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tidyverse/dplyr

dplyr: A grammar of data manipulation

dplyr

Overview

dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges:

mutate() adds new variables that are functions of existing variables
select() picks variables based on their names.
filter() picks cases based on their values.
summarise() reduces multiple values down to a single summary.
arrange() changes the ordering of the rows.

These all combine naturally with group_by() which allows you to perform any operation “by group”. You can learn more about them in vignette("dplyr"). As well as these single-table verbs, dplyr also provides a variety of two-table verbs, which you can learn about in vignette("two-table").

If you are new to dplyr, the best place to start is the data transformation chapter in R for Data Science.

Backends

In addition to data frames/tibbles, dplyr makes working with other computational backends accessible and efficient. Below is a list of alternative backends:

arrow for larger-than-memory datasets, including on remote cloud storage like AWS S3, using the Apache Arrow C++ engine, Acero.
dbplyr for data stored in a relational database. Translates your dplyr code to SQL.
dtplyr for large, in-memory datasets. Translates your dplyr code to high performance data.table code.
duckplyr for large, in-memory datasets. Translates your dplyr code to high performance duckdb queries with zero extra copies and an automatic R fallback when translation isn’t possible.
sparklyr for very large datasets stored in Apache Spark.

Installation

# The easiest way to get dplyr is to install the whole tidyverse:
install.packages("tidyverse")

# Alternatively, install just dplyr:
install.packages("dplyr")

Development version

To get a bug fix or to use a feature from the development version, you can install the development version of dplyr from GitHub.

# install.packages("pak")
pak::pak("tidyverse/dplyr")

Cheat Sheet

Usage

library(dplyr)

starwars |>
  filter(species == "Droid")
#> # A tibble: 6 × 14
#>   name   height  mass hair_color skin_color  eye_color birth_year sex   gender  
#>   <chr>   <int> <dbl> <chr>      <chr>       <chr>          <dbl> <chr> <chr>   
#> 1 C-3PO     167    75 <NA>       gold        yellow           112 none  masculi…
#> 2 R2-D2      96    32 <NA>       white, blue red               33 none  masculi…
#> 3 R5-D4      97    32 <NA>       white, red  red               NA none  masculi…
#> 4 IG-88     200   140 none       metal       red               15 none  masculi…
#> 5 R4-P17     96    NA none       silver, red red, blue         NA none  feminine
#> # ℹ 1 more row
#> # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
#> #   vehicles <list>, starships <list>

starwars |>
  select(name, ends_with("color"))
#> # A tibble: 87 × 4
#>   name           hair_color skin_color  eye_color
#>   <chr>          <chr>      <chr>       <chr>    
#> 1 Luke Skywalker blond      fair        blue     
#> 2 C-3PO          <NA>       gold        yellow   
#> 3 R2-D2          <NA>       white, blue red      
#> 4 Darth Vader    none       white       yellow   
#> 5 Leia Organa    brown      light       brown    
#> # ℹ 82 more rows

starwars |>
  mutate(name, bmi = mass / ((height / 100)^2)) |>
  select(name:mass, bmi)
#> # A tibble: 87 × 4
#>   name           height  mass   bmi
#>   <chr>           <int> <dbl> <dbl>
#> 1 Luke Skywalker    172    77  26.0
#> 2 C-3PO             167    75  26.9
#> 3 R2-D2              96    32  34.7
#> 4 Darth Vader       202   136  33.3
#> 5 Leia Organa       150    49  21.8
#> # ℹ 82 more rows

starwars |>
  arrange(desc(mass))
#> # A tibble: 87 × 14
#>   name      height  mass hair_color skin_color eye_color birth_year sex   gender
#>   <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
#> 1 Jabba De…    175  1358 <NA>       green-tan… orange         600   herm… mascu…
#> 2 Grievous     216   159 none       brown, wh… green, y…       NA   male  mascu…
#> 3 IG-88        200   140 none       metal      red             15   none  mascu…
#> 4 Darth Va…    202   136 none       white      yellow          41.9 male  mascu…
#> 5 Tarfful      234   136 brown      brown      blue            NA   male  mascu…
#> # ℹ 82 more rows
#> # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
#> #   vehicles <list>, starships <list>

starwars |>
  group_by(species) |>
  summarise(
    n = n(),
    mass = mean(mass, na.rm = TRUE)
  ) |>
  filter(
    n > 1,
    mass > 50
  )
#> # A tibble: 9 × 3
#>   species      n  mass
#>   <chr>    <int> <dbl>
#> 1 Droid        6  69.8
#> 2 Gungan       3  74  
#> 3 Human       35  81.3
#> 4 Kaminoan     2  88  
#> 5 Mirialan     2  53.1
#> # ℹ 4 more rows

Getting help

If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub. For questions and other discussion, please use forum.posit.co.

