假设我必须跟踪数据帧
dput(df)
structure(list(Date_time = structure(c(1641025800, 1641025800,
1641025800, 1641025800, 1641025800, 1641025800, 1641025800, 1641025800,
1641027600, 1641027600, 1641027600, 1641027600, 1641027600, 1641027600,
1641027600, 1641027600, 1641027600, 1641027600, 1641027600, 1641027600,
1641027600, 1651396800, 1651396800, 1651396800, 1651396800, 1651396800,
1651396800, 1651396800, 1651396800, 1651396800, 1651401000, 1651401000,
1651401000, 1651401000, 1651401000, 1669966200, 1669966200, 1669966200,
1669966200, 1669966200, 1669966200, 1669966200, 1669966200, 1669969800,
1669969800, 1669969800, 1669969800, 1669969800, 1669969800, 1669969800,
1669969800, 1669969800, 1669969800, 1669969800, 1669969800), class = c("POSIXct",
"POSIXt"), tzone = "Europe/London"), Category = c("heat", "heat",
"heat", "heat", "heat", "heat", "heat", "heat", "cold", "cold",
"cold", "cold", "cold", "cold", "cold", "medium", "medium", "medium",
"medium", "medium", "medium", "heat", "heat", "heat", "heat",
"cold", "cold", "cold", "cold", "cold", "cold", "cold", "medium",
"medium", "medium", "heat", "heat", "heat", "heat", "heat", "cold",
"cold", "cold", "cold", "cold", "cold", "cold", "medium", "medium",
"medium", "medium", "medium", "medium", "heat", "heat"), SubCat = c("r",
"r", "r", "r", "n", "n", "n", "r", "r", "r", "r", "n", "n", "n",
"n", "r", "r", "r", "n", "n", "n", "n", "n", "n", "r", "r", "r",
"r", "n", "n", "n", "n", "r", "r", "r", "r", "r", "r", "n", "n",
"n", "n", "n", "n", "r", "r", "r", "r", "n", "n", "r", "r", "r",
"n", "n"), Site = c("1a", "1a", "1a", "1a", "1a", "1a", "1a",
"1a", "1a", "1a", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b",
"1b", "1b", "1b", "2c", "2c", "2c", "2c", "2c", "2c", "2c", "2c",
"2c", "2c", "2c", "2c", "2c", "2c", "7c", "7c", "7c", "7c", "7c",
"7c", "7c", "7c", "7c", "7c", "7c", "7c", "7c", "7c", "7c", "7c",
"7c", "7c", "7c", "7c")), row.names = c(NA, -55L), class = "data.frame")
>
我会产生每个类别的累计和的一些情节，按日期，子类别(可能网站也是，但不确定这是否有意义). 基本上，每天将每一类别的总数相加，然后将其添加到数据帧总数中的第二天，依此类推. 所以我以下面这样的东西结束，我可以画出
Date Category Subcategory Count
1 01/01/2022 Heat r 5
2 01/01/2022 Cold r 6
3 01/01/2022 Medium r 9
4 01/01/2022 Heat n 3
5 01/01/2022 Cold n 6
6 01/01/2022 Medium n 10
7 05/01/2022 Heat r 3
8 05/01/2022 Cold r 6
9 05/01/2022 Medium r 9
10 05/01/2022 Heat n 4
11 05/01/2022 Cold n 8
12 05/01/2022 Medium n 12
13 12/01/2022 Heat r 3
14 12/01/2022 Cold r 6
15 12/01/2022 Medium r 10
16 12/01/2022 Heat n 3
17 12/01/2022 Cold n 3
18 12/01/2022 Medium n 5
