I get anxiety when I smoke and am way too young to attribute it to any sort of “reeder madness” - so it’s definitely a thing. I don’t smoke super often, and don’t always get anxiety when I do, but it definitely gave me terrible anxiety when I was younger.
If anyone else gets anxiety when they smoke, I recommend smoking sativa instead of indica (nicknamed “in da couch” because of its anxiety effects). I was given the tip from a stoner friend, and it’s fsirly true in my experience.
The same happens to me, but the next day I feel a significant decrease in anxiety. I wonder if the pro-anxiety effects of THC are overcome by the anti-anxiety effects of CBD over time (e.g. the anxiety effects of THC last for a few hours, the milder anti-anxiety effects of CBD last for a couple of days).
FWIW, I have the opposite reaction to different strains. Sativa gives me anxiety, while indica makes me want to relax and take a nap. Really hope researchers can start testing effects so we can figure out where this can help anxiety. So many people I know struggle with it; I'd love for them to have a way to treat it.
I hear this repeated a lot in different variations, some people say the exact opposite, but it's untrue.
There's no real definition of what a '100% sativa' or '100% indica' even is. All we have to go by are characteristic leaf patterns between indica/sativa. In short, the whole 'indica body buzz' thing is unfounded. In fact, I remember hearing the EXACT opposite of what you heard a few years ago. I was told that sativa caused anxiety and indica was more calming.
The reality is that the only differences between strains are in combinations of THC to CBD. Plants with higher CBD and lower THC will be more 'calming' and plants with higher THC and lower CBD will get you more high, which can produce more anxiety in people sensitive to THC.
This is a good first-order approximation, but there are other cannabinoids which influence the effects: CBN, THCV, and others [0]. Additionally, based on first-hand anecdata, I think there is good reason to believe that the terpene profile can have a big effect on the experience, as well as the flavor [1].
But, yeah, if THC causes anxiety for you, try to never go above a 1:1 THC:CBD ratio. For comparison, most sativas are around 200:1 (20% THC : .1% CBD). Since CBD is legal over the counter, you can usually enforce this ratio, regardless of the THC source.
I agree I was oversimplifying, of course there are other cannabinoids in weed that can affect the high. What doesn't though, is how sativa or indica a strain is.
1) you got the 2 mixed up, it's indica that's the in-da-couch. Sativa is the brain-high.
2) there's little evidence to prove that sativa/indica strains affect your high. The curing, drying, growing of each individual plant alters the THC-CBD-CBG ratios way more than strain or species.
Point number 2 is utterly untrue as any experienced cannabis enthusiast could tell you. Even if people don't know the strain or what the effects are it's pretty reliable that it will either energize them or send them to sleep.
What they were saying is anyone with experience in cannabis can tell you after a few minute of smoking if it's sativa or indica. Doesn't matter if there is no genetic definition, there are obvious differences.
There aren't, and those subjective reports can't tell us anything related to how 'sativa' or 'indica' some strain is. The only thing that affects high is CBD-THC ratios (and possibly other alkaloids) & those are independent of strain.
Interesting - I had assumed the opposite! I picked up a few different blends a while back hoping to address some (relatively mild) social anxiety and it worked well at first but later started giving me horrible anxiety (oddly only later that night - I'd take a small dose in the morning and be fine all day, then freak out from 1am to 4am.) Tried a bunch of different blends of thc:cbd with little luck, but I was sticking mostly to indica thinking sativa was more likely to cause anxiety.
I think they have this the wrong way round. Indica is known for its relaxation effects. Couch lock isn't due to anxiety but relaxed passivity, and sativa strains tend to promote overthinking and paranoia in those (inc. myself) prone to it.
No. It depends on the person entirely and if you start digging you will find about equal distribution of people who are calmed by a sativa and people who are calmed by an indica. I personally suspect that people who feel calm from a sativa are a little ADHD and it seems to make them think better and more calmly and indicas make them much more anxious because it exacerbates and existing condition.
anecdotal, but i am certainly an individual with adhd and i personally find strains marketed as indica dominant to be far more effective for anxiety relief and just general functionality. it also may just be a random correlation, but i always recalled seeing higher levels of cbd in indica based strains when I lived in Washington and the actual levels of cbd and thc in the product was listed on the packaging. i certainly don't think this is strong evidence that such a thing is the case, in an overarching empirical sense, but i can at least say, with confidence, that for me, indica dominant is much less anxiety inducing
This kind of matches my experience - the anxiety I get is from feeling like my mind is slowing down / not totally working properly and not knowing if it will ever come back. I think I am a bit ADHD, and caffeine has a kind of calming effect on me as well, so might be worth trying a sativa blend.
