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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Visualizing Time Series in R Using tidyverse and tidyquant</title>
<meta charset="utf-8" />
<meta name="author" content="StatistikinDD" />
<script src="libs/header-attrs/header-attrs.js"></script>
<link rel="stylesheet" href="libs/_css/xaringan-themer.css" type="text/css" />
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<textarea id="source">
class: center, middle, inverse, title-slide
# Visualizing Time Series in R<br><br>Using tidyverse and tidyquant
## Daily Stock Prices and Moving Averages
### StatistikinDD
### Created: 2021-06-25
---
# 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**
--
<img src="libs/_Images/tidyquant-tidyverse.png" width="75%" height="75%" style="display: block; margin: auto;" />
Source: Youtube video, see *Introduction to tidyquant* vignette
`help(package = "tidyquant")`
---
# Getting Amazon Stock Prices
```r
library(tidyverse)
library(tidyquant)
AMZN <- tq_get("AMZN", from = "2015-01-01", to = "2021-05-31")
head(AMZN) %>% kable()
```
|symbol |date | open| high| low| close| volume| adjusted|
|:------|:----------|------:|------:|------:|------:|-------:|--------:|
|AMZN |2015-01-02 | 312.58| 314.75| 306.96| 308.52| 2783200| 308.52|
|AMZN |2015-01-05 | 307.01| 308.38| 300.85| 302.19| 2774200| 302.19|
|AMZN |2015-01-06 | 302.24| 303.00| 292.38| 295.29| 3519000| 295.29|
|AMZN |2015-01-07 | 297.50| 301.28| 295.33| 298.42| 2640300| 298.42|
|AMZN |2015-01-08 | 300.32| 303.14| 296.11| 300.46| 3088400| 300.46|
|AMZN |2015-01-09 | 301.48| 302.87| 296.68| 296.93| 2592400| 296.93|
---
# A First Line Plot: Amazon Stock Prices
.pull-left[
```r
AMZN %>%
ggplot(aes(x = date, y = adjusted)) +
geom_line()
```
]
.pull-right[
<!-- -->
]
---
# Some Finetuning
.pull-left[
```r
*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[
<!-- -->
]
---
# Zooming in: From 2020-01-01, into Covid
.pull-left[
```r
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[
<!-- -->
]
---
# Adding A 30 Day Moving Average
.pull-left[
```r
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[
<!-- -->
]
---
# Adding A 90 Day Moving Average
.pull-left[
```r
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[
<!-- -->
]
---
# Adding A Custom Legend
.pull-left[
```r
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[
<!-- -->
]
---
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}*.
</textarea>
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