我想建立一个新的吸毒人群（Ray 2003）。我的原始数据集大约有 1900 万行，因此循环证明效率低下。这是一个虚拟数据集（用水果而不是药物完成）：
df2
names dates age sex fruit
1 tom 2010-02-01 60 m apple
2 mary 2010-05-01 55 f orange
3 tom 2010-03-01 60 m banana
4 john 2010-07-01 57 m kiwi
5 mary 2010-07-01 55 f apple
6 tom 2010-06-01 60 m apple
7 john 2010-09-01 57 m apple
8 mary 2010-07-01 55 f orange
9 john 2010-11-01 57 m banana
10 mary 2010-09-01 55 f apple
11 tom 2010-08-01 60 m kiwi
12 mary 2010-11-01 55 f apple
13 john 2010-12-01 57 m orange
14 john 2011-01-01 57 m apple
我已经确定了在 04-2010 和 10-2010 之间服用苹果的人：
temp2
names dates age sex fruit
6 tom 2010-06-01 60 m apple
5 mary 2010-07-01 55 f apple
7 john 2010-09-01 57 m apple
我想在原始 DF 中创建一个名为“索引”的新列，这是一个人在定义的日期范围内被处方药物的第一个日期。这就是我试图将 temp 中的日期转换为 df$index 的方法：
df2$index<-temp2$dates
df2$index<-df2$dates == temp2$dates
df2$index<-df2$dates %in% temp2$dates
df2$index<-ifelse(as.Date(df$dates)==as.Date(temp2$dates), as.Date(temp2$dates),NA)
我做的不对——因为这些都不起作用。这是所需的输出。
df2
names dates age sex fruit index
1 tom 2010-02-01 60 m apple <NA>
2 mary 2010-05-01 55 f orange <NA>
3 tom 2010-03-01 60 m banana <NA>
4 john 2010-07-01 57 m kiwi <NA>
5 mary 2010-07-01 55 f apple 2010-07-01
6 tom 2010-06-01 60 m apple 2010-06-01
7 john 2010-09-01 57 m apple 2010-09-01
8 mary 2010-07-01 55 f orange <NA>
9 john 2010-11-01 57 m banana <NA>
10 mary 2010-09-01 55 f apple <NA>
11 tom 2010-08-01 60 m kiwi <NA>
12 mary 2010-11-01 55 f apple <NA>
13 john 2010-12-01 57 m orange <NA>
14 john 2011-01-01 57 m apple <NA>
一旦我得到了想要的输出，我想从索引日期追溯过去 180 天内是否有人吃过苹果。如果他们没有苹果 - 我想保留他们。如果他们确实有一个苹果（例如，汤姆），我想丢弃他。这是我在所需输出上尝试过的代码：
df4<-df2[df2$fruit!='apple' & df2$index-180,]
df4<-df2[df2$fruit!='apple' & df2$dates<=df2$index-180,] ##neither work for me
我将不胜感激有关这些问题的任何指导 - 甚至是我应该阅读的内容以帮助我学习如何做到这一点。也许我的逻辑有缺陷，我的方法不起作用——如果是这样，请告诉我！先感谢您。
这是我的df：
names<-c("tom", "mary", "tom", "john", "mary",
"tom", "john", "mary", "john", "mary", "tom", "mary", "john", "john")
dates<-as.Date(c("2010-02-01", "2010-05-01", "2010-03-01",
"2010-07-01", "2010-07-01", "2010-06-01", "2010-09-01",
"2010-07-01", "2010-11-01", "2010-09-01", "2010-08-01",
"2010-11-01", "2010-12-01", "2011-01-01"))
fruit<-as.character(c("apple", "orange", "banana", "kiwi",
"apple", "apple", "apple", "orange", "banana", "apple",
"kiwi", "apple", "orange", "apple"))
age<-as.numeric(c(60,55,60,57,55,60,57,55,57,55,60,55, 57,57))
sex<-as.character(c("m","f","m","m","f","m","m",
"f","m","f","m","f","m", "m"))
df2<-data.frame(names,dates, age, sex, fruit)
df2
这是temp2：
data1<-df2[df2$fruit=="apple"& (df2$dates >= "2010-04-01" & df2$dates< "2010-10-01"), ]
index <- with(data1, order(dates))
temp<-data1[index, ]
dup<-duplicated(temp$names)
temp1<-cbind(temp,dup)
temp2<-temp1[temp1$dup!=TRUE,]
temp2$dup<-NULL
解决方案
df2 <- df2[with(df2, order(names, dates)), ]
df2$first.date <- ave(df2$date, df2$name, df2$fruit,
FUN=function(dt) dt[dt <="2010-10-31" & dt>="2010-04-01"][1]) ##DWin code for assigning index date for each fruit in the pre-period
df2$x<-df2$fruit=='apple' & df2$dates>df2$first.date-180 & df2$dates<df2$first.date ##assigns TRUE to row that tom is not a new user
ids <- with(df2, unique(names[x == "TRUE"])) ##finding the id which has one value of true
new_users<-subset(df2, !names %in% ids) ##gets rid of id that has at least one value of true