R 使用data.table创建序列
我有一个数据表的格式R 使用data.table创建序列,r,data.table,aggregate-functions,R,Data.table,Aggregate Functions,我有一个数据表的格式 id | pet | name 2011-01-01 | "dog" | "a" 2011-01-02 | "dog" | "b" 2011-01-03 | "cat" | "c" 2011-01-04 | "dog" | "a" 2011-01-05 | "dog" | "some" 2011-01-06 | "cat" | "thing" 我想执行一个聚合，将猫出现之前出现的所有狗名连接起来，例如 id | pet | name
id | pet | name
2011-01-01 | "dog" | "a"
2011-01-02 | "dog" | "b"
2011-01-03 | "cat" | "c"
2011-01-04 | "dog" | "a"
2011-01-05 | "dog" | "some"
2011-01-06 | "cat" | "thing"
id | pet | name | prior
2011-01-01 | "dog" | "a" |
2011-01-02 | "dog" | "b" |
2011-01-03 | "cat" | "c" | "a b"
2011-01-04 | "dog" | "a" |
2011-01-05 | "dog" | "some" |
2011-01-06 | "cat" | "thing" | "a some"
df1这里是另一个选项
indx <- setDT(DT)[, list(.I[.N], paste(name[-.N], collapse = ' ')),
by = list(c(0L, cumsum(pet == "cat")[-nrow(DT)]))]
DT[indx$V1, prior := indx$V2]
DT
# id pet name prior
# 1: 2011-01-01 dog a NA
# 2: 2011-01-02 dog b NA
# 3: 2011-01-03 cat c a b
# 4: 2011-01-04 dog a NA
# 5: 2011-01-05 dog some NA
# 6: 2011-01-06 cat thing a some
indx我在数据集中运行了每个解决方案，并将运行时间与rbenchmark进行了比较
我无法共享数据集，但这里有一些基本信息：
dim(event_source_causal_parts)
[1] 311127 4
用于比较的代码
require(rbenchmark)
benchmark({
event_source_causal_parts <- augmented_data_no_software[, list(PROD_ID, Source, Event_Date, Causal_Part_Number)]
setDT(event_source_causal_parts)[, prior := paste(Causal_Part_Number[-.N], collapse = ' '), .(group=cumsum(c(0,diff(Source == "Warranty")) < 0))][Source != 'Warranty', prior := '']
})
benchmark({
event_source_causal_parts <- augmented_data_no_software[, list(PROD_ID, Source, Event_Date, Causal_Part_Number)]
setDT(event_source_causal_parts)[, prior := paste(Causal_Part_Number[-.N], collapse = ' '), .(group=cumsum(shift(Source, fill="Warranty") == "Warranty"))][Source != 'Warranty', prior := '']
})
benchmark({
event_source_causal_parts <- augmented_data_no_software[, list(PROD_ID, Source, Event_Date, Causal_Part_Number)]
indx <- setDT(event_source_causal_parts)[, list(.I[.N], paste(Causal_Part_Number[-.N], collapse = " ")),
by = list(c(0L, cumsum(Source == "Warranty")[-nrow(event_source_causal_parts)]))]
})
我的环境,
R version 3.1.2 (2014-10-31)
Platform: x86_64-w64-mingw32/x64 (64-bit)
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] rbenchmark_1.0.0 stringr_0.6.2 data.table_1.9.5 vimcom_1.2-6
loaded via a namespace (and not attached):
[1] chron_2.3-45 grid_3.1.2 lattice_0.20-30 tools_3.1.2 zoo_1.7-11
R使用了英特尔MKL数学库
基于这些结果，我认为@akrun的第二个解决方案是最快的
我再次运行了测试，但现在我用-O3重新编译了data.table，并将R更新为3.2.0。结果非常不同：
replications elapsed relative user.self sys.self user.child sys.child
1 100 21.22 1 20.73 0.48 NA NA
replications elapsed relative user.self sys.self user.child sys.child
1 100 11.31 1 10.39 0.92 NA NA
replications elapsed relative user.self sys.self user.child sys.child
1 100 35.77 1 35.53 0.25 NA NA
因此，在新的R和O3条件下，最佳溶液的速度更快，但次优溶液的速度要慢得多 你尝试了什么？请不要在这里“扩展问题”。如果你有一个新问题，你可能应该把它作为一个新问题发布，最好用一个小的容易重复的例子来解决。对不起，真管用！谢谢。我将对我的问题进行一些基准测试，以比较解决方案。@Anton，出于好奇，我的答案也不起作用？或者你就是懒得为你在这里得到的免费帮助提供一些反馈？@DavidArenburg我认为OP打算在两篇文章中比较解决方案（尽管不确定），如果你的第二个解决方案获胜，我将获得7.5分：）@DavidArenburg我也将测试你的解决方案。
require(rbenchmark)
benchmark({
event_source_causal_parts <- augmented_data_no_software[, list(PROD_ID, Source, Event_Date, Causal_Part_Number)]
setDT(event_source_causal_parts)[, prior := paste(Causal_Part_Number[-.N], collapse = ' '), .(group=cumsum(c(0,diff(Source == "Warranty")) < 0))][Source != 'Warranty', prior := '']
})
benchmark({
event_source_causal_parts <- augmented_data_no_software[, list(PROD_ID, Source, Event_Date, Causal_Part_Number)]
setDT(event_source_causal_parts)[, prior := paste(Causal_Part_Number[-.N], collapse = ' '), .(group=cumsum(shift(Source, fill="Warranty") == "Warranty"))][Source != 'Warranty', prior := '']
})
benchmark({
event_source_causal_parts <- augmented_data_no_software[, list(PROD_ID, Source, Event_Date, Causal_Part_Number)]
indx <- setDT(event_source_causal_parts)[, list(.I[.N], paste(Causal_Part_Number[-.N], collapse = " ")),
by = list(c(0L, cumsum(Source == "Warranty")[-nrow(event_source_causal_parts)]))]
})
replications elapsed relative user.self sys.self user.child sys.child
1 100 12.91 1 12.76 0.05 NA NA
replications elapsed relative user.self sys.self user.child sys.child
1 100 12.7 1 12.66 0.05 NA NA
replications elapsed relative user.self sys.self user.child sys.child
1 100 61.97 1 61.65 0 NA NA
R version 3.1.2 (2014-10-31)
Platform: x86_64-w64-mingw32/x64 (64-bit)
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] rbenchmark_1.0.0 stringr_0.6.2 data.table_1.9.5 vimcom_1.2-6
loaded via a namespace (and not attached):
[1] chron_2.3-45 grid_3.1.2 lattice_0.20-30 tools_3.1.2 zoo_1.7-11
replications elapsed relative user.self sys.self user.child sys.child
1 100 21.22 1 20.73 0.48 NA NA
replications elapsed relative user.self sys.self user.child sys.child
1 100 11.31 1 10.39 0.92 NA NA
replications elapsed relative user.self sys.self user.child sys.child
1 100 35.77 1 35.53 0.25 NA NA