Sometimes, peer review (and procrastination) help. Other times, the delays generate more net work. I was discussing this workflow with a colleague regarding a paper that was submitted two-years ago, rejected, then we both ran out of steam. This was the gold-standard workflow we proposed (versus reformat and submit to another journal immediately).
Hit web of science and check for new papers on topic.
Download the pdfs.
Think about what to cite or add.
Add citations and rebuild biblio.
Update writing to mention new citations especially if they are really relevant (intro and discussion).
Take whatever pearls of wisdom you can from rejection in first place and revise ideas, plots, or stats.
Format for new journal.
Check requirements for that journal.
Search the table of contents for the journal and check your lit cited to ensure you cite a few papers from that journal – if not, assess whether that the right journal for this contribution.
Download pdfs from new journal, read, cite, and interpret.
Then, look up referees and emails.
Write cover letter.
Set up account for that new and different annoying journal system – register and wait.
Fight with system to submit and complete all the little boxes/fields.
Many if not most data clean up, tidying, wrangling, and joining can be done directly in R. There are many advantages to this approach – i.e. read in data in whatever format (from excel to json to zip) and then do your tidying – including transparency, a record of what you did, reproducibility (if you ever have to do it again for another experiment or someone else does), and reproducibility again if your data get updated and you must rinse and repeat! Additionally, an all-R workflow forces you to think about your data structure, the class of each vector (or what each variable means/represents), missing data, and facilitates better QA/QC. Finally, your data assets are then ready to go for the #tidyverse once read in for joining, mutating in derived variables, or reshaping. That said, I propose that good thinking precedes good rstats. For me, this ripples backwards from projects where I did not think ahead sufficiently and certainly got the job done with R and the tidyverse in particular, but it took some time that could have been better spent on the statistical models later in the workflow. Consequently, here are some recent tips I have been thinking about this season of data collection and experimental design that are pre-R for R and how I know I like to subsequently join and tidy.
Keep related data in separate csv files. For instance, I have a site with 30 long-term shrubs that I measure morphology and growth, interactions with/associations with the plant and animal community, and microclimate. I keep each set of data in a separate csv (no formatting, keeps it simple, reads in well) including shrub_morphology.csv, associations.csv, and microclimate.csv. This matches how I collect the data in the field, represents the levels of thinking and sampling, and at times I sample asynchronously so open each only as needed. You can have a single excel file instead with multiple sheets and read those in using R, but I find that there is a tendency for things to get lost, and it is hard for me to have parallel checking with sheets versus just opening up each file side-by-side and doing some thinking. Plus, this format ensures and reminds me to write a meta-data file for each data stream.
Always have a key vector in each data file that represents the unique sampling instance. I like the #tidyverse dyplr::join family to put together my different data files. Here is an explanation of the workflow. So, in the shrub example, the 30 individual shrubs structure all observations for how much they grow, what other plants and animals associate with them, and what the microclimate looks like under their canopy so I have a vector entitled shrub_ID that demarcates each instance in space that I sample. I often also have a fourth data file for field sampling that is descriptive using the same unique ID as the key approach wherein I add lat, long, qualitative observations, disturbances, or other supporting data.
Ensure each vector/column is a single class. You can resolve this issue later, but I prefer to keep each vector a single class, i.e. all numeric, all character, or all date and time.
Double-code confusing vectors for simplicity and error checking. I double-code data and time vectors to simpler vectors just to be safe. I try to use readr functions like read_csv that makes minimal and more often than not correct assumptions about the class of each vector. However, to be safe for vectors that I have struggled with in the past, and fixed in R using tidytools or others, I now just set up secondary columns that match my experimental design and sampling. If I visit my site with 30 shrubs three times in a growing season, I have a date vector that captures this rich and accurate sampling process, i.e. august 14, 2019 as a row, but I also prefer a census column that for each row has 1,2, or 3. This helps me recall how often I sampled when I reinspect the data and also provides a means for quick tallies and other tools. Sometimes, if I know it is long-term data over many years, I also add a simple year column that simply lists 2017, 2018, and 2019. Yes, I can reverse engineer this in R, but I like the structure – like a backbone or scaffold to my dataframe to support my thinking about statistics to match design.