Code of conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

satijalab/seurat

R toolkit for single cell genomics

Seurat v5

Seurat is an R toolkit for single-cell genomics, developed and maintained by the Satija Lab at NYGC.

We are excited to release Seurat v5! This update introduces new functionality for spatial, multimodal, and scalable single-cell analysis.

Seurat v5 is backwards-compatible with previous versions, so that users will continue to be able to re-run existing workflows.

Installation instructions, documentation, and tutorials can be found at:

https://satijalab.org/seurat

Seurat can be successfully installed in R on any system (Mac OS X, Linux, Windows).

Improvements and new features are added on a regular basis. For the most up-to-date development version, refer to the main branch. For the most recent version released on CRAN, refer to the cran branch.

Please post under Issues with any bug reports, feature requests, etc., and use Discussions to engage with the wider Seurat community on analysis questions and other topics. PRs are welcome if you would like to contribute.

For a version history/changelog, please see the NEWS file.

tidyverse/ggplot2

An implementation of the Grammar of Graphics in R

ggplot2

Overview

ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.

Installation

# The easiest way to get ggplot2 is to install the whole tidyverse:
install.packages("tidyverse")

# Alternatively, install just ggplot2:
install.packages("ggplot2")

# Or the development version from GitHub:
# install.packages("pak")
pak::pak("tidyverse/ggplot2")

Cheatsheet

Usage

It’s hard to succinctly describe how ggplot2 works because it embodies a deep philosophy of visualisation. However, in most cases you start with ggplot(), supply a dataset and aesthetic mapping (with aes()). You then add on layers (like geom_point() or geom_histogram()), scales (like scale_colour_brewer()), faceting specifications (like facet_wrap()) and coordinate systems (like coord_flip()).

library(ggplot2)

ggplot(mpg, aes(displ, hwy, colour = class)) +
  geom_point()

Lifecycle

ggplot2 is now 18 years old and is used by hundreds of thousands of people to make millions of plots. That means, by-and-large, ggplot2 itself changes relatively little. When we do make changes, they will be generally to add new functions or arguments rather than changing the behaviour of existing functions, and if we do make changes to existing behaviour we will do them for compelling reasons.

If you are looking for innovation, look to ggplot2’s rich ecosystem of extensions. See a community maintained list at https://exts.ggplot2.tidyverse.org/gallery/.

Learning ggplot2

If you are new to ggplot2 you are better off starting with a systematic introduction, rather than trying to learn from reading individual documentation pages. Currently, there are several good places to start:

The Data Visualization and Communication chapters in R for Data Science. R for Data Science is designed to give you a comprehensive introduction to the tidyverse, and these two chapters will get you up to speed with the essentials of ggplot2 as quickly as possible.
If you’d like to take an online course, try Data Visualization in R With ggplot2 by Kara Woo.
If you’d like to follow a webinar, try Plotting Anything with ggplot2 by Thomas Lin Pedersen.
If you want to dive into making common graphics as quickly as possible, I recommend The R Graphics Cookbook by Winston Chang. It provides a set of recipes to solve common graphics problems.
If you’ve mastered the basics and want to learn more, read ggplot2: Elegant Graphics for Data Analysis. It describes the theoretical underpinnings of ggplot2 and shows you how all the pieces fit together. This book helps you understand the theory that underpins ggplot2, and will help you create new types of graphics specifically tailored to your needs.
For articles about announcements and deep-dives you can visit the tidyverse blog.

Getting help

There are two main places to get help with ggplot2:

The Posit Community (formerly RStudio Community) is a friendly place to ask any questions about ggplot2.
Stack Overflow is a great source of answers to common ggplot2 questions. It is also a great place to get help, once you have created a reproducible example that illustrates your problem.

