这是 Ronak Shah 解决方案的修改版,将同一位置的非连续事件视为单独的事件周期。
df <- data.frame(
DATE = as.Date(c("2000-01-01", "2000-01-02", "2000-01-03", "2000-01-05",
"2000-02-05", "2000-02-06", "2000-02-07"), format = "%Y-%m-%d"),
LONG = c(23, 23, 23, 23, 22, 19, 22),
LAT = c(45, 45, 45, 45, 56, 21, 56),
TYPE = c("A", "B", "B", "A", "A", "B", "A")
)
library(dplyr)
df %>%
group_by(LONG, LAT) %>%
arrange(DATE) %>%
mutate(DATE.diff = c(1, diff(DATE))) %>%
mutate(PERIOD = cumsum(DATE.diff != 1)) %>%
ungroup() %>%
group_by(LONG, LAT, PERIOD) %>%
summarise(START_DATE = min(DATE),
END_DATe = max(DATE),
TYPE = paste(TYPE, collapse = "")) %>%
ungroup()
LONG LAT PERIOD START_DATE END_DATe TYPE
<dbl> <dbl> <int> <date> <date> <chr>
1 19 21 0 2000-02-06 2000-02-06 B
2 22 56 0 2000-02-05 2000-02-05 A
3 22 56 1 2000-02-07 2000-02-07 A
4 23 45 0 2000-01-01 2000-01-03 ABB
5 23 45 1 2000-01-05 2000-01-05 A
编辑以添加“PERIOD”变量的解释。
为了简单起见,让我们考虑在同一位置发生的一些连续和不连续事件,这样我们就可以跳过group_by(LONG,LAT)
和arrange(DATE)
步骤:
df2 <- data.frame(
DATE = as.Date(c("2001-01-01", "2001-01-02", "2001-01-03",
"2001-01-05", "2001-01-06",
"2001-02-01", "2001-02-02", "2001-02-03", "2001-02-04",
"2001-04-01"), format = "%Y-%m-%d"),
LONG = rep(23, 10),
LAT = rep(45, 10),
TYPE = LETTERS[1:10]
)
作为中间步骤,我们创建了一些辅助变量:
"DATE.diff" 计算当前行日期与上一行日期之间的差异。由于第一行没有日期在 "2001-01-01" 之前,我们将差异默认为 1。
"non.consecutive" 表示计算出的日期差异是否不是 1(即与前一天不连续),或者是 1(即与前一天连续)。如果您需要考虑数据集中同一位置的同一天事件,则可以将计算从 DATE.diff != 1
更改为 DATE.diff > 1
。
"PERIOD" 跟踪 "non.consecutive" 变量中 TRUE 结果的数量。从第一行开始,每当一行与前一行不连续时,“PERIOD” 就会增加 1。
由于辅助变量的存在,"PERIOD" 对于每组连续日期都有不同的值。
df2.intermediate <- df2 %>%
mutate(DATE.diff = c(1, diff(DATE))) %>%
mutate(non.consecutive = DATE.diff != 1) %>%
mutate(PERIOD = cumsum(non.consecutive))
> df2.intermediate
DATE LONG LAT TYPE DATE.diff non.consecutive PERIOD
1 2001-01-01 23 45 A 1 FALSE 0
2 2001-01-02 23 45 B 1 FALSE 0
3 2001-01-03 23 45 C 1 FALSE 0
4 2001-01-05 23 45 D 2 TRUE 1
5 2001-01-06 23 45 E 1 FALSE 1
6 2001-02-01 23 45 F 26 TRUE 2
7 2001-02-02 23 45 G 1 FALSE 2
8 2001-02-03 23 45 H 1 FALSE 2
9 2001-02-04 23 45 I 1 FALSE 2
10 2001-04-01 23 45 J 56 TRUE 3
我们可以将“PERIOD”视为分组变量,以便在每个期间内查找开始/结束日期和事件。
df2.intermediate %>%
group_by(PERIOD) %>%
summarise(START_DATE = min(DATE),
END_DATe = max(DATE),
TYPE = paste(TYPE, collapse = "")) %>%
ungroup()
# A tibble: 4 x 4
PERIOD START_DATE END_DATe TYPE
<int> <date> <date> <chr>
1 0 2001-01-01 2001-01-03 ABC
2 1 2001-01-05 2001-01-06 DE
3 2 2001-02-01 2001-02-04 FGHI
4 3 2001-04-01 2001-04-01 J