# R R is a free software environment for statistical computing and graphics. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Bringing Interactive Computing to Zed: Introducing REPL Support DevFeed: [Bringing Interactive Computing to Zed: Introducing REPL Support]() Original publisher: [Read original article]() Author: Kyle Kelley Published: 2024-07-24T00:00:00Z Content type: release Language: en Sources: [Zed Industries - Blog]() Topics: [Development](), [jupyter](), [kernels](), [Python](), [Deno](), [R](), [Markdown]() Tags: [analysis](), [code](), [development](), [jupyter](), [jupyter-notebooks](), [kernel](), [kernels](), [language](), [markdown](), [python](), [release](), [typescript]() ### AI overview Zed introduces a built-in REPL that lets users run code line by line, block by block, or from a selection, visualize data, and iterate within the editor. The feature integrates REPL-style interaction with Jupyter-like output and supports installed kernels for languages including Python, TypeScript through Deno, and R. ### Source excerpt Run code, visualize data, and iterate right in your editor with Zed's new REPL support. ## Datalore Enterprise 2021.3: ячейки SQL, подключение Jupyter-ноутбуков к БД, интерактивные отчеты и многое другое DevFeed: [Datalore Enterprise 2021.3: ячейки SQL, подключение Jupyter-ноутбуков к БД, интерактивные отчеты и многое другое]() Original publisher: [Read original article]() Author: alenaguzharina (JetBrains) Published: 2021-12-07T14:10:12Z Content type: release Language: ru Sources: [JetBrains RU]() Topics: [Data Science](), [jupyter](), [SQL](), [Databases](), [SQLAlchemy](), [R](), [Scala](), [Amazon Redshift](), [Azure SQL](), [MariaDB](), [MySQL](), [Oracle Database]() Tags: [azure-sql](), [data](), [data-science](), [database](), [datalore](), [jupyter](), [jupyterhub](), [mysql](), [oracle](), [plus-a5524f17cbdd](), [postgresql](), [python](), [scala](), [sql](), [tag-4d3456108bed](), [tag-b0c5f1e2d8ce](), [tag-baf5012a40ff]() ### AI overview This release article presents Datalore Enterprise 2021.3, a data science platform for collaborative Jupyter notebooks. The update adds database connections and native SQL cells, with query results transferred to pandas DataFrames, support for R and Scala notebooks, reactive features, chart builders, and interactive report publishing. ### Source excerpt Привет, Хабр! Datalore Enterprise -- это data science платформа для совместной работы с Jupyter-ноутбуками. Ее можно установить в частное облако или на приватный сервер компании. Новая версия 2021.3 позволяет специалистам по анализу и обработке данных работать с базами данных и SQL-кодом внутри Jupyter-ноутбуков, а также легко делиться результатами работы с коллегами. Также мы интегрировали поддержку ноутбуков R и Scala, добавили новый реактивный режим, реализовали конструкторы графиков и множество других функций. Читайте дальше, чтобы узнать о новых возможностях Datalore Enterprise 2021.3! Читать далее ## Ask a Developer: Your Questions Answered DevFeed: [Ask a Developer: Your Questions Answered]() Original publisher: [Read original article]() Author: Ankia Wolf Published: 2020-03-20T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Programming](), [Job](), [Frameworks](), [Go Language](), [JavaScript](), [PHP](), [Angular](), [React](), [Python](), [Vue.js](), [R](), [Open Source]() Tags: [angular](), [backend](), [developer](), [engineering-culture](), [frameworks](), [full-stack](), [github](), [go](), [hackathons](), [interviewing](), [javascript](), [kotlin](), [open-source](), [php](), [python](), [q-a](), [react](), [requirements](), [tech](), [tech-stack](), [vue]() ### AI overview A trivago developer Q&A covers opportunities for React developers, interview expectations, ways for newcomers to contribute, full-stack requirements, and the company's technology stack. It identifies Kotlin, Go, and PHP for backend work; JavaScript for frontend; Python and R for data science; and Angular, React, Vue, and Melody as JavaScript frameworks. ### Source excerpt We recently hosted