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.

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

## Import data
stocks <- tq_get("NFLX", get = "stock.prices", from = "2017-01-13")
stocks

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

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

10515000 stocks were traded on that date

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 example:Names of things Logical data example:Dates and times

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.

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

## Import data
stocks <- tq_get("NFLX", get = "stock.prices", from = "2017-01-13")
stocks

## 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 Netflix stock has performed since the beginning of 2019.

Netflix stocks shot up in the beginning of 2019 then it leveled out to a degree.

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.

knitr::opts_chunk$set(echo = TRUE, message = FALSE, results = "markup")
## Load package
library(tidyverse) # for cleaning, plotting, etc
library(tidyquant) # for financial analysis

## Import data
stocks <- tq_get("NFLX", get = "stock.prices", from = "2017-01-13")
stocks
## # A tibble: 772 x 7
##    date        open  high   low close   volume adjusted
##    <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 2017-01-13  131.  134.  131.  134. 10515000     134.
##  2 2017-01-17  135.  135.  132.  133. 12220200     133.
##  3 2017-01-18  133.  134.  131.  133. 16168600     133.
##  4 2017-01-19  142.  143.  138.  138. 23203400     138.
##  5 2017-01-20  139.  141.  138.  139.  9497400     139.
##  6 2017-01-23  139.  139.  137.  137.  7433900     137.
##  7 2017-01-24  138.  141.  137.  140.  7754700     140.
##  8 2017-01-25  141.  141.  139.  140.  7238100     140.
##  9 2017-01-26  140.  141.  139.  139.  6038300     139.
## 10 2017-01-27  139.  142.  139   142.  8323900     142.
## # … with 762 more rows
## Import data
stocks.2 <- tq_get("AMZN", get = "stock.prices", from = "2017-01-13")
stocks.2
## # A tibble: 772 x 7
##    date        open  high   low close  volume adjusted
##    <date>     <dbl> <dbl> <dbl> <dbl>   <dbl>    <dbl>
##  1 2017-01-13  814.  822.  811.  817. 3791900     817.
##  2 2017-01-17  816.  816   803.  810. 3670500     810.
##  3 2017-01-18  810.  812.  804.  807. 2354200     807.
##  4 2017-01-19  810   814.  807.  809. 2540800     809.
##  5 2017-01-20  815.  816.  806.  808. 3376200     808.
##  6 2017-01-23  807.  818.  805.  818. 2797500     818.
##  7 2017-01-24  822   824.  814.  822. 2971700     822.
##  8 2017-01-25  826.  837.  825.  837. 3922600     837.
##  9 2017-01-26  836.  844.  833   839. 3586300     839.
## 10 2017-01-27  839   840.  829.  836. 2998700     836.
## # … with 762 more rows
## Visualize
stocks %>%
  ggplot(aes(x = date, y = adjusted)) +
  geom_line()

## Visualize
stocks.2 %>%
  ggplot(aes(x = date, y = adjusted)) +
  geom_line()

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.