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R and RStudio Installation
More about RStudio - interface, projects, working directory
R package installation
Check your version number and make sure to download the correct version of R/RStudio.
Download R from https://cran.r-project.org/bin/macosx/, and select the .pkg link based on your macOS version.
Download RStudio at https://posit.co/download/rstudio-desktop/#download
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Windows /Linux |
Mac |
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Run current command/selection |
Ctrl+Enter |
Cmd+Enter |
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Show shortcuts |
Alt+Shift+K |
Option+Shift+K |
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Insert <- |
Alt+- |
Option+- |
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Comment lines |
Ctrl+Shift+C |
Cmd+Shift+C |
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Insert code section |
Ctrl+Shift+R |
Cmd+Shift+R |
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Insert %>% |
Ctrl+Shift+M |
Cmd+Shift+M |
Objects, functions, and comments
Vectors and data types
Missing data
Data SAFI
Data frames and tibbles
Formatting dates
The data used for this lesson are in the figshare repository at: https://figshare.com/articles/SAFI_Survey_Results/6262019
This lesson uses SAFI_clean.csv. The direct download link for this file is: https://ndownloader.figshare.com/files/11492171
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Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle--transform your datasets into a form convenient for analysis Program--learn powerful R tools for solving data problems with greater clarity and ease Explore--examine your data, generate hypotheses, and quickly test them Model--provide a low-dimensional summary that captures true "signals" in your dataset Communicate--learn R Markdown for integrating prose, code, and results
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