Estructura

.Rhistory

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+tinytex::install_tinytex()
+#-----Practica 2
+#----Ejercicio1a -----
+library(dplyr)
+library(tidyr)
+library(ggplot2)
+library(lubridate)
+library(fpp3)
+library(slider)
+library(seasonal)
+data(canadian_gas)
+canadian_gas
+autoplot(canadian_gas, Volume) +
+ylab("volumen")+
+xlab("fecha") +
+ggtitle("produccion nacional de gas en canada")
+dcmp_can <- canadian_gas %>%
+model(STL(Volume))
+dcmp_can
+# Componente trend-cycle
+canadian_gas %>% autoplot(Volume, color='gray') +
+autolayer(components(dcmp_can), trend, color='blue') +
+xlab("fecha") + ylab("volumen") +
+ggtitle("Produccion de Gas Canada - Tendencia")
+#hay una tendencia del crecimiento y del decrecimiento en la produccion del gas, se puede observar que la tendencia es de manera ascendente, se puede observar a su ves que hay fluctuaciones estructurales que afectan a la producion de gas
+# Datos ajustados por estacionalidad
+canadian_gas %>%
+autoplot(Volume, color='gray') +
+autolayer(components(dcmp_can), season_adjust, color='green') +
+xlab("fecha") + ylab("volumen") +
+ggtitle("Produccion de Gas Canada - Desestacionalizado")
+#se tiene un aumento general en la produccion de gas con ligeras fluctuaciones estacionales
+#Graficos extras (no se como interpretarlas)
+gg_season(canadian_gas,Volume)+
+labs(tittle="Estacionalidad Gas Canada",
+y="volumen",
+x="mes")
+#el volumen de produccion varia segun el mes, se puede observar un patron estacional dado que en las epocas de invierno este incrementa y en la temporada de verano este tiende a disminuir
+gg_season(canadian_gas, Volume, polar = TRUE) +
+labs(title = "Estacionalidad Gas Canada (Polar)",
+y = "Volumen",
+x = "Mes")
+#----Ejercicio 1b-----
+x11_dcmp_can  <- canadian_gas %>%
+model(x11 = X_13ARIMA_SEATS(Volume ~ x11()))
+x11_dcmp_can
+x11_component_can <- components(x11_dcmp_can)
+head(x11_component_can,10)
+autoplot(x11_component_can) + labs(title = "Decomposicion de la produccion total de canada using X-11.")
+x11_component_can %>% ggplot(aes(x = Month)) +
+geom_line(aes(y = Volume, colour = "Data")) +
+geom_line(aes(y = season_adjust, colour = "Seasonally Adjusted")) +
+geom_line(aes(y = trend, colour = "Trend")) +
+labs(y = "Produccion Gas", title = "Produccion de gas Canada") +
+scale_colour_manual(values = c("gray", "#0072B2", "#D55E00"),
+breaks = c("Data", "Seasonally Adjusted", "Trend"))
+x11_component_can %>%
+gg_subseries(seasonal)

.quarto/_freeze/index/execute-results/html.json

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+{
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+  "result": {
+    "engine": "knitr",
+    "markdown": "# Bienvenida {.unnumbered}\n\nThis is a Quarto book.\n\nTo learn more about Quarto books visit <https://quarto.org/docs/books>.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n1 + 1\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 2\n```\n\n\n:::\n:::\n",
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.quarto/_freeze/intro/execute-results/html.json

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+{
+  "hash": "f878a345de1703db0e940d24bf6c7523",
+  "result": {
+    "engine": "knitr",
+    "markdown": "# Introducción\n\nThis is a book created from markdown and executable code.\n\nSee @knuth84 for additional discussion of literate programming.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n1 + 1\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 2\n```\n\n\n:::\n:::\n",
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.quarto/_freeze/summary/execute-results/html.json

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+{
+  "hash": "3febd69255a43159ce5a13968b282099",
+  "result": {
+    "engine": "knitr",
+    "markdown": "# Índice\n\nIn summary, this book has no content whatsoever.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n1 + 1\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 2\n```\n\n\n:::\n:::\n",
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+    "preserve": {},
+    "postProcess": true
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.quarto/cites/index.json

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