如何使用`purrr::accumulate`进行累积过滤? [英] How to do cumulative filtering with `purrr::accumulate`?
问题描述
我正在寻找一种方法来做这样的事情
I'm looking for an approach to do something like this
# this doesnt work
# accumulate(1:8, ~filter(mtcars, carb >= .x))
这样我就可以检查不同截止值的一些汇总统计数据.我可以简单地做
So that I can examine some summary statistics at different cutoff values. I could simply do
# this works but redundant filtering is done
map2(list(mtcars), 1:8, ~filter(.x, carb >= .y))
但由于我的数据相当大，过滤掉之前步骤中已经过滤掉的值是没有意义的.本质上，这只是多次复制原始数据帧，然后分别过滤每一个.我正在查看 purrr 包中的积累，但该功能似乎不适合这个问题(我希望我错了).base-R 解决方案可能是
But since my data is rather large, it doesn't make sense to filter out values that were already filtered out in the step just before. In essence, this just duplicates the original dataframe a number of times and then filters each one separately. I was looking at accumulate from the purrr package, but that function doesn't seem fit to this problem (I'm hoping that I'm wrong on this). The base-R solution could be
# something like this works, but is ugly
output <- vector("list", length(1:8) + 1)
output[[1]] <- mtcars
for (i in 1:8) {
output[[i + 1]] <- filter(output[[i]], carb >= i)
}
output[[1]] <- NULL
但这并不是特别优雅.我怎样才能更好地做到这一点?
but that's not particularly elegant. How can I accomplish this better?
# the above code assumes
library(tidyverse)
mtcars <- as_tibble(mtcars)
这是输出可用于的示例:
This is an example of something the output could be used for:
推荐答案
您的初始示例 accumulate(1:8, ~filter(mtcars, carb >= .x)) 不起作用因为它使用累积值 (.x) 作为过滤标准，而不是下一个"值 (.y).试试这个:
Your initial example accumulate(1:8, ~filter(mtcars, carb >= .x)) doesn't work because it uses the accumulated value (.x) as the filtering criteria, rather than the "next" value (.y). Try this:
library(tidyverse)
accumulate(2:8, function(x,y) filter(x, carb >= y), .init=mtcars)
#> [[1]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#>
#> [[2]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> 3 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 4 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 5 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 6 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 7 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 8 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 9 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 10 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 11 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 12 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 13 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 14 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 15 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 16 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> 17 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> 18 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 19 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> 20 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 21 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 22 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 23 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 24 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> 25 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#>
#> [[3]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> 3 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 4 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 5 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 6 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 7 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 8 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 9 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 10 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 11 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 12 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 13 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 14 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 15 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#>
#> [[4]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> 3 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 4 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 5 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 6 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 7 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 8 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 9 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 10 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 11 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 12 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#>
#> [[5]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6
#> 2 15.0 8 301 335 3.54 3.57 14.6 0 1 5 8
#>
#> [[6]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6
#> 2 15.0 8 301 335 3.54 3.57 14.6 0 1 5 8
#>
#> [[7]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 15 8 301 335 3.54 3.57 14.6 0 1 5 8
#>
#> [[8]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 15 8 301 335 3.54 3.57 14.6 0 1 5 8
Created on 2019-11-21 by the reprex package (v0.3.0)
.init 参数从 mtcars 开始，然后每个步骤都使用序列 (y) 的增量进行过滤，并将过滤后的数据帧作为累积"值 (x) 传递给下一次迭代.
The .init argument starts you off with mtcars, and then each step filters with an increment from the sequence (y) and passes off the filtered dataframe as the "accumulated" value (x) to the next iteration.
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