基于向量长度的向量递归函数
Recursive function over a vector based on the length of it
我有几个月经间隔 6 个月。首字母“in_talls_temp_6”和结尾“f_talls_temp_6”。
`in_talls_temp_6 <- seq.Date(from=i_preImp_preref, to=f_postImp, by="6 months")
f_talls_temp_6 <- in_talls_temp_6 + months(6) - days(1)
我有这样的数据：
name <- paste0("time_point", seq(1:13))
a <- round(runif(length(name), 200, 500), 0)
data <- data.frame(name, a)
name a
1 time_point1 361
2 time_point2 444
3 time_point3 221
4 time_point4 434
5 time_point5 400
6 time_point6 438
7 time_point7 411
8 time_point8 367
9 time_point9 409
10 time_point10 337
11 time_point11 481
12 time_point12 201
13 time_point13 417
而且我想为每个“time_point”分配 x 期的初始日期和结束日期。
到目前为止，我都是用愚蠢的方式来做的：
data %>%
mutate( i.date.time.point = case_when (name == "time_point1" ~ in_talls_temp_6[1],
name == "time_point2" ~ in_talls_temp_6[2],
name == "time_point3" ~ in_talls_temp_6[3],
name == "time_point4" ~ in_talls_temp_6[4],
name == "time_point5" ~ in_talls_temp_6[5],
name == "time_point6" ~ in_talls_temp_6[6],
name == "time_point7" ~ in_talls_temp_6[7],
name == "time_point8" ~ in_talls_temp_6[8],
name == "time_point9" ~ in_talls_temp_6[9],
name == "time_point10" ~ in_talls_temp_6[10],
name == "time_point11" ~ in_talls_temp_6[11],
name == "time_point12" ~ in_talls_temp_6[12],
name == "time_point13" ~ in_talls_temp_6[13]) ) %>%
mutate( f.date.time.point = case_when (name == "time_point1" ~ f_talls_temp_6[1],
name == "time_point2" ~ f_talls_temp_6[2],
name == "time_point3" ~ f_talls_temp_6[3],
name == "time_point4" ~ f_talls_temp_6[4],
name == "time_point5" ~ f_talls_temp_6[5],
name == "time_point6" ~ f_talls_temp_6[6],
name == "time_point7" ~ f_talls_temp_6[7],
name == "time_point8" ~ f_talls_temp_6[8],
name == "time_point9" ~ f_talls_temp_6[9],
name == "time_point10" ~ f_talls_temp_6[10],
name == "time_point11" ~ f_talls_temp_6[11],
name == "time_point12" ~ f_talls_temp_6[12],
name == "time_point13" ~ f_talls_temp_6[13])
)
得到这个：
name a i.date.time.point f.date.time.point
1 time_point1 361 2014-07-01 2014-12-31
2 time_point2 444 2015-01-01 2015-06-30
3 time_point3 221 2015-07-01 2015-12-31
4 time_point4 434 2016-01-01 2016-06-30
5 time_point5 400 2016-07-01 2016-12-31
6 time_point6 438 2017-01-01 2017-06-30
7 time_point7 411 2017-07-01 2017-12-31
8 time_point8 367 2018-01-01 2018-06-30
9 time_point9 409 2018-07-01 2018-12-31
10 time_point10 337 2019-01-01 2019-06-30
11 time_point11 481 2019-07-01 2019-12-31
12 time_point12 201 2020-01-01 2020-06-30
13 time_point13 417 2020-07-01 2020-12-31
我觉得有更好的方法，但我做不到。我被困在这里是因为我想扩大项目规模，现在我想做同样的事情：
in_talls_temp_3 <- seq.Date(from=i_preImp_preref, to=f_postImp, by="3 months")
f_talls_temp_3 <- in_talls_temp_3 + months(3) - days(1)
更多 time_point 秒。这可能会在未来增长......
