Clase 2

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(fpp3)

── Attaching packages ────────────────────────────────────────────── fpp3 0.5 ──
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✔ tsibbledata 0.4.1     ✔ fabletools  0.3.4
✔ feasts      0.3.1     
── Conflicts ───────────────────────────────────────────────── fpp3_conflicts ──
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library(plotly)

Attaching package: 'plotly'

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library(patchwork)
library(tidyquant)

Loading required package: PerformanceAnalytics
Loading required package: xts
Loading required package: zoo

Attaching package: 'zoo'

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    as.Date, as.Date.numeric


######################### Warning from 'xts' package ##########################
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# The dplyr lag() function breaks how base R's lag() function is supposed to  #
# work, which breaks lag(my_xts). Calls to lag(my_xts) that you type or       #
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# Use stats::lag() to make sure you're not using dplyr::lag(), or you can add #
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# Code in packages is not affected. It's protected by R's namespace mechanism #
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Attaching package: 'xts'

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Attaching package: 'PerformanceAnalytics'

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Loading required package: quantmod
Loading required package: TTR
Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 

Attaching package: 'tidyquant'

The following object is masked from 'package:fable':

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empleo_tsbl <- us_employment |> 
  filter(year(Month) >= 1990, Title == "Retail Trade") |> 
  select(-Series_ID)

empleo_decomp <- empleo_tsbl |> 
  model(
    clasica = classical_decomposition(Employed,
                                      type = "additive")
  )

empleo_decomp

# A mable: 1 x 1
          clasica
          <model>
1 <DECOMPOSITION>

empleo_components <- empleo_decomp |> 
  components()
empleo_components

# A dable: 357 x 7 [1M]
# Key:     .model [1]
# :        Employed = trend + seasonal + random
   .model     Month Employed  trend seasonal random season_adjust
   <chr>      <mth>    <dbl>  <dbl>    <dbl>  <dbl>         <dbl>
 1 clasica 1990 Jan   13256.    NA    -75.5   NA           13331.
 2 clasica 1990 Feb   12966.    NA   -273.    NA           13239.
 3 clasica 1990 Mar   12938.    NA   -253.    NA           13191.
 4 clasica 1990 Apr   13012.    NA   -190.    NA           13203.
 5 clasica 1990 May   13108.    NA    -88.9   NA           13197.
 6 clasica 1990 Jun   13183.    NA    -10.4   NA           13193.
 7 clasica 1990 Jul   13170. 13178.   -13.3    5.65        13183.
 8 clasica 1990 Aug   13160. 13161.    -9.99   8.80        13169.
 9 clasica 1990 Sep   13113. 13141.   -87.4   59.9         13201.
10 clasica 1990 Oct   13185. 13117.    34.6   33.8         13151.
# ℹ 347 more rows

empleo_components |> 
  autoplot()

Warning: Removed 6 rows containing missing values (`geom_line()`).

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