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我正在尝试在一个data.frame或data.table两个条件下创建一个列。我看到的帖子和我在下面尝试修改的帖子的不同之处在于，我没有条件的“价值”，但条件取决于data.frame.

假设这是我的数据框：

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"))

我想知道这 3 名受试者在过去 5 年内经历了多少次事件。但是，如果事件日期可以追溯到 5 年以上，我不想包括它。我想我可以做一个指标变量的总和（如果受试者在一年中经历了事件，则设置为 1），同时指定一些类似的东西Year < Year & Year >= Year-5。所以基本上总结了小于焦点年和大于或等于焦点年前5年的经验。

我已经创建了一个求和指标和一个焦点年的变量 - 5

mydf$Ind <- 1
mydf$Yearm5 <- mydf$Year-5

然后我转换为速度数据表（原始df有+60k obs）

library(data.table)
mydf <- data.table(mydf)

现在的问题是我无法让这两个条件起作用。我所看到的帖子似乎都知道一个特定的值来进行子集化（例如R data.table 在多个条件下进行子集化。），但在我的情况下，值会从观察变为观察（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的东西：

mydf[, c("Exp"):= sum(Ind), by = c("Name")][Year < Year & Year >= Yearm5]

给出：

Empty data.table (0 rows) of 5 cols: Year,Name,Ind,Yearm5,Exp

仅使用一个条件

mydf1 <- mydf[, c("Exp"):= sum(Ind), by = c("Name")][Year >= Yearm5]

给出了总体经验，所以我假设Year < Year情况有问题。

我不太确定是什么。我还尝试修改以下建议： 如何在 R 中的一个向量中累积添加值 而再次不走运，我指定条件的方式似乎有问题。

library(dplyr)
mytest1 <- mydf %>%
           group_by(Name, Year) %>%
           filter(Year < Year & Year >= Yearm5) %>%
           mutate(Exp = sum(Ind))

结果应如下所示：

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))

任何帮助或指示将不胜感激！

4

3 回答 3

3

这是一种更多的data.table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
于 2014-08-07T15:48:25.347 回答
2

这是一种使用rollapply 和的方法data.table

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
于 2014-08-07T04:57:34.427 回答
2

使用data.table，我认为您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]
于 2014-08-07T01:22:21.060 回答