I felt exactly the same way, and this as someone who spent their entire life surrounded by motorcycles. The philosophical insights just felt a bit... corny? And the rest was just plain boring.
I skimmed through the book, and think it does a very poor job at showcasing how R and Python are juxtaposed in industry.
To be fair, the book advertises showing R and Python code side-by-side. And that’s what it does. But it does it unlike how the languages are most often used in industry.
As a quick example, I saw no tidyverse code, which is essentially the only thing keeping R in the game. Learning R from this book won’t prepare you for writing R in most R shops.
I don’t see the utility in knowing how to do the same thing in both python and R if you’re a beginner. This is even more true if you’re not taking advantage of the strengths/weaknesses of either language.
Instead, just learn one of the languages well, and then learn the other well. Shallow dives in both will make you weak in both.
Unfortunately, 90% of data science content seems to be geared at beginners.
> tidyverse code, which is essentially the only thing keeping R in the game
From my experience this is not the case. In biomedicine and bioinformatics few people actually use tidyverse because the data is much better represented as a matrix, and not in the "tidy" form.
Outside of that corporations (well at least 2 I contracted with) used `data.table` explicitly. Join 3 ad-click dataframes matching by userID, sessionID and closest possible time-point - that's one line in `data.table`.
Tidyverse is well suited for learning and for managing (relatively) simple datasets. But becomes cumbersome for more complex data. It can be used for those data too of course, just that it will be adding ad-hoc solutions and maybe get in a way more than help.
I've only use base R for my medical data (subsetting dataframe and such). Very rarely do I need tidy and also I find the pipe operator makes debugging harder. If and when I need it I'll use it that's that.
I think R have much more packages in medical, especially statistical packages, where many fields within medical cares about inferences not just prediction/forecasting. So I disagree with the "essentially the only thing keeping R in the game". The breath of packages in R is one of the many things that keep R in the game.
The tribalism and highly bias comments makes it very toxic and harder to have an honest discord.
They are just tools, use what makes you happy and get the job done.
I have a similar feeling. And that is why I spent one whole chapter in data.table (and pandas). Hope more R users would like to learn and use data.table.
I agree for the most part, but R does have a few things beyond the tidyverse: built-in dataframe support, lots of domain-specific packages, more consistent interfaces for basic statistics and machine learning models, etc. Python is definitely better for matrices (because of NumPy) and anything involving custom gradient descent methods (because of TensorFlow).
I think 90% of data science content is for beginners because anything more advanced isn't best described as data science. As soon as you get beyond the initial stages of data analysis (cleaning and processing data), you're doing something best described as some other word (statistics, machine learning, etc.) - although, granted, there isn't much content in these areas if you don't know _exactly_ what you're looking for.
Even if you ignore the tidyverse, the example code for "roll your own linear regressions by hand" uses the R6 object system, which is... not even one of the two popular object systems for R (which are S3 and S4). No beginner needs to learn how to write classes in R.
`no beginner needs to learn how to write classes in R`.
a) using classes properly is great for all level R users;
b) a major reason that classes are not widely used (for beginners) is that S3/S4 are not easy to follow. R6 provides a natural and clear way to understand and write classes (especially for beginners).
Using classes at all is unnecessary for most R users. R is really, to the extent that paradigms matter to the average R user at all (which is: not much) a functional-first language. The idiomatic way to deal with the things you would use classes for is to use functions and closures. There are people who need objects in R, which is why R has object systems available, but it is of no help to a beginner to know them -- it doesn't help them to interact with the code they are going to see, and they don't have the background to understand why you would use classes instead of functions.
Not an advantage if you ask me - exactly because data.frame is built in, people have been building their own versions (tibble, data.table) instead of improving it. That's how R ended up with 3 different structures that are similar but have inconsistent apis and behaviour.
