I like thinking about workflow - I don’t think people talk about it enough. So in an effort to discuss and improve my own workflow, below is a description of tools I’m actively using. If you have any suggestions, please let me know!

Computing

I use R Studio for working in R. In R, I sometimes use the tidyverse, although I typically use a mix of base R and tidyverse packages. Favourite R packages: beepr, dplyr, ggplot2, and magrittr.

I also do a lot of work in Python. I used to work in Spyder, mainly because it was a smooth transition from R Studio. Ultimately though I’ve landed on a combination of Jupyter notebooks for testing, and then Python scripts run at the command line. I like Jupyter because I can set it up using ssh, so I can work on large datasets and not crash my computer.

I use a Mac Air, which has been a strange transition from Windows, but so far it’s worked superbly. I used to work on a Windows machine (Asus), and run Windows Subsystem for Linux. For accessing computing resources remotely, I now just use the Mac terminal, but I used to use PuTTY and WinSCP.

I use Git and GitHub as version control via Git bash.

For making things fast (enough), I typically rely on the wizardy of Numba.

For fitting Bayesian models, I like Stan. On that note, I like this fun blog from Gelman, and others.

Research

I use Zotero. I sometimes write with LateX, and so I export the bibtex file from Zotero, but Zotero does most of the organizing information etc.

My favourite way to write is using a Google Doc. In grad school I would write the text in LateX (as if it were to appear in a LaTeX editor), but edit it as a normal google doc. This makes revisions easy with co-authors. Only until the final stages do I switch it over to compiling in LaTeX. I use TeXWorks for editing and compiling just because it’s easy, and export the BiBtex document from Mendeley.

I only sometimes find working in Latex makes sense now, post grad school. It was great for doing very little thesis formatting, and also for papers with lots of equations, but now I’m not convinced it saves much time.

I like to write things down, and for that I use Leuchtturm. Or whiteboards. I love whiteboards and would prefer to have them on all walls.

For posters and presentations I use powerpoint - I know there are alternatives, but these have been sufficient so far! Suggestions very welcome.

This website was made based off of Jekyll Now and GitHub pages.

For benchwork, I watch a lot of YouTube tutorials. I like AddGene protocols a lot, with their associated videos! I also have found the reddit thread /labrats to be hilariously helpful. The Barrick Lab website has a bunch of well-described protocols that are super helpful.

Organizational

I organize all my projects the same way. This is to improve reproducibility, but also it just helps me find things down the road.

I have a ‘Projects’ folder that is synced with Google Drive, so everything is backed up as I work (for large datasets I typically back them up separately so they don’t take up too much space).

Within each project, I have consistent file structure: ‘~/data’, ‘~/scripts’, ‘~/writing’, ‘~/tables’, ‘~/figures’, ‘~/meetings’. Within ‘~/data’, I sometimes put ‘~/data/intermediate_data’, when an analysis takes a long time to reproduce. Within each folder I typically put a ‘~/../deprecated’ subfolder, as I am a bit of a hoarder.

I also put important information (i.e. conda environment names etc.) in the main folder – it’s easy and annoying to forget these things after long pauses and this helps me get back to speed!

For many of my projects, I extensively use published data. To keep things organized, I put a README in the data subfolder whenever I put a new spreadsheet or data source, that gives the link for where I downloaded it.