Framework

data.table

Overview

data.table is a package for fast and memory-efficient processing of large datasets in R. Its unique syntax enables filtering, aggregation, joining, and other operations with extremely high performance.

Details

data.table is a high-performance data manipulation package for R, developed by Matt Dowle and others. Development began in 2006, and its internal processing implemented in C achieves orders of magnitude faster processing than standard data.frames. The unique DT[i, j, by] syntax can express operations equivalent to SQL's WHERE, SELECT/UPDATE, and GROUP BY in a single line. A distinctive feature is update by reference (using the := operator), which significantly improves memory efficiency by modifying data directly without creating copies. It includes all features necessary for the big data era, such as fast file I/O with fread() and fwrite(), parallel processing support, and memory mapping. It is widely adopted in fields handling large-scale data, such as finance, pharmaceuticals, and telecommunications. With no external dependencies, it is suitable for production environments.

Pros and Cons

Pros

• Overwhelming Processing Speed: Extremely fast due to C implementation
• Memory Efficiency: Minimizes copies through update by reference
• Concise Syntax: Express complex operations in one line with DT[i, j, by]
• Fast File I/O: High-speed processing of large files with fread/fwrite
• Parallel Processing Support: Automatically utilizes multi-core CPUs
• No Dependencies: Stable with no dependencies beyond base R

Cons

• Learning Curve: Takes time to master the unique syntax
• Readability: Can be hard to read for those unfamiliar
• Differences from tidyverse: Feels different for dplyr users
• Debugging Difficulty: Update by reference is hard to track

Main Use Cases

• High-speed financial data analysis
• Genomic data processing
• Large-scale log file analysis
• Real-time data processing
• High-frequency trading data analysis
• IoT sensor data aggregation
• Large-scale simulation result processing

Basic Usage

Installation

# Install from CRAN
install.packages("data.table")

# Install development version
install.packages("data.table", 
                 repos = "https://Rdatatable.gitlab.io/data.table")

# Load library
library(data.table)

Basic Operations

# Create data.table
DT <- data.table(
  id = 1:1000000,
  group = sample(LETTERS[1:5], 1000000, replace = TRUE),
  value = rnorm(1000000),
  date = seq(as.Date("2020-01-01"), by = "hour", length.out = 1000000)
)

# i: Row filtering
DT[group == "A"]
DT[value > 0 & group %in% c("A", "B")]

# j: Column selection/computation
DT[, .(id, value)]  # Column selection
DT[, mean_value := mean(value)]  # Add new column (update by reference)
DT[, .(avg = mean(value), sum = sum(value))]  # Aggregation

# by: Grouping
DT[, .(mean_value = mean(value)), by = group]
DT[, .(count = .N), by = .(group, year = year(date))]

# Compound operations
DT[value > 0, 
   .(avg_value = mean(value), 
     count = .N), 
   by = group][order(-avg_value)]

Advanced Operations

# Update multiple columns by reference
DT[, `:=`(
  value_squared = value^2,
  value_log = log(abs(value) + 1),
  group_mean = mean(value)
), by = group]

# Conditional update
DT[group == "A", value := value * 1.1]

# Fast joins
DT2 <- data.table(
  group = LETTERS[1:5],
  multiplier = c(1.1, 1.2, 1.3, 1.4, 1.5)
)
DT[DT2, on = "group", value := value * multiplier]

# Rolling joins
DT[, rolling_mean := frollmean(value, 24), by = group]

# Shift operations (lag/lead)
DT[, prev_value := shift(value, 1), by = group]
DT[, next_value := shift(value, -1), by = group]

# Fast file read/write
fwrite(DT, "large_data.csv")
DT_read <- fread("large_data.csv")

Practical Examples

# Time series data aggregation
hourly_stats <- DT[, .(
  mean_value = mean(value),
  median_value = median(value),
  sd_value = sd(value),
  count = .N
), by = .(
  group,
  hour = hour(date),
  date = as.Date(date)
)]

# Window functions
DT[order(date), `:=`(
  cumsum_value = cumsum(value),
  rank_value = frank(value),
  pct_rank = percent_rank(value)
), by = group]

# Efficient subsetting
setkey(DT, group, date)  # Set keys
DT["A"]  # Fast binary search
DT[.("A", "2020-06-01")]  # Compound key search

Latest Trends (2025)

• Enhanced Parallel Processing: More operations automatically parallelized
• GPU Support Consideration: Integration with NVIDIA RAPIDS
• Streaming Processing: Enhanced real-time data processing
• Cloud Optimization: Direct reading from S3/GCS
• Arrow Format Support: Faster data exchange

Summary

data.table maintains its position as the fastest data manipulation package in R in 2025. It has become an essential tool particularly in fields handling large-scale data, such as finance and pharmaceuticals. While its unique syntax has a high learning cost, once mastered, its processing speed and memory efficiency are unmatched. It remains the first choice for achieving high-performance data processing in R in the big data era.