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One Year with R

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Re: One Year with R

#201

Post by dash2 »

bachmeier wrote: post #127
delusional wrote: post #117

Earlier quoted context omitted.

> R is a highly optimized, well-oiled machine if you're using it for its highly-optimized, well-oiled purposes. This hits home for me. We are just starting to use R for risk modeling where I work. R, more than any language I've ever used, makes me appreciate "worse is better". From a theoretical "aesthetic" perspective R is a mess. Yet for data processing all those theoretical concerns don't matter. It just works. It…

I hadn't thought about R as a "worse is better" language, but that's a good way to think about it. Makes sense, too, since it came from the place that inspired worse is better.

R comes from New Zealand, no?

Re: One Year with R

#202

Post by dash2 »

qudat wrote: post #97
tel wrote: post #61

R, and by R I mean R+tidyverse, is the world's best graphing calculator attached to an OK scheme. To which I mean R is a highly optimized, well-oiled machine if you're using it for its highly-optimized, well-oiled purposes. I tend to have notebooks full of tiny fragments like this dat_min %>% group_by(ymd = make_date(year(date), month(date), day(date))) %>% summarize(vol_btc=sum(vol_btc), vol_usdt=sum(vol_usdt), trad…

My wife is a researcher and started delving into doing her own statistical analysis. It's be fun (and frustrating) learning R with her. I agree that dplyr and the tidyverse are some fantastic packges for a software engineer who thinks about spreadsheets as SQL tables. I would say the most frustrating part about RStudio is that it is a workbook where you can execute code based on your cursor. For my wife, these workbo…

Here are two good strategies:

1. Always run from the top, using the "run previous chunks" button. When this gets too slow, you know that it's time to think harder about your workflow. For a more extreme version of the same idea, regularly restart R using Ctrl-Shift-0, and run from the top. It'll ensure your code is working right.

2. Have a setup chunk that always gets you to the same state. Make sure every other chunk works directly after calling the setup chunk. Then just alternate between "run setup chunk" and "run current chunk".

Re: One Year with R

#203

Post by BrokrnAlgorithm »

mtizim wrote: post #18

Fun fact: there exists a "Why R? Foundation" which holds yearly conferences, because after all these years the R community just cannot find a sensible reason to use the language.

I used to do R intensly. What I found, after moving to more "SE"-centric languages such as Python or C++, was that R becomes quite frustrating when you need to build something maintainable. Which is basically a definition that should apply to every package. As soon as S3, R6 and whatnot comes into play at multiple levels, I'm better off moving to C in order to minimize interacting with R's class system and having that done only at the topmost layer.

Re: One Year with R

#204

Post by bachmeier »

dash2 wrote: post #201
bachmeier wrote: post #127

Earlier quoted context omitted.

I hadn't thought about R as a "worse is better" language, but that's a good way to think about it. Makes sense, too, since it came from the place that inspired worse is better.

R comes from New Zealand, no?

R is an implementation of S. John Chambers worked on it at Bell Labs starting in 1975.

https://en.wikipedia.org/wiki/S_%28programming_language%29

Re: One Year with R

#205

Post by dash2 »

OT: does anyone know what happened to rweekly.org? It hasn't been updated in a long time and I don't know a good substitute.

Re: One Year with R

#206

Post by Hasnep »

dm319 wrote: post #128
tel wrote: post #61

R, and by R I mean R+tidyverse, is the world's best graphing calculator attached to an OK scheme. To which I mean R is a highly optimized, well-oiled machine if you're using it for its highly-optimized, well-oiled purposes. I tend to have notebooks full of tiny fragments like this dat_min %>% group_by(ymd = make_date(year(date), month(date), day(date))) %>% summarize(vol_btc=sum(vol_btc), vol_usdt=sum(vol_usdt), trad…

This is just a quick example - I would be grateful if people could recreate this brief look at UK COVID figures in another language: library(tidyverse) library(scales) download.file(url = "https://api.coronavirus.data.gov.uk/v2/data?areaType=overview&metric=covidOccupiedMVBeds&metric=newAdmissions&metric=newCasesBySpecimenDate&metric=newDeaths28DaysByDeathDate&metric=newPeopleReceivingFirstDose&format=csv", destfile…

A Julia solution with Chain and Gadfly might look something like this, although I've translated the R fairly directly so it might not be very idiomatic.

    import CSV
    using Chain: @chain
    using DataFrames
    import Downloads
    using Gadfly
    using Dates

