[英]Nest Time-Series object into a DataFrame
library(fpp)
library(purrr)
library(tidyr)
data(austourists)
tr <- window(austourists,end=c(2007,12))
te <- window(austourists, start=c(2008,1))
I have the Australian tourists data from the FPP package. 我有FPP数据包中的澳大利亚游客数据。 I'd like to create multiple time series objects that are trimmed based on different starting years. 我想创建多个时间序列对象,这些对象将根据不同的开始年份进行修剪。
df <- as.data.frame(1999:2005)
colnames(df) <- "yr_start"
df$yr_end <- 2008
I'd like to repeat the window function as seen above but with the given inputs in df
. 我想重复上述的window函数,但要在df
使用给定的输入。 I was trying to use map
and nest
to create a timeseries object and the nest it into place. 我试图使用map
和nest
创建时间序列对象并将其嵌套到位。
I'm aiming for a dataframe with the structure of 我的目标是具有以下结构的数据框
head(df)
yr_start yr_end ts.object
<num> <num> <list>
1992 2008 <S3 class: ts object>
1993 2008 <S3 class: ts object>
1994 2008 <S3 class: ts object>
1995 2008 <S3 class: ts object>
1996 2008 <S3 class: ts object>
1997 2008 <S3 class: ts object>
The goal is to use these ts objects later to run Exponential Smoothing models using the map function on these ts objects. 目标是稍后使用这些ts对象在这些ts对象上使用map函数运行指数平滑模型。
You can use map2
over yr_start
and yr_end
columns and construct a ts
object for each pair of start-end
years: 您可以在yr_start
和yr_end
列上使用map2
,并为每对start-end
年份构造一个ts
对象:
df %>%
mutate(ts.object = map2(yr_start, yr_end, ~ window(austourists, start=c(.x, 1), end=c(.y, 4)))) %>%
as.tibble()
# A tibble: 7 x 3
# yr_start yr_end ts.object
# <int> <dbl> <list>
#1 1999 2008 <S3: ts>
#2 2000 2008 <S3: ts>
#3 2001 2008 <S3: ts>
#4 2002 2008 <S3: ts>
#5 2003 2008 <S3: ts>
#6 2004 2008 <S3: ts>
#7 2005 2008 <S3: ts>
df_ts <- df %>%
mutate(ts.object = map2(yr_start, yr_end, ~ window(austourists, start=c(.x, 1), end=c(.y, 4)))) %>%
as.tibble()
Here are the last two rows in ts.object
column: 这是ts.object
列中的最后两行:
df_ts$ts.object[[6]]
# Qtr1 Qtr2 Qtr3 Qtr4
#2004 41.27360 26.65586 28.27986 35.19115
#2005 41.72746 24.04185 32.32810 37.32871
#2006 46.21315 29.34633 36.48291 42.97772
#2007 48.90152 31.18022 37.71788 40.42021
#2008 51.20686 31.88723 40.97826 43.77249
df_ts$ts.object[[7]]
# Qtr1 Qtr2 Qtr3 Qtr4
#2005 41.72746 24.04185 32.32810 37.32871
#2006 46.21315 29.34633 36.48291 42.97772
#2007 48.90152 31.18022 37.71788 40.42021
#2008 51.20686 31.88723 40.97826 43.77249
Or use Map
from base R: 或使用基于R的Map
:
df %>% mutate(ts.object = Map(function(x, y) window(austourists, start=c(x, 1), end=c(y, 4)), yr_start, yr_end))
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