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: 1,092 x 8
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 AAPL   2016-01-04 103.  105.  102   105.  67649400     98.2
##  2 AAPL   2016-01-05 106.  106.  102.  103.  55791000     95.8
##  3 AAPL   2016-01-06 101.  102.   99.9 101.  68457400     93.9
##  4 AAPL   2016-01-07  98.7 100.   96.4  96.4 81094400     89.9
##  5 AAPL   2016-01-08  98.6  99.1  96.8  97.0 70798000     90.4
##  6 AAPL   2016-01-11  99.0  99.1  97.3  98.5 49739400     91.9
##  7 AAPL   2016-01-12 101.  101.   98.8 100.  49154200     93.2
##  8 AAPL   2016-01-13 100.  101.   97.3  97.4 62439600     90.8
##  9 AAPL   2016-01-14  98.0 100.   95.7  99.5 63170100     92.8
## 10 AAPL   2016-01-15  96.2  97.7  95.4  97.1 79833900     90.6
## # … with 1,082 more rows
## Visualize
stocks %>%
  ggplot(aes(x = date, y = close)) +
  geom_line()

Q1 Import Microsoft 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("MSFT", get = "stock.prices", from = "2016-01-01")
stocks
## # A tibble: 1,092 x 8
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 MSFT   2016-01-04  54.3  54.8  53.4  54.8 53778000     50.4
##  2 MSFT   2016-01-05  54.9  55.4  54.5  55.0 34079700     50.6
##  3 MSFT   2016-01-06  54.3  54.4  53.6  54.0 39518900     49.7
##  4 MSFT   2016-01-07  52.7  53.5  52.1  52.2 56564900     48.0
##  5 MSFT   2016-01-08  52.4  53.3  52.2  52.3 48754000     48.1
##  6 MSFT   2016-01-11  52.5  52.8  51.5  52.3 36943800     48.1
##  7 MSFT   2016-01-12  52.8  53.1  52.1  52.8 36095500     48.5
##  8 MSFT   2016-01-13  53.8  54.1  51.3  51.6 66883600     47.5
##  9 MSFT   2016-01-14  52    53.4  51.6  53.1 52381900     48.8
## 10 MSFT   2016-01-15  51.3  52.0  50.3  51.0 71820700     46.9
## # … with 1,082 more rows
## Visualize
stocks %>%
  ggplot(aes(x = date, y = close)) +
  geom_line()

Q2 How many shares of the stock were traded on January 13, 2018?

19422300

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.

Logical data can be true false. Character data associates data with a given letter or symbol

Q4 Plot the adjusted 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 adjusted to the y-axis, instead of close.

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 = adjusted)) +
  geom_line()

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

2019 had a steady rise before dipping in February 2020 and then rising again

Q6 Import two stocks: Microsoft 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. Do this by using the tq_get() function once, not twice.

## Import data
stocks <- tq_get(c("MSFT", "AMZN"), get = "stock.prices", from = "2017-01-13", results = 'markup')
stocks
## # A tibble: 1,664 x 8
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 MSFT   2017-01-13  62.6  62.9  62.3  62.7 19422300     59.2
##  2 MSFT   2017-01-17  62.7  62.7  62.0  62.5 20664000     59.1
##  3 MSFT   2017-01-18  62.7  62.7  62.1  62.5 19670100     59.1
##  4 MSFT   2017-01-19  62.2  63.0  62.2  62.3 18451700     58.9
##  5 MSFT   2017-01-20  62.7  62.8  62.4  62.7 30213500     59.3
##  6 MSFT   2017-01-23  62.7  63.1  62.6  63.0 23097600     59.5
##  7 MSFT   2017-01-24  63.2  63.7  62.9  63.5 24672900     60.0
##  8 MSFT   2017-01-25  64.0  64.1  63.5  63.7 23672700     60.2
##  9 MSFT   2017-01-26  64.1  64.5  63.5  64.3 43554600     60.7
## 10 MSFT   2017-01-27  65.4  65.9  64.9  65.8 44818000     62.2
## # … with 1,654 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.