What is Polars? | Documentation and tutorials | Series and DataFrames | Methods and pipelines | Subset | Aggregate and modify | Reshape | Join | Lazy execution | Data import | Execute R functions within a Polars query | Data types

Introduction | A Quick Example | The Usage | Loading Fonts | Working with R Markdown | CJK Fonts | How showtext Works | Compatibility with RStudio

Writing functions using polars expressions | Using purrr | Conclusion

How to install | From R-multiverse (recommended) | From GitHub | Details of installation | Pre-built Rust library binaries | Rust build time options | Features | Profile | Minimum Supported Rust Version (MSRV) | Builds for WASM/Emscripten

Basic usage | Multiple functions | Current column | Gotchas | Other verbs | filter() and filter_out() | _if, _at, _all | Why do we like across()? | Why did it take so long to discover across()? | How do you convert existing code?

Overview | One table verbs | arrange(): Arrange rows by variables | distinct(): Select distinct/unique rows | filter(): Return rows with matching conditions | mutate(): Create or transform variables | pull(): Pull out a single variable | relocate(): Change column order | rename(): Rename variables by name | rename_with(): Rename variables with a function | select(): Select variables by name | summarise(): Reduce multiple values down to a single value | slice(): Choose rows by position | Two-table verbs | Mutating joins | Filtering joins

group_by() | Group metadata | Changing and adding to grouping variables | Removing grouping variables | Verbs | summarise() | select(), rename(), and relocate() | arrange() | mutate() | filter() | slice() and friends

Data: starwars | Single table verbs | The pipe | Filter rows with filter() | Arrange rows with arrange() | Choose rows using their position with slice() | Select columns with select() | Add new columns with mutate() | Change column order with relocate() | Summarise values with summarise() | Commonalities | Combining functions with |> | Patterns of operations | Selecting operations | Mutating operations

Introduction | Data masking | Data- and env-variables | Indirection | Name injection | Tidy selection | The tidyselect DSL | How-tos | User-supplied data | One or more user-supplied expressions | Any number of user-supplied expressions | Creating multiple columns | Transforming user-supplied variables | Loop over multiple variables | Use a variable from an Shiny input

Creating | Per row summary statistics | Row-wise summary functions | List-columns | Motivation | Subsetting | Modelling | Repeated function calls | Simulations | Multiple combinations | Varying functions | Previously | rowwise() | do()

Types of window functions | Ranking functions | Lead and lag | Cumulative aggregates | Recycled aggregates

Introduction | case_when() | replace_when() | Type stability | recode_values() | replace_values() | Comparisons | if_else() | coalesce() | na_if() | SQL

Introduction | Using tidyr in packages | Fixed column names | Continuous integration | tidyr v0.8.3 -> v1.0.0 | Conditional code | New syntax for nest() | New syntax for unnest() | nest() preserves groups | nest_() and unnest_() are defunct

Introduction | GitHub users | GitHub repos | Game of Thrones characters | Geocoding with google | Sharla Gelfand's discography

Overview | Example values | Implementation

Automatic guessing | Legacy behavior

Vector parsers | Atomic vectors | Flexible numeric parser | Date/times | Factors | Column specification | Rectangular parsers | Overriding the defaults | Available column specifications | Output

Dates and times | Names of months and days | Timezones | Default formats | Character | Numbers

Referring to ggplot2 functions | Using aes() and vars() in a package function | Best practices for common tasks | Using ggplot2 to visualize an object | Creating a new theme | Testing ggplot2 output | ggplot2 in Suggests | Read more

Colour and fill | Lines | Line type | Linewidth | Line end/join paramters | Polygons | Point | Shape | Text | Font family | Font face | Font size | Justification

ggproto | Creating a new stat | The simplest stat | Stat parameters | Picking defaults | Variable names and default aesthetics | Exercises | Creating a new geom | A simple geom | Collective geoms | Inheriting from an existing Geom | Geoms and Stats with multiple orientation | Omnidirectional stats | Omnidirecitonal geoms | Dealing with required aesthetics | Ambiguous layers | Creating your own theme | Overriding elements | Global elements | Complete vs incomplete | Creating a new faceting | Creating a layout specification | Mapping data into panels | Laying out the panels | Assembling the Facet class | Doing more with facets | Extending existing facet function | Creating new guides | Overriding scale extraction | Guide constructors | Custom drawings

Data | Mapping | Layers | Scales | Facets | Coordinates | Theme | Combining

Performance focused changes across versions | vr packageVersion('ggplot2') | v3.1.0

Introduction | Map: A better way to loop | Progress bars | Parallel computing | Output variants | Input variants | Combinatorial explosion | Filtering and finding with predicates

