Clase 1

library(tidyverse)

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library(fpp3)

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library(plotly)

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library(patchwork)
library(tidyquant)

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aus_accommodation

# A tsibble: 592 x 5 [1Q]
# Key:       State [8]
      Date State                        Takings Occupancy   CPI
     <qtr> <chr>                          <dbl>     <dbl> <dbl>
 1 1998 Q1 Australian Capital Territory    24.3      65    67  
 2 1998 Q2 Australian Capital Territory    22.3      59    67.4
 3 1998 Q3 Australian Capital Territory    22.5      58    67.5
 4 1998 Q4 Australian Capital Territory    24.4      59    67.8
 5 1999 Q1 Australian Capital Territory    23.7      58    67.8
 6 1999 Q2 Australian Capital Territory    25.4      61    68.1
 7 1999 Q3 Australian Capital Territory    28.2      66    68.7
 8 1999 Q4 Australian Capital Territory    25.8      60    69.1
 9 2000 Q1 Australian Capital Territory    27.3      60.9  69.7
10 2000 Q2 Australian Capital Territory    30.1      64.7  70.2
# ℹ 582 more rows

aus_accommodation |> 
  distinct(State)

# A tibble: 8 × 1
  State                       
  <chr>                       
1 Australian Capital Territory
2 New South Wales             
3 Northern Territory          
4 Queensland                  
5 South Australia             
6 Tasmania                    
7 Victoria                    
8 Western Australia           

tasmania <- aus_accommodation |> 
  filter(State == "Tasmania") |> 
  # select(Date, Takings, Occupancy) opción 1: escoger todas manuales
  # select(Date, Takings:Occupancy) opción 2: date y de takings hasta occupancy
  select(-c(State, CPI)) |> # opción 3: todas menos state y cpi
  mutate(
    'Año unión Nª1' = 1
  ) |> 
    
  janitor::clean_names()
    
tasmania

# A tsibble: 74 x 4 [1Q]
      date takings occupancy ano_union_na1
     <qtr>   <dbl>     <dbl>         <dbl>
 1 1998 Q1    28.7      67               1
 2 1998 Q2    19.0      45               1
 3 1998 Q3    16.1      39               1
 4 1998 Q4    25.9      56               1
 5 1999 Q1    28.4      66               1
 6 1999 Q2    20.1      48               1
 7 1999 Q3    17.3      41               1
 8 1999 Q4    24.3      56               1
 9 2000 Q1    30.0      66.2             1
10 2000 Q2    21.7      49.7             1
# ℹ 64 more rows

tasmania |> 
  autoplot(takings, color="#F2A7DFFF", size=2)

p <- tasmania |> 
  autoplot(log(takings), color="#F2A7DFFF", size=2) +
  geom_point()

ggplotly(p, dynamicTicks = TRUE) # dynamicTicks permite seleccionar segmento para hacer zoom

tasmania |> 
  gg_season(log(takings)) # para ver la estacionalidad, te grafica cada año

tasmania |> 
  gg_subseries(log(takings))

tasmania |> 
  gg_tsdisplay(log(takings),plot_type = "auto")

stocks <- c("MCK", "META", "NVDA")

stocks_data <- tq_get(
  x = stocks,
  get = "stock.prices",
  from = "2010-01-01",
  to = "2024-01-25"
)

stocks_data

# A tibble: 10,015 × 8
   symbol date        open  high   low close  volume adjusted
   <chr>  <date>     <dbl> <dbl> <dbl> <dbl>   <dbl>    <dbl>
 1 MCK    2010-01-04  63.0  63.5  62.5  63.4 2035200     56.4
 2 MCK    2010-01-05  63.3  63.6  63.2  63.6 2133200     56.7
 3 MCK    2010-01-06  62.8  62.8  60.8  61   4230000     54.3
 4 MCK    2010-01-07  62.1  62.1  60.4  61.1 3789500     54.5
 5 MCK    2010-01-08  61.2  61.4  60.2  60.8 2608500     54.1
 6 MCK    2010-01-11  60.7  61.6  60.7  61.5 1276100     54.8
 7 MCK    2010-01-12  61.4  62.5  61.0  62.3 3582000     55.5
 8 MCK    2010-01-13  62.3  62.9  61.9  62.5 2064300     55.7
 9 MCK    2010-01-14  62.6  62.6  61.9  62.0 1846900     55.2
10 MCK    2010-01-15  61.9  62.2  61.5  61.9 2652000     55.2
# ℹ 10,005 more rows

stocks_data |> 
  distinct(symbol)

# A tibble: 3 × 1
  symbol
  <chr> 
1 MCK   
2 META  
3 NVDA  

stocks_data <- stocks_data |> 
  as_tsibble(index = date, key = symbol) # convierte una tabla en una tsibble
stocks_data

# A tsibble: 10,015 x 8 [1D]
# Key:       symbol [3]
   symbol date        open  high   low close  volume adjusted
   <chr>  <date>     <dbl> <dbl> <dbl> <dbl>   <dbl>    <dbl>
 1 MCK    2010-01-04  63.0  63.5  62.5  63.4 2035200     56.4
 2 MCK    2010-01-05  63.3  63.6  63.2  63.6 2133200     56.7
 3 MCK    2010-01-06  62.8  62.8  60.8  61   4230000     54.3
 4 MCK    2010-01-07  62.1  62.1  60.4  61.1 3789500     54.5
 5 MCK    2010-01-08  61.2  61.4  60.2  60.8 2608500     54.1
 6 MCK    2010-01-11  60.7  61.6  60.7  61.5 1276100     54.8
 7 MCK    2010-01-12  61.4  62.5  61.0  62.3 3582000     55.5
 8 MCK    2010-01-13  62.3  62.9  61.9  62.5 2064300     55.7
 9 MCK    2010-01-14  62.6  62.6  61.9  62.0 1846900     55.2
10 MCK    2010-01-15  61.9  62.2  61.5  61.9 2652000     55.2
# ℹ 10,005 more rows

stocks_data |> 
  ggplot(aes(x = date, y = close, color = symbol)) +
  geom_line()

stocks_data |> 
  ggplot(aes(x = date, open = open, high = high, low = low, close = close)) +
  geom_candlestick() +
  facet_wrap(~ symbol, ncol = 2)

c(3, 6, 12, 89)

[1]  3  6 12 89

c("pablo", "juan", "maría")

[1] "pablo" "juan"  "maría"

c(TRUE, FALSE, FALSE, TRUE)

[1]  TRUE FALSE FALSE  TRUE

# listas

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

us_retail_employment |> 
  write_csv("empleo_eeuu.csv")

Cedasticidad:

Homocedasticidad: Para varianza constante.

Heterocedasticidad: Para varianza variable.

Componentes:

Estacionalidad: Periodicidad fija.

Ciclos: No se sabe con exactitud cuando va a ser la siguiente.

Tendencia

Se calcula a través de medias móviles.

Quarto

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Running Code

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1 + 1

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