R

I gave an invited talk at the National Cancer Institute Division of Cancer Prevention Multi-Cancer Detection Working Group entitled “Accessible geospatial socio-economic metrics.” The talk was an introduction to my open-source R package on CRAN named ndi that I developed as a postdoctoral Cancer Prevention Fellow.

My fourth R package is on CRAN named ndi. It computes various geospatial neighborhood deprivation indices (NDI) and other metrics of social vulnerability in the United States. Two types of NDI are available in the initial version:

Here is an example of plotting a neighborhoods network with ggplot2 using the sf and sfdep packages for counties in a U.S. state. We can display the “weights” feature of the neighborhoods network as the size of the line segments by scaling the size aesthetic with + scale_size_identity().

My third R package is on CRAN named envi. It estimates the ecological niche using presence/absence data and the spatial relative risk function via the sparr package. See the public Github repository for more details.

My second R package is on CRAN named gateR. It estimates clustering of cytometry cells using markers and the spatial relative risk function via the sparr package. See the public Github repository for more details.

I present code to create the hexsticker for the sparrpowR package using the hexSticker and spatstat packages. The sparrpowR calculated the statistical power for a spatial relative risk function from the sparr package.

My first R package is on CRAN named sparrpowR. It provides a statistical power calculation for the spatial relative risk function via the sparr package. See the public Github repository for more details.

I present an update to my previous posts #1 and #2. This update can also be found on a public GitHub repository. Starting May 17, 2020 the DC Mayoral Office began releasing testing information by neighborhood on their coronavirus data portal.

I received an Informatics Tool Challenge Award from the NCI Division of Cancer Epidemiology and Genetics for a project entitled “sparrpowR: A flexible R package and webtool to estimate statistical power of a spatial cluster detection technique.

I present code to identify relative spatial clustering in multidimensional scaling / principal coordinate analysis (MDS/PCoA) space. I use a spatial relative risk function risk from the sparr package.