Invited talk at Chinese Academy of Sciences
Data visualization is a way to communicate information clearly and efficiently via statistical graphics, plots and information graphics. Effective visualization helps users analyze and reason about data and evidence. It makes complex data more accessible, understandable and usable. In the talk, I will emphasize the importance of data visualization and communication in the workflow of data science, introduce a general scheme for data visualization: grammar of graphics, demonstrate exhaustive usage of ggplot2, showcase some commonly used interactive plot libraries in R, and present a real example of how to generate publication level report using rmarkdown. At last, I will also share some professional experience of mine at BCG.
install.packages(c('tidyverse','repr','ggthemes','ggformula','GGally','ggmosaic','plotly','radarchart','dygraphs','visNetwork','DT','leaflet'))
It may take few minutes for these packages to download and compile. Please install them before the talk starts.
The jupyter notebook Invited Talk at CAS.ipynb is the presentation slides.
Opening it on GitHub shows all the code and ggplot graphs in a flat notebook view. If you want to open it in a slide view, you can just go to the link at the top of the notebook: Slides on nbviewer. The way to generate the link it is to go to nbviewer and copy the URL of the notebook into the blank, hit Go！ and then click View as Slides bottom (in a shape of wrapped present) on the upper-right corner.
If you want to run the notebook, you have to install Python and Jupyter (recommend install Anaconda as your Python environment), and then follow the instruction of IRkernel. Or you can click the View as Code bottom (in a shape of </>) in the Slides on nbviewer. That will render a webpage with all the R code from the notebook, and then you can copy and paste it into your Rstudio and run each line of them.