THE PROBLEM IT SOLVES
SEARCH BEFORE YOU LOAD
Standard R functions like read.csv() require loading entire files into memory before you can filter them. For massive logs or data dumps, this exhausts RAM. Grepreaper leverages system-level grep to filter lines at the OS level, returning only matching rows to R. This reduces memory overhead by orders of magnitude.
USE CASES
- Log Analysis: Search millions of log lines for specific errors without loading them.
- Data Exploration: Quickly scan folders of raw data to understand what's inside.
- Performance Pipelines: Pre-filter massive CSV exports early in your pipeline to slash computation time.
CORE CAPABILITIES
EFFICIENT FILTERING
Search through gigabytes of data in seconds by filtering at the system level before R even touches it. Support for complex regex patterns.
BULK AGGREGATION
Count matches, sum numeric columns, and find unique values across multiple files instantly without writing complex loops in R.
RECURSIVE SEARCHING
Search entire directory trees automatically. Just point Grepreaper at a root folder and it will traverse all nested files seamlessly.
TERMINAL PLAYGROUND
Experience how Grepreaper commands work in an interactive R console emulator. Click a quick command to auto-fill, or type your own custom searches against the dataset. Try typing help for a list of commands!
FAQ
Yes. By utilizing system-level grep instead of loading entire files into memory first, Grepreaper can process datasets far larger than your available RAM. You search the data at the file system level, extract only the useful rows, and only those rows enter the R environment.
Grepreaper works seamlessly with CSV files, plain text, and common compressed formats like gzip. It auto-detects file types and applies the correct parsing rules, allowing you to mix file types within the same directory search.
Grepreaper supports both plain text and full regular expressions (regex). You can utilize simple keyword searches, wildcard patterns, and complex POSIX regex syntax to match exactly the rows you need.
Absolutely. Grepreaper's output is formatted as clean R data frames, making it perfectly compatible with dplyr, ggplot2, and other tidyverse tools. Use Grepreaper as your fast ingestion layer, then analyze normally.