考虑这个数据集:
> DATA <- data.frame(Agreement_number = c(1,1,1,1,2,2,2,2),
+ country = c("Canada","Canada", "USA", "USA", "Canada","Canada", "USA", "USA"),
+ action = c("signature", "ratification","signature", "ratification", "signature", "ratification","signature", "ratification"),
+ signature_date = c(2000,NA,2000,NA, 2001, NA, 2002, NA),
+ ratification_date = c(NA, 2001, NA, 2002, NA, 2001, NA, 2002))
> DATA
Agreement_number country action signature_date ratification_date
1 Canada signature 2000 NA
1 Canada ratification NA 2001
1 USA signature 2000 NA
1 USA ratification NA 2002
2 Canada signature 2001 NA
2 Canada ratification NA 2001
2 USA signature 2002 NA
2 USA ratification NA 2002
如您所见，一半的行有重复的信息。对于像这样的小数据集，删除重复项真的很容易。我可以使用
coalesce 函数( dplyr package )，去掉“action”列，然后删除所有不相关的行。虽然，还有很多其他的方法。最终结果应如下所示:> DATA <- data.frame( Agreement_number = c(1,1,2,2),
+ country = c("Canada", "USA", "Canada","USA"),
+ signature_date = c(2000,2000,2001,2002),
+ ratification_date = c(2001, 2002, 2001, 2002))
> DATA
Agreement_number country signature_date ratification_date
1 Canada 2000 2001
1 USA 2000 2002
2 Canada 2001 2001
2 USA 2002 2002
问题是我的真实数据集要大得多(102000 x 270)，而且还有更多的变量。真实数据也更不规则，缺失值更多。
coalesce 函数看起来很慢。到目前为止我能做的最好的循环仍然需要 5-10 分钟才能运行。有没有一种简单的方法可以更快地做到这一点？我有一种感觉，R 中必须有一些函数用于这种操作，但我找不到任何函数。
最佳答案
OP 告诉他，他的生产数据有 10 万行 x 270 列，速度是他关心的问题。因此，我建议使用 data.table 。
我知道 Harland 也建议使用 data.table 和 dcast() 但下面的解决方案是一种不同的方法。它以正确的顺序引入行并将 ratification_date 复制到签名行。经过一些清理，我们得到了想要的结果。
library(data.table)
# coerce to data.table,
# make sure that the actions are ordered properly, not alphabetically
setDT(DATA)[, action := ordered(action, levels = c("signature", "ratification"))]
# order the rows to make sure that signature row and ratification row are
# subsequent for each agreement and country
setorder(DATA, Agreement_number, country, action)
# copy the ratification date from the row below but only within each group
result <- DATA[, ratification_date := shift(ratification_date, type = "lead"),
by = c("Agreement_number", "country")][
# keep only signature rows, remove action column
action == "signature"][, action := NULL]
result
数据
OP 提到他的生产数据有 270 列。为了模拟这一点，我添加了两个虚拟列:
set.seed(123L)
DATA <- data.frame(Agreement_number = c(1,1,1,1,2,2,2,2),
country = c("Canada","Canada", "USA", "USA", "Canada","Canada", "USA", "USA"),
action = c("signature", "ratification","signature", "ratification", "signature", "ratification","signature", "ratification"),
signature_date = c(2000,NA,2000,NA, 2001, NA, 2002, NA),
ratification_date = c(NA, 2001, NA, 2002, NA, 2001, NA, 2002),
dummy1 = rep(sample(4), each = 2L),
dummy2 = rep(sample(LETTERS[1:4]), each = 2L))
set.seed() 用于采样时可重复的结果。附录:带有附加列的
dcast() Harland 建议使用 data.table 和 dcast() 。除了他的回答中的其他几个缺陷外，它还没有处理 OP 提到的其他列。下面的
dcast() 方法还将返回附加列:library(data.table)
# coerce to data table
setDT(DATA)[, action := ordered(action, levels = c("signature", "ratification"))]
# use already existing column to "coalesce" dates
DATA[action == "ratification", signature_date := ratification_date]
DATA[, ratification_date := NULL]
# dcast from long to wide form, note that ... refers to all other columns
result <- dcast(DATA, Agreement_number + country + ... ~ action,
value.var = "signature_date")
result
请注意，此方法将更改列的顺序。
关于r - 如何避免大数据集的缓慢循环？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/45427161/