Use the given code below to answer the questions.

## Load package
library(tidyverse) # for cleaning, plotting, etc
library(tidyquant) # for financial analysis

## 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.

stocks <- tq_get("NFLX", get = "stock.prices", from = "2016-01-01")
stocks

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

On January 13th, 2017 there were 10,515,000 Netflix stocks 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.

Character Data: An opened ended question could a character data type. An example of this could be an opinion on a subject.

Logical Data: True, 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.

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

stocks %>%
  ggplot(aes(x = date, y = close)) +
  geom_line()

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

In the beginning of 2019, Netlfix closing stock price was relatively low. It then took off, experiencing fast growth in early 2019. It leveles off around $350 for half of 2019 unitl it experiences a large drop off of about $100. It then began to rise again into 2020.

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.

mult_stocks <- tq_get(c("NFLX", "AMZN"), get= "stock.prices", from = "2016-01-01")
stocks
## # A tibble: 1,034 x 7
##    date        open  high   low close   volume adjusted
##    <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 2016-01-04  109   110   105.  110. 20794800     110.
##  2 2016-01-05  110.  111.  106.  108. 17664600     108.
##  3 2016-01-06  105.  118.  105.  118. 33045700     118.
##  4 2016-01-07  116.  122.  112.  115. 33636700     115.
##  5 2016-01-08  116.  118.  111.  111. 18067100     111.
##  6 2016-01-11  112.  117.  111.  115. 21920400     115.
##  7 2016-01-12  116.  118.  115.  117. 15133500     117.
##  8 2016-01-13  114.  114.  105.  107. 24921600     107.
##  9 2016-01-14  106.  109.  101.  107. 23664800     107.
## 10 2016-01-15  102.  106.  102.  104. 19775100     104.
## # … with 1,024 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.