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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.

Screenshot of Data Tips and Tricks - Creating Population Pyramid Plots in R with ggplot2

Data Tips and Tricks - Creating Population Pyramid Plots in R with ggplot2

In this tutorial, Steve guides us through creating population pyramid plots in R using the ggplot2 library. Ideal for visualizing demographic data, these plots compare population distribution across age groups and genders or different time periods. The post includes a step-by-step guide, beginning with installing ggplot2, to loading libraries and preparing data. It covers generating a basic bar chart for one gender and extending it to combine both genders, thereby constructing the desired population pyramid plot. Readers will learn how to manipulate plot aesthetics for visual clarity and symmetry in demographic presentations.

Screenshot of Data Visualization

Data Visualization

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

Screenshot of Data Visualization: A Practical Introduction

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

Screenshot of 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.

Screenshot of DBI


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.

Screenshot of dbplyr


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

Screenshot of devtools


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 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.