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RStudio Cheat Sheets No ratings yet.




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RStudio Cheat Sheets

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RStudio

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Open Data Science

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“These cheat sheets below make it easy to learn about and use some of our favorite packages.”

  • Data Import Cheat Sheet: “reminds you how to read in flat files…,work with the results as tibbles, and reshape messy data with tidyr. Use tidyr to reshape your tables into tidy data, the data format that works the most seamlessly with R and the tidyverse.”
  • Data Transformation Cheat Sheet: “dplyr provides a grammar for manipulating tables in R.This cheat sheet will guide you through the grammar, reminding you how to select, filter, arrange, mutate, summarise, group, and join data frames and tibbles.”
  • Sparklyr Cheat Sheet: “Sparklyr provides an R interface to Apache Spark, a fast and general engine for processing Big Data. With sparklyr, you can connect to a local or remote Spark session, use dplyr to manipulate data in Spark, and run Spark’s built in machine learning algorithms.”
  • R Markdown Cheat Sheet: “R Markdown is an authoring format that makes it easy to write reusable reports with R. You combine your R code with narration written in markdown…and then export the results as an html, pdf, or Word file. You can even use R markdown to build interactive documents and slideshows.”
  • RStudio IDE Cheat Sheet: “No matter what you do with R, the RStudio IDE can help you do it faster. This cheat sheet will guide you through the most useful features of the IDE, as well as the long list of keyboard shortcuts built into the RStudio IDE.”
  • Shiny Cheat Sheet: “If you’re ready to build interactive web apps with R, say hello to Shiny. This cheat sheet provides a tour of the Shiny package and explains how to build and customize for an interactive app.”
  • Data Visualization Cheat Sheet: “The ggplot2 package lets you make beautiful and customizable plots for your data. It implements the grammar of graphics, an easy to use system for building graphs.”
  • Package Development Cheat Sheet: “The devtools package makes it easy to build your own R packages, and packages make it easy to share your R code.”
  • R Markdown Reference Guide: “R markdown marries together three pieces of software: markdown, knitr, and pandoc. This five page guide lists each of the options from markdown, knitr, and pandoc that you can use to customize your R markdown documents.”

Recommended Prerequisites: none

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