    @chain begin
        Downloads.download(
            "https://api.coronavirus.data.gov.uk/v2/data?areaType=overview&metric=covidOccupiedMVBeds&metric=newAdmissions&metric=newCasesBySpecimenDate&metric=newDeaths28DaysByDeathDate&metric=newPeopleReceivingFirstDose&format=csv",
        )
        CSV.File
        DataFrame
        stack(
            [:newCasesBySpecimenDate, :covidOccupiedMVBeds, :newAdmissions, :newDeaths28DaysByDeathDate];
            variable_name = :Data,
        )
        transform(
            :Data =>
                (
                    x -> replace(
                        x,
                        "newCasesBySpecimenDate" => "NewCases",
                        "newAdmissions" => "Admissions",
                        "newDeaths28DaysByDeathDate" => "Deaths",
                        "covidOccupiedMVBeds" => "Ventilated",
                    )
                ) => :Data,
        )
        subset(:value => ByRow(!ismissing)) # Can't plot Geom.smooth with missings
        plot(
            _,
            x = :date,
            y = :value,
            colour = :Data,
            layer(Geom.smooth(method = :loess, smoothing = 0.1)),
            layer(Geom.point),
            Scale.y_log10(),
            Guide.xlabel("Date"),
            Guide.ylabel("Daily rate"),
            Guide.xlabel("Angle"),
            Guide.colorkey(title = "UK COVID-19"),
        )
    end

Re: One Year with R

#207

Post by j_m_b »

substation13 wrote: post #129
j_m_b wrote: post #91

My favorite operator is the pipe operator. When I first found out you could do a simple `ls | more` to read long outputs, it was an eye opening experience. In Clojure, we have the threading macros, `->` and `->>` that do a very similar thing. In R, we have `%>%` and now the native `|>`. Whenever a language has this operator and it is widely used, I know I am going to love it.

I credit F# with much of the popularity of the forward pipe operator. Unlike Haskell etc. which emphasize function binding (>>), idiomatic F# has pipes all over. [1..10] |> Seq.filter (fun x -> x % 2 = 0) |> Seq.map (fun x -> x * x * x)

Looks like I need to check out F#! Thanks!

Re: One Year with R

#208

Post by kgwgk »

It's a good reading but some of the complaints are hard to understand.

For example, in 4.5.1:

  Selecting and deleting at the same time doesn’t work either. For example, data[c(-1, 5)] is an error.
What would it mean for that to work? He seems to acknowledge that "selecting and deleting at the same time" doesn't make sense in 4.11.1

  Can you guess what data[-1:5] returns? I can’t either, so don’t ever try it. If you must know, it’s actually an error.
Also in 4.11.1:

  The : operator is absolutely lovely… until it screws you. The solution is to prefer the seq() functions to using : [....] As I’ve said, seq() and its related functions usually fix this issue.
Maybe the "related functions" fix some issues but seq(a,b) is not different from a:b

In 4.11:

  Now what do you think names(foo)
How is that surprising? Can the author also think of four realistic guesses about the effect of A[1,2] In 4.13:

  The index in a for loop uses the same environment as its caller, so loops like for(i in 1:10) will overwrite any variable called i in the parent environment and set it to 10 when the loop finishes. [...] This sounds awful, but I’ve never encountered it in practice.
Is it awful? The same happens in other languages like Python or C if I'm not mistaken.

  The plot() function has some strange defaults. For example, you need to have a plot before you can plot points [...]
I have no idea what that means. You can plot points using plot() without having a plot beforehand.

Edited to add: In 4.5.3:

  The $ operator is another case of R quietly changing your data structures.
Is it unexpected that when we extract an element from a data structure we get a different kind of data structure? Is A[1,1] another example of silently changing one data structure (matrix) to another (number)?

Re: One Year with R

#209

Post by antipaul »

It would help to know this person's background a bit more. Is this a former software engineer, or a manager trying to pick up a technical skill?

That info could help contextualize the entire piece.

Re: One Year with R

#210

Post by jointpdf »

mbreese wrote: post #172
hadley wrote: post #108

Earlier quoted context omitted.

Ah yeah, connecting the dots in ggplot2 docs is hard. It's hard for us to document because, under the hood, the pieces quite decoupled and different pieces are responsible for different arguments. But since we last took a deep dive on the ggplot2 docs, we've gotten much better at generating docs with code, so maybe it's time to have another look. I've filed an issue ( https://github.com/tidyverse/ggplot2/issues/4770…

Is there a tutorial someplace that explains how ggplot actually manages plotting? Or the architecture and layers between the high level code and how a plot is drawn? Meaning, I love being able to express what I want and ggplot figures out a good plot for me. But I know there are many layers that can be manipulated, but I just don’t understand the layers. One of the best compliments I can think of is that with ggplot,…

That should be covered in the original A layered grammar of graphics paper: https://vita.had.co.nz/papers/layered-grammar.html

And then there is an entire ggplot2 book (there are many, but this one was written by Hadley): https://ggplot2-book.org/

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