Background In a previous blog post, I discussed the use of application program interfaces (APIs) in R. Specifically, I focused on accessing data from the US Department of Agriculture’s National Agricultural Statistics Survey (NASS) Quick Stats API. Since I wrote that previous blog post in January 2019, several R packages that aid access to the NASS Quick Stats API have come to my attention.
Background Much of my postdoctoral research is focused on studying (and building tools to help decision makers mitigate) the impacts of climate change on communities in North Carolina. I’m also committed to reducing my carbon footprint and have read over and over that flying makes up the largest fraction of an average person’s carbon dioxide emissions. In early January I decided that I wanted to try to reduce the number of flights I took in 2019.
Background There are lots of resources out there for R users and I thought it might be helpful to compile some that might be especially helpful for water scientists. I’ll also admit that this is a 50% selfish task meant to help me keep track of resources for my own learning.
Goals of This Post The main goal of this blog post is to compile links to R resources including: packages, tutorials, books, etc.
Background This post was originally posted on Jan. 4, 2019 but was revised on May 14, 2020.
Application program interfaces (APIs) help users access (“API request”) and retrieve (“API response”) data from web-based, data servers via programs like R, Python, etc. If you’re interested in more details, several others before me have done a great job writing about API’s and R: this post by C.
Background I recently participated in Thomas Mock’s Tidy Tuesday R challenge with other members of the NC State University R Learning Group. It was a lot of fun and after participating, I felt motivated to share what I’ve been doing to tidy up my own data sets.
Specifically, I’ve been working with collaborator, Kelly Suttles, to tidy up model outputs from the Soil and Water Assessment Tool (SWAT). SWAT is a hydrologic model used to predict daily and monthly streamflow and water quality at the watershed-scale.
Background While attending rstudio::conf 2018, I heard about the tibbletime package developed by Davis Vaughan and Matt Dancho for analysis of time series data. In his conference talk, Davis Vaughan presented several business/finance examples to showcase tibbletime’s functionality and mentioned a few, general non-business applications at the end of his talk. I couldn’t help but think about how this package might be especially helpful for environmental scientists working with time series data.
Welcome to the wateR blog! I’ve been considering starting a water-centric data science blog for a while now. Getting this blogdown website up and running has helped solidify my motivation to write regularly about water-related data science advances that are available to R users…and maybe Python users too (both languages are so useful!).
Some of the specific goals of this blog are to (in no special order):
Practice developing my science communication and R skills.