# dplyr — A Grammar of Data Manipulation for R

> dplyr is an R package that provides a consistent set of verbs (filter, select, mutate, summarise, arrange) for transforming and summarizing tabular data. It is the most popular data wrangling tool in the R ecosystem and a core member of the tidyverse.

## Install

Save as a script file and run:

# dplyr — A Grammar of Data Manipulation for R

## Quick Use
```r
install.packages("dplyr")
library(dplyr)
starwars %>%
  filter(species == "Human") %>%
  select(name, height, mass) %>%
  mutate(bmi = mass / (height/100)^2) %>%
  arrange(desc(bmi))
```

## Introduction
dplyr is an R package by Hadley Wickham that provides a concise, consistent grammar for the most common data manipulation tasks. Instead of remembering different base R functions with different interfaces, dplyr offers five core verbs that cover the vast majority of data transformation needs. It is designed to work with the pipe operator and integrates tightly with the rest of the tidyverse.

## What dplyr Does
- Filters rows based on conditions with `filter()`
- Selects, renames, and reorders columns with `select()` and `rename()`
- Creates new columns or transforms existing ones with `mutate()` and `transmute()`
- Aggregates data with `summarise()` combined with `group_by()` for split-apply-combine workflows
- Joins tables with `left_join()`, `inner_join()`, `anti_join()`, and other relational verbs

## Architecture Overview
dplyr operates on data frames and tibbles through a verb-based API. Internally, it uses a lazy evaluation system where expressions are captured as quosures and evaluated in the data context. The package can dispatch operations to different backends: in-memory R data frames, database tables via dbplyr (translating dplyr verbs to SQL), or Apache Arrow datasets via arrow. This backend-agnostic design lets the same code work on datasets ranging from kilobytes to terabytes.

## Self-Hosting & Configuration
- Install from CRAN with `install.packages("dplyr")` or as part of the tidyverse
- Works with R 3.6+; recommended to use with tibbles for cleaner printing
- Enable database backends by installing dbplyr and a DBI-compatible driver (RPostgres, RSQLite, etc.)
- Use `.by` argument (dplyr 1.1+) for per-operation grouping without persistent `group_by()`
- Combine with purrr for row-wise or list-column operations

## Key Features
- Consistent verb-based API makes code readable and composable via the pipe operator
- Grouped operations with `group_by()` handle split-apply-combine patterns in a single pipeline
- Backend-agnostic: the same dplyr code runs on data frames, databases, and Arrow tables
- Tidy selection helpers (`starts_with()`, `where()`, `everything()`) simplify column selection
- Window functions (lag, lead, cumsum, row_number, dense_rank) work naturally inside mutate

## Comparison with Similar Tools
- **pandas (Python)** — similar capabilities but uses method chaining and bracket indexing; dplyr's verb API is often considered more readable
- **data.table (R)** — faster for very large in-memory datasets; dplyr trades raw speed for a more approachable syntax
- **SQL** — dplyr verbs map closely to SQL clauses; dbplyr translates dplyr pipelines to SQL automatically
- **Polars (Python/Rust)** — newer, very fast DataFrame library; dplyr has a larger ecosystem of extensions and educational resources in R
- **base R** — subset(), transform(), aggregate() cover similar ground but with inconsistent interfaces; dplyr unifies them

## FAQ
**Q: Is dplyr fast enough for large datasets?**
A: dplyr handles millions of rows efficiently. For very large data (100M+ rows), data.table or Arrow backends may be faster. Use `dtplyr` to get data.table speed with dplyr syntax.

**Q: Can dplyr query databases directly?**
A: Yes. With dbplyr, dplyr verbs are translated to SQL and executed on the database. Call `collect()` to pull results into R.

**Q: What is the difference between dplyr and the tidyverse?**
A: dplyr is one package focused on data manipulation. The tidyverse is a collection of packages (ggplot2, tidyr, readr, purrr, dplyr, and more) that share a common design philosophy.

**Q: How do I apply a function to multiple columns at once?**
A: Use `across()` inside `mutate()` or `summarise()`: `summarise(across(where(is.numeric), mean))`.

## Sources
- https://github.com/tidyverse/dplyr
- https://dplyr.tidyverse.org/

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