这就是我的数据框架的样子。最右边的两列是我想要的列。我计算每行的唯一FundTypes的累计数量。第4列是所有＆＃34; ActivityType＆＃34;的累积唯一计数。并且第5列仅为＆＃34; ActivityType ==＆＃34; Sale＆＃34;的累积唯一计数。
dt <- read.table(text='
Name ActivityType FundType UniqueFunds(AllTypes) UniqueFunds(SaleOnly)
John Email a 1 0
John Sale a;b 2 2
John Webinar c;d 4 2
John Sale b 4 2
John Webinar e 5 2
John Conference b;d 5 2
John Sale b;e 5 3
Tom Email a 1 0
Tom Sale a;b 2 2
Tom Webinar c;d 4 2
Tom Sale b 4 2
Tom Webinar e 5 2
Tom Conference b;d 5 2
Tom Sale b;e;f 6 4
', header=T, row.names = NULL)
我试过了dt[, UniqueFunds := cumsum(!duplicated(FundType)& !FundType=="") ,by = Name]但是例如它算了一个＆amp; a; b＆amp; c; d为3个唯一值，而不是所需的4个唯一值，因为因子用分号分隔。请告诉我一个解决方案。
更新：我的真实数据集看起来更像是这样：
dt <- read.table(text='
Name ActivityType FundType UniqueFunds(AllTypes) UniqueFunds(SaleOnly)
John Email "" 0 0
John Conference "" 0 0
John Email a 1 0
John Sale a;b 2 2
John Webinar c;d 4 2
John Sale b 4 2
John Webinar e 5 2
John Conference b;d 5 2
John Sale b;e 5 3
John Email "" 5 3
John Webinar "" 5 3
Tom Email a 1 0
Tom Sale a;b 2 2
Tom Webinar c;d 4 2
Tom Sale b 4 2
Tom Webinar e 5 2
Tom Conference b;d 5 2
Tom Sale b;e;f 6 4
', header=T, row.names = NULL)
唯一累积向量需要考虑缺失值。
答案 0 :(得分：7)
nrussell建议编写自定义函数的简洁解决方案。让我放弃我得到的东西。我尝试过使用cumsum()和duplicated()。我做了两个主要的操作。一个用于alltype，另一个用于saleonly。首先，我为每个名字创建了索引。然后，我拆分FundType并使用splitstackshape包中的cSplit()格式化长格式的数据。然后，我为每个名称的每个索引号选择了最后一行。最后，我只选择了一列alltype。
library(splitstackshape)
library(zoo)
library(data.table)
setDT(dt)[, ind := 1:.N, by = "Name"]
cSplit(dt, "FundType", sep = ";", direction = "long")[,
alltype := cumsum(!duplicated(FundType)), by = "Name"][,
.SD[.N], by = c("Name", "ind")][, list(alltype)] -> alltype
第二次行动是为了saleonly。基本上，我对待销售的子集化数据重复了相同的方法，即ana。我还创建了一个没有销售的数据集，即ana2。然后，我创建了一个包含两个数据集的列表（即l）并绑定它们。我用Name和ind更改了数据集的顺序，取每个名称和索引号的最后一行，处理NAs（填充NA并用0替换每个Name的第一个NA），最后选择了一列。最后一项操作是将原始dt，alltype和saleonly合并。
# data for sale only
cSplit(dt, "FundType", sep = ";", direction = "long")[
ActivityType == "Sale"][,
saleonly := cumsum(!duplicated(FundType)), by = "Name"] -> ana
# Data without sale
cSplit(dt, "FundType", sep = ";", direction = "long")[
ActivityType != "Sale"] -> ana2
# Combine ana and ana2
l <- list(ana, ana2)
rbindlist(l, use.names = TRUE, fill = TRUE) -> temp
setorder(temp, Name, ind)[,
.SD[.N], by = c("Name", "ind")][,
saleonly := na.locf(saleonly, na.rm = FALSE), by = "Name"][,
saleonly := replace(saleonly, is.na(saleonly), 0)][, list(saleonly)] -> saleonly
cbind(dt, alltype, saleonly)
Name ActivityType FundType UniqueFunds.AllTypes. UniqueFunds.SaleOnly. ind alltype saleonly
