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
## # A tibble: 1,033 x 7
##    date        open  high   low close   volume adjusted
##    <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 2016-01-04 103.  105.  102   105.  67649400     98.2
##  2 2016-01-05 106.  106.  102.  103.  55791000     95.8
##  3 2016-01-06 101.  102.   99.9 101.  68457400     93.9
##  4 2016-01-07  98.7 100.   96.4  96.4 81094400     89.9
##  5 2016-01-08  98.6  99.1  96.8  97.0 70798000     90.4
##  6 2016-01-11  99.0  99.1  97.3  98.5 49739400     91.9
##  7 2016-01-12 101.  101.   98.8 100.  49154200     93.2
##  8 2016-01-13 100.  101.   97.3  97.4 62439600     90.8
##  9 2016-01-14  98.0 100.   95.7  99.5 63170100     92.8
## 10 2016-01-15  96.2  97.7  95.4  97.1 79833900     90.6
## # … with 1,023 more rows
## 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 = "2016-01-01")
stocks
## # A tibble: 1,033 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 1,023 more rows

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

There are 26111900 shares of the stock that were traded on Janurary 13, 2017.

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.

An example of Character Data would be something eihter numerical, or have letters. An example of logical data is something more along the lines of right or wrong, 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.

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

## Visualize
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.

Since the beginning of 2019, Netflix stock has gone up.

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(c("NFLX", "AMZN"), get = "stock.prices", from = "2017-01-01")
stocks
## # A tibble: 1,562 x 8
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 NFLX   2017-01-03  125.  128.  124.  127.  9437900     127.
##  2 NFLX   2017-01-04  127.  130.  127.  129.  7843600     129.
##  3 NFLX   2017-01-05  129.  133.  129.  132. 10185500     132.
##  4 NFLX   2017-01-06  132.  134.  130.  131. 10657900     131.
##  5 NFLX   2017-01-09  131.  132.  130.  131.  5771800     131.
##  6 NFLX   2017-01-10  131.  132.  129.  130.  5985800     130.
##  7 NFLX   2017-01-11  131.  132.  129.  130.  5615100     130.
##  8 NFLX   2017-01-12  131.  131.  128.  129.  5388900     129.
##  9 NFLX   2017-01-13  131.  134.  131.  134. 10515000     134.
## 10 NFLX   2017-01-17  135.  135.  132.  133. 12220200     133.
## # … with 1,552 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.