添加行并有条件地更新后续行

Question

我想在我的数据框中的某些条件之前添加一行。 在这种情况下，每次“天数”列 >= 3 时，我都想在之前插入一行。

问题涉及从一个城市到另一个城市的旅行，因此如果旅行之间的天数 >= 3，我希望该团队返回其家乡城市，然后从那里开始下一次旅行。 因此，新行应反映家乡城市的正确坐标，并更新下一次旅行（下一行）的坐标，因为团队将从不同的城市出发。

下面是数据集的一个例子

Date <- c("2017-10-20", "2017-10-22", "2017-10-25", "2017-10-26", "2017-10-27", "2017-11-01", "2017-11-03")
Team <-  c("Team A", "Team A", "Team A", "Team A", "Team A", "Team A", "Team A")
Opponent <- c("Opp 1 ", "Opp 2", "Opp 3", "Opp 4", "Opp 5", "Opp 6", "Opp 7")
Location <- c("Away", "Away", "Away", "Away", "Home", "Away", "Home")
City <- c("Opp 1 City",  "Opp 2 City", "Opp 3 City", "Opp 4 City", "Home City", "Opp 6 City", "Home City")
lat <- c(35.20, 40.67, 25.78, 41.84, 33.76, 40.01, 33.76)
lon <- c(-80.83, -73.94, -80.21, -87.68, -84.42, -75.13, -84.42)
destlat <- c(32.79, 35.20, 40.67, 25.78, 41.84, 33.76, 40.01)
destlon <- c(-96.77, -80.83, -73.94, -80.21, -87.68, -84.42, -75.13)
Days <- c(NA, 2, 3, 1, 1, 5, 2)

df <- tibble(Date, Team, Opponent, Location, City, lat, lon, destlat, destlon, Days) %>% 
  dplyr::mutate(Date = as.Date(Date))

df

这是添加条件行后正确输出应该是什么样子的手动添加的解决方案。

# desired output

Date <- c("2017-10-20", "2017-10-22", "2017-10-24", "2017-10-25", "2017-10-26", "2017-10-27", "2017-11-01", "2017-11-03")
Team <-  c("Team A", "Team A", "Team A", "Team A", "Team A", "Team A", "Team A", "Team A")
Opponent <- c("Opp 1 ", "Opp 2", NA, "Opp 3", "Opp 4", "Opp 5", "Opp 6", "Opp 7")
Location <- c("Away", "Away", NA, "Away", "Away", "Home", "Away", "Home")
City <- c("Opp 1 City",  "Opp 2 City", "Home City", "Opp 3 City" ,"Opp 4 City", "Home City", "Opp 6 City", "Home City")
lat <- c(35.20, 40.67, 33.76, 25.78, 41.84, 33.76, 40.01, 33.76)
lon <- c(-80.83, -73.94, -84.41, -80.21, -87.68, -84.42, -75.13, -84.42)
destlat <- c(32.79, 35.20, 40.67, 33.76, 25.78, 41.84, 33.76, 40.01)
destlon <- c(-96.77, -80.83, -73.94, -84.42, -80.21, -87.68, -84.42, -75.13)
Days <- c(NA, 2, NA, 3, 1, 1, 5, 2)

df <- tibble(Date, Team, Opponent, Location, City, lat, lon, destlat, destlon, Days) %>% 
  dplyr::mutate(Date = as.Date(Date))

df

理想情况下寻找 dplyr 解决方案，但如果需要，可以使用其他选项。

Answer 1

此解决方案适用于多个团队：

# Create home team location df
Team <- c("Team A", "Team B")
lat <- c(33.8, 50)
lon <- c(-84.4, -90)
df_loc <- tibble(Team, lat, lon)

# function to create new row
new.row <- function(x) {
    Date <- x$Date - 1
    Team <- x$Team
    City <- x$City
    lat <- df_loc$lat[df_loc$Team == Team]
    lon <- df_loc$lon[df_loc$Team == Team]
    destlat <- x$destlat
    destlon <- x$destlon
    new_row <- tibble(Date = Date, 
                      Team = Team,
                      Opponent = NA,
                      Location = NA,
                      City = City,
                      lat = lat,
                      lon = lon,
                      destlat = destlat,
                      destlon = destlon,
                      Days = NA)
} 

# find the row where Days >= 3
row_loc <- which(df$Days >= 3)[1]

# call the function
new_row <- new.row(df[row_loc, ])

# add the row
df <- df %>% add_row(new_row, .before = row_loc)

# set the old Days (>=3) to 1
df$Days[row_loc +1] <- 1

df

Answer 2

不是最优雅的解决方案，但我所做的是通过修改过滤数据框中的现有行来创建所需的新行，然后将它们与其他所有内容绑定。

bind_rows(
  filter(df,Days>=3) %>% mutate(destlat="33.76", destlon="-84.42"),
  filter(df,Days>=3 & lag(Location!="Home")| lead(Days>=3) & Location!="Home") %>% 
    mutate(Date=lag(Date)+lag(Days), Location="Home",City="Home City",destlat=lag(lat),destlon=lag(lon),lat="33.76",lon="-84.42",Opponent=NA) %>% 
    filter(Days>=3) %>% 
    mutate(Days=NA),
  filter(df,Days<3)) %>%
  arrange(Date)

# A tibble: 7 x 10
  Date       Team   Opponent Location City       lat   lon    destlat destlon  Days
  <date>     <chr>  <chr>    <chr>    <chr>      <chr> <chr>  <chr>   <chr>   <dbl>
1 2017-10-22 Team A Opp 2    Away     Opp 2 City 40.67 -73.94 35.20   -80.83      2
2 2017-10-24 Team A NA       Home     Home City  33.76 -84.42 40.67   -73.94     NA
3 2017-10-25 Team A Opp 3    Away     Opp 3 City 25.78 -80.21 33.76   -84.42      3
4 2017-10-26 Team A Opp 4    Away     Opp 4 City 41.84 -87.68 25.78   -80.21      1
5 2017-10-27 Team A Opp 5    Home     Home City  33.76 -84.42 41.84   -87.68      1
6 2017-11-01 Team A Opp 6    Away     Opp 6 City 40.01 -75.13 33.76   -84.42      5
7 2017-11-03 Team A Opp 7    Home     Home City  33.76 -84.42 40.01   -75.13      2