This was my first time attending RStudio::conf, and I went primarily to explore my career options in data science. I mainly stuck to teaching and modeling related talks since that’s how I already use R. Here are my major takeaways from the conference.
Shiny is the new hotness Shiny apps are interactive web apps that run on R code, and there was a big focus on Shiny development at the conference this year.
I recently gave a talk on some of my work as a PhD student on experiments manipulating densities of the tea green leafhopper (Empoasca onukii) on tea plants. What the audience liked most, I think, were my methods for finding leafhopper eggs in the field and rearing them in the lab (well, a guest room at a tea farm). You see, leafhoppers (including at least the tea green leafhopper and the small green leafhopper, Empoasca vitis) lay their eggs inside plant tissues, making them impossible to find with the naked eye.
I’m currently in Hangzhou, China at the Tea Research Institute(TRI) for my fourth and last time. It’s bitter sweet (like my favorite teas ;-) ) since I’m both glad to be nearing the end of my PhD, and sad to say goodbye to all the friends I’ve made and a city I’ve really grown to enjoy living in.
Fieldwork This final summer, I’ve been focusing on a few experiments having to do with leafhoppers and their effects on tea chemistry (see the project page for more info).
My PhD has involved learning a lot more than I expected about analytical chemistry, and as I’ve been learning, I’ve been trying my best to make my life easier by writing R functions to help me out. Some of those functions have found a loving home in the webchem package, part of rOpenSci.
Papers that use gas chromatography to separate and measure chemicals often include a table of the compounds they found along with experimental retention indices and literature retention indices.
Last semester I took a class that used Python. It was my first time really seriously using any programing language other than R. The students were about half engineers and half biologists. The vast majority of the biologists knew R to varying degrees, but had no experience with Python, and the engineers seemed to generally have some experience with Python, or at least with languages more similar to it than R.
I know you’re all waiting on the edge of your seats for an update on the cupcakes vs. muffins data science project, but unfortunately I don’t have any answers to that age-old question* yet.
As silly as it may sound, I’m actually considering using this data set for a paper about using PLS (partial least squares regression) for ecological data. So for now, I’m holding off on blogging about any results of analyses in case I end up wanting to use them for the publication.
One thing I’ve learned from my PhD at Tufts is that I really enjoy working data wrangling, visualization, and statistics in R. I enjoy it so much, that lately I’ve been strongly considering a career in data science after graduation. As a way to showcase my data science skills, I’ve been working on a side project to use webscraping and multivariate statistics to answer the age old question: Are cupcakes really that different from muffins?
The LI-6400XT is a portable device used to measure photosynthesis in plant leaves. As you take measurements by pressing a button on the device, they are recorded into memory. In order to keep track of which measurments go with which plants (or experimental treatments), there is an “add remark” option where you can enter sample information before taking measurements.
When the data are exported, you get a series of .
As part of my fieldwork in China, I collected harvested tea leaves that were damaged by the tea green leafhopper. I want to quantify the amount of leafhopper damage for each harvest. I was able to find several solutions for quantifying holes in leaves or even damage to leaf margins, but typical leafhopper damage is just tiny brown spots on the undersides of leaves. I did find some tutorials on using ImageJ to analyze diseased area on leaves, but found that the leafhopper damage spots were too small and too similar in color to undamaged leaves for these tools to work reliably and be automated.