Rdatatable/data.table

R's data.table package extends data.frame:

data.table

data.table provides a high-performance version of base R's data.frame with syntax and feature enhancements for ease of use, convenience and programming speed.

The data.table project uses a custom governance agreement and is fiscally sponsored by NumFOCUS. Consider making a tax-deductible donation to help the project pay for developer time, professional services, travel, workshops, and a variety of other needs.

Why `data.table`?

concise syntax: fast to type, fast to read
fast speed
memory efficient
careful API lifecycle management
community
feature rich

Features

fast and friendly delimited file reader: ?fread, see also convenience features for small data
fast and feature rich delimited file writer: ?fwrite
low-level parallelism: many common operations are internally parallelized to use multiple CPU threads
fast and scalable aggregations; e.g. 100GB in RAM (see benchmarks on up to two billion rows)
fast and feature rich joins: ordered joins (e.g. rolling forwards, backwards, nearest and limited staleness), overlapping range joins (similar to IRanges::findOverlaps), non-equi joins (i.e. joins using operators >, >=, <, <=), aggregate on join (by=.EACHI), update on join
fast add/update/delete columns by reference by group using no copies at all
fast and feature rich reshaping data: ?dcast (pivot/wider/spread) and ?melt (unpivot/longer/gather)
any R function from any R package can be used in queries not just the subset of functions made available by a database backend, also columns of type list are supported
has no dependencies at all other than base R itself, for simpler production/maintenance
the R dependency is as old as possible for as long as possible, currently R 3.5.0 (2018), and we continuously test against that version

Installation

install.packages("data.table")

# latest development version (only if newer available)
data.table::update_dev_pkg()

# latest development version (force install)
install.packages("data.table", repos="https://rdatatable.gitlab.io/data.table")

See the Installation wiki for more details.

Usage

Use data.table subset [ operator the same way you would use data.frame one, but...

no need to prefix each column with DT$ (like subset() and with() but built-in)
any R expression using any package is allowed in j argument, not just list of columns
extra argument by to compute j expression by group

library(data.table)
DT = as.data.table(iris)

# FROM[WHERE, SELECT, GROUP BY]
# DT  [i,     j,      by]

DT[Petal.Width > 1.0, mean(Petal.Length), by = Species]
#      Species       V1
#1: versicolor 4.362791
#2:  virginica 5.552000

Getting started

Introduction to data.table vignette
Getting started wiki page
Examples produced by example(data.table)

Cheatsheets

Community

data.table is widely used by the R community. It is being directly used by hundreds of CRAN and Bioconductor packages, and indirectly by thousands. It is one of the top most starred R packages on GitHub, and was highly rated by the Depsy project. If you need help, the data.table community is active on StackOverflow.

A list of packages that significantly support, extend, or make use of data.table can be found in the Seal of Approval document.

Stay up-to-date

click the Watch button at the top and right of GitHub project page
read NEWS file
follow #rdatatable and the r_data_table account on X/Twitter
follow #rdatatable and the r_data_table account on fosstodon
follow the data.table community page on LinkedIn
watch recent Presentations
read recent Articles
read posts on The Raft

Contributing

Guidelines for filing issues / pull requests: Contribution Guidelines.

swirldev/swirl_courses

🎓 A collection of interactive courses for the swirl R package.

swirl courses

This is a collection of interactive courses for use with the swirl R package. You'll find instructions for installing courses further down on this page. Some courses are still in development and we'd love to hear any suggestions you have as you work through them.

For more information regarding swirl, visit swirlstats.com or the swirl GitHub repository. If you'd like to write your own interactive content, please visit the Instructors page of our website.

Here are our current offerings, organized by level of difficulty:

Beginner

R Programming: The basics of programming in R
R Programming E: Same as the original, but modified slightly for in-class use (see below ***)
The R Programming Environment

*** R Programming E is identical to R Programming, except we've eliminated the prompts for Coursera credentials at the end of each lesson and instead give students the option to send an email to their instructor notifying them of completion. Admittedly, it's sort of a hack until we come up with a more robust solution for in-class use (i.e. an instructor "dashboard").

