mespe

I have 2x USB-C, 1X USB-A, and HDMI plugged in.

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.

Typing this on 12th gen running Fedora, and I have none of the issues you describe. I've been daily driving this laptop for 2 years, and my only complaint is the mediocre battery life (I get 5-6 hours with mixed use and around 50% display brightness).

I did switch from the glossy to matte display, which was a massive improvement for use on the go.

If I recall correctly, I had issues primarily with more complicated documents, and mostly due to some RStudio "project" and file path configuration issues - I would have to look to see what the specific issues were. Simple, one-file .Rmd docs are typically fine either way.

Funny thing is this has actually motivated me to move away from using rmarkdown. To the point that I went "backwards" and wrote several reports and an academic paper in Sweave after using rmarkdown for years. My primary motivation was that I needed to know the documents could be built by any R session, not just from within RStudio. I didn't want to be in a situation where my "reproducible" document relied on a specific IDE to build correctly.

I've been using Rmarkdown and knitr for a long while, and have watched its evolution over the years (roughly 8 years now). As someone who does not use RStudio, it's become a bit of a pain for me to use. The authors seem to expect it is being used from RStudio, and using it in a different environment has become a bit fragile.

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.

Every time I think code readability isn't important, I am inevitably proven wrong. Either I need to go back months later and need to figure out what I did, or I end up sharing with others. Even those one-off, "never going to touch this again" scripts.

This.

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.

Fallow fields are essential to maintaining soil health, a successful practice going back 10k years. Additionally, some of the "barren" fields are actually being used to alternate crop - it is a technique to grow crops with limited natural water. Modern best-practices avoid a lot of erosion by using no-till or low-till systems.

So, in my grad program in ag, we had a Bay-area ML startup come in to give a seminar on how they were revolutionizing agriculture. The presented their findings on how to increase yields (which they claimed could only be understood from their algorithm).

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.

One last thing - to be a successful farmer has very little to do with growing crops. Take business classes - the rest is relatively easy to figure out.

Hi - coming from a PhD in agriculture (focus on sustainable ag), graduate level courses are going to be tough to jump into unless you have a strong background in ecology. Most of my grad-level courses assumed years of training in e.g. genetics, soil science and chemistry, plant physiology, ecology, weed science, entomology, etc. Agriculture is a very broad life science field.

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.

You probably can grow them (you can "grow" a potato in a cup of water on your counter), but probably not profitably. Potatoes have a fairly low commodity price relative to their light and space demands. Additionally, they store and transport really well.