我有两个数据框如下。它们的长度不等：
library(lubridate)
id <- c(1, 2, 2, 2, 2, 3, 4, 4, 6, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9,
10, 10, 10, 11, 11, 12, 13, 14, 15, 15, 5451396, 5451396, 5451396, 5451396, 5451396)
admDt <- ymd(c("2000-02-24", "2000-04-30", "2000-06-06", "2001-01-29", "2004-06-10", "2001-05-21",
"2000-01-25", "2000-04-18", "2000-01-14", "1991-10-06", "1992-02-25", "2000-05-17",
"2003-06-06", "2009-02-16", "2000-01-23", "2000-03-10", "2000-04-05", "2000-06-16",
"2000-07-04", "2000-07-27", "2001-01-19", "2002-08-16", "2002-09-19", "2004-04-17",
"2005-08-02", "2005-09-21", "2006-07-10", "2000-02-24", "2000-05-05", "2000-08-29",
"2001-01-24", "2000-01-27", "2000-03-09", "2000-04-15", "2000-03-20", "2002-11-13",
"2000-06-28", "2000-07-02", "2000-06-13", "1999-12-27", "2008-09-10", "2000-04-09",
"2000-06-01", "2002-11-25", "2006-08-04", "2006-10-07"))
sepDt <- ymd(c("2000-02-25", "2000-05-25", "2000-06-06", "2001-02-15", "2004-07-12", "2001-06-01",
"2000-01-31", "2000-04-20", "2000-01-31", "1991-11-07", "1992-03-26", "2000-05-31",
"2003-06-17", "2009-02-23", "2000-03-06", "2000-03-17", "2000-04-06", "2000-06-28",
"2000-07-17", "2000-07-31", "2002-04-19", "2002-09-11", "2003-05-06", "2004-05-03",
"2005-08-31", "2006-05-29", "2009-06-19", "2000-03-09", "2000-05-06", "2000-09-12",
"2001-01-24", "2000-02-15", "2000-03-17", "2000-04-16", "2000-04-20", "2002-12-05",
"2000-07-27", "2000-08-15", "2000-06-22", "2000-02-12", "2008-09-17", "2000-05-26",
"2000-08-29", "2003-02-24", "2006-09-22", "2006-11-10"))
adm <- data.frame(id, admDt, sepDt)
id <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 5451396)
birthDt <- ymd(c("1971-07-22", "1982-08-09", "1976-01-30", "1972-02-03", "1958-05-26", "1979-05-24",
"1971-11-03", "1980-02-05", "1978-06-08", "1969-10-14", "1962-01-01", "1977-03-09",
"1952-01-24", "1974-12-16", "1956-05-05", "1963-07-16"))
dxDt <- ymd(c("2000-02-24", "2000-04-30", "2000-03-03", "2000-01-31", "2000-06-20", "2000-12-13",
"2000-05-14", "2000-01-23", "2000-03-09", "2000-02-15", "2000-05-01", "2000-06-30",
"2000-08-15", "2000-06-22", "2000-01-27", "2000-06-01"))
admPreDx <- c("No", "No", "No", "Yes", "No", "No", "No", "No", "Yes", "Yes","Yes", "Yes", "Yes",
"Yes", "Yes", "Yes")
admPreDxNbr <- c(0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1)
admPreDxDur <- c(0, 0, 0, 6, 0, 0, 0, 0, 14, 19, 20, 2, 31, 9, 31, 25)
admPostDx <- c("Yes", "Yes", "No", "No", "No", "No", "Yes", "Yes", "No", "Yes", "No", "Yes", "No",
"No", "Yes", "Yes")
admPostDxNbr <- c(1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 3)
admPostDxDur <- c(1, 25, 0, 0, 0, 0, 14, 31, 0, 6, 0, 27, 0, 0, 16, 31)
admDx <- data.frame(id, birthDt, dxDt, admPreDx, admPreDxNbr, admPreDxDur, admPostDx, admPostDxNbr,
admPostDxDur)
> head(adm)
id admDt sepDt
1 1 2000-02-24 2000-02-25
2 2 2000-04-30 2000-05-25
3 2 2000-06-06 2000-06-06
4 2 2001-01-29 2001-02-15
5 2 2004-06-10 2004-07-12
6 3 2001-05-21 2001-06-01
> head(admDx)
id birthDt dxDt admPreDx admPreDxNbr admPreDxDur admPostDx admPostDxNbr admPostDxDur
1 1 1971-07-22 2000-02-24 No 0 0 Yes 1 1
2 2 1982-08-09 2000-04-30 No 0 0 Yes 1 25
3 3 1976-01-30 2000-03-03 No 0 0 No 0 0
4 4 1972-02-03 2000-01-31 Yes 1 6 No 0 0
5 5 1958-05-26 2000-06-20 No 0 0 No 0 0
6 6 1979-05-24 2000-12-13 No 0 0 No 0 0
实际数据集的范围为10,000到1,000,000+行。
adm中的每一行都指的是不同的入院时间。注意：id是患者的ID号，而admDt和sepDt分别表示入院和出院日期。有些患者有多次入院。
