timeseries-moving-average.Rmd

---
title: "Visualizing Time Series in R<br><br>Using tidyverse and tidyquant"
subtitle: "Daily Stock Prices and Moving Averages"
author: "StatistikinDD"
date: "Created: `r Sys.Date()`"
output:
  xaringan::moon_reader:
    chakra: libs/remark-latest.min.js
    lib_dir: libs
    css: ["libs/_css/xaringan-themer.css", "libs/_css/my_css.css"]
    nature:
      highlightStyle: github
      highlightLines: true
      countIncrementalSlides: false
      slideNumberFormat: "%current%"
      ratio: 16:9
---

```{r setup, include = FALSE}

options(htmltools.dir.version = FALSE)
# knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE, comment = "")
knitr::opts_chunk$set(echo = FALSE, comment = "")

library(knitr)
library(ggplot2)
theme_set(theme_gray(base_size = 16))

```

# The tidyquant package

* Authors: Matt Dancho, Davis Vaughan
* Maintainer: Matt Dancho
* Check out https://www.business-science.io/

** A Bridge between Time Series specific packages and the Tidyverse**

--

```{r, out.height = "75%", out.width = "75%", fig.align = "center"}
knitr::include_graphics("libs/_Images/tidyquant-tidyverse.png")
```

Source: Youtube video, see *Introduction to tidyquant* vignette  
`help(package = "tidyquant")`

---

# Getting Amazon Stock Prices

```{r get-data, echo = TRUE, message = FALSE}

library(tidyverse)
library(tidyquant)

AMZN <- tq_get("AMZN", from = "2015-01-01", to = "2021-05-31")

head(AMZN) %>% kable()

```

---

# A First Line Plot: Amazon Stock Prices

.pull-left[
```{r first-plot, echo = TRUE, eval = FALSE}

AMZN %>%
  ggplot(aes(x = date, y = adjusted)) +
  geom_line() 

```
]

.pull-right[
```{r, ref.label = "first-plot"}
```
]

---

# Some Finetuning

.pull-left[
```{r finetuning, echo = TRUE, eval = FALSE}

theme_set(ggthemes::theme_wsj()) #<<

AMZN %>%
  ggplot(aes(x = date, y = adjusted)) +
  geom_line() +
  scale_y_continuous( #<<
    labels = scales::label_dollar(), #<<
    position = "right") + #<<
  labs(title = "Amazon Stock Price",
       x = "")

```
]

.pull-right[
```{r, ref.label = "finetuning"}
```
]

---

# Zooming in: From 2020-01-01, into Covid

.pull-left[
```{r zoom-in, echo = TRUE, eval = FALSE}

AMZN %>%
  ggplot(aes(x = date, y = adjusted)) +
  geom_line() +
  scale_y_continuous(
    labels = scales::label_dollar(),
    position = "right") +
  coord_x_date( #<<
      xlim = c("2020-01-01", "2021-05-31"), #<<
      ylim = c(1500, 3500)) + #<<
  labs(title = "Amazon Stock Price",
       x = "")
```
]

.pull-right[
```{r, ref.label = "zoom-in"}
```
]

---

# Adding A 30 Day Moving Average

.pull-left[
```{r sma-30, echo = TRUE, eval = FALSE}

AMZN %>%
  ggplot(aes(x = date, y = adjusted)) +
  geom_line() +
  geom_ma(ma_fun = SMA, n = 30, size = 1.2) + #<<
  scale_y_continuous(
    labels = scales::label_dollar(),
    position = "right") +
  coord_x_date( 
      xlim = c("2020-01-01", "2021-05-31"), 
        ylim = c(1500, 3500)) + 
  labs(title = "Amazon Stock Price",
       x = "")
```
]

.pull-right[
```{r, ref.label = "sma-30"}
```
]

---

# Adding A 90 Day Moving Average

.pull-left[
```{r sma-90, echo = TRUE, eval = FALSE}

AMZN %>%
  ggplot(aes(x = date, y = adjusted)) +
  geom_line() +
  geom_ma(ma_fun = SMA, n = 30, size = 1.2) + #<<
  geom_ma(ma_fun = SMA, n = 90, #<<
          color = "green", size = 1.2) + #<<
  scale_y_continuous(
    labels = scales::label_dollar(),
    position = "right") +
  coord_x_date( 
      xlim = c("2020-01-01", "2021-05-31"), 
        ylim = c(1500, 3500)) + 
  labs(title = "Amazon Stock Price",
       x = "")
```
]

.pull-right[
```{r, ref.label = "sma-90"}
```
]

---

# Adding A Custom Legend

.pull-left[
```{r legend, echo = TRUE, eval = FALSE}

AMZN %>%
  ggplot(aes(x = date, y = adjusted)) +
  geom_line(aes(color = "Daily")) + #<<
  scale_y_continuous(
    labels = scales::label_dollar(),
    position = "right") +
  coord_x_date(xlim = c("2020-01-01", "2021-05-31"),
                 ylim = c(1500, 3500)) +
  labs(title = "Amazon Stock Price", x = "",
       subtitle = "Daily Adjusted / \nSimple Moving Averages (SMA)",
       caption = "Created using R, tidyverse, \n ggthemes, and tidyquant") +
  geom_ma(ma_fun = SMA, n = 30,
      aes(color = "30 Day SMA")) + #<<
  geom_ma(ma_fun = SMA, n = 90,
      aes(color = "90 Day SMA")) + #<<
  scale_color_manual(name = "", #<<
      values = c("Daily" = "black", #<<
                 "30 Day SMA" = "blue", #<<
                 "90 Day SMA" = "green")) #<<
```
]

.pull-right[
```{r, ref.label = "legend"}
```
]

---

class: center, middle

# Thanks!

### Youtube: StatistikinDD

### Twitter: @StatistikinDD

### github: fjodor

Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan).

The chakra comes from [remark.js](https://remarkjs.com), [**knitr**](https://yihui.org/knitr), and [R Markdown](https://rmarkdown.rstudio.com).

Thanks to **Yihui Xie** for *{knitr}* and *{xaringan}* and **Garrick Aden-Buie** for *{xaringanthemer}*.