我想到了一个“递归函数”？ （这是它的正确名称吗？）像这样（只是一个想法）：
repeat_v <- function(x){
n <- length(x)
for (y in 1:n) {
return(x[[y]])
}
}
我不知道使用 for 循环是否是正确的方法（应用会更好？）。我也怀疑这个想法，不知道它是否适合这份工作，否则我以后会后悔的，因为会很费时间..
有什么想法吗？
如有任何想法，我们将不胜感激！ ^^
就这样：
generate_df <- function(months, time_points, min_val=200, max_val=500,
from=i_preImp_preref,
to=f_postImp) {
dates <- seq.Date(from=from, to=to, by=paste0(months, " months"))
data.frame(name = paste0("time_point", 1:time_points),
a = round(runif(length(name), min_val, max_val), 0),
i.date.time.point = dates,
f_talls_temp_3 = dates + months(months) - days(1))
}
第一个 df 会是这样的：
generate_df(6, 13, 200, 500, i_preImp_preref, postImp)
第二个：
generate_df(3, 13, 200, 500, i_preImp_preref, postImp)
我们可以只使用标准 R [ 子集：
n = readr::parse_number(data$name)
data$i.date.time.point = in_talls_temp_6[n]
data$f.date.time.point = f_talls_temp_6[n]
# name a i.date.time.point f.date.time.point
# 1 time_point1 267 2014-07-01 2014-12-31
# 2 time_point2 208 2015-01-01 2015-06-30
# 3 time_point3 332 2015-07-01 2015-12-31
# 4 time_point4 325 2016-01-01 2016-06-30
# 5 time_point5 455 2016-07-01 2016-12-31
# 6 time_point6 345 2017-01-01 2017-06-30
# 7 time_point7 425 2017-07-01 2017-12-31
# 8 time_point8 212 2018-01-01 2018-06-30
# 9 time_point9 359 2018-07-01 2018-12-31
# 10 time_point10 297 2019-01-01 2019-06-30
# 11 time_point11 230 2019-07-01 2019-12-31
# 12 time_point12 334 2020-01-01 2020-06-30
# 13 time_point13 457 2020-07-01 2020-12-31
我有几个月经间隔 6 个月。首字母“in_talls_temp_6”和结尾“f_talls_temp_6”。
`in_talls_temp_6 <- seq.Date(from=i_preImp_preref, to=f_postImp, by="6 months")
f_talls_temp_6 <- in_talls_temp_6 + months(6) - days(1)
我有这样的数据：
name <- paste0("time_point", seq(1:13))
a <- round(runif(length(name), 200, 500), 0)
data <- data.frame(name, a)
name a
1 time_point1 361
2 time_point2 444
3 time_point3 221
4 time_point4 434
5 time_point5 400
6 time_point6 438
7 time_point7 411
8 time_point8 367
9 time_point9 409
10 time_point10 337
11 time_point11 481
12 time_point12 201
13 time_point13 417
而且我想为每个“time_point”分配 x 期的初始日期和结束日期。
到目前为止，我都是用愚蠢的方式来做的：
data %>%
mutate( i.date.time.point = case_when (name == "time_point1" ~ in_talls_temp_6[1],
name == "time_point2" ~ in_talls_temp_6[2],
name == "time_point3" ~ in_talls_temp_6[3],
name == "time_point4" ~ in_talls_temp_6[4],
name == "time_point5" ~ in_talls_temp_6[5],
name == "time_point6" ~ in_talls_temp_6[6],
name == "time_point7" ~ in_talls_temp_6[7],
name == "time_point8" ~ in_talls_temp_6[8],
name == "time_point9" ~ in_talls_temp_6[9],
name == "time_point10" ~ in_talls_temp_6[10],
name == "time_point11" ~ in_talls_temp_6[11],
name == "time_point12" ~ in_talls_temp_6[12],
name == "time_point13" ~ in_talls_temp_6[13]) ) %>%
mutate( f.date.time.point = case_when (name == "time_point1" ~ f_talls_temp_6[1],
