Use the given code below to answer the questions.

## Load package
library(tidyverse) # for cleaning, plotting, etc
## ── Attaching packages ───────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.2.1     ✓ purrr   0.3.3
## ✓ tibble  2.1.3     ✓ dplyr   0.8.4
## ✓ tidyr   1.0.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.4.0
## ── Conflicts ──────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(tidyquant) # for financial analysis
## Loading required package: lubridate
## 
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
## 
##     date
## Loading required package: PerformanceAnalytics
## Loading required package: xts
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
## 
## Attaching package: 'xts'
## The following objects are masked from 'package:dplyr':
## 
##     first, last
## 
## Attaching package: 'PerformanceAnalytics'
## The following object is masked from 'package:graphics':
## 
##     legend
## Loading required package: quantmod
## Loading required package: TTR
## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo
## Version 0.4-0 included new data defaults. See ?getSymbols.
## ══ Need to Learn tidyquant? ══════════════════════════════════════
## Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
## </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>
## Import data
stocks <- tq_get("AAPL", get = "stock.prices", from = "2016-01-01")
stocks

## Visualize
stocks %>%
  ggplot(aes(x = date, y = adjusted)) +
  geom_line()

Q1 Import Netflix stock prices, instead of Apple.

Hint: Insert a new code chunk below and type in the code, using the tq_get() function above. Replace the ticker symbol. Find ticker symbols from Yahoo Finance.

## Import data 
stocks <- tq_get("NFLX", get = "stock.prices", from = "2017-01-10")
stocks

Q2 How many shares of the stock were traded on January 13, 2017?

On January 13, 2017 10,515,000 shares of Netflix stock were traded.

Q3 Stock prices in this data would be a good example of numeric data. Character and logical are two other basic data types in R. List one example of character data and one example of logical data.

Hint: Watch the video, “Basic Data Types”, in DataCamp: Introduction to R for Finance: Ch1 The Basics.

A character object is used to represent a string of values in R. An example of character data would be " aws ". An example logical data would be true, or false.

Q4 Plot the closing price in a line chart.

Hint: Insert a new code chunk below and type in the code, using the ggplot() function above. Revise the code so that it maps close to the y-axis, instead of adjusted.

## Visualize
stocks %>%
  ggplot(aes(x = date, y = adjusted)) +
  geom_line()

For more information on the ggplot() function, refer to Ch2 Introduction to ggplot2 in one of our e-textbooks, Data Visualization with R.

Q5 From the chart you created in Q4, briefly describe how the Netflix stock has performed since the beginning of 2019.

Since the beginning of 2019 Netflix stock shot up in price from aproximently 250 usd to 350 usd and stayed around that price for some time. It then dropped to its previous low in 2019 and went on a run from there into 2020 at around 350 usd.

Q6 Import two stocks: Netflix and Amazon for the same time period.

Hint: Insert a new code chunk below and type in the code, using the tq_get() function above. You may refer to the manual of the tidyquant r package. Or, simply Google the tq_get function and see examples of the function’s usage.

## Import data 
stocks <- tq_get("NFLX", get = "stock.prices", from = "2017-01-10")
stocks 
## # A tibble: 776 x 7
##    date        open  high   low close   volume adjusted
##    <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 2017-01-10  131.  132.  129.  130.  5985800     130.
##  2 2017-01-11  131.  132.  129.  130.  5615100     130.
##  3 2017-01-12  131.  131.  128.  129.  5388900     129.
##  4 2017-01-13  131.  134.  131.  134. 10515000     134.
##  5 2017-01-17  135.  135.  132.  133. 12220200     133.
##  6 2017-01-18  133.  134.  131.  133. 16168600     133.
##  7 2017-01-19  142.  143.  138.  138. 23203400     138.
##  8 2017-01-20  139.  141.  138.  139.  9497400     139.
##  9 2017-01-23  139.  139.  137.  137.  7433900     137.
## 10 2017-01-24  138.  141.  137.  140.  7754700     140.
## # … with 766 more rows
## Import data 
stocks <- tq_get("AMZN", get = "stock.prices", from = "2017-01-10")
stocks 
## # A tibble: 776 x 7
##    date        open  high   low close  volume adjusted
##    <date>     <dbl> <dbl> <dbl> <dbl>   <dbl>    <dbl>
##  1 2017-01-10  797.  798   790.  796. 2558400     796.
##  2 2017-01-11  794.  800.  790.  799. 2992800     799.
##  3 2017-01-12  800.  814.  800.  814. 4873900     814.
##  4 2017-01-13  814.  822.  811.  817. 3791900     817.
##  5 2017-01-17  816.  816   803.  810. 3670500     810.
##  6 2017-01-18  810.  812.  804.  807. 2354200     807.
##  7 2017-01-19  810   814.  807.  809. 2540800     809.
##  8 2017-01-20  815.  816.  806.  808. 3376200     808.
##  9 2017-01-23  807.  818.  805.  818. 2797500     818.
## 10 2017-01-24  822   824.  814.  822. 2971700     822.
## # … with 766 more rows

Q7 Hide the messages and the results of the code, but display the code on the webpage.

Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.

Q8 Make an exception to the code chunk in Q6 by displaying both the code and its results.

Hint: Use echo and results in the chunk option. Note that this question only applies to the individual code chunk of Q6.

Q9 Display the title and your name correctly at the top of the webpage.

Q10 Use the correct slug.