Keep track of total unique observation instances. I like tidy data. In each dataframe, I like a vector that provides me a total tally of the length of the data as a representation of unique observations. You can wrangle in later, and this vector does not replace the unique ID key vector at all. In planning an experiment, I do some math. One site, 30 shrubs, 3 census events per season/year, and a total of 3 years. So, 1 x 30 x 3 x 3 should be 270 unique observations or rows. I hardcode that into the data to ensure that I did not miss or forget to collect data. It is also fulfilling to have them all checked off. The double-check using tibble::rowid_to_column should confirm that count, and further to tip #2, you can have a variable or set of variables to join different dataframes so this becomes fundamentally useful if I measured shrub growth and climate three times each year for three years in my join (i.e. I now have a single observation_ID vector I generated that should match my hardcoded collection_ID data column and I can ensure it lines up with the census column too etc per year). A tiny bit of rendundancy just makes it so much easier to check for missing data later.
Leave blanks blank. Ensures your data codes true and false zeros correctly (for me this means I observed a zero, i.e. no plants under the shrub at all versus missing data) and also stick to tip #3. My quick a priori rule that I annotate in meta-data for each file is that missing altogether is coded as blank (i.e. no entry in that row/instance but I still have the unique_ID and row there as placeholder) and an observed zero in the field or during experiment is coded as 0. Do not record ‘NA’ as characters in a numeric column in the csv because it flips the entire vector to character, and read_csv and other functions sorts this out better with blanks anyway. I can also impute in missing values if needed by leaving blanks blank.
Never delete data. Further to tip #1, and other ideas described in R for Data Science, once I plan my experiment and decide on my data collecting and structural rules a priori, the data are sacred and stay intact. I have many colleagues delete data that they did not ‘need’ once they derived their site-level climate estimates (then lived to regret it) or delete rows because they are blank (not omit in #rstats workflow but opened up data file and deleted). Sensu tip #5, I like the tally that matches the designed plan for experiment and prefer to preserve the data structure.
Avoid automatic factorassignments. Further to simple data formats like tip #1 and tip #4, I prefer to read in data and keep assumptions minimal until I am ready to build my models. Many packages and statistical tools do not need the vector be factor class, and I prefer to make assignments directly to ensure statistics match the planned design for the purpose of each variable. Sometimes, variables can be both. The growth of the shrub in my example is a response to the growing season and climate in some models but a predictor in other models such as the effect of the shrub canopy on the other plants and animals. The r-package forcats can help you out when you need to enter into these decisions and challenges with levels within factors.
Putting the different pieces together in science and data science is important. The construction of each project element including the design of experiment, evidence and data collection, and #rstats workflow for data wrangling, viz, and statistical models suggest that a little thinking beforehand and planning (like visual lego instruction guides) ensures that all these different pieces fit together in the process of project building and writing. Design them so that connect easily.
Sometimes you can get away without instructions and that is fun, but jamming pieces together that do not really fit and trying to pry them apart later is never really fun.
Meetings are universally reviled. Even those that are obligated to call or host them likely do not relish the notion.
Challenge. Do not use the term meeting for two weeks.
