来自tidyr的完整/填充的data.table等效项 [英] data.table equivalent of complete/fill from tidyr
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问题描述
我有以下数据
library(tidyr)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(data.table)
#>
#> Attaching package: 'data.table'
#> The following objects are masked from 'package:dplyr':
#>
#> between, first, last
df <- structure(list(filename = c("PS92_019-6_rovT_irrad.tab", "PS92_019-6_rovT_irrad.tab",
"PS92_019-6_rovT_irrad.tab", "PS92_019-6_rovT_irrad.tab"), depth = c(5,
10, 20, 75), ps = c(3.26223404971255, 3.38947945477306, 3.97380593851983,
0.428074807655144)), row.names = c(NA, -4L), class = c("tbl_df", "tbl",
"data.frame"), .Names = c("filename", "depth", "ps"))
df
#> # A tibble: 4 x 3
#> filename depth ps
#> <chr> <dbl> <dbl>
#> 1 PS92_019-6_rovT_irrad.tab 5 3.2622340
#> 2 PS92_019-6_rovT_irrad.tab 10 3.3894795
#> 3 PS92_019-6_rovT_irrad.tab 20 3.9738059
#> 4 PS92_019-6_rovT_irrad.tab 75 0.4280748
在此数据中,深度= 0时缺少观测值。使用tidyr,
我可以用以下命令完成它:
In this data, there is a missing observation at depth = 0. Using tidyr, I can complete it with:
df %>% tidyr::complete(depth = c(0, unique(depth))) %>% fill(everything(), .direction = "up") ## use the last observations to fill the new line
#> # A tibble: 5 x 3
#> depth filename ps
#> <dbl> <chr> <dbl>
#> 1 0 PS92_019-6_rovT_irrad.tab 3.2622340
#> 2 5 PS92_019-6_rovT_irrad.tab 3.2622340
#> 3 10 PS92_019-6_rovT_irrad.tab 3.3894795
#> 4 20 PS92_019-6_rovT_irrad.tab 3.9738059
#> 5 75 PS92_019-6_rovT_irrad.tab 0.4280748
问题是我必须在大型数据集上运行我发现
的完成/填充功能有点慢。因此,我想给
一个data.table来看看它是否可以加快速度。但是,我
无法解决这个问题。任何帮助表示赞赏。
The problem is that I have to run this on a large dataset and I found that the complete/fill functions are a bit slow. Thus, I wanted to give it a go with data.table to see if it can speed up things. However I can't get my head around it. Any help appreciated.
推荐答案
没有特定功能,但您可以使用以下方法实现相同功能:
There is no specific function for it, but you can achieve the same with:
# load package
library(data.table)
# convert to a 'data.table'
setDT(df)
# expand and fill the dataset with a rolling join
df[.(c(0, depth)), on = .(depth), roll = -Inf]
给出:
filename depth ps
1: PS92_019-6_rovT_irrad.tab 0 3.2622340
2: PS92_019-6_rovT_irrad.tab 5 3.2622340
3: PS92_019-6_rovT_irrad.tab 10 3.3894795
4: PS92_019-6_rovT_irrad.tab 20 3.9738059
5: PS92_019-6_rovT_irrad.tab 75 0.4280748
直接向@Frank提出改进建议。
Heads up to @Frank for the improvement suggestion.
旧解:
df[CJ(depth = c(0,unique(depth))), on = 'depth'
][, c(1,3) := lapply(.SD, zoo::na.locf, fromLast = TRUE), .SDcols = c(1,3)][]
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