They do what you expect them to do, but were a glaring omission among common data frame functions. nrow and dim return big data objects because they act on each group when the group function is used. For instance:
mtcars.in %|% nrow nrow
1 32
mtcars.in %|% group(cyl) %|% nrow cyl nrow
1 4 11
1.1 6 7
1.2 8 14
Notwithstanding the size of this example, you see the possibility for nrow to produce billions of rows, therefore an implicit conversion to data.frame would reduce its usefulness. Just pipe it through as.data.frame if that's what you want.
It is convenient to type bind.cols(mtcars, cyl/2), but what do you do when you want to parametrize the data and the column? Typically, you are writing a function
halve.some.column = function(data, col) bind.cols(data, col/2)Should work right?
halve.some.column(mtcars, cyl)Error: object 'cyl' not found
halve.some.column(mtcars, "cyl")Error: non-numeric argument to binary operator
halve.some.column(mtcars, as.name("cyl"))Error: non-numeric argument to binary operator
Nada, nope, rien. Try VAR.
halve.some.column =
function(data, col) bind.cols(data, VAR(col)/2)
head(halve.some.column(mtcars, "cyl")) mpg cyl disp hp drat wt qsec vs am gear carb col.divided.by.2
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 3
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 3
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 2
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 3
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 4
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 3
Finally! And it works also with column numbers
mtcars.in %|% bind.cols(z = VAR(1)/2) mpg cyl disp hp drat wt qsec vs am gear carb z
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 10.50
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 10.50
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 11.40
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 10.70
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 9.35
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 9.05
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 7.15
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 12.20
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 11.40
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 9.60
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 8.90
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 8.20
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 8.65
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 7.60
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 5.20
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 5.20
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 7.35
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 16.20
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 15.20
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 16.95
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 10.75
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 7.75
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 7.60
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 6.65
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 9.60
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 13.65
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 13.00
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 15.20
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 7.90
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 9.85
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 7.50
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 10.70
When dealing with big data, wasteful recomputation should not be taken lightly. plyrmr now does that on your behalf.
mtcars.in %|% ncol[1] 11
This does actual work,
mtcars.in %|% ncol[1] 11
This one doesn't. It's that easy.
For example:
mtcars.in %|% count(carb:cyl, carb:gear) carb_cyl.carb carb_cyl.cyl carb_cyl.freq carb_gear.carb carb_gear.gear carb_gear.freq
1 2 4 6 4 3 5
2 4 8 6 1 4 4
3 1 4 5 2 3 4
4 2 8 4 2 4 4
5 4 6 4 4 4 4
6 3 8 3 1 3 3
7 1 6 2 3 3 3
8 6 6 1 2 5 2
9 8 8 1 4 5 1
10 NA NA NA 6 5 1
11 NA NA NA 8 5 1
Let me try to spell it out. For each ... argument, there are as many columns in the result as there are variables in that argument, plus 1. The last one is for the counts. The others are for the combination of values being counted. All the column names start with an id for the ... arg followed by a column specific part. The arg identifier is an underscore separated concatenation of variables in that argument. The column specific part is either the name of a variable or "freq". NA is used as filler when necessary. It's probably easier to look at the example.
When using the dplyr extension pack you may notice two new functions by a similar name: summarize and summarize_mergeable. The latter should be used when the summarization is associative and commutative, like a sum, to reap important performance benefits, but can't be used with operations like mean, which do no enjoy the same properties. It's the same as the .mergeable argument to transmute, but instead of trying to monkey patch summarize to accept an additional argument I went for the additional function. Remember summarize can only be used for single row summaries and gives important performance benefits when summarizing many small groups.
Just a little convenience for when you'd like to apply the same vector function to each column of a data frame.
log.data.frame = each.column(log)
mtcars %|% log.data.frame %|% head mpg cyl disp hp drat wt qsec vs am gear carb
1 3.045 1.792 5.075 4.700 1.361 0.9632 2.801 -Inf 0 1.386 1.3863
2 3.045 1.792 5.075 4.700 1.361 1.0561 2.834 -Inf 0 1.386 1.3863
3 3.127 1.386 4.682 4.533 1.348 0.8416 2.924 0 0 1.386 0.0000
4 3.063 1.792 5.553 4.700 1.125 1.1678 2.967 0 -Inf 1.099 0.0000
5 2.929 2.079 5.886 5.165 1.147 1.2355 2.834 -Inf -Inf 1.099 0.6931
6 2.896 1.792 5.416 4.654 1.015 1.2413 3.007 0 -Inf 1.099 0.0000
With this function, you can do things like:
mtcars.in %|% gapply(log.data.frame) %|% as.data.frame %|% head mpg cyl disp hp drat wt qsec vs am gear carb
1 3.045 1.792 5.075 4.700 1.361 0.9632 2.801 -Inf 0 1.386 1.3863
2 3.045 1.792 5.075 4.700 1.361 1.0561 2.834 -Inf 0 1.386 1.3863
3 3.127 1.386 4.682 4.533 1.348 0.8416 2.924 0 0 1.386 0.0000
4 3.063 1.792 5.553 4.700 1.125 1.1678 2.967 0 -Inf 1.099 0.0000
5 2.929 2.079 5.886 5.165 1.147 1.2355 2.834 -Inf -Inf 1.099 0.6931
6 2.896 1.792 5.416 4.654 1.015 1.2413 3.007 0 -Inf 1.099 0.0000
As part of a major clean-up of how non-standard evaluation is handled (a.k.a ... expression arguments), base functions transform and subset are not supported anymore. They were part of an extension pack and they required lots of complex code to make up for a brittle way of handling ... arguments. There are plenty of alternatives anyway: where, select, bind.cols, and, from the dplyr extension pack, filter and mutate .
What bugs? Nothing major to report for plyrmr but the new use cases that it makes possible put pressure on dear old rmr2, hence it's necessary to upgrade it to version 3.3.0.