我试图在data.frame或data.table中创建两个条件的列。我所看到的帖子的不同之处在于，我试图在下面修改的是，我对条件没有"价值"，但条件取决于data.frame中的其他变量。
让我们假设这是我的数据帧:
mydf <- data.frame (Year = c(2000, 2001, 2002, 2004, 2005,
2007, 2000, 2001, 2002, 2003,
2003, 2004, 2005, 2006, 2006, 2007),
Name = c("Tom", "Tom", "Tom", "Fred", "Gill",
"Fred", "Gill", "Gill", "Tom", "Tom",
"Fred", "Fred", "Gill", "Fred", "Gill", "Gill"))
我想知道在过去的5年里，这3名受试者经历了多少次相同的事件。但是，如果事件的日期超过5年，我不想包括它。我认为我可以对一个指标变量求和(如果主体在一年中经历了该事件，则将其设置为1)，同时按照Year < Year & Year >= Year-5的方式指定一些东西。所以基本上是把小于焦点年和大于或等于焦点年前5年的经验加起来。
我已经创建了一个求和指示器和一个焦点年- 5的变量
mydf$Ind <- 1
mydf$Yearm5 <- mydf$Year-5
然后我转换到数据表的速度(原来的df有+60k obs)
library(data.table)
mydf <- data.table(mydf)
现在的问题是我不能让这两个条件工作。我所看到的帖子似乎都知道一个特定的值来子集(例如R数据)。)，但在我的情况下，值随着观察值的变化而变化(不确定这是否意味着我需要做一些循环?)。
我想我需要一些类似这样的东西:
mydf[, c("Exp"):= sum(Ind), by = c("Name")][Year < Year & Year >= Yearm5]
给:
Empty data.table (0 rows) of 5 cols: Year,Name,Ind,Yearm5,Exp
只使用一个条件
mydf1 <- mydf[, c("Exp"):= sum(Ind), by = c("Name")][Year >= Yearm5]
给出了总经验，所以我假设Year < Year条件有问题。
我不太确定是什么。我也试着修改了以下的建议:如何累计添加值在一个向量在R运气不佳，我指定条件的方式似乎出了问题。
library(dplyr)
mytest1 <- mydf %>%
group_by(Name, Year) %>%
filter(Year < Year & Year >= Yearm5) %>%
mutate(Exp = sum(Ind))
结果如下:
myresult <- data.frame (Year = c(2003, 2004, 2004, 2006,
2007, 2000, 2001, 2005,
2005, 2006, 2007, 2000,
2001, 2002, 2002, 2003),
Name = c("Fred", "Fred", "Fred", "Fred",
"Fred", "Gill", "Gill", "Gill",
"Gill", "Gill", "Gill", "Tom",
"Tom", "Tom", "Tom", "Tom"),
Ind = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
Exp = c(0, 1, 1, 3, 4, 0, 1, 1, 1, 2, 3, 0, 1, 2, 2, 4),
Yearm5 = c(1998, 1999, 1999, 2001, 2002,
1995, 1996, 2000, 2000, 2001,
2002, 1995, 1996, 1996, 1997, 1998))
任何帮助或指针将不胜感激!
这里有一个更data.table的方法，使用roll。
setDT(mydf)
# this is our desired end point
boundary = mydf[, list(Name, year.end = Year + 4)]
# set the key for the following merges
setkey(mydf, Name, Year)
setkey(boundary, Name, year.end)
# add indices that will keep track of the positions to compute deltas
mydf[, idx := .I]
boundary[, idx := .I]
# merge, rolling to match the end correctly, and then subtract the indices
# to get the desired delta.
# Note that we need to unique data because of duplicates.
# Depending on data you may also need to add `allow.cartesian = TRUE`.
# Final note - in data.table <= 1.9.2 you should omit the `by = .EACHI` part.
mydf[unique(boundary)[unique(mydf), list(Exp = i.idx - idx),
roll = -Inf, by = .EACHI]]
# Year Name idx Exp
# 1: 2003 Fred 1 0
# 2: 2004 Fred 2 1
# 3: 2004 Fred 3 1
# 4: 2006 Fred 4 3
# 5: 2007 Fred 5 4
# 6: 2000 Gill 6 0
# 7: 2001 Gill 7 1
# 8: 2005 Gill 8 1
# 9: 2005 Gill 9 1
#10: 2006 Gill 10 2
#11: 2007 Gill 11 3
#12: 2000 Tom 12 0
#13: 2001 Tom 13 1
#14: 2002 Tom 14 2
#15: 2002 Tom 15 2
#16: 2003 Tom 16 4
下面是使用rollapply和data.table的方法
library(zoo)
setDT(mydf)
setkey(mydf, Name,Year)
# create a data.table that has all Years and incidences including the 5 year window
# and sum up the number of incidences per year for each subject
m <- mydf[CJ(unique(Name),seq(min(Year)-5, max(Year))),allow.cartesian=TRUE][,
list(Ind = unique(Ind), I2 = sum(Ind,na.rm=TRUE)),
keyby=list(Name,Year)]
# use rollapply over this larger data.table to get the number of
# incidences in the previous 5 years (not including this year (hence head(x,-1))
m[,Exp := rollapply(I2, 5, function(x) sum(head(x,-1)),
align = 'right', fill=0),by=Name]
# join with the original to create your required data
m[mydf, !'I2']
Name Year Ind Exp
# 1: Fred 2003 1 0
# 2: Fred 2004 1 1
# 3: Fred 2004 1 1
# 4: Fred 2006 1 3
# 5: Fred 2007 1 4
# 6: Gill 2000 1 0
# 7: Gill 2001 1 1
# 8: Gill 2005 1 1
# 9: Gill 2005 1 1
# 10: Gill 2006 1 2
# 11: Gill 2007 1 3
# 12: Tom 2000 1 0
# 13: Tom 2001 1 1
# 14: Tom 2002 1 2
# 15: Tom 2002 1 2
# 16: Tom 2003 1 4
与data.table，我认为你正在寻找的语法应该是这样的:
setDT(mydf)
mydf[ , Exp := rank(x=Year,ties.method="min")-1, by=Name]