> lots of domain-specific packages
That's true.
> more consistent interfaces for basic statistics and machine learning models
Can't disagree more - there is no one go-to library for ML in R (like sklearn in Python) and each package has it's own strange interface and implementation.
> Not an advantage if you ask me - exactly because data.frame is built in, people have been building their own versions (tibble, data.table) instead of improving it. That's how R ended up with 3 different structures that are similar but have inconsistent apis and behaviour.
I've been fortunate to only work on projects that use built-in data frames, never encountered tibble or data.table in the wild.
> there is no one go-to library for ML in R (like sklearn in Python) and each package has it's own strange interface and implementation.
I still disagree here - one example being the unified interface for generalized linear models. Also, the vast majority of classifiers (RF, SVM, etc.) have similar or identical interfaces. Also, the unified `predict` interface as well. Granted, `sklearn` does have a consistent API as well.
That said, some of this is just a personal preference for the vaguely functional interface in R. The object-orientedness in Python feels a little forced for some tasks in `sklearn`.
> because data.frame is built in, people have been building their own versions (tibble, data.table) instead of improving it.
To make what I think your point is more explicit, people build their own things in R because R must maintain compatibility with S. So by and large, changes happen in packages and not the base language. This does lead to a proliferation of solutions for the same kinds of problems.
You mean like keras? or tensorflow?
Or base random forest. You know, like the original Breiman implementation.
Python has utility. But R is far superior in its the quality of the packages, their documentation, their ability to behave predictably on a given data type.
I run a machine learning shop. Right now all of the training, application, and data management is handled via R. R is simply superior in too many ways for us to be bothered with python for the scale of work we are doing.
Since we're moving some big applications to keras/ TF we do use python and will be using more in the future. However, for almost all data management, munging, movement visualization, reporting, its an R world.
> You mean like keras? or tensorflow? Or base random forest. You know, like the original Breiman implementation.
> ...
> Since we're moving some big applications to keras/ TF we do use python and will be using more in the future.
Not sure if I misunderstood, or you're contradicting yourself there.
> R is far superior in its the quality of the packages, their documentation, their ability to behave predictably on a given data type.
I not only disagree but I think that the exact opposite is true for each one of these points. But if things are working well in our shop, I'm not going to try to convince you otherwise.
> > R is far superior in its the quality of the packages, their documentation, their ability to behave predictably on a given data type.
> I not only disagree but I think that the exact opposite is true for each one of these points. But if things are working well in our shop, I'm not going to try to convince you otherwise.
I partially agree with you here. I'm extremely careful about what non-standard packages I use in R. Code quality varies wildly outside of these, likewise for documentation. But outside of neural networks, I've never found a package in Python that I felt better about in terms of code quality or documentation than its equivalent in R.
My point behind the keras/ TF comment is that the libraries have front ends in both python and R, so its mix mox/ dealers choice on what you like to work in (since the backends of both are identical).
The primary reason to moving these to python is due to convenience/ the community. Most new work is published in python. If we find a new/ interesting model we want to implement, its probably written in python. Rather than reskin the thing in its entirety, its easier here to work in python.
A couple disclaimers: my group works primarily in geospatial data, and principally in LiDAR and multispectral imagery.
The coarse division I see between R/ Python, is that if you come from a research/ academic background (non-engineering), you probably learned to program in R. If you were an engineer, you probably learned matlab. If you are self taught/ coursera/ youtube, you probably learned in python.
R libraries are generally more geared towards academic research, and specifically, working within existing frameworks (handling geospatial data as geospatial data rather then turning them into a numpy arrays). Working in python, there is far more re-invention of the wheel, and its always a pain the ass to get things back into the structures they came in as.
Python has huge utility and is an important tool for certain work. But its really really not faster than R (it def used to be, this isnt the case any more).
R has better support for more scientific programming than python.
> My point behind the keras/ TF comment is that the libraries have front ends in both python and R, so its mix mox/ dealers choice on what you like to work in (since the backends of both are identical).
Not as a point of argument, just additional information:
R's support for keras and TF is a wrapper around the Python interface to those libraries.
numpy is significantly faster and arguably more usable (e.g. broadcasting) than anything in R, and only recently has there been progress in more efficient matrix manipulation in R like rray[0], a wrapper for xtensor.
