mespe
To be perfectly honest, I don't really know how much battery I would lose - the laptop is rarely untouched for more than a day or so and in the rare situations when it has been, it has been either completely powered off or left plugged in. I don't know if it's ever been in a situation where it was left in a sleep-state for multiple days.
I did switch from the glossy to matte display, which was a massive improvement for use on the go.
Examples:
https://www.vigoltd.com/AdditionalDepartments/Processes/Appl...
It's also a bit telling that this "definitive guide" does not include any troubleshooting/debugging sections - the expectation seems to be that it "just works" so long as you use it in RStudio, but otherwise you are on your own.
Not sure that I am aware of many other R packages with this mentality, but I am personally not a fan.
Just a reminder: https://news.ycombinator.com/newsguidelines.html
My first thought was "I hope I never encounter code that uses this." It's interesting as a proof of concept, but it makes your R code difficult to understand to 99.9% of R programmers. In my opinion, that is a pretty high cost for what I would consider mostly syntactic sugar.
The problem they were diving into was well understood, and has been researched to death for the last 100+ years. And they had the relationship backwards, not understanding their "input" to increase yields was actually a response to low yields. They were the opposite of helpful, but rather a waste of our time.
As with anything, it helps to know the current state of knowledge before you jump into contribute. An understanding of math doesn't get you there.
That said, if you want a quick primer, "Crop ecology: productivity and management in agricultural systems" is a good primer on most of the basic ecological systems in agriculture. I've read it cover to cover many times.
However, you don't need a grad-level education to farm (believe me, I have been reminded this endlessly) - this is more for people doing research. For applied/actionable specifics for cold climates, your best friend is going to be local crop-extension services (in the US, most land-grants run an extension service). They will have tested techniques for your area and will be able to point you to good resources for farmers, not people researching agriculture.
One thing missed by a lot of the comments: Indoor systems tend to be incredibly fragile affairs. If you've ever been in a well managed commercial greenhouse, you will notice a ton of sanitation procedures. There are greenhouse pests and diseases which are never an issue in the field, in large part because there is an entire ecological system of checks and balances working out in a field. Even in modern intensive ag fields. The truth is an agricultural field is an amazingly complex system which we don't fully understand (we are only starting to explore soil ecosystems and plant roots). Vertical farms are disconnected from this, though the costs might not be obvious. As a consultant, I watched a "trendy" aquaponics startup crash and burn because they underestimated this.
I use Rscript all the time. I was taught to use it by one of R's developers. While not typical, it is absolutely an "intended" use case.
In fact, if you would like to learn more, Software Carpentry has an entire module on using R as a CLI: https://swcarpentry.github.io/r-novice-inflammation/05-cmdli...
I think this really depends on the end point. If you want to learn to read data into R and do basic manipulations, plotting, modeling, etc., the Tidyverse absolutely has a lower bar to entry. Once you get into writing functions, it gets a little trickier. Knowing some of the base R programming concepts and skills will make you much more efficient. If you start debugging and profiling code, only knowing the Tidyverse becomes a liability because you fundamentally do not understand R's computational model (the Tidyverse does not follow it). Hence, if your end goal is to write and debug functions in R, the steeper learning curve of base R can more than pay off. If not, then the Tidyverse's low bar to entry can be more attractive.
That said, I have heard my mentor say "I don't get the point of <insert tidyverse function> - I did this 20 years ago."
This article only briefly touches on what I think is the biggest issue with the Tidyverse. The Tidyverse is incredibly limiting. The "tidy" workflow hides many details of the language, which leads many users to think all data has to be in a tidy "data.frame" (or now tibble) and organized just so. The functions the Tidyverse authors have chosen to implement are seen as the limits of R's capabilities. Every data problem has to be forced into the Tidyverse box (or it is impossible). I cannot tell you how many Tidyverse scripts I have seen where 90% of the operations are munging the data into the correct format for the Tidyverse functions, when the original data can be handled with a single base R function (e.g. lapply). Most Tidyverse users accept this as just the way R is.
Most R users who only learn the Tidyverse never hit its limits, and for them the Tidyverse is perfectly OK. Those that do either resign themselves to the perceived limits of R, or (hopefully) start learning some base R. One friend who took the latter path once exclaimed to me "Logical subsetting is amazing!?!" - this is a foundation piece of the language. That she went 2+ years in the "Tidyverse" without even knowing it was an option was eye opening to me.
To its credit, the Tidyverse is empowering - with very little programming knowledge a beginner can do a lot of data science. However, the majority of these people get stuck as "expert beginners". Some of these become fierce advocates of the Tidyverse without truly understanding base R. Meanwhile, none of the truly advanced R users I encounter use the Tidyverse.
ed.typo