Set up

1. Download the materials in this repository using the "Clone or download" button and click the "Download ZIP" link. Unzip the file locally.

2. Ensure you have R and R Studio installed on your machine. Use the links and follow the instructions to download each locally.

Alternatively, you can use RollApp to create a free account and run R and R Studio on a cloud service. This option has more issues with saving so this is only an option if you want to avoid downloading R/R Studio locally.

Open R Studio and run the following command to ensure you have all of the R libraries:

packages <- c("flexdashboard", "readr", "dplyr", "lubridate", "htmlwidgets", "htmltools",
              "KernSmooth", "sp", "devtools", "xts", "dygraphs", "reshape2", "visNetwork",
              "igraph", "leaflet", "plotly", "highcharter","d3heatmap","forecast","treemap",
              "viridisLite","arules","ggplot2")

lapply(packages, install.packages(packages), character.only = TRUE)

devtools::install_github("hrbrmstr/streamgraph")

Before we get into the visualizations, we'll start with two introductory tutorials to R from Brooke Anderson's R Programming for Research materials: tutorial 1 and tutorial 2.

For users looking for a great resource on learning R as a whole, I strongly recommend Brooke Anderson's course GitHub repository.

Visualization Demos

• Spatial (Leaflet) Demo

• Time Series Demo

• Network Demo

Links for Tools

• RMarkdown

• HTMLWidgets

• Flexdashboard

• Shiny

Links for Publishing

• RPubs

• Shinyapps.io

Great R References

• Winston Chang's Cookbook for R

• Dean Attali's Shiny Demos

• Dean Attali's Shiny Tutorial