1: John Email a 1 0 1 1 0
2: John Sale a;b 2 2 2 2 2
3: John Webinar c;d 4 2 3 4 2
4: John Sale b 4 2 4 4 2
5: John Webinar e 5 2 5 5 2
6: John Conference b;d 5 2 6 5 2
7: John Sale b;e 5 3 7 5 3
8: Tom Email a 1 0 1 1 0
9: Tom Sale a;b 2 2 2 2 2
10: Tom Webinar c;d 4 2 3 4 2
11: Tom Sale b 4 2 4 4 2
12: Tom Webinar e 5 2 5 5 2
13: Tom Conference b;d 5 2 6 5 2
14: Tom Sale b;e;f 6 4 7 6 4
修改
对于新数据集，我尝试了以下方法。基本上，我使用我的方法将saleonly数据用于这个新数据集。修订仅在alltype部分。首先，我添加了索引，用NA替换了“”，并用具有非NA值的行对数据进行了子集化。这是temp。其余部分与之前的答案相同。现在我想在FundType中使用NA的数据集，所以我使用了setdiff()。使用rbindlist()，我合并了两个数据集并创建了temp。其余部分与之前的答案相同。销售部分没有任何变化。我希望这对你的真实数据有用。
### all type
setDT(dt)[, ind := 1:.N, by = "Name"][,
FundType := replace(FundType, which(FundType == ""), NA)][FundType != ""] -> temp
cSplit(temp, "FundType", sep = ";", direction = "long")[,
alltype := cumsum(!duplicated(FundType)), by = "Name"] -> alltype
whatever <- list(setdiff(dt, temp), alltype)
rbindlist(whatever, use.names = TRUE, fill = TRUE) -> temp
setorder(temp, Name, ind)[,.SD[.N], by = c("Name", "ind")][,
alltype := na.locf(alltype, na.rm = FALSE), by = "Name"][,
alltype := replace(alltype, is.na(alltype), 0)][, list(alltype)] -> alltype
### sale only
cSplit(dt, "FundType", sep = ";", direction = "long")[
ActivityType == "Sale"][,
saleonly := cumsum(!duplicated(FundType)), by = "Name"] -> ana
cSplit(dt, "FundType", sep = ";", direction = "long")[
ActivityType != "Sale"] -> ana2
l <- list(ana, ana2)
rbindlist(l, use.names = TRUE, fill = TRUE) -> temp
setorder(temp, Name, ind)[,
.SD[.N], by = c("Name", "ind")][,
saleonly := na.locf(saleonly, na.rm = FALSE), by = "Name"][,
saleonly := replace(saleonly, is.na(saleonly), 0)][, list(saleonly)] -> saleonly
cbind(dt, alltype, saleonly)
Name ActivityType FundType UniqueFunds.AllTypes. UniqueFunds.SaleOnly. ind alltype saleonly
1: John Email NA 0 0 1 0 0
2: John Conference NA 0 0 2 0 0
3: John Email a 1 0 3 1 0
4: John Sale a;b 2 2 4 2 2
5: John Webinar c;d 4 2 5 4 2
6: John Sale b 4 2 6 4 2
7: John Webinar e 5 2 7 5 2
8: John Conference b;d 5 2 8 5 2
9: John Sale b;e 5 3 9 5 3
10: John Email NA 5 3 10 5 3
11: John Webinar NA 5 3 11 5 3
12: Tom Email a 1 0 1 1 0
13: Tom Sale a;b 2 2 2 2 2
14: Tom Webinar c;d 4 2 3 4 2
15: Tom Sale b 4 2 4 4 2
16: Tom Webinar e 5 2 5 5 2
17: Tom Conference b;d 5 2 6 5 2
18: Tom Sale b;e;f 6 4 7 6 4
答案 1 :(得分：6)
我认为这是实现目标的一种方式。首先添加一个辅助索引变量来维护输入顺序;和key Name：
Dt <- copy(dt[, 1:3, with = FALSE])[, gIdx := 1:.N, by = "Name"]
setkeyv(Dt, "Name")
为清楚起见，我使用了这个功能
n_usplit <- function(x, spl = ";") length(unique(unlist(strsplit(x, split = spl))))
