## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 4.5,
  fig.align = "center",
  message = FALSE,
  warning = FALSE
)

## ----setup--------------------------------------------------------------------
library(trendseries)
library(dplyr)
library(tidyr)
library(ggplot2)

## ----libs---------------------------------------------------------------------
# library(trendseries)
# # Optional
# library(dplyr)
# library(tidyr, include.only = "pivot_longer")

## -----------------------------------------------------------------------------
library(ggplot2)

theme_series <- theme_minimal(paper = "#fefefe") +
  theme_sub_panel(grid.minor = element_blank()) +
  theme_sub_plot(margin = margin(10, 10, 10, 10)) +
  theme_sub_axis_x(
    line = element_line(color = "gray20"),
    ticks = element_line(color = "gray20", linewidth = 0.35),
    title = element_blank()
  ) +
  theme(
    legend.position = "bottom",
    # Use colors
    palette.colour.discrete = c(
      "#2c3e50",
      "#e74c3c",
      "#f39c12",
      "#1abc9c",
      "#9b59b6"
    )
  )

## -----------------------------------------------------------------------------
head(electric)

ggplot(electric, aes(date, consumption)) +
  geom_line(lwd = 0.7) +
  theme_series

## -----------------------------------------------------------------------------
elec_trend <- augment_trends(
  electric,
  date_col = "date",
  value_col = "consumption",
  methods = "stl"
)

head(elec_trend)

## ----eval = FALSE-------------------------------------------------------------
# elec_trend <- augment_trends(
#   electric,
#   date_col = "date",
#   value_col = "consumption",
#   methods = "stl",
#   frequency = 12
# )

## -----------------------------------------------------------------------------
# Prepare data for plotting
plot_data <- elec_trend |>
  tidyr::pivot_longer(
    cols = -date,
    names_to = "series",
    values_to = "value"
  ) |>
  mutate(
    series = case_when(
      series == "consumption" ~ "Data (original)",
      series == "trend_stl" ~ "Trend (STL)"
    )
  )

# Create the plot
ggplot(plot_data, aes(x = date, y = value, color = series)) +
  geom_line(linewidth = 0.7) +
  labs(
    title = "Residential Electricity Consumption",
    x = NULL,
    y = "Electric Consumption (GWh)",
    color = NULL
  ) +
  theme_series

## -----------------------------------------------------------------------------
ggplot(elec_trend, aes(x = date)) +
  geom_line(
    aes(y = consumption, color = "Original"),
    linewidth = 0.7,
    alpha = 0.5
  ) +
  geom_line(
    aes(y = trend_stl, color = "Trend (STL)"),
    linewidth = 1
  ) +
  scale_color_manual(values = c("#1E3A5F", "#1E3A5F")) +
  labs(
    title = "Residential Electricity Consumption",
    subtitle = "Decomposition using an STL trend",
    x = NULL,
    y = "Electric Consumption (GWh)",
    color = NULL
  ) +
  theme_series

## -----------------------------------------------------------------------------
elec_sub_trend <- electricity |>
  dplyr::filter(date >= as.Date("1995-01-01")) |>
  augment_trends(
    date_col = "date",
    value_col = "value",
    group_cols = "name_series",
    methods = "stl"
  )

ggplot(elec_sub_trend, aes(date)) +
  geom_line(aes(y = value), alpha = 0.5, color = "#1E3A5F") +
  geom_line(aes(y = trend_stl), color = "#1E3A5F") +
  facet_wrap(vars(name_series), ncol = 1) +
  theme_series

## -----------------------------------------------------------------------------
ggplot(retail_autofuel, aes(date, value)) +
  geom_line(lwd = 0.7, color = "#1E3A5F") +
  theme_series

## ----compare-methods----------------------------------------------------------
fuel_trends <- retail_autofuel |>
  filter(date >= as.Date("2012-01-01")) |>
  augment_trends(
    methods = c("stl", "hp", "loess")
  )

comparison_plot <- fuel_trends |>
  tidyr::pivot_longer(
    cols = c(value, starts_with("trend_")),
    names_to = "method",
  ) |>
  mutate(
    method = case_when(
      method == "value" ~ "Data (original)",
      method == "trend_hp" ~ "HP Filter",
      method == "trend_stl" ~ "STL",
      method == "trend_loess" ~ "LOESS"
    )
  )

ggplot(comparison_plot, aes(x = date, y = value, color = method)) +
  geom_line(linewidth = 0.7) +
  labs(
    title = "Comparing Different Trend Extraction Methods",
    subtitle = "Same data, different methods",
    x = "Date",
    y = "Retail Sales Index",
    color = "Method"
  ) +
  theme_series

## -----------------------------------------------------------------------------
elec_trends <- electric |>
  rename(value = consumption) |>
  # window controls the s.window argument by default
  augment_trends(methods = "stl", window = 17) |>
  # Creates a 11-month moving median
  augment_trends(methods = "median", window = 11) |>
  # Creates a (centered) 5-month moving average
  augment_trends(methods = "ma", window = 5) |>
  # Creates a (centered) 2x12 moving average
  augment_trends(methods = "ma", window = 12)

## -----------------------------------------------------------------------------
comparison_plot <- elec_trends |>
  tidyr::pivot_longer(
    cols = c(value, starts_with("trend_")),
    names_to = "method",
  ) |>
  mutate(
    method = case_when(
      method == "value" ~ "Data (original)",
      method == "trend_median" ~ "Median",
      method == "trend_stl" ~ "STL",
      method == "trend_ma" ~ "MA (5)",
      method == "trend_ma_1" ~ "MA (2x12)"
    )
  ) |>
  filter(date >= as.Date("2018-01-01"))

ggplot(comparison_plot, aes(x = date, y = value, color = method)) +
  geom_line(linewidth = 0.7) +
  labs(
    title = "Comparing Different Trend Extraction Methods",
    subtitle = "Same data, different methods",
    x = "Date",
    y = "Retail Sales Index",
    color = "Method"
  ) +
  theme_series

## -----------------------------------------------------------------------------
gdp_cons <- ts(
  gdp_construction$index,
  frequency = 4,
  start = c(1996, 1)
)

# Or, using lubridate to extract year and month
gdp_cons <- ts(
  gdp_construction$index,
  frequency = 4,
  start = c(
    lubridate::year(min(gdp_construction$date)),
    lubridate::quarter(min(gdp_construction$date))
  )
)

## -----------------------------------------------------------------------------
gdp_trend_hp <- mFilter::hpfilter(gdp_cons, 1600)

## -----------------------------------------------------------------------------
# Convert back to data frame using tsbox
trend_df <- tsbox::ts_df(gdp_trend_hp$trend)
names(trend_df) <- c("date", "trend_hp")

# Join with original data
gdp_manual <- left_join(gdp_construction, trend_df, by = "date")

## -----------------------------------------------------------------------------
gdp_auto <- augment_trends(gdp_construction, value_col = "index", methods = "hp")