columns to it.

Summarization Function

Next, we define a summarization function that will be applied to each chunk of data read into memory. This function takes two inputs:

The chunk of data (x)
A position indicator (pos)

The function performs the following operations:

Splits the values in x into bins using the cut() function with custom breaks.
Creates a new column added_bin containing these bin labels.
Counts the occurrences of each bin label.

Here’s the summarization function:

summarise_for_hist <- function(x, pos) {
  x %>% 
    mutate(added_bin = cut(V1, breaks = -6:6)) %>% 
    count(added_bin)
}

Note that we’ve manually set the breaks argument to customize the bin labels. You’ll need to adjust these based on your specific data and domain expertise.

Reading Data in Chunks

Now, let’s use read_csv_chunked to read our sample data into chunks of 200 rows each:

small_read <- read_csv_chunked(temp,
                               DataFrameCallback$new(summarise_for_hist),
                               chunk_size = 200
                              )

In this example, we create an object small_read that will store the processed data from each chunk.

Plotting the Histogram

Once we’ve summarized all chunks of data, we can combine and plot the results:

# Combine the summaries into a single dataframe
combined_data <- small_read %>% 
  group_by(added_bin) %>% 
  summarise(total = sum(n))

# Generate a histogram using ggplot2
ggplot(combined_data, aes(x = added_bin, y = total)) + 
  geom_col()

This code groups the combined data by added_bin, calculates the cumulative sum of observations (total), and plots a histogram using geom_col().

Results

The resulting plot will display the distribution of values in your original matrix, aggregated across all chunks processed individually. You can adjust parameters such as chunk_size to optimize performance for different data sizes.

Keep in mind that while this approach mitigates memory issues, it may still be computationally intensive for very large datasets.

Conclusion

Loading and plotting large matrices in R requires careful consideration of memory constraints. By employing techniques like chunking and summarization, you can effectively visualize the distribution of your matrix without overwhelming the system’s resources. In this article, we explored how to plot a matrix “by parts” using readr, dplyr, and ggplot2.

Last modified on 2025-01-21