而不是动态地键入正文的表达式 - 下面的操作足够密集，因为它没有一堆嵌套函数调用卷积的东西。
最后，
Dt[Dt, allow.cartesian = TRUE][
gIdx <= i.gIdx,
.("UniqueFunds(AllTypes)" = n_usplit(FundType),
"UniqueFunds(SaleOnly)" = n_usplit(FundType[ActivityType == "Sale"])),
keyby = "Name,i.gIdx,i.ActivityType,i.FundType"][,-2, with = FALSE]
# Name i.ActivityType i.FundType UniqueFunds(AllTypes) UniqueFunds(SaleOnly)
# 1: John Email a 1 0
# 2: John Sale a;b 2 2
# 3: John Webinar c;d 4 2
# 4: John Sale b 4 2
# 5: John Webinar e 5 2
# 6: John Conference b;d 5 2
# 7: John Sale b;e 5 3
# 8: Tom Email a 1 0
# 9: Tom Sale a;b 2 2
# 10: Tom Webinar c;d 4 2
# 11: Tom Sale b 4 2
# 12: Tom Webinar e 5 2
# 13: Tom Conference b;d 5 2
# 14: Tom Sale b;e;f 6 4
我觉得我可以用SQL解释这个问题，但是我们走了：
Dt本身（Name）gIdx），只考虑序列中的前一行（包含） - 这会产生一种累积效应（缺少更好的术语）UniqueFunds(...)列 - 注意在第二种情况下完成的额外子集 - n_usplit(FundType[ActivityType == "Sale"]) i.gIdx）。 我不确定这会因为使用笛卡尔连接而缩放，所以希望您的真实数据集不是数百万行。
数据：
library(data.table)
##
dt <- fread('
Name ActivityType FundType UniqueFunds(AllTypes) UniqueFunds(SaleOnly)
John Email a 1 0
John Sale a;b 2 2
John Webinar c;d 4 2
John Sale b 4 2
John Webinar e 5 2
John Conference b;d 5 2
John Sale b;e 5 3
Tom Email a 1 0
Tom Sale a;b 2 2
Tom Webinar c;d 4 2
Tom Sale b 4 2
Tom Webinar e 5 2
Tom Conference b;d 5 2
Tom Sale b;e;f 6 4
', header = TRUE)
答案 2 :(得分：2)
我实现了您正在寻找的目标：
library(data.table)
library(stringr)
dt <- data.table(read.table(text='
Name ActivityType FundType UniqueFunds(AllTypes) UniqueFunds(SaleOnly)
John Email a 1 0
John Sale a;b 2 2
John Webinar c;d 4 2
John Sale b 4 2
John Webinar e 5 2
John Conference b;d 5 2
John Sale b;e 5 3
Tom Email a 1 0
Tom Sale a;b 2 2
Tom Webinar c;d 4 2
Tom Sale b 4 2
Tom Webinar e 5 2
Tom Conference b;d 5 2
Tom Sale b;e;f 6 4
', header=T, row.names = NULL))
dt[,UniqueFunds.AllTypes. := NULL][,UniqueFunds.SaleOnly. := NULL]
#Get the different Fund Types
vals <- unique(unlist(str_extract_all(dt$FundType,"[a-z]")))
#Construct a new set of columns indicating which fund types are present
dt[,vals:=data.table(1*t(sapply(FundType,str_detect,vals))),with=FALSE]
#Calculate UniqueFunds.AllTypes
dt[, UniqueFunds.AllTypes. :=
rowSums(sapply(.SD, cummax)), .SDcols = vals, by = Name]
#Calculate only when ActicityType == "Sale" and use cummax to achieve desired output
dt[,UniqueFunds.SaleOnly. := 0
][ActivityType == "Sale", UniqueFunds.SaleOnly. :=
rowSums(sapply(.SD, cummax)), .SDcols = vals, by = Name
][,UniqueFunds.SaleOnly. := cummax(UniqueFunds.SaleOnly.), by = Name
]
#Cleanup vals
dt[,vals := NULL, with = FALSE]