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: 927 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     98.7
##  2 2016-01-05 106.  106.  102.  103.  55791000     96.3
##  3 2016-01-06 101.  102.   99.9 101.  68457400     94.4
##  4 2016-01-07  98.7 100.   96.4  96.4 81094400     90.4
##  5 2016-01-08  98.6  99.1  96.8  97.0 70798000     90.9
##  6 2016-01-11  99.0  99.1  97.3  98.5 49739400     92.4
##  7 2016-01-12 101.  101.   98.8 100.0 49154200     93.7
##  8 2016-01-13 100.  101.   97.3  97.4 62439600     91.3
##  9 2016-01-14  98.0 100.   95.7  99.5 63170100     93.3
## 10 2016-01-15  96.2  97.7  95.4  97.1 79833900     91.0
## # … with 917 more rows
## Visualize
stocks %>%
  ggplot(aes(x = date, y = adjusted)) +
  geom_line()

Q1 Import Tesla 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("TSLA", get = "stock.prices", from = "2016-01-01")
stocks
## # A tibble: 927 x 7
##    date        open  high   low close  volume adjusted
##    <date>     <dbl> <dbl> <dbl> <dbl>   <dbl>    <dbl>
##  1 2016-01-04  231.  231.  219   223. 6827100     223.
##  2 2016-01-05  226.  227.  220   223. 3186800     223.
##  3 2016-01-06  220   220.  216.  219. 3779100     219.
##  4 2016-01-07  214.  218.  214.  216. 3554300     216.
##  5 2016-01-08  218.  220.  211.  211  3628100     211 
##  6 2016-01-11  214.  214.  203   208. 4089700     208.
##  7 2016-01-12  212.  214.  205.  210. 3091900     210.
##  8 2016-01-13  212.  213.  200   200. 4126400     200.
##  9 2016-01-14  202.  210   193.  206. 6490700     206.
## 10 2016-01-15  199.  205.  197.  205. 5578600     205.
## # … with 917 more rows

Q2 How many shares of the stock were traded on January 13, 2016?

4,126,400 Tesla shares were traded on January 13, 2016.

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 a name or something not numerical like Telsa, and an example of 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 = close)) +
  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 Tesla stock has performed since the beginning of 2019.

Since the beginning of 2019, the closing price for Tesla stock prices has dramatically decreased from around 300 dollars per share and now at about 200 dollars per share.

Q6 Import two stocks: Tesla and Netflix 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.

## # A tibble: 927 x 7
##    date        open  high   low close  volume adjusted
##    <date>     <dbl> <dbl> <dbl> <dbl>   <dbl>    <dbl>
##  1 2016-01-04  231.  231.  219   223. 6827100     223.
##  2 2016-01-05  226.  227.  220   223. 3186800     223.
##  3 2016-01-06  220   220.  216.  219. 3779100     219.
##  4 2016-01-07  214.  218.  214.  216. 3554300     216.
##  5 2016-01-08  218.  220.  211.  211  3628100     211 
##  6 2016-01-11  214.  214.  203   208. 4089700     208.
##  7 2016-01-12  212.  214.  205.  210. 3091900     210.
##  8 2016-01-13  212.  213.  200   200. 4126400     200.
##  9 2016-01-14  202.  210   193.  206. 6490700     206.
## 10 2016-01-15  199.  205.  197.  205. 5578600     205.
## # … with 917 more rows

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

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

Q8 Hide the code in Q6 but display 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.