Intermediate

Regression Models: The basics of regression modeling in R
Getting and Cleaning Data: dplyr, tidyr, lubridate, oh my!

Advanced

Statistical Inference: This intermediate to advanced level course closely follows the Statistical Inference course of the Johns Hopkins Data Science Specialization on Coursera. It introduces the student to basic concepts of statistical inference including probability, hypothesis testing, confidence intervals and p-values. It concludes with an initiation to topics of particular relevance to big data, issues of multiple testing and resampling.
Advanced R Programming

Since our users come from a variety backgrounds, it's very hard to label material as Beginner, Intermediate, or Advanced. If you find something that is labelled Beginner to be challenging, please don't be discouraged. The first step of learning anything is to acknowledge that you are capable of understanding it. True understanding will come with time and practice.

Course Authors

Writing swirl Courses: An interactive guides and example for swirl course authors. The first group of lessons cover basics. The rest cover special topics useful primarily as samples--points of departure for one's own material. For more comprehensive documentation about writing your own swirl courses see http://swirlstats.com/swirlify/.

Install and run a course automatically from swirl

This is the preferred method of installing courses. It automates the process by allowing you to do everything right from the R console.

Make sure you have a recent version version of swirl:

install.packages("swirl")

Enter the following from the R console, substituting the name of the course that you wish to install:

library(swirl)
install_course("Course Name Here")
swirl()

For example, install_course("R Programming") will install the R Programming course. Please note that course names are case sensitive!

If that doesn't work for you...

Install and run a course manually

If the automatic course installation method outlined above does not work for you, then there's a simple alternative.

Find the course you want to install on the Swirl Course network website.
Follow the manual installation instructions on the course page.

If that does not work for you, consider taking a look at the legacy manual install instructions.

Uninstall a course

If you'd like to remove a course at any time, you can use uninstall_course("Course Name Here").

Using swirl in the classroom

Instructors around the world are using swirl in their classrooms. We think this is awesome. If you're an instructor, please feel free to do the same -- free of charge. While your students may be paying to take your course or attend your institution, we simply ask that you don't charge people directly for the use of our software or instructional content.

If you are not sure about a particular use case, don't hesitate to post a question to our Google Group.

rstudio/shiny

Easy interactive web applications with R

shiny

Easily build rich and productive interactive web apps in R — no HTML/CSS/JavaScript required.

Features

An intuitive and extensible reactive programming model which makes it easy to transform existing R code into a "live app" where outputs automatically react to new user input.
- Compared to event-based programming, reactivity allows Shiny to do the minimum amount of work when input(s) change, and allows humans to more easily reason about complex MVC logic.
A prebuilt set of highly sophisticated, customizable, and easy-to-use widgets (e.g., plots, tables, sliders, dropdowns, date pickers, and more).
An attractive default look based on Bootstrap which can also be easily customized with the bslib package or avoided entirely with more direct R bindings to HTML/CSS/JavaScript.
Seamless integration with R Markdown, making it easy to embed numerous applications natively within a larger dynamic document.
Tools for improving and monitoring performance, including native support for async programming, caching, load testing, and more.
Modules: a framework for reducing code duplication and complexity.
An ability to bookmark application state and/or generate code to reproduce output(s).
A rich ecosystem of extension packages for more custom widgets, input validation, unit testing, and more.

Installation

To install the stable version from CRAN:

install.packages("shiny")

Getting Started

Once installed, load the library and run an example:

library(shiny)
# Launches an app, with the app's source code included
runExample("06_tabsets")
# Lists more prepackaged examples
runExample()

For more examples and inspiration, check out the Shiny User Gallery.

For help with learning fundamental Shiny programming concepts, check out the Mastering Shiny book and the Shiny Tutorial. The former is currently more up-to-date with modern Shiny features, whereas the latter takes a deeper, more visual, dive into fundamental concepts.

Join the conversation

If you want to chat about Shiny, meet other developers, or help us decide what to work on next, join us on Discord.

Getting Help

To ask a question about Shiny, please use the RStudio Community website.

For bug reports, please use the issue tracker and also keep in mind that by writing a good bug report, you're more likely to get help with your problem.

Contributing

We welcome contributions to the shiny package. Please see our CONTRIBUTING.md file for detailed guidelines of how to contribute.

