1

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

实际数据集的范围从 10,000 到 1,000,000+ 行。

中的每一行adm指的是离散的住院。注：id为患者身份证号，而admDt和sepDt分别指入院和出院日期。一些患者有多次入院。

中的每一行代表admDx一个患者：id是患者的 ID 号（与 中提供的一致adm），而birthDt和dxDt分别是患者的出生日期和诊断日期。

我正在进行一些纵向/时间序列分析，并想确定患者在诊断前后的不同时间段是否住院。为简洁起见，这个问题与诊断前后的一个月有关。理想情况下，我想：

  • 创建一个二分变量（“是”/“否”），指示给定患者是否在该时间段内住院（即，我不关心他们是否在该时间段开始之前入院或是否出院时间段偏移后）
  • 计算该时间段内每位患者住院的次数
  • 计算该时间段内每位患者住院的时间（天数）

我已经在几天内查看了许多帖子（例如，R 时间段重叠，通过 id 和重叠日期范围加入数据框，如何显示 R 中两个日期之间发生的事件）；但是，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；按“组”[或个人]）。

我是 R 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, lubridate, 或%overlaps%从"DescTools"包中；但是，我不确定如何构建相关公式。

任何帮助将不胜感激！

编辑＃1：

虽然@sirallen 的建议适用于所提供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，患者在一天内多次入院 ["2000-01-25" - "2000-01-26"]据报道在医院度过了零天。这对我来说似乎很奇怪，因为代码似乎被用来回答类似的例子。这个问题是否与我为这些患者有几个重叠的日期范围这一事实有关？此外，正如@所指出的那样sirallen，代码没有突出显示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) 入院次数，为我的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 代码仍然不起作用，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

在测试其他情况后，似乎问题 re: pmin / pmax 可能与>vs的使用有关>=：>使用时，返回正确的Dur值；但是，当>=使用时，Dur返回值 0。

如何修改此代码以使我能够计算到诊断日期（包括诊断日期）的天数？

4

1 回答 1

1

我们可以在(>=v1.9.8)中使用非 equi 连接来做到这一点：data.table

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
于 2016-12-22T19:08:16.887 回答