admDx中的每一行都指单个患者：id是患者的ID号（与adm中提供的ID号一致），而birthDt dxDt分别是患者的出生和诊断日期。
我正在进行一些纵向/时间序列分析，并希望确定患者在诊断前后的不同时间段内是否住院。为简洁起见，这个问题涉及诊断前后一个月。理想情况下，我想：
我在几天内审核了一些帖子（例如R Time periods overlapping，Join dataframes by id and overlapping date range，how to show an event happened between two dates in R）;然而，它们似乎都没有结合我感兴趣的三个方面（计算重叠日期之间的时间;多个数据框;通过＆＃34;组＆＃34; [或个人]）。
我是R的新手，对循环和更高级的公式几乎没有经验。似乎可以使用foverlaps包中的lubridate，%overlaps%或"DescTools";但是，我不确定如何构建相关的公式。
非常感谢任何帮助！
编辑＃1：
虽然@ sirallen的建议适用于所提供的示例中的特定时间段，但sum(pmin(dxDt, sepDt) - pmax(admDt, dxDt)), by = "id"在我的真实数据集中返回了不准确的值（例如，持续一天多次入院的患者[＆＃34;据报道，2000-01-25＆＃34; - ＆＃34; 2000-01-26＆＃34;]在医院度过了零天。这对我来说似乎很奇怪，因为代码似乎用来回答类似的例子。这个问题是否与这些患者有几个重叠的日期范围有关？此外，正如@sirallen所述，该代码没有强调患者在该时间段内有一次或多次入院。
下面的代码通过确定a）患者是否在医院度过时间和b）入院人数，为我问题的前两部分提供了更直接的途径：
library(data.table)
setDT(adm)
setDT(admDx)[, (4:9) := NULL]
#Period bounds
admDx[, `:=`(dxDtN1 = dxDt %m-% months(1), dxDtP1 = dxDt %m+% months(1))]
#Hospitalised in the month preceding diagnosis
admDx <- adm[admDx, on = .(id, admDt < dxDt, sepDt > dxDtN1), .N, by = .EACHI]
admDx[, `:=` (admPreDx = factor(ifelse(N > 0, "Yes", "No")))]
但是，pmin / pmax代码仍无效，返回负值：
admDx[, `:=` (birthDt = birthDt, dxDt = dxDt, dxDtN1 = dxDt %m-% months(1), dxDtP1 = dxDt %m+% months(1))]
admDx[, `:=` (admPreDxDur=as.numeric(sum(pmin(dxDt, adm$sepDt) - pmax(dxDtN1, adm$admDt)))), by = "id"]
admDx <- select(admDx, admPreDx, N, admPreDxDur)
> head(admDx)
admPreDx N admPreDxDur
1: No 0 -28573
2: No 0 -27160
3: No 0 -28366
4: Yes 1 -29357
5: No 0 -26701
6: No 0 -28044
编辑＃2
在测试其他案例之后，问题似乎是：pmin / pmax可能与> vs >=的使用有关：当使用>时，正确{{1返回值;但是，使用Dur时，>=会返回值0。
如何调整此代码以使我能够计算诊断日期（包括诊断日期）的天数？
答案 0 :(得分：1)
我们可以使用data.table中的non-equi joins（＆gt; = v1.9.8）执行此操作：
library(data.table)
setDT(adm)
setDT(admDx)[, (4:9):= NULL]
# period bounds
admDx[, `:=`(dxDtLo=dxDt-31, dxDtHi=dxDt+31)]
# hospitalized pre-dxnosis?
admDx = adm[, .(id, admDt, sepDt, dxDt=admDt, dxDtLo=sepDt)][admDx,
on=.(id, dxDt < dxDt, dxDtLo > dxDtLo)]
admDx[, admPreDx:= as.numeric(!is.na(admDt))]
admDx[, `:=`(admPreDxNbr=sum(admPreDx), admPreDxDur=as.numeric(
sum(pmin(dxDt,sepDt) - pmax(admDt,dxDtLo)))), by='id']
admDx[, c('admDt','sepDt'):= NULL]
# hospitalized post-dxnosis?
admDx = adm[, .(id, admDt, sepDt, dxDtHi=admDt, dxDt=sepDt)][admDx,
on=.(id, dxDtHi < dxDtHi, dxDt > dxDt)]
admDx[, admPostDx:= as.numeric(!is.na(admDt))]
admDx[, `:=`(admPostDxNbr=sum(admPostDx), admPostDxDur=as.numeric(
sum(pmin(sepDt,dxDtHi) - pmax(dxDt,admDt)))), by='id']
admDx[, c('admDt','sepDt'):= NULL]
admDx[is.na(admDx)] = 0
admDx = unique(admDx)[, c('dxDtLo','dxDtHi'):= NULL]
> admDx
# id dxDt birthDt admPreDx admPreDxNbr admPreDxDur admPostDx admPostDxNbr admPostDxDur
# 1: 1 2000-02-24 1971-07-22 0 0 0 1 1 1
# 2: 2 2000-04-30 1982-08-09 0 0 0 1 1 25
# 3: 3 2000-03-03 1976-01-30 0 0 0 0 0 0
# 4: 4 2000-01-31 1972-02-03 1 1 6 0 0 0
# 5: 5 2000-06-20 1958-05-26 0 0 0 0 0 0
# 6: 6 2000-12-13 1979-05-24 0 0 0 0 0 0
# 7: 7 2000-05-14 1971-11-03 0 0 0 1 1 14
# 8: 8 2000-01-23 1980-02-05 0 0 0 1 1 31
# 9: 9 2000-03-09 1978-06-08 1 1 14 0 0 0
# 10: 10 2000-02-15 1969-10-14 1 1 19 1 1 8
# 11: 11 2000-05-01 1962-01-01 1 1 20 0 0 0
# 12: 12 2000-06-30 1977-03-09 1 1 2 1 1 27
# 13: 13 2000-08-15 1952-01-24 1 1 31 0 0 0
# 14: 14 2000-06-22 1974-12-16 1 1 9 0 0 0
# 15: 15 2000-01-27 1956-05-05 1 1 31 1 1 16
# 16: 5451396 2000-06-01 1963-07-16 1 1 25 1 1 31