License

The shiny package as a whole is licensed under the MIT License. See the LICENSE file for more details.

R version support

Shiny is supported on the latest release version of R, as well as the previous four minor release versions of R. For example, if the latest release R version is 4.3, then that version is supported, as well as 4.2, 4.1, 4.0, 3.6.

jinworks/CellChat

R toolkit for inference, visualization and analysis of cell-cell communication from single-cell and spatially resolved transcriptomics

Update

CellChat v3 (Spatial CellChat) is an updated version that

enables the inference of cell-cell communication at single-cell resolution from spatial transcriptomics data.
is applicable to diverse technologies of spatial transcriptomics data. We add Frequently Asked Questions (FAQ) when analyzing spatially resolved transcriptomics datasets, particularly on how to apply Spatial CellChat to different technologies of spatial transcriptomics data, including sequencing-based and in-situ imaging-based readouts.

CellChat v2 is a version that includes

inference of spatially proximal cell-cell communication between interacting cell groups from spatially resolved transcriptomics
expanded database CellChatDB v2 by including more than 1000 protein and non-protein interactions (e.g. metabolic and synaptic signaling) with rich annotations. A function named updateCellChatDB is also provided for easily updating CellChatDB.
new functionalities enabling easily interface with other computational tools for single-cell data analysis and cell-cell communication analysis
interactive web browser function to allow exploration of CellChat outputs of spatially proximal cell-cell communication

For the version history and detailed important changes, please see the NEWS file.

A step-by-step protocol for cell-cell communication analysis using CellChat is available at Jin et al., Nature Protocols 2024. Please kindly cite this paper when using CellChat version >= 1.5. We greatly appreciate the users' support and suggestions that make it possible for us to update CellChat since we published the first version in the year of 2021.

Capabilities

In addition to infer the intercellular communication from any given scRNA-seq data and spatially resolved transcriptomics data, CellChat provides functionality for further data exploration, analysis, and visualization.

It can quantitatively characterize and compare the inferred cell-cell communication networks using an integrated approach by combining social network analysis, pattern recognition, and manifold learning approaches.
It provides an easy-to-use tool for extracting and visualizing high-order information of the inferred networks. For example, it allows ready prediction of major signaling inputs and outputs for all cell populations and how these populations and signals coordinate together for functions.
It enables comparative analysis of cell-cell communication across different conditions and identification of altered signaling and cell populations.
It provides several visualization outputs to facilitate intuitive user-guided data interpretation.

Installation

To ensure efficient and scalable inference of cell-cell communication at single-cell resolution from spatial transcriptomics data, Spatial CellChat optimizes the data structure within CellChat object. To enable users still can run their previously calculated CellChat v1/v2 object and smoothly upgrade to CellChat v3, we currently deposite the source codes and tutorials of Spatial CellChat at another GitHub repository.

CellChat v3 (Spatial CellChat) R package can be easily installed from Github using devtools:

devtools::install_github("jinworks/SpatialCellChat")

Installation of other dependencies

Install BiocNeighbors using BiocManager::install("BiocNeighbors") if you encounter any issue.
Install MERINGUE using devtools::install_github("JEFworks-Lab/MERINGUE") if you encounter any issue.
Install ALRA using devtools::install_github("KlugerLab/ALRA") if you encounter any issue.
Install RcppML using devtools::install_github("zdebruine/RcppML") if you encounter any issue.

CellChat v1/v2 R package can be easily installed from Github using devtools:

devtools::install_github("jinworks/CellChat")

Installation of other dependencies

Install NMF (>= 0.23.0) using install.packages('NMF'). Please check here for other solutions if you encounter any issue. You might can set Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS=TRUE) if it throws R version error.
Install circlize (>= 0.4.12) using devtools::install_github("jokergoo/circlize") if you encounter any issue.
Install ComplexHeatmap using devtools::install_github("jokergoo/ComplexHeatmap") if you encounter any issue.
Install UMAP python pacakge for dimension reduction: pip install umap-learn. Please check here if you encounter any issue.

Some users might have issues when installing CellChat pacakge due to different operating systems and new R version. Please check the following solutions:

Installation on Mac OX with R > 3.6: Please re-install Xquartz.
Installation on Windows, Linux and Centos: Please check the solution for Windows and Linux.

