R, the master troll of statistical languages (2012)
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R, the master troll of statistical languages (2012)
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Re: R, the master troll of statistical languages (2012)
#2I think this article nails exactly what's right and wrong with R.
This in particular sums up the learning curve of R.
> Thankfully, I’m long past the point where R syntax is perpetually confusing. I’m now well into the phase where it’s only frequently confusing, and I even have high hopes of one day making it to the point where it barely confuses me at all.
Warning personal opinion ahead...
R, the language can get you up and running alot faster than other languages for statistics like say python with Pandas or scipy but even people who use it on a daily basis will curse the languages "quirks". I find most of the confusion comes from R trying to be too friendly to the user via type conversions. The ease in which the R's type system will convert values has probably caused me more grief when first learning the language than any other issue I ran into.
And this illustrates the down side of using R
> library(Hmisc) apply(ice.cream, 2, all.is.numeric)
> …which had the desirable property of actually working. But it still wasn’t very satisfactory, because it requires loading a pretty large library (Hmisc) with a bunch of dependencies just to do something very simple that should really be doable in the base R distribution.
Since R is rarely a programmer's most used language, I find there tends to be an above average use of google and paste type code that pulls in 50 different packages, each of which is used on 1-2 lines of a 1000 line script. Perhaps this is just a function of most programmers not really understanding the mathematical domain and hence they slowly google and iterate their way towards a solution.
Often I'll see people pull in 5 different time series libraries just because each of them operate on a ts object, so they all can work on the same object, and each one provides one additional method the other's don't and the programmer needs to create their solution.
You'll hear people talk about writing R in the Hadley universe or the basic R universe but there isn't much talk about what a canonical R solution looks like. R is a great language in the sense that Perl and C++. It allows you to do anything but there often isn't an agreed upon way of writing it and two different programmers can come up with wildly different but valid solutions to the same problem.
Re: R, the master troll of statistical languages (2012)
#3R is a language with a lot of gotcha's. I usually get burned by characters being converted to factors in read.csv() and converting factors to numeric (it works, but not how you intend). The R Inferno (http://www.burns-stat.com/documents/books/the-r-inferno/) has a lot of other gotcha's and is worth a read for people who use the language.
That said, the power, flexibility and user community make it my go-to for any first crack at an analysis of data.
Re: R, the master troll of statistical languages (2012)
#4The problem is people using R without trying to learn about the language itself, just assuming it works like their favourite language.
For example complaining that R is slow and then writing iterative solution instead of using vectorization. When I saw the example the author gave my first thought was "sapply/lapply". Lapply is essential to the R use, and is being taught early on in every book/course on R I've ever saw.
"In 2012, I’m the kind of person who uses apply() a dozen times a day, and is vaguely aware that R has a million related built-in functions like sapply(), tapply(), lapply(), and vapply(), yet still has absolutely no idea what all of those actually do. "
Re: R, the master troll of statistical languages (2012)
#5Most of my university classmates' first exposure to programming is using R in a statistics class. It's awful. I wish they'd make Python or something a prerequisite, so that giant swaths of people don't get turned off of computing or start with the strange ideas it teaches.
Re: R, the master troll of statistical languages (2012)
#6R is a language with a lot of gotcha's. I usually get burned by characters being converted to factors in read.csv() and converting factors to numeric (it works, but not how you intend). The R Inferno ( http://www.burns-stat.com/documents/books/the-r-inferno/ ) has a lot of other gotcha's and is worth a read for people who use the language. That said, the power, flexibility and user community make it my go-to for any…
Factor is one of the worst thing of R-world. I don't recall ever needing factors, yet they creep in with many functions (read.csv, cut).
Btw there's a nice readr package (from Hadleyverse) that has a read_csv method that does away with factors by default.
Re: R, the master troll of statistical languages (2012)
#7I think this article nails exactly what's right and wrong with R. This in particular sums up the learning curve of R. > Thankfully, I’m long past the point where R syntax is perpetually confusing. I’m now well into the phase where it’s only frequently confusing, and I even have high hopes of one day making it to the point where it barely confuses me at all. Warning personal opinion ahead... R, the language can get yo…
I think it's also due the fact that installing R package is just one command away, install.package("abc") and library(abc) and you're done. It a blessing, but encourages loading swaths of libraries.
Re: R, the master troll of statistical languages (2012)
#8R is a language with a lot of gotcha's. I usually get burned by characters being converted to factors in read.csv() and converting factors to numeric (it works, but not how you intend). The R Inferno ( http://www.burns-stat.com/documents/books/the-r-inferno/ ) has a lot of other gotcha's and is worth a read for people who use the language. That said, the power, flexibility and user community make it my go-to for any…
Factor is one of the worst thing of R-world. I don't recall ever needing factors, yet they creep in with many functions (read.csv, cut). Btw there's a nice readr package (from Hadleyverse) that has a read_csv method that does away with factors by default.
You will (inevitably?) run into factors when importing data from SPSS files... sure, you can discard them upon reading... but are you sure you don't want access to the value labels in the future?
Re: R, the master troll of statistical languages (2012)
#9Way too many languages are trolls, or have troll features. Or in other words, too many languages have features that don't do what a reasonable person would expect them to do.
I've long considered implicit type conversion to be a troll feature, especially how Javascript does it. Another one is how differently Java treats primitives and Object types. Oracle databases treat nulls and empty strings the same.
At times like this, all I can do is lament and search in vain for a language with no troll features.
Re: R, the master troll of statistical languages (2012)
#10R is a language with a lot of gotcha's. I usually get burned by characters being converted to factors in read.csv() and converting factors to numeric (it works, but not how you intend). The R Inferno ( http://www.burns-stat.com/documents/books/the-r-inferno/ ) has a lot of other gotcha's and is worth a read for people who use the language. That said, the power, flexibility and user community make it my go-to for any…
Factor is one of the worst thing of R-world. I don't recall ever needing factors, yet they creep in with many functions (read.csv, cut). Btw there's a nice readr package (from Hadleyverse) that has a read_csv method that does away with factors by default.
How would you represent categorical data then? R's primary use case isn't text processing. And HW isn't always right.