查找R中给定日期范围之间每个月的开始和结束日期

我是R编程的初学者，需要帮助在数据集上执行日期操作（希望有一种方法可以在R中实现）

我已将给定数据集作为“交易”导入

deals <- read.csv("deals_dates.csv")

我想找到每笔交易在给定开始日期/结束日期范围内的每月开始日期和结束日期

（预期产出）

---

谢谢大家!

我们可以首先将“日期”列转换为

Date

类，然后使用

seq

按“月”获取“日期”序列，

floor

日期，以及

unest

library(tidyverse)
df1 %>%
   mutate_at(vars(ends_with("Date")), mdy) %>%
   group_by(Deal_Id, Deal_Name) %>% 
   nest %>% 
   mutate(data = map(data, ~ 
       seq(.x$Start_Date, .x$End_Date, by = "1 month") %>%
       {x1 <- floor_date(., unit = "month")
        x2 <-  (x1[-1]-1)
       tibble(Start_Date = c(.x$Start_Date[1], x1[-1]),
              End_date = c(x2, last(.x$End_Date)))})) %>% 
   unnest

库（tidyverse）
df1%>%
在（变量（以“日期”结尾）、mdy%%>
分组依据（交易Id、交易名称）%>%
嵌套%>%
变异（数据=映射（数据，~
seq（.x$Start\u Date，.x$End\u Date，by=“1个月”）%>%
{x1data.table使用
tidyverse
进行一些准备工作的解决方案。实际的周期图片是由数据完成的。table的速度非常快
foverlaps（）
。如果您使用的是大数据集，请尝试此解决方案

library( tidyverse )
library( data.table )

#create df with start and end dates
dt1 <- data.table( start = seq( as.Date("2017-01-01"), 
                               length.out = 24,
                               by = "month") ,
                  end = seq( as.Date("2017-02-01"), 
                               length.out = 24,
                               by = "month" ) - 1,
                  stringsAsFactors = FALSE)

#read data with deals
df2 <- read.table(text=" Deal_Id  Deal_Name       Start_Date   End_Date
1        Samsung_Mobile  3/5/2018     6/23/2018
2        Apple_Watch     12/21/2017   2/9/2018", header = T)

#set the dates as actual dates
dt2 <- df2 %>%
  mutate( start = as.Date( as.character(Start_Date), format = "%m/%d/%Y"),
          end = as.Date( as.character(End_Date), format = "%m/%d/%Y") ) %>%
  select( -Start_Date, -End_Date) %>%
  setDT()

setkey(dt1, start, end)
setkey(dt2, start, end)

#create overlap join
dt3 <- foverlaps( dt1, dt2, type = "any", nomatch = 0L)
#set start 
dt3[start < i.start, start := i.start]
#set end
dt3[end > i.end, end := i.end]

#print results
dt3[, c("i.end", "i.start") := NULL ][]

#    Deal_Id      Deal_Name      start        end
# 1:       2    Apple_Watch 2017-12-21 2017-12-31
# 2:       2    Apple_Watch 2018-01-01 2018-01-31
# 3:       2    Apple_Watch 2018-02-01 2018-02-09
# 4:       1 Samsung_Mobile 2018-03-05 2018-03-31
# 5:       1 Samsung_Mobile 2018-04-01 2018-04-30
# 6:       1 Samsung_Mobile 2018-05-01 2018-05-31
# 7:       1 Samsung_Mobile 2018-06-01 2018-06-23