Tutorials

Please check the tutorial directory of the repo. Example datasets are publicly available at figshare. Please check the Jin et al., Nature Protocols 2024 for a comprehensive protocol of cell-cell communication analysis using CellChat.

Analysis of single-cell transcriptomics data

Analysis of spatially resolved omics data

Additional utilities

Web-based “CellChat Explorer”

We build a user-friendly web-based “CellChat Explorer” that contains two major components:

Ligand-Receptor Interaction Explorer that allows easy exploration of our novel ligand-receptor interaction database, a comprehensive recapitulation of known molecular compositions including multimeric complexes and co-factors. Our database CellChatDB is a manually curated database of literature-supported ligand-receptor interactions in both human and mouse. Of note, this Explorer currently only shows the original CellChatDB, but did not include the new interactions in CellChatDB v2.
Cell-Cell Communication Atlas Explorer that allows easy exploration of the cell-cell communication for any given scRNA-seq dataset that has been processed by our R toolkit CellChat.

We also developed an Interactive Web Browser that allows exploration of CellChat outputs of spatially proximal cell-cell communication using a built-in function runCellChatApp, and a standalone CellChat Shiny App for the above Cell-Cell Communication Atlas Explorer.

Help, Suggestion and Contribution

If you have any question, comment or suggestion, please use github issue tracker to report coding related issues of CellChat.

Before reporting an issue

First check the GitHub issues to see if the same or a similar issues has been reported and resolved. This relieves the developers from addressing the same issues and helps them focus on adding new features!
The best way to figure out the issues is running the sources codes of the specific functions by yourself. This will also relieve the developers and helps them focus on the common issues! I am sorry, but I have to say I have no idea on many errors except that I can reproduce the issues.
Minimal and reproducible example are required when filing a GitHub issue. In certain cases, please share your CellChat object and related codes to reproduce the issues.
Users are encouraged to discuss issues and bugs using the github issues instead of email exchanges.

Contribution

CellChat is an open source software package and any contribution is highly appreciated!

We use GitHub's Pull Request mechanism for reviewing and accepting submissions of any contribution. Issue a pull request on the GitHub website to request that we merge your branch's changes into CellChat's master branch. Be sure to include a description of your changes in the pull request, as well as any other information that will help the CellChat developers involved in reviewing your code.

System Requirements

Hardware requirements: CellChat package requires only a standard computer with enough RAM to support the in-memory operations.
Software requirements: This package is supported for macOS, Windows and Linux. The package has been tested on macOS: Ventura (version 13.5) and Windows 10. Dependencies of CellChat package are indicated in the Description file, and can be automatically installed when installing CellChat pacakge. CellChat can be installed on a normal computer within few mins.

About CellChat and CellChatDB

CellChat is an R package designed for inference, analysis, and visualization of cell-cell communication from single-cell and spatially resolved transcriptomics. CellChat aims to enable users to identify and interpret cell-cell communication within an easily interpretable framework, with the emphasis of clear, attractive, and interpretable visualizations.

CellChatDB is a manually curated database of literature-supported ligand-receptor interactions in mutiple species, leading to a comprehensive recapitulation of known molecular interaction mechanisms including multi-subunit structure of ligand-receptor complexes and co-factors.

If you use CellChat or CellChatDB in your research, please considering citing our papers:

Suoqin Jin et al., CellChat for systematic analysis of cell–cell communication from single-cell transcriptomics, Nature Protocols 2024 [CellChat v2] (Please kindly cite this paper when using CellChat version >= 1.5)
Suoqin Jin et al., Inference and analysis of cell-cell communication using CellChat, Nature Communications 2021 [CellChat v1]

sqjin/CellChat

R toolkit for inference, visualization and analysis of cell-cell communication from single-cell data

CAUTION

We have updated CellChat to v2 and migrated CellChat to a new repository. This repository will be NOT updated and maintained any more. Please check the new repository jinworks/CellChat for the new updates, and the CellChat v2 paper for a comprehensive protocol of CellChat.

About CellChat and CellChatDB

CellChat is an R package designed for inference, analysis, and visualization of cell-cell communication from single-cell data. CellChat aims to enable users to identify and interpret cell-cell communication within an easily interpretable framework, with the emphasis of clear, attractive, and interpretable visualizations.

