如何计算不在列表中的日期 [英] How to count the dates that are not in a list

问题描述

我在与客户的两个日期（date1 date2）和到达日期之间有一个数据框。

I have a dataframe between two dates (date1 date2) with clients and the date of arrive.

date1<- "2019-07-29"
date2<- "2019-08-08"

clients<-data.frame(id= c(1:10),
                    arrive=c("2019-07-31", "2019-07-29", "2019-08-01",
                             "2019-08-03", "2019-08-05", "2019-08-08", 
                             "2019-08-02", "2019-08-06", "2019-07-29", 
                             "2019-08-02"),
                    hotel= c(rep(900067, 5), rep(9001649,5)))

我想计算两次约会之间的间隔时间，即每家酒店没有新客户的天数。

I want to count between does dates, how many days each hotel did not have a new clients.

酒店900067在以下几天没有新客户：2019-07-30、2019-08-02、2019-08-04、2019-08-06， 2019-08-07，2019-08-08。这是介于date1和date2之间的6天，没有任何新客户。

Hotel 900067 did not have new clients the following days: 2019-07-30, 2019-08-02, 2019-08-04, 2019-08-06, 2019-08-07, 2019-08-08. This are 6 days between date1 and date2 without any new client.

数据框结果应类似于：

Result<- data.frame(hotel= c(900067, 9001649), 
                    days_without_new_clients= c(6, 7))

谢谢。

推荐答案

也许您可以创建一个包含所有酒店日期的数据框，然后查看<$中不存在的日期c $ c> clients data。

Perhaps you could create a data frame containing all hotel-dates and then see how many that do not exist in the clientsdata.

library(dplyr)

all_hotel_dates <- expand.grid(arrive = seq.Date(as.Date("2019-07-29"), as.Date("2019-08-08"), "day"), hotel = c(900067, 9001649))

clients %>%
  mutate(arrive = as.Date(arrive)) %>%
  full_join(all_hotel_dates) %>%
  group_by(hotel) %>%
  summarise(days_without_new_clients = sum(is.na(id)))

# A tibble: 2 x 2
    hotel days_without_new_clients
    <dbl>                    <int>
1  900067                        6
2 9001649                        7

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