库（tidyverse）
库（data.table）
#创建带有开始和结束日期的df
dt1%
setDT（）
设置键（dt1，开始，结束）
设置键（dt2，开始，结束）
#创建重叠连接
dt3 i.end，end:=i.end]
#打印结果
dt3[，c（“i.end”，“i.start”）：=NULL][]
#交易\u Id交易\u名称开始结束
#1:2苹果手表2017-12-21 2017-12-31
#2:2苹果手表2018-01-01 2018-01-31
#3:2苹果手表2018-02-01 2018-02-09
#4:1三星移动2018-03-05 2018-03-31
#5:1三星移动2018-04-01 2018-04-30
#6:1三星移动2018-05-01 2018-05-31
#7:1三星移动2018-06-01 2018-06-23
您也可以只使用
vanillar R
执行某些操作。例如，使用以下功能：

myDateFunction <-function(dealName,stdate,endate){

 firstDay <- cut(as.Date(stdate,format = "%m/%d/%Y"), "month")
 Start <-seq(as.Date(firstDay), as.Date(endate,format = "%m/%d/%Y"), by = "month")
 DealName <- rep(dealName,length(Start))
 plusOne<-seq(as.Date(firstDay), by = "month", length = 2)[2]
 End<-seq(as.Date(plusOne),length=4,by="months")-1

 data <- data.frame(DealName,Start,End)
 data$Start[1] <- as.Date(stdate,format = "%m/%d/%Y")
 data$End[-1][3] <- as.Date(endate,format = "%m/%d/%Y")
 return(data)

}

产生以下输出：

library( tidyverse )
library( data.table )

#create df with start and end dates
dt1 <- data.table( start = seq( as.Date("2017-01-01"), 
                               length.out = 24,
                               by = "month") ,
                  end = seq( as.Date("2017-02-01"), 
                               length.out = 24,
                               by = "month" ) - 1,
                  stringsAsFactors = FALSE)

#read data with deals
df2 <- read.table(text=" Deal_Id  Deal_Name       Start_Date   End_Date
1        Samsung_Mobile  3/5/2018     6/23/2018
2        Apple_Watch     12/21/2017   2/9/2018", header = T)

#set the dates as actual dates
dt2 <- df2 %>%
  mutate( start = as.Date( as.character(Start_Date), format = "%m/%d/%Y"),
          end = as.Date( as.character(End_Date), format = "%m/%d/%Y") ) %>%
  select( -Start_Date, -End_Date) %>%
  setDT()

setkey(dt1, start, end)
setkey(dt2, start, end)

#create overlap join
dt3 <- foverlaps( dt1, dt2, type = "any", nomatch = 0L)
#set start 
dt3[start < i.start, start := i.start]
#set end
dt3[end > i.end, end := i.end]

#print results
dt3[, c("i.end", "i.start") := NULL ][]

#    Deal_Id      Deal_Name      start        end
# 1:       2    Apple_Watch 2017-12-21 2017-12-31
# 2:       2    Apple_Watch 2018-01-01 2018-01-31
# 3:       2    Apple_Watch 2018-02-01 2018-02-09
# 4:       1 Samsung_Mobile 2018-03-05 2018-03-31
# 5:       1 Samsung_Mobile 2018-04-01 2018-04-30
# 6:       1 Samsung_Mobile 2018-05-01 2018-05-31
# 7:       1 Samsung_Mobile 2018-06-01 2018-06-23

myDateFunction <-function(dealName,stdate,endate){

 firstDay <- cut(as.Date(stdate,format = "%m/%d/%Y"), "month")
 Start <-seq(as.Date(firstDay), as.Date(endate,format = "%m/%d/%Y"), by = "month")
 DealName <- rep(dealName,length(Start))
 plusOne<-seq(as.Date(firstDay), by = "month", length = 2)[2]
 End<-seq(as.Date(plusOne),length=4,by="months")-1

 data <- data.frame(DealName,Start,End)
 data$Start[1] <- as.Date(stdate,format = "%m/%d/%Y")
 data$End[-1][3] <- as.Date(endate,format = "%m/%d/%Y")
 return(data)

}

myDateFunction("Samsung Mobile","3/5/2018","6/23/2018")

    DealName      Start        End
1 Samsung Mobile 2018-03-05 2018-03-31
2 Samsung Mobile 2018-04-01 2018-04-30
3 Samsung Mobile 2018-05-01 2018-05-31
4 Samsung Mobile 2018-06-01 2018-06-23