About the AuthorFollow on Twitter Visit Website More Content by Mine Çetinkaya-Rundel
- Learning Roadmap
- Important RStudio Sites
Home » RStudio Webinars » General data science overview - data acquisition and wrangling, exploratory data analysis, data visualization, and effective communication.
In this talk we describe an introductory data science course that is our (working) answer to these questions. The courses focuses on data acquisition and wrangling, exploratory data analysis, data visualization, and effective communication and approaching statistics from a model-based, instead of an inference-based, perspective. A heavy emphasis is placed on a consitent syntax (with tools from the tidyverse), reproducibility (with R Markdown) and version control and collaboration (with git/GitHub). We help ease the learning curve by avoiding local installation and supplementing out-of-class learning with interactive learnr modules. By the end of the semester teams of students work on fully reproducible data analysis projects on data they acquired, answering questions they care about.
This talk will discuss in detail course structure, logistics, and pedagogical considerations as well as give examples from the case studies used in the course. We will also share student feedback and assessment of the success of the course in recruiting students to the statistical science major.
Data science case study an analysis in R, using a variety of packages for web scraping and processing non-tidy data into tidy data frames
An analysis in R, using a variety of packages for web scraping and processing non-tidy data into tidy data ...
Error - something went wrong!
The Latest Resources
In this webinar, we'll cover strategies to create reproducible environments in R and the tools you need to implement them.
What Every Data Scientist Should Know About Education - Greg Wilson
Thousands of programmers teach coding, web design, robotics, and other skills in the workplace or after-school programs and bootcamps, but most have never been taught how to teach well.
Best Practices for Administering RStudio in Production - Nathan Stephens
Most organizations are unfamiliar with the R programming language. As a result they often struggle to onboard and manage R in production. In this webinar we introduce the RStudio Quickstart...
Programación con R - Edgar Ruiz
Hay ocasiones que, cuando trabajamos en un análisis en R, necesitamos dividir nuestros datos en grupos, y después tenemos que correr la misma operación sobre cada grupo.
Accessing and responding to Plotly events in Shiny - Carson Sievert
For several years now, the Plotly package has provided an `event_data()` function for accessing click, hover and drag event information in Shiny. This functionality provides a powerful way to build...
Make PowerPoint Presentations with R Markdown - Nathan Stephens
This webinar demonstrates how to create feature rich PowerPoint presentations from R Markdown and how to use these presentations to share insights, visualizations, Shiny apps, and more.
Convenient analysis with broom - Alex Hayes
In this webinar I’ll demonstrate how to use to broom to work with many models at once.
Reproducible Finance with R - Jonathan Regenstein
In this webinar, we will create and code a real (but simple) portfolio analysis in order to explore R's data import, wrangling, and visualization tools in the world of investment management.
Introduction to the RStudio Package Manager - Sean Lopp
This webinar will introduce RStudio Package Manager, a new product to organize R packages across teams, departments, and organizations.
Help me help you. Creating reproducible examples - Jenny Bryan
What is a reprex? It’s a reproducible example. Making a great reprex is both an art and a science and this webinar will cover both aspects.
Load testing Shiny - Alan Dipert
shinyloadtest and shinycannon are two new tools that work together to help you answer the question: how many users can my app support?
R, RStudio 1.2 & Python a love story - Sean Lopp
We'll discuss R’s history of interoperability and the philosophy of Reticulate, the Reticulate-powered features in RStudio 1.2, and talk through a case study of a reticulated Shiny app
How to Work with List Columns - Garrett Grolemund
This webinar breaks down one of the most esoteric concepts in the tidyverse: list columns.
Plumbing APIs with plumber - Jeff Allen
Plumber is an open-source R package that converts your existing R code into a web API; this enables you to leverage your R code from other platforms or programing languages.
Comunicando resultados con R - Edgar Ruiz
Professional R tooling and integration - Nathan Stephens
Many organizations do not officially recognize R as a standard or integrate R with IT managed systems. Yet these same organizations employ data science teams that use R on a daily basis.
Debugging techniques in RStudio
Fortunately, there are excellent tools built into R and RStudio that can make debugging easier.
Scaling Shiny apps with asynchronous programming
Asynchronous programming offers a way to offload certain classes of long-running operations from the main R thread, such that Shiny apps can remain responsive.
Usando R para la Ciencia de Datos - Using R for Data Science
La extracción de conocimiento mediante el análisis de datos es usualmente una tarea compleja y ardua. Las extensiones de R, llamadas paquetes, que son parte de lo que en inglés llamamos “tidyverse”.