name == "time_point2" ~ f_talls_temp_6[2],
name == "time_point3" ~ f_talls_temp_6[3],
name == "time_point4" ~ f_talls_temp_6[4],
name == "time_point5" ~ f_talls_temp_6[5],
name == "time_point6" ~ f_talls_temp_6[6],
name == "time_point7" ~ f_talls_temp_6[7],
name == "time_point8" ~ f_talls_temp_6[8],
name == "time_point9" ~ f_talls_temp_6[9],
name == "time_point10" ~ f_talls_temp_6[10],
name == "time_point11" ~ f_talls_temp_6[11],
name == "time_point12" ~ f_talls_temp_6[12],
name == "time_point13" ~ f_talls_temp_6[13])
)
得到这个：
name a i.date.time.point f.date.time.point
1 time_point1 361 2014-07-01 2014-12-31
2 time_point2 444 2015-01-01 2015-06-30
3 time_point3 221 2015-07-01 2015-12-31
4 time_point4 434 2016-01-01 2016-06-30
5 time_point5 400 2016-07-01 2016-12-31
6 time_point6 438 2017-01-01 2017-06-30
7 time_point7 411 2017-07-01 2017-12-31
8 time_point8 367 2018-01-01 2018-06-30
9 time_point9 409 2018-07-01 2018-12-31
10 time_point10 337 2019-01-01 2019-06-30
11 time_point11 481 2019-07-01 2019-12-31
12 time_point12 201 2020-01-01 2020-06-30
13 time_point13 417 2020-07-01 2020-12-31
我觉得有更好的方法，但我做不到。我被困在这里是因为我想扩大项目规模，现在我想做同样的事情：
in_talls_temp_3 <- seq.Date(from=i_preImp_preref, to=f_postImp, by="3 months")
f_talls_temp_3 <- in_talls_temp_3 + months(3) - days(1)
更多 time_point 秒。这可能会在未来增长......
我想到了一个“递归函数”？ （这是它的正确名称吗？）像这样（只是一个想法）：
repeat_v <- function(x){
n <- length(x)
for (y in 1:n) {
return(x[[y]])
}
}
我不知道使用 for 循环是否是正确的方法（应用会更好？）。我也怀疑这个想法，不知道它是否适合这份工作，否则我以后会后悔的，因为会很费时间..
有什么想法吗？
如有任何想法，我们将不胜感激！ ^^
就这样：
generate_df <- function(months, time_points, min_val=200, max_val=500,
from=i_preImp_preref,
to=f_postImp) {
dates <- seq.Date(from=from, to=to, by=paste0(months, " months"))
data.frame(name = paste0("time_point", 1:time_points),
a = round(runif(length(name), min_val, max_val), 0),
i.date.time.point = dates,
f_talls_temp_3 = dates + months(months) - days(1))
}
第一个 df 会是这样的：
generate_df(6, 13, 200, 500, i_preImp_preref, postImp)
第二个：
generate_df(3, 13, 200, 500, i_preImp_preref, postImp)
我们可以只使用标准 R [ 子集：
n = readr::parse_number(data$name)
data$i.date.time.point = in_talls_temp_6[n]
data$f.date.time.point = f_talls_temp_6[n]
# name a i.date.time.point f.date.time.point
# 1 time_point1 267 2014-07-01 2014-12-31
# 2 time_point2 208 2015-01-01 2015-06-30
# 3 time_point3 332 2015-07-01 2015-12-31
# 4 time_point4 325 2016-01-01 2016-06-30
# 5 time_point5 455 2016-07-01 2016-12-31
# 6 time_point6 345 2017-01-01 2017-06-30
# 7 time_point7 425 2017-07-01 2017-12-31
# 8 time_point8 212 2018-01-01 2018-06-30
# 9 time_point9 359 2018-07-01 2018-12-31
# 10 time_point10 297 2019-01-01 2019-06-30
# 11 time_point11 230 2019-07-01 2019-12-31
# 12 time_point12 334 2020-01-01 2020-06-30
# 13 time_point13 457 2020-07-01 2020-12-31