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Comment by educationdata | Hacker News Reader

> If you're better at writing/composing dplyr, you can often make up the speed difference between the two in terms of the savings in time spent writing and reading code.

dplyr syntax is definitely more concise and readable than base R, but comparing to data.table I don't think it has any advantage in terms of saving time writing or reading code.

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I think the article sort of punts on providing examples of a complicated set of operations on a data frame. dplyr's author provides what I think is a good example of the differences between data.table and dplyr on a reasonably complex problem:

https://stackoverflow.com/questions/21435339/data-table-vs-d...

I feel like the first example is far more readable than the second. People can disagree on this, but the adoption rates of dplyr versus data.table do suggest (don't prove, but suggest) that the consensus on the issue leans towards dplyr. As we've noted, people certainly aren't adopting dplyr for the speed.

The second example should be written like this:

  diamondsDT[cut != "Fair", .(AvgPrice = mean(price),
                              MedianPrice = as.numeric(median(price)),
                              Count = .N), cut][order(-Count)]

There is no need to break it to 10 lines.

I honestly find that less readable than Hadley's version, especially turning "by = cut" into "cut." This is where terseness really cuts into readability (and positional arguments is one of my least favorite features about R in terms of long-term readability of code).

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