我需要创建一个新的变量"控制日期";从日期变量";casedates";。这个新变量将由与案件日期在同一周的同一天、与案件日期位于同一月份和年份的日期组成。例如,如果我的病例日期是7月的第三个星期三,我的控制日将是7月第一个星期三、7月第二个星期三和7月第四个星期三。此外,我想为创建的每组日期创建一个指标变量。我想在r.中使用dplyr来完成这项工作
起始数据:
Casedate
"01-03-2015"
"08-27-2017"
"10-23-2019"
这就是我希望它看起来的样子
Casedate Controldate Index
"01-03-2015" "01-03-2015" 1
"01-03-2015" "01-10-2015" 1
"01-03-2015" "01-17-2015" 1
"01-03-2015" "01-24-2015" 1
"01-03-2015" "01-31-2015" 1
"08-12-2017" "08-05-2017" 2
"08-12-2017" "08-12-2017" 2
"08-12-2017" "08-19-2017" 2
"08-12-2017" "08-26-2017" 2
"10-23-2019" "10-02-2019" 3
"10-23-2019" "10-09-2019" 3
"10-23-2019" "10-16-2019" 3
"10-23-2019" "10-23-2019" 3
"10-23-2019" "10-30-2019" 3
这里有一个tidyverse
选项。用lubridate
将"Casedate"转换为Date
类,然后用map
循环元素,在list
、unnest
和list
列中创建seq
日期影响
library(dplyr)
library(purrr)
library(lubridate)
df1 %>%
mutate(Index = row_number(),
Casedate = mdy(Casedate),
wd = wday(Casedate, label = TRUE),
Controldate = map2(floor_date(Casedate, 'month'), wd, ~ {
x1 <- seq(.x, length.out = 7, by = '1 day')
seq(x1[wday(x1, label = TRUE) == .y],
ceiling_date(.x, 'month'), by = '7 day')})) %>%
unnest(c(Controldate)) %>%
select(Casedate, Controldate, Index)
-输出
# A tibble: 14 x 3
# Casedate Controldate Index
# <date> <date> <int>
# 1 2015-01-03 2015-01-03 1
# 2 2015-01-03 2015-01-10 1
# 3 2015-01-03 2015-01-17 1
# 4 2015-01-03 2015-01-24 1
# 5 2015-01-03 2015-01-31 1
# 6 2017-08-27 2017-08-06 2
# 7 2017-08-27 2017-08-13 2
# 8 2017-08-27 2017-08-20 2
# 9 2017-08-27 2017-08-27 2
#10 2019-10-23 2019-10-02 3
#11 2019-10-23 2019-10-09 3
#12 2019-10-23 2019-10-16 3
#13 2019-10-23 2019-10-23 3
#14 2019-10-23 2019-10-30 3
数据
df1 <- structure(list(Casedate = c("01-03-2015", "08-27-2017", "10-23-2019"
)), class = "data.frame", row.names = c(NA, -3L))
由于一个月内一个日期最多只能在前4周或后4周(总共9个值(,因此可以通过一些序列一次性计算该范围。这样就可以避免对每个值进行显式循环。
计算完这些值后,将其子集设置为与单个扫描中的原始值相同月份的值。使用@akrun的df1
示例数据如下:
d <- as.Date(df1$Casedate, format="%m-%d-%Y")
r <- rep(d, each=9)
o <- r + (7 * -4:4)
i <- rep(seq_along(d), each=9)
s <- format(o, "%m") == format(r, "%m")
data.frame(
Casedate = r,
Controldate = o,
Index = i
)[s,]
# Casedate Controldate Index
#5 2015-01-03 2015-01-03 1
#6 2015-01-03 2015-01-10 1
#7 2015-01-03 2015-01-17 1
#8 2015-01-03 2015-01-24 1
#9 2015-01-03 2015-01-31 1
#11 2017-08-27 2017-08-06 2
#12 2017-08-27 2017-08-13 2
#13 2017-08-27 2017-08-20 2
#14 2017-08-27 2017-08-27 2
#20 2019-10-23 2019-10-02 3
#21 2019-10-23 2019-10-09 3
#22 2019-10-23 2019-10-16 3
#23 2019-10-23 2019-10-23 3
#24 2019-10-23 2019-10-30 3
如果你想在数据集中保留所有原始变量,这是一个简单的解决方案:
cbind(
df1[i,],
data.frame(Controldate = o, Index = i)
)[s,]
例如:
# Casedate othvar1 othvar2 Controldate Index
#1.4 01-03-2015 a B 2015-01-03 1
#1.5 01-03-2015 a B 2015-01-10 1
#1.6 01-03-2015 a B 2015-01-17 1
#1.7 01-03-2015 a B 2015-01-24 1
#...
即使在中等规模的数据集(300K行(上,生成序列运行(2秒(和在每个值上循环(2分钟(之间的时间也存在显著差异:
顺序:
df1 <- df1[rep(1:3,each=1e5),,drop=FALSE]
system.time({
d <- as.Date(df1$Casedate, format="%m-%d-%Y")
r <- rep(d, each=9)
o <- r + (7 * -4:4)
i <- rep(seq_along(d), each=9)
s <- format(o, "%m") == format(r, "%m")
data.frame(
Casedate = r,
Controldate = o,
Index = i
)[s,]
})
# user system elapsed
# 1.909 0.128 2.038
循环:
library(dplyr)
library(purrr)
library(lubridate)
system.time({
df1 %>%
mutate(Index = row_number(),
Casedate = mdy(Casedate),
wd = wday(Casedate, label = TRUE),
Controldate = map2(floor_date(Casedate, 'month'), wd, ~ {
x1 <- seq(.x, length.out = 7, by = '1 day')
seq(x1[wday(x1, label = TRUE) == .y],
ceiling_date(.x, 'month'), by = '7 day')})) %>%
unnest(Controldate) %>%
select(Casedate, Controldate, Index)
})
# user system elapsed
# 131.466 1.143 132.623