As far as I can tell, R uses BLAS for matrix operations, and Python probably does the same, so in terms of efficiency I wouldn't expect a big difference between the two.
Both R and numpy use BLAS, and if both are linked to the same library, say OpenBLAS or Intel MKL, then performance is in fact almost identical for expensive operations like matrix multiplication. (R also ships with its own internal BLAS implementation, which is reliable but not very fast, and I believe is still single threaded, so the first thing you should do if you are using R and care about performance is to swap it out.)
For more sophisticated linear algebra algorithm, such as SVD, both will use typically LAPACK, and again, both will exhibit essential identical performance.
There is one important difference though: when R is compiled for 64-bit machines, it can only use 64-bit floats! While numpy can support 32 and even (through software emulation) 16 bit floats. This can halve memory usage, which in turn halves cache misses, which results in a significant speed up in cases where 64-bits of precision is not needed.
This is really interesting! I had always just claimed that Python was faster (see the benchmark I linked above [1]) based on personal experience. I wonder if this internal implementation has something to do with it...
So then I would assume you must be working with tables of less than 1000 rows, because thats pretty much the only case where it doesn't matter. At anything more than 1k rows, the differences are substantial.
Hundreds of rows is about usual for me. I do analysis on clinical studies with human participants. Nothing too tricky, most of my munging runs in effectively zero time.
I was going to make this point, but yeah.
The only thing I think people have a bit of a time with is how you do operations in data.table. If you are coming from plyr/dplyr, the transition can be difficult. However, I've found that the more I do, the more I prefer it, inspite of the fact that the main reason I use dt over tidy is the phenomenal performance gain.
That's pretty much the only group of people who will use this though. Those that are serious about it or have some background won't really look at another book on data science and probably do the necessary research themselves
I can’t think of any other retailer that does this. Best Buy, Walmart, Famous Footwear, Bed Bath and Beyond, etc. all honor the same prices in person as they do online.
Best Buy got in to legal trouble because they'd point 'bestbuy.com' from within the store to a different online store with prices that matched the in-store prices (i.e. higher prices).
Over the past few years, products at my local Best Buy are regularly 20-40% more than on their site. (Unless you order through the site for local pickup... then you can get the online price.)
Walgreens is the same. Might be $12 online but $18 in my local store.
Barnes and Noble is pretty par for the course in this.
My experience is that BestBuy will price match bestbuy.com, Newegg and Amazon. Their price match policy says they match: Amazon.com, Crutchfield.com, Dell.com, HP.com, Newegg.com, and TigerDirect.com, but I’ve gotten them to match online.
My experience with the office supply places and Target is that they'll happily match their online price, but the in store price is generally higher on items that aren't specifically on sale - sometimes dramatically so. Not sure about Best Buy, but they'll match a bunch of other online retailers so I'm more likely doing that anyway.
I'm surprised by the experience related here - I'd absolutely expect the store to price match online when asked about it even though I believe they were separate divisions.
i also noticed the app matching the in-store price based on location. and yup, store employees will match online prices without question, but only if you ask.
i don't shop there much anymore, but i've done bopus (buy online, pick up in store) before to get the online price directly.
On top of the stores others have mentioned, Costco seems to have randomly different prices between their physical stores and their online store. (With the physical store more frequently being the cheaper price.)
They strongly imply that the difference is the shipping being included in the online price. In-store displays often say "May be available on costco.com at a shipped price."
Exactly. Her past pattern of poor behavior really speaks for itself.
I’d be interested if her husband has a similar reputation in his field. Shame we don’t have any names, if only so their actions yield some sort of consequence.
Hopefully court pans out in the author’s favor (assuming what he said is indeed the true rendition of events).
Assuming the story is true as presented. If he’s regularly getting in the habit of publicly attacking people his girlfriend is having conflict with, I am sure his reputation is about as toxic as hers.
If anyone else gets anxiety when they smoke, I recommend smoking sativa instead of indica (nicknamed “in da couch” because of its anxiety effects). I was given the tip from a stoner friend, and it’s fsirly true in my experience.