You can’t use R for data analysis unless you can get your data into R. Getting your data into R can be a major hassle, so in the last few months Hadley Wickham has been working hard to make it easier. In this webinar Hadley will discuss the places you most often find data (databases, excel, text files, other statistical packages, web apis, and web pages) and the packages (DBI, xml2, jsonlite, haven, readr, exel) that make it easy to get your data into R.
- Learning Roadmap
- Important RStudio Sites
The Latest Resources
How to Start with Shiny Part 3
How to Start with Shiny Part 2 - 50:44
How to Start with Shiny Part 1 - 41:57
htmlwidget, Data Tables & Leaflet - 56:26
Shinyapps.io Overview & Tour - 50:31
Introduction to Debugging in R - 11:21
The Grammar and Graphics of Data Science
Data science is the process of turning data into understanding and actionable insight. Two key data science tools are data manipulation and visualization.
It doesn’t matter how great your analysis is unless you can share it with others – easily. R Markdown and knitr make it easy to intermingle code and text to generate compelling reports and presentatio
Shiny Server Pro Architecture - 18:47
In a static report, you answer known questions. With a dynamic report, you give the reader the tools to answer their own questions.
RStudio Server Pro Overview - 9:40
A Shiny app demo - :40
Managing package dependencies in R with packrat - 46:59
Data wrangling with R and RStudio
RStudio & git-github Demonstration - 8:20
Collaboration and time travel- version control with git, github and RStudio
Hadley Wickham presents and demonstrates how understanding git & github will give you two data science superpowers.
Yihui Xie on RMarkdown - 26:54
Hadley Wickham on ggvis - 15:39
Joe Cheng on RStudio Shiny - 23:28
Meet Packrat - 4:10