Framework

dplyr

Overview

dplyr is a package for intuitive and efficient data manipulation in R, serving as a core component of the tidyverse ecosystem. It provides consistent "verb" functions for selecting, filtering, transforming, and aggregating data concisely.

Details

dplyr (pronounced "deep-liar") is an R package dedicated to data manipulation, developed by Hadley Wickham. First released in 2014, it has since become the de facto standard for data analysis in R. It enables SQL-like operations on R data frames, providing intuitive verb functions such as select(), filter(), mutate(), summarize(), and arrange(). A distinctive feature is its combination with the pipe operator (%>%), allowing complex data transformations to be written readably. Internal processing implemented in C++ achieves high-speed operation, supporting large dataset processing. Integration with backends like data.table, arrow, and duckdb enables processing of big data that doesn't fit in memory. Perfect integration with the tidyverse package suite allows building consistent workflows from data loading to visualization.

Pros and Cons

Pros

• Intuitive Grammar: SQL-like verb functions with low learning curve
• Highly Readable Code: Clear processing flow with pipe operators
• Tidyverse Integration: Seamless collaboration with other packages
• Fast Processing: Efficient handling of large data through C++ implementation
• Rich Documentation: Comprehensive tutorials and community support
• Backend Extensions: Integration possible with data.table, arrow, duckdb

Cons

• Memory Usage: Requires attention with large-scale data
• Learning Curve: Need to understand the entire tidyverse
• Performance: Can be slower than data.table in some cases
• Non-standard Evaluation: Requires caution when programming

Main Use Cases

• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Report creation and dashboard development
• Statistical analysis preprocessing
• Machine learning data preparation
• Business intelligence
• Research data formatting

Basic Usage

Installation

# Install from CRAN
install.packages("dplyr")

# Install entire tidyverse
install.packages("tidyverse")

# Install development version
devtools::install_github("tidyverse/dplyr")

Basic Operations

library(dplyr)

# Sample data
df <- data.frame(
  name = c("Alice", "Bob", "Charlie", "David"),
  age = c(25, 30, 35, 28),
  salary = c(50000, 60000, 70000, 55000),
  department = c("Sales", "IT", "IT", "Sales")
)

# select: Column selection
df %>% 
  select(name, salary)

# filter: Row filtering
df %>% 
  filter(age > 28)

# mutate: Adding new columns
df %>% 
  mutate(bonus = salary * 0.1)

# group_by + summarize: Grouping and aggregation
df %>% 
  group_by(department) %>% 
  summarize(
    avg_salary = mean(salary),
    count = n()
  )

# arrange: Sorting
df %>% 
  arrange(desc(salary))

# Chaining multiple operations
df %>% 
  filter(department == "IT") %>% 
  mutate(total_comp = salary * 1.1) %>% 
  select(name, total_comp) %>% 
  arrange(desc(total_comp))

Advanced Operations

# Filtering with complex conditions
df %>% 
  filter(age >= 28 & salary > 55000)

# Multiple grouping
df %>% 
  group_by(department, age_group = cut(age, breaks = c(0, 30, 40))) %>% 
  summarize(
    count = n(),
    avg_salary = mean(salary),
    .groups = 'drop'
  )

# Operations on multiple columns with across()
df %>% 
  mutate(across(where(is.numeric), ~ . * 1.05))

# Join operations
departments <- data.frame(
  department = c("Sales", "IT"),
  location = c("Tokyo", "Osaka")
)

df %>% 
  left_join(departments, by = "department")

Latest Trends (2025)

• DuckDB Backend Integration: Faster large-scale data processing
• Mature Arrow Integration: Efficient Parquet file processing
• Tidypolars Collaboration: Using Polars' speed with dplyr syntax
• Improved Type Safety: Stricter type checking features
• Enhanced Parallel Processing: Optimized multi-core processing

Summary

dplyr maintains its unshakeable position as the first choice for data manipulation in R in 2025. With its intuitive grammar, powerful features, and active community, it is supported by a wide range of users from beginners to advanced practitioners. Its scope of application continues to expand through integration with new backends adapted to the big data era.