a Q&A on our trivago tech Twitter and Life at trivago Instagram page,... ## Reproducibility and Old Faithful DevFeed: [Reproducibility and Old Faithful]() Original publisher: [Read original article]() Author: Dominic Steinitz Published: 2018-02-25T09:49:10Z Content type: article Language: en Sources: [Maths, Stats & Functional Programming]() Topics: [reproducibility](), [data](), [dataset](), [R](), [Haskell](), [Nix]() Tags: [data](), [dataset](), [documentation](), [haskell](), [r](), [reproducibility](), [statistics]() ### AI overview This article investigates the provenance and reproducibility of the Old Faithful geyser datasets available in R. It compares the datasets with cited source tables, notes differing observation counts and apparent differences between versions, and reports that one dataset covers continuous observations collected from August 1 to August 15, 1985. The investigation uses Haskell, embedded R, data frames, and Nix. ### Source excerpt Introduction For the blog post still being written on variatonal methods, I referred to the still excellent Bishop (2006) who uses as his example data, the data available in R for the geyser in Yellowstone National Park called "Old Faithful". While explaining this to another statistician, they started to ask about the dataset. Since I ... Continue reading Reproducibility and Old Faithful ## Using an R Container for Analytical Models DevFeed: [Using an R Container for Analytical Models]() Original publisher: [Read original article]() Published: 2015-06-30T15:25:24Z Content type: tutorial Language: en Sources: [Jessie Frazelle]() Topics: [R](), [Docker](), [Data Science](), [plotting](), [Docker Image]() Tags: [container](), [data-science](), [docker](), [docker-image](), [plotting](), [python]() ### AI overview A tutorial on building and using an R Docker container for data science. It covers creating a custom image from an R base image, installing selected R packages, running the container with mounted data and X11 display support, and plotting the iris dataset with ggplot2. ### Source excerpt So it turns out I'm pretty bad at vacation. I had this idea for a blog post and one thing lead to another and here we are... You probably know by now I hate installing things on my host. At my previous job we did a lot of work with using Python and R for data science. I still love plotting data with ggplot and my favorite R package, wes anderson color palette. Here's a fast intro into how to do this with an R Docker image. Now everyone loves their share of different packages, without a doubt I bet most of them are written by Hadley Wickham ;). Can you imagine if the percentage of packages contributed by Hadley to CRAN was mirrored by someone to NPM or pip? It would be crazy. We are going to start with an R base and build our ideal (aka you can make yours different, chill...) R data science container, with the following Dockerfile: # our R base image FROM r-base # install packages # these are ones I like RUN echo 'install.packages(c("ggplot2", "plyr", "reshape2", "RColorBrewer", "scales","grid", "wesanderson"), repos="http://cran.us.r-project.org", dependencies=TRUE)' > /tmp/packages.R \ && Rscript /tmp/packages.R # create an R user ENV HOME /home/user RUN useradd --create-home --home-dir $HOME user \ && chown -R user:user $HOME WORKDIR $HOME USER user # set the command CMD ["R"] Build the image: $ docker build --rm --force-rm -t jess/r-custom . Run and use the image: # we need X11 for the graph to display, alternatively # you can save to a file that is in a bind-mounted dir # or you can docker cp the file to the host :) $ docker run -it --name analytics \ -v /tmp/.X11-unix:/tmp/.X11-unix \ -e DISPLAY=unix$DISPLAY \ jess/r-custom # bind mount your data $ docker run -v $(pwd)/data:/home/user/data \ -it --name analytics \ -v /tmp/.X11-unix:/tmp/.X11-unix \ -e DISPLAY=unix$DISPLAY \ jess/r-custom Now plot something: library(wesanderson) library(ggplot2) ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) + geom_point(size = 3) + scale_color_manual(values = wes_pal