If you use CellChat in your research, please considering citing our papers:

Capabilities

In addition to infer the intercellular communication from any given single-cell data, CellChat provides functionality for further data exploration, analysis, and visualization.

It can quantitatively characterize and compare the inferred cell-cell communication networks using a systems approach by combining social network analysis, pattern recognition, and manifold learning approaches.
It provides an easy-to-use tool for extracting and visualizing high-order information of the inferred networks. For example, it allows ready prediction of major signaling inputs and outputs for all cell populations and how these populations and signals coordinate together for functions.
It enables comparative analysis of cell-cell communication across different conditions and identification of altered signaling and cell populations.
It provides several visualization outputs to facilitate intuitive user-guided data interpretation.

r-spatial/sf

Simple Features for R

A package that provides simple features access for R.

Blogs, links • Cheatsheet • Installing • Contributing • Acknowledgment • How to cite

Package sf:

represents simple features as records in a data.frame or tibble with a geometry list-column
represents natively in R all 17 simple feature types for all dimensions (XY, XYZ, XYM, XYZM)
interfaces to GEOS for geometrical operations on projected coordinates, and (through R package s2) to s2geometry for geometrical operations on ellipsoidal coordinates
interfaces to GDAL, supporting all driver options, Date and POSIXct and list-columns
interfaces to PRØJ for coordinate reference system conversion and transformation
uses well-known-binary serialisations written in C++/Rcpp for fast I/O with GDAL and GEOS
reads from and writes to spatial databases such as PostGIS using DBI
is extended by
- lwgeom for selected liblwgeom/PostGIS functions
- stars for raster data, and raster or vector data cubes (spatial time series)
- sfnetworks for geospatial network data

Books, journal articles, blogs, presentations, vignettes, sp-sf wiki

an open access R Journal article summarizes the package
two books: Spatial Data Science: with applications in R, Geocomputation with R
package vignettes: first, second, third, fourth, fifth, sixth, seventh
blog posts: first, second, third, fourth
the original R Consortium ISC proposal, the R Consortium blog post
presentations: rstudio::conf 2018 (video), UseR! 2016
wiki page describing sp-sf migration

Cheatsheet

CC 4.0 BY Ryan Garnett

Installing

Install either from CRAN with:

install.packages("sf")

This will install binary packages on Windows and MacOS, unless you configured R such that it tries to install source packages; in that case, see below.

Install development versions from GitHub with:

library(remotes)
install_github("r-spatial/sf")

Windows

Installing sf from source works under Windows when Rtools is installed.

MacOS

MacOS users are strongly encouraged to install the sf binary packages from CRAN, unless they are familiar with compilers, linking, C++ source code, and homebrew. If you experience that R tries to install sf from source (or otherwise your install fails but you don't understand what is going on) try again by explicitly installing the binary, using

install.packages("sf", type = "binary")

The remainder of this section is for those who understand what source installs mean, and imply.

Perhaps the easiest way of an install from source is to first install gdal using Homebrew. Recent versions of Homebrew include a full-featured up-to-date gdal formula, which installs proj and gdal at the same time:

brew install pkg-config
brew install gdal

Once gdal is installed, you may be able to install sf package from source in R. With the current version of proj on homebrew, installation requires additional configuration:

install.packages("sf", type = "source", configure.args = "--with-proj-lib=$(brew --prefix)/lib/")

Or the development version:

library(remotes)
install_github("r-spatial/sf", configure.args = "--with-proj-lib=$(brew --prefix)/lib/")

Alternatively, these instructions explain how to install gdal using kyngchaos frameworks.

For Mac OS 11 Big Sur source install instruction, see here

Linux

For Unix-alikes, GDAL (>= 2.0.1), GEOS (>= 3.4.0) and PROJ (>= 4.8.0) are required.

Ubuntu

Dependencies for recent versions of Ubuntu (18.04 and later) are available in the official repositories; you can install them with:

sudo apt -y update && apt install -y libudunits2-dev libgdal-dev libgeos-dev libproj-dev libsqlite3-dev

However, to get more up-to-date versions of dependencies such as GDAL, GEOS and PROJ we recommend adding the ubuntugis-unstable PPA to the package repositories and installing them as follows:

sudo add-apt-repository ppa:ubuntugis/ubuntugis-unstable
sudo apt update
sudo apt install libudunits2-dev libgdal-dev libgeos-dev libproj-dev libsqlite3-dev

Adding this PPA is required for installing sf on older versions of Ubuntu (e.g. Xenial).

