---
title: "Plotting Seasonality and Correlation"
author: "Matt Dancho"
output: 
  rmarkdown::html_vignette:
    toc: true
    toc_depth: 2
vignette: >
  %\VignetteIndexEntry{Plotting Seasonality and Correlation}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  message = FALSE,
  warning = FALSE,
  fig.width = 8, 
  fig.height = 4.5,
  fig.align = 'center',
  out.width='95%', 
  dpi = 100,
  collapse = TRUE,
  comment = "#>"
)
```

This tutorial focuses on 3 functions for visualizing time series diagnostics:

- __ACF Diagnostics:__ `plot_acf_diagnostics()`
- __Seasonality Diagnostics:__ `plot_seasonal_diagnostics()`
- __STL Diagnostics:__ `plot_stl_diagnostics()`

# Libraries

Run the following code to set up for this tutorial.

```{r setup}
library(dplyr)
library(timetk)

# Setup for the plotly charts (# FALSE returns ggplots)
interactive <- TRUE
```

# Correlation Plots

## Grouped ACF Diagnostics

```{r, fig.height=6}
m4_hourly %>%
    group_by(id) %>%
    plot_acf_diagnostics(
        date, value,               # ACF & PACF
        .lags = "7 days",          # 7-Days of hourly lags
        .interactive = interactive
    )
```

## Grouped CCF Plots

```{r, fig.height=8}
walmart_sales_weekly %>%
    select(id, Date, Weekly_Sales, Temperature, Fuel_Price) %>%
    group_by(id) %>%
    plot_acf_diagnostics(
        Date, Weekly_Sales,        # ACF & PACF
        .ccf_vars    = c(Temperature, Fuel_Price),   # CCFs
        .lags        = "3 months",    # 3 months of weekly lags
        .interactive = interactive
    )
```

# Seasonality

## Seasonal Visualizations

```{r, fig.height=8}
taylor_30_min %>%
    plot_seasonal_diagnostics(date, value, .interactive = interactive)
```

## Grouped Seasonal Visualizations

```{r, fig.height=8}
m4_hourly %>%
    group_by(id) %>%
    plot_seasonal_diagnostics(date, value, .interactive = interactive)
```


# STL Diagnostics

```{r, fig.height=8}
m4_hourly %>%
    group_by(id) %>%
    plot_stl_diagnostics(
        date, value,
        .frequency = "auto", .trend = "auto",
        .feature_set = c("observed", "season", "trend", "remainder"),
        .interactive = interactive)
```

# Learning More

<p>
	<iframe width="100%" height="450" src="https://www.youtube.com/embed/elQb4VzRINg" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" style="box-shadow: 0 0 5px 2px rgba(0, 0, 0, .5);"><span id="selection-marker-1" class="redactor-selection-marker"></span><span id="selection-marker-1" class="redactor-selection-marker"></span><span id="selection-marker-1" class="redactor-selection-marker"></span><span id="selection-marker-1" class="redactor-selection-marker"></span>
	</iframe>
</p>

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<p class="text-center" style="font-size:30px;">
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</p>