To facilitate this proceess, here is a set of synonyms or alt-terms to consider. Most importantly, using an alternative term that is more specific and coupled to the functional purpose of calling a meeting will ensure that outcomes are aligned with expectations. Hopefully, it will also reduce the implicit biases associated with calling, attending, and participating in meetings.
implies brevity and light-hearted discussion
present novel findings
about to make a play as team
zen and outside
meet but move
implies skills and tool development
focus on technical problem solving
change, social good, positive cheer
promote successes and recognize contributions
focus on ideation
sole purpose is to ideate
updates provided rapidly in succession
implies end of talk and open forum
seek answers to pressing questions
formal presentation and exploration of theory
propose big theories
part of the flock and family
connect as team
two-way dialogue implied
talking, not reporting
promotes individual discovery
sharing food together is primal
eating provides a focus & changes time perception
pirates but making dispute resolution fun
conflict resolution without stating it
crafty and creative ideas to sell
generate marketable ideas
implies entertainment but critical analyses
permission to be critical
implies bringing in an item or idea you care about
presentations of work but focus on tangible
informal chance to sound off on ideas or projects
rapid consumption of a treat
short and sweet exchange
Metadata Synonyms are very loosely defined here as terms with similar meanings. Consider using any term that is aligned with the purpose of calling people together virtually or in person. Imagine lecture, lab, viewing or any term that means bring people together to do something. Meetings are no different but have really come to mean mostly negative opportunities to convene.
Implication is the first thing that pops into your mind when you see or hear the term.
Novel function is the sole and express purpose of the time allocated and need to bring people together. If you cannot think of a function for calling your team together, then you likely do not need to.
Relatedness is scored from 1 to 5 with 5 being closely aligned with the most likely purposes or functions of meetings and 1 is unrelated. Most terms here roughly approximate the traditional needs for a meeting but feel free to get radical. After all, office parties are really meetings that have the capacity to be fun or at least provide libation.
Preamble Colleagues and I had some sweet telemetry data, we did some simple models (& some relatively more complex ones too), we drew maps, and we wrote a paper. However, I thought it would be great to also provide stakeholders with the capacity to engage with the models, data, and maps. I published the data with a DOI, published the code at zenodo (& online at GitHub), and submitted paper to a journal. We elected not to pre-print because this particular field of animal ecology is not an easy place. My goal was to rapidly spin up some interactive capacity via two apps.
Adventures Map appis simple but was really surprising once rendered. Very different and much more clear finding through interactivity. This was a fascinating adventure! Model app exploring the distribution of data and the resource selection function application for this species confirmed what we concluded in the paper.
Workflow Shiny app steps development flow is straightforward, and I like the logic!! 1. Use RStudio 2. Set up ashiny app account (free for up to 25hrs total use per month) 3. Set up a single r script with three elements (i) ui (ii) server (iii) generate app (typically single line) 4. Click run app in RStudio to see it. 5. Test and play. 6. Publish (click publish button).
There is a bit more to it but not much more.
Rationale A user interface makes it an app (haha), the server serves up the rstats or your work, and the final line generates app using shiny package. I could have an interactive html page published on GitHub and use plotly and leaflet etc, but I wanted to have the sliders and select input features more like a web app – because it is.
Main challenge to adventure was leaflet and reactive data The primary challenge, adventure time style, was the reactive data calls and leaflet. If you have to produce an interactive map that can be updated with user input, you change your workflow a tiny bit. a. The select input becomes an input$var that is in essence the name of vector you can use in your rstats code. So, this intuitive in conventional shiny app to me. b. To take advantage of user input to render an updated map, I struggled a bit. You still use the input but want to filter your data to replot map. Novel elements include introducing a reactive function call to rewrite your dataframe in server chunk and then in leaflet first renderLeaflet map but them use an observe function to update the map with the reactive, i.e. user-defined, subset of the data. Simple in concept now that I get it, but it was still a bit tweaky to call specific elements from reactive data for mapping.
Summary Apps from your work can illuminate patterns for others and for you.
Apps can provide a mechanism to interact with your models and see the best fits or outcomes in a more parallel, extemporary capacity
Apps are a gratifying mean to make statistics and data more accessible
Updates Short-cut/parsimony coding: If you wrap your data script or wrangling into the renderPlot call, your data becomes reactive (without the formal reactive function).
The position of scripts is important – check this – numerous options where to read in data and this has consequences.