Resources

Data cleaning for data sharing by Crystal Lewis in tutorials February 14, 2023.

Libby Heeren is a self-professed Data Human on a mission to speak candidly about the day-to-day work of data professionals and tear down the veil of mystery that hangs over the world of data jobs. Find her at datahumans.club

Data Science for the Biomedical Sciences is a book that provides an introduction to data science concepts and tools specifically tailored for the biomedical sciences. It covers topics such as spreadsheets, R and RStudio, data loading, descriptive calculations, data cleaning, visualization, analysis, working with multiple datasets, APIs, functions, survival analysis, machine learning, and more.

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.

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

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.

Data wrangling for spatial analysis: R Workshop

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.

VI lubridate - YouTube

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.

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.