R：有条件地将数据从一个数据帧提取到另一个数据帧

Question

我有两个数据框，我想有条件地从一个数据框的一列中提取数据，并将其放入另一个数据框的新列中。

数据帧 1 如下所示：

df1 <- data.frame(date.start = c("2019-06-10 11:52:00",
  "2019-06-11 11:52:00", "2019-06-12 11:51:00"), date.end =
  c("2019-06-10 11:53:00", "2019-06-11 11:53:00", "2019-06-12 11:53:00"))

数据帧 2 如下所示：

df2 <- data.frame(date.start = c("2019-06-11 11:50:00",
  "2019-06-10 11:51:00", "2019-06-12 11:50:00"), date.end =
  c("2019-06-11 11:54:00", "2019-06-11 08:59:00", "2019-06-12 11:57:00"),
  day = c(1, 15, 64))

如果 df.1 的 date.start 和 date.end 落在 df2 的任何行的 date.start 或 date.end 范围内，我想提取df2 中的变量 day 并将其放入 df1.

的匹配行

预期结果如下所示：

expected.out <- data.frame(date.start = c("2019-06-10 11:52:00", "2019-06-11 11:52:00", "2019-06-12 11:51:00"),
                           date.end = c("2019-06-10 11:53:00", "2019-06-11 11:53:00", "2019-06-12 11:53:00"),
                           day = c(15, 1, 64))

我目前有以下循环可以工作，但是当我在我的大数据帧（行 = 1135133）上运行它时它非常慢，我想知道是否有更快的方法来做到这一点.

for(i in 1:nrow(df1)){
  find.match <- which(df1$date.start[i] >= df2$date.start &
                        df1$date.end[i] <= df2$date.end)
  if(length(find.match) !=0){
    df1$day[i] <- df2$day[find.match]
  }
  
}

Answer 1

使用library(fuzzyjoin)

library(tidyverse)
library(lubridate)
library(fuzzyjoin)

df1 <- data.frame(
  date.start = c("2019-06-10 11:52:00", "2019-06-11 11:52:00", "2019-06-12 11:51:00"),
  date.end = c("2019-06-10 11:53:00", "2019-06-11 11:53:00", "2019-06-12 11:53:00"), stringsAsFactors = F)

df2 <- data.frame(date.start = c("2019-06-11 11:50:00", "2019-06-10 11:51:00", "2019-06-12 11:50:00"),
                  date.end = c("2019-06-11 11:54:00", "2019-06-11 08:59:00", "2019-06-12 11:57:00"),
                  day = c(1, 15, 64), stringsAsFactors = F)

df1 <- df1 %>% 
  mutate(across(where(is.character), ymd_hms)) %>% 
  as_tibble()

df2 <- df2 %>% 
  mutate(across(where(is.character), ymd_hms)) %>% 
  as_tibble()


fuzzy_left_join(df1, df2, by = c("date.start", "date.end"), match_fun = list(`>=`, `<=`))
# A tibble: 3 x 5
  date.start.x        date.end.x          date.start.y        date.end.y            day
  <dttm>              <dttm>              <dttm>              <dttm>              <dbl>
1 2019-06-10 11:52:00 2019-06-10 11:53:00 2019-06-10 11:51:00 2019-06-11 08:59:00    15
2 2019-06-11 11:52:00 2019-06-11 11:53:00 2019-06-11 11:50:00 2019-06-11 11:54:00     1
3 2019-06-12 11:51:00 2019-06-12 11:53:00 2019-06-12 11:50:00 2019-06-12 11:57:00    64

^{由 reprex package (v0.3.0)}

于 2020-09-23 创建

不确定该方法是否快速

Answer 2

您可以在 sapply 中使用 match 来获取 df2 的第一行，其中日期在给定时间范围内。

df1[] <- lapply(df1, as.POSIXct) #Convert character to POSIXct
df2[1:2] <- lapply(df2[1:2], as.POSIXct)

df1$day <- df2$day[sapply(asplit(df1, 1), function(x) {match(TRUE,
 x[1] >= df2[,1] & x[2] <= df2[,2])})]
df1
#           date.start            date.end day
#1 2019-06-10 11:52:00 2019-06-10 11:53:00  15
#2 2019-06-11 11:52:00 2019-06-11 11:53:00   1
#3 2019-06-12 11:51:00 2019-06-12 11:53:00  64

Answer 3

将 data.table 中的 between 与 outer 结合使用。 which.max 扫描匹配矩阵中的 TRUE 值。

library(data.table)
FUN <- Vectorize(function(x, y) all(between(unlist(df1[x, ]), df2[y, 1], df2[y, 2])))
res <- transform(df1, day=df2[apply(outer(1:3, 1:3, FUN), 1, which.max), 3])
res
#            date.start            date.end day
# 1 2019-06-10 11:52:00 2019-06-10 11:53:00  15
# 2 2019-06-11 11:52:00 2019-06-11 11:53:00   1
# 3 2019-06-12 11:51:00 2019-06-12 11:53:00  64

您可能需要事先转换为 POSIXct 格式以应用解决方案。

df1[1:2] <- lapply(df1[1:2], as.POSIXct)
df2[1:2] <- lapply(df2[1:2], as.POSIXct)

数据：

df1 <- structure(list(date.start = structure(c(1560160320, 1560246720, 
1560333060), class = c("POSIXct", "POSIXt"), tzone = ""), date.end = structure(c(1560160380, 
1560246780, 1560333180), class = c("POSIXct", "POSIXt"), tzone = "")), row.names = c(NA, 
-3L), class = "data.frame")

df2 <- structure(list(date.start = structure(c(1560246600, 1560160260, 
1560333000), class = c("POSIXct", "POSIXt"), tzone = ""), date.end = structure(c(1560246840, 
1560236340, 1560333420), class = c("POSIXct", "POSIXt"), tzone = ""), 
    day = c(1, 15, 64)), row.names = c(NA, -3L), class = "data.frame")

R：有条件地将数据从一个数据帧提取到另一个数据帧

R: Conditionally extract data from one dataframe to another

loops

r

vectorization