The Tidyverse is a true gem in modern programming that deals with data.<p>This release feels like a nice incremental improvement rather than a revolution. Adding lubridate as a core package will save me a few keystrokes. I also did not know about the conflicts package, but will certainly be using it in the future.<p>I'm a bit surprised that other "better" programming languages haven't done more to replicate the Tidyverse. The Tidyverse is a masterclass in metaprogramming, and has a pretty big following. While R has some warts as a language, I'm not sure I've seen anything comparable to Tidyverse in terms of scope and influence. I see Julia trying with the Dataframes ecosystem, but it's got a ways to go before that feels cohesive like Tidyverse.<p>If I could wave a magic wand, I'd get Hadley and the RStudio crew really excited about Julia or Racket. R <i>does</i> have warts, and I dream of the day when something better is as good.
My 2¢ on the tidyverse is that it is a bit misleading to call it "a collection of packages". I think it would be more accurate to call it a "framework". Though I suppose "opinionated collection of packages" is perhaps a fuzzy synonym for framework.<p>The difference between "collection of packages" and "framework" is that "collection of packages" implies interoperable via API conventions, but otherwise standalone packages, which could easily be used in isolation alongside base R.<p>Whereas "framework" generally means tightly coupled components, in a "do things my way or the highway" fashion.<p>Technically speaking, it's not "impossible" to mix tidyverse with base R, but in general if you wanted to mix and match tidy vs base R syntax, you'd find yourself working "against" the tidy api rather than alongside it most of the time.<p>R is not my main language, but every time I come across a task and I need to make a decision between R vs tidyR, the two feel like completely different languages.<p>I also have some beef against some of the decisions/conventions used (e.g. abusing metaprogramming to pass variables and use the variable's label as a value, completely ignoring the variable's actual value, instead of passing it as a string or as an actual variable ... seriously confusing when you first come across it).
> You’ll notice one other small change to the tidyverse message: we now advertise the conflicted package.<p>Not a fan of the nature in which tidyverse promotes itself. They used to be more aggressive - i.e. advertising their books in package load messages [1]. But these new changes still feel like an ad.<p>To me, as an outsider, tidyverse seems to mainly be about "dplyr". The main thing holding all of it together otherwise is just the fact that majority of their functions return a "tibble" instead of a data.frame, forcing you to load all the jungle of the rest of their stuff whenever you use one of their packages.<p>[1]: <a href="https://win-vector.com/2019/08/30/it-is-time-for-cran-to-ban-package-ads/" rel="nofollow">https://win-vector.com/2019/08/30/it-is-time-for-cran-to-ban...</a>
There's a bit of circular logic in here. Can somebody make a short explanation on what's this is about?<p><pre><code> Overview
The tidyverse is a set of packages that work in harmony because they share common data representations and API design. The tidyverse package is designed to make it easy to install and load core packages from the tidyverse in a single command.</code></pre>
I have been using R almost daily for years now and its no hyperbole when I say that if ggplot2/dplyr wasn't a thing I would have never bothered with R.
Has anybody switched from R (tidyverse and ggplot2) to python and found they can be equally or more productive in tabular data wrangling, data exploration and visualisations?<p>Still using R because I find python cannot do exploratory data analysis of visualisation as well as R.
I am an unapologetic Hadley fanboy. He is a genius. Never in my working life has anyone made so many good decisions that directly benefitted me. My R productivity is so incredibly much higher than it would have been without him.<p>The worst thing I can say about tidyverse is that I find the stack traces increasingly gnarly. The information you need to debug is there, there's just a lot of filler. I have some faith that they know and they're working on it.
I’ve had to dip my toes into R recently to help my wife with data cleaning. `dplyr` is what I reach for when helping her.<p>The pipe operator as well as treating csv tables as SQL was intuitive for me as a software engineer.
I have some concerns about the direction that certain packages within tidyverse, particularly dplyr, are heading.<p>One issue I've noticed is the increasing frequency of warnings, such as when performing left or inner joins. These warnings can be a bit overwhelming at times.<p>However, it's worth noting that you can suppress these warnings by using the "multiple='all'" parameter, which is easy to fumble when you’re exploring data, why not simply "all=TRUE”? It seems like a lack of ergonomics and considering user experience.