Another option, for advanced users, is to install dependencies from source; see e.g. an older Travis config file for hints.

Fedora

The following command installs all required dependencies:

sudo dnf install gdal-devel proj-devel geos-devel sqlite-devel udunits2-devel

Arch

Get gdal, proj, geos and podofo from the main repos, and udunits from the AUR:

pacman -S gdal proj geos arrow podofo
yay/pacaur/yaourt/whatever -S udunits

`renv` or `conda`

There are several reports that sf fails to install as a source package when R is used with renv, or when R is installed in a conda environment. If you experience this, please only raise an issue here if the problem persists without renv or conda, and otherwise

try to sort this out with the renv developers or the conda maintainers, or
try to use binary installs of the sf package, e.g. from r2u, or the Posit package manager

Other

To install on Debian, the rocker geospatial Dockerfiles may be helpful. Ubuntu Dockerfiles are found here.

Support for (Geo)Parquet and Arrow

sf links to GDAL, but does not control how GDAL was configured and built. Not every GDAL installation has built-in support for GeoParquet or Arrow. Please see this issue, or configure and compile GDAL with Parquet support e.g. using this Dockerfile.

Multiple GDAL, GEOS and/or PROJ versions on your system

If you use dynamic linking (installation from source) and have multiple versions of these libraries installed (e.g. one from ubuntugis-unstable, another installed from source in /usr/local/lib) then this will in general not work, even when setting LD_LIBRARY_PATH manually. See here for the reason why.

lwgeom

Functions and methods that require liblwgeom, including ellipsoidal (not spherical or Euclidean) metrics (area, distances), are provided by and used from lwgeom, which is also on CRAN.

Contributing

Contributions of all sorts are most welcome, issues and pull requests are the preferred ways of sharing them.
When contributing pull requests, please adhere to the package style (in package code use = rather than <-; don't change indentation; tab stops of 4 spaces are preferred).
This project is released with a Contributor Code of Conduct. By participating in this project, you agree to abide by its terms.

How to cite

Package sf can be cited as:

Edzer Pebesma, 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10:1, 439-446.
Pebesma, E.; Bivand, R. (2023). Spatial Data Science: With Applications in R (1st ed.). 314 pages. Chapman and Hall/CRC.

Acknowledgment

This project gratefully acknowledges financial support from the

hadley/r4ds

R for data science: a book

R for Data Science

This repository contains the source of R for Data Science book. The book is built using Quarto.

Images

Omnigraffle drawings

Font: 12pt Guardian Sans Condensed / Ubuntu mono
Export as 300 dpi png.
Website font is 18 px = 13.5 pt, so scale dpi to match font sizes: 270 = 300 * 12 / 13.5. (I also verified this empirically by screenshotting.)
```
#| echo: FALSE
#| out.width: NULL
knitr::include_graphics("diagrams/transform.png", dpi = 270)
```

Screenshots

Make sure you're using a light theme. For small interface elements (eg. toolbars), zoom in twice.
Screenshot with Cmd + Shift + 4.

Don't need to set dpi:

#| echo: FALSE
#| out.width: NULL
knitr::include_graphics("screenshots/rstudio-wg.png")

O'Reilly

To generate book for O'Reilly, build the book then:

# pak::pak("hadley/htmlbook")
htmlbook::convert_book()

html <- list.files("oreilly", pattern = "[.]html$", full.names = TRUE)
file.copy(html, "../r-for-data-science-2e/", overwrite = TRUE)

pngs <- list.files("oreilly", pattern = "[.]png$", full.names = TRUE, recursive = TRUE)
dest <- gsub("oreilly", "../r-for-data-science-2e/", pngs)
fs::dir_create(unique(dirname(dest)))
file.copy(pngs, dest, overwrite = TRUE)

Then commit and push to atlas.

Code of Conduct

Please note that r4ds uses a Contributor Code of Conduct. By contributing to this book, you agree to abide by its terms.

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