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R for the Rest of Us: A Statistics-Free Introduction comes out June 25th. Or you can read the online version today. Check it out →
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This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.

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Data Visualization

Use R, ggplot2, and the principles of graphic design to create beautiful and truthful visualizations of data

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Data Visualization: A Practical Introduction

This is a book about data visualization using R and ggplot. It covers various topics such as working with plain text, making plots, showing the right numbers, graphing tables, working with models, and drawing maps.

Screenshot of Data wrangling for spatial analysis: R Workshop

Data wrangling for spatial analysis: R Workshop

Data wrangling for spatial analysis: R Workshop

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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.

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The DBI package helps connecting R to database management systems (DBMS). It separates the connectivity to the DBMS into a “front-end” and a “back-end” and provides an interface that is implemented by different DBI backends. The package supports operations like connecting to a DBMS, executing statements, extracting results, and handling errors. The DBI package is typically installed automatically when you install one of the supported database backends.

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dbplyr is a database backend for the dplyr package in R. It allows you to use remote database tables as if they are in-memory data frames by automatically converting dplyr code into SQL.

Screenshot of Deep dive intro dplyr

Deep dive intro dplyr

Dive into dplyr tutorial on Kaggle

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devtools is an R package that aims to make package development easier by providing functions that simplify and expedite common tasks. It includes functions for loading code, updating documentation, running tests, building and installing packages, checking and releasing packages, and more. It is widely used for R package development and there are several resources available to learn more about package development using devtools.

Screenshot of Don't use Quarto documents to clean or analyze data

Don't use Quarto documents to clean or analyze data

This content advocates against using Quarto, R Markdown, or Jupyter for data cleaning and analysis, emphasizing that these platforms should be used for communication rather than exploratory tasks. Diego Catalan Molina advises that data inputs should be clean before being loaded into documents which should serve as a vehicle to tell a story. He suggests creating engaging outlines focused on findings' importance and using these documents exclusively to share results, not every plot or table during the exploratory phase of data analysis.

Screenshot of dplyr


dplyr is a package in R that provides a grammar of data manipulation. It offers a consistent set of verbs to solve common data manipulation challenges, such as adding new variables, selecting variables, filtering cases, summarizing data, and arranging rows. It also provides support for working with different computational backends, including arrow, dtplyr, dbplyr, duckplyr, duckdb, and sparklyr. The package can be installed as part of the tidyverse or separately.