Introduction | Key differences | Direct translations | Map functions | Extractor shorthands | Predicates | Other vector transforms | Examples | Varying inputs | One input | Two inputs | Any number of inputs | Outputs | Pipes

Mutating joins | Controlling how the tables are matched | Types of join | Observations | Filtering joins | Set operations | Multiple-table verbs

Join helpers | Data masking and tidy selection NOTEs | Deprecation | Multiple dplyr versions | Deprecation of mutate_*() and summarise_*() | Data frame subclasses

Basics | Nested data and models

Introduction | Longer | String data in column names | Numeric data in column names | Many variables in column names | Multiple observations per row | Wider | Capture-recapture data | Aggregation | Generate column name from multiple variables | Tidy census | Implicit missing values | Unused columns | Contact list | Longer, then wider | World bank | Multi-choice | Manual specs | By hand | Theory

Introduction | Tidy selection | Indirection

Data tidying | Defining tidy data | Data structure | Data semantics | Tidying messy datasets | Column headers are values, not variable names | Multiple variables stored in one column | Variables are stored in both rows and columns | Multiple types in one table | One type in multiple tables

Evolution of movie lengths over time | Results from Catalan regional elections, 1980-2015 | Temperatures in Lincoln, Nebraska | Visualization of Poisson random samples with different means | Height of Australian athletes | A cheese plot

Geoms | Ridgelines | Density ridgeline plots | Varying fill colors along the x axis | Stats | Quantile lines and coloring by quantiles or probabilities | Jittering points | Using alternative stats | Themes | Cyclical scales

Lazy vs eager execution | Order of operations | How does polars help? | Use the streaming engine | Use polars functions

Themes | Arranging plots into a grid | Generic plot annotations

Overview

Digits | Basic differences | Terminal zeros | Trailing dot | Showing more digits | Fixed number of digits | Scientific notation | When is it used? | Enforce notation

Options | Per-column number formatting | Rule-based number formatting | Computing on num | Arithmetics | Mathematics | Override | Recovery

Conventions | Column extraction | Definition of x[[j]] | Definition of x$name | Column subsetting | Definition of x[j] | Definition of x[, j] | Definition of x[, j, drop = TRUE] | Row subsetting | Definition of x[i, ] | Definition of x[i, , drop = TRUE] | Row and column subsetting | Definition of x[] and x[,] | Definition of x[i, j] | Definition of x[[i, j]] | Column update | Definition of x[[j]] <- a | Definition of x$name <- a | Column subassignment: x[j] <- a | a is a list or data frame | a is a matrix or array | a is another type of vector | a is NULL | a is not a vector | Row subassignment: x[i, ] <- list(...) | Row and column subassignment | Definition of x[i, j] <- a | Definition of x[[i, j]] <- a

Creating | Coercion | Tibbles vs data frames | Printing | Subsetting | Recycling | Arithmetic operations

broom: let's tidy up a bit | Tidying functions | Other Examples | Generalized linear and non-linear models | Hypothesis testing | Conventions | All functions | tidy functions | augment functions | glance functions

Parsing dates and times | Setting and Extracting information | Time Zones | Time Intervals | Arithmetic with date times | If anyone drove a time machine, they would crash | Vectorization | Further Resources

tl;dr | Introduction | The Color Scales | Comparison | Green-Blind (Deuteranopia) | Red-Blind (Protanopia) | Blue-Blind (Tritanopia) | Desaturated | Usage | Gallery

Formats | Offsets | References

Topics documented elsewhere | Data frame subclasses | Tibble example | Data frame example

Example plots | Basic use | Controlling layout | Stacking and packing plots | Annotating the composition | Want more?

Abstract | Introduction and basics | Additional pipe operators | Aliases | Development

Code transformation | Desired properties | Implications of design decisions | Placeholder binding | Masking environment | Laziness | Numbered placeholders | Three implementations | Nested pipe | Multiple placeholders r fail() | Lazy evaluation r pass() | Persistence and eager unbinding r pass() | Progressive stack r fail() | Lexical effects r pass() | Continuous stack r pass() | Eager lexical pipe | Multiple placeholders r pass() | Lazy evaluation r fail() | Persistence: r fail() | Eager unbinding: r pass() | Progressive stack: r pass() | Lexical effects and continuous stack: r pass() | Lazy masking pipe | Persistence: r pass() | Progressive stack: r fail() | Lexical effects r fail() | Continuous stack r fail()

Tidying k-means clustering

Value labels | labelled() | Missing values | Tagged missing values | User defined missing values

Font Fundamentals | Why sans-serif? | Kern-what? | Tabula Figura | Clean and compact | Weight. What? | TTF. Meh. | Spacing Out