Handling Large Investment Datasets with R: A Powerful Solution to Data Management Challenges

Dealing with large datasets—where observations run into the millions and file sizes reach gigabytes or more—can be daunting for many data practitioners. However, there is no shortage of specialized tools, many of which are open source, that offer efficient solutions for such challenges.

I illustrate the challenges of handling large data sets with a solution. Recently, Statistics Canada released a comprehensive dataset on investor-owned condominiums. I intend to conduct an in-depth analysis of this data in the near future. But even before diving into that, I want to illustrate the superior data-handling capabilities of R, specifically the data.table package, and how it enables rapid and efficient data manipulation.

The Dataset: Size and Complexity

To set the context, the dataset in its compressed (zipped) form was around 244 MB. At first glance, this may seem like a small file; most modern software can easily handle this size. However, once uncompressed, the CSV file expands to a considerable 5.86 GB, comprising 24,227,546 rows. For context, this scale of data can be quite demanding for analysts working on standard laptops or desktops with limited RAM and processing power. Handling and processing such a large dataset requires a robust toolset, and this is where R shines.
The Solution: Efficient Data Handling with data.table in R

R has long been a staple in the toolkit of statisticians and data scientists and its data.table package is particularly powerful when working with large datasets. After downloading the "entire table" comprising the investor condominium data from Statistics Canada's website, my initial task was to import and inspect the dataset's contents. Using data.table, not only was I able to load the full 5.86 GB dataset within seconds, but I also efficiently navigated through the rows and columns, removing unnecessary columns and optimizing the dataset for future analysis.

The magic lies in how data.table handles memory and its optimized use of indexing, which greatly reduces the time needed to import and manipulate data. While R’s base functions are capable, data.table goes a step further by providing faster processing speeds and a syntax tailored for concise and efficient operations.

Compression and Storage Efficiency

One surprising outcome of this exercise was the significant reduction in file size after processing. The original zipped file, at 244 MB, ballooned to 5.86 GB when unzipped as a CSV. However, after importing the data into R, performing some light cleaning, and saving it in R’s native format (.rda or .RData), the file size was reduced to a mere 18.6 MB on the hard drive. This is a remarkable reduction from the original CSV and far smaller than the original zipped format.

Read the rest of the blog at https://www.linkedin.com/pulse/handling-large-investment-datasets-r-powerful-solution-murtaza-haider-3j2tc/.