我怎样才能使这个数据框的值逐渐增加。前任。第二个等于第一个+第二个以此类推,为连续时间
How can I make the values of this dataframe add progressively. Ex. the second is equal to the first + second and so on, as continuous time
我的代码是这样写的:
HoraLLamadaSimulado2 <- function(n = 50)
{
tiempodellamada <- 0
x <- 1
HORA <- 0
vec1 <- data.frame()
while (x < n)
{
tiempoentrellamada <- (runif(n, 1000, 1010))
LLX <- tiempoentrellamada
HORA <- as.POSIXct(LLX, origin = "2019-01-01", tz = "GMT")
#HORA <- HORA + tiempoentrellamada
vec1 <- data.frame(HORA)
x <- x + 1
}
return(vec1)
}
HoraLLamadaSimulado2()
这就是我得到的。即均匀分布50次。我无法计算连续时间的总和。
霍拉
2019-01-01 00:16:46
2019-01-01 00:16:46
2019-01-01 00:16:43
2019-01-01 00:16:47
2019-01-01 00:16:47
2019-01-01 00:16:47
2019-01-01 00:16:47
2019-01-01 00:16:48
2019-01-01 00:16:48
2019-01-01 00:16:43
cumsum
的使用情况如何:
set.seed(42)
as.POSIXct(cumsum(runif(10, 1000, 1010)), origin = "2019-01-01")
# [1] "2018-12-31 16:16:49 PST" "2018-12-31 16:33:38 PST"
# [3] "2018-12-31 16:50:21 PST" "2018-12-31 17:07:09 PST"
# [5] "2018-12-31 17:23:56 PST" "2018-12-31 17:40:41 PST"
# [7] "2018-12-31 17:57:28 PST" "2018-12-31 18:14:10 PST"
# [9] "2018-12-31 18:30:56 PST" "2018-12-31 18:47:43 PST"
我的代码是这样写的:
HoraLLamadaSimulado2 <- function(n = 50)
{
tiempodellamada <- 0
x <- 1
HORA <- 0
vec1 <- data.frame()
while (x < n)
{
tiempoentrellamada <- (runif(n, 1000, 1010))
LLX <- tiempoentrellamada
HORA <- as.POSIXct(LLX, origin = "2019-01-01", tz = "GMT")
#HORA <- HORA + tiempoentrellamada
vec1 <- data.frame(HORA)
x <- x + 1
}
return(vec1)
}
HoraLLamadaSimulado2()
这就是我得到的。即均匀分布50次。我无法计算连续时间的总和。
霍拉
2019-01-01 00:16:46
2019-01-01 00:16:46
2019-01-01 00:16:43
2019-01-01 00:16:47
2019-01-01 00:16:47
2019-01-01 00:16:47
2019-01-01 00:16:47
2019-01-01 00:16:48
2019-01-01 00:16:48
2019-01-01 00:16:43
cumsum
的使用情况如何:
set.seed(42)
as.POSIXct(cumsum(runif(10, 1000, 1010)), origin = "2019-01-01")
# [1] "2018-12-31 16:16:49 PST" "2018-12-31 16:33:38 PST"
# [3] "2018-12-31 16:50:21 PST" "2018-12-31 17:07:09 PST"
# [5] "2018-12-31 17:23:56 PST" "2018-12-31 17:40:41 PST"
# [7] "2018-12-31 17:57:28 PST" "2018-12-31 18:14:10 PST"
# [9] "2018-12-31 18:30:56 PST" "2018-12-31 18:47:43 PST"