In this exercise, use Chapter 4.2 Quantitative vs. Quantitative Data Visualization with R.

# Load packages
library(tidyquant)
library(tidyverse)

# Import stock prices
stock_prices <- tq_get(c("AAPL", "MSFT", "AMZN"), get  = "stock.prices", from = "2021-01-01")

# Calculate daily returns
stock_returns <-
  stock_prices  %>%
    group_by(symbol) %>%
    tq_mutate(select = adjusted, mutate_fun = periodReturn, period = "daily") 
stock_returns 
## # A tibble: 129 x 9
## # Groups:   symbol [3]
##    symbol date        open  high   low close    volume adjusted daily.returns
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>         <dbl>
##  1 AAPL   2021-01-04  134.  134.  127.  129. 143301900     129.       0      
##  2 AAPL   2021-01-05  129.  132.  128.  131.  97664900     131.       0.0124 
##  3 AAPL   2021-01-06  128.  131.  126.  127. 155088000     126.      -0.0337 
##  4 AAPL   2021-01-07  128.  132.  128.  131. 109578200     131.       0.0341 
##  5 AAPL   2021-01-08  132.  133.  130.  132. 105158200     132.       0.00863
##  6 AAPL   2021-01-11  129.  130.  128.  129. 100620900     129.      -0.0232 
##  7 AAPL   2021-01-12  128.  130.  127.  129.  91951100     129.      -0.00140
##  8 AAPL   2021-01-13  129.  131.  128.  131.  88636800     131.       0.0162 
##  9 AAPL   2021-01-14  131.  131   129.  129.  90221800     129.      -0.0151 
## 10 AAPL   2021-01-15  129.  130.  127   127. 111598500     127.      -0.0137 
## # ... with 119 more rows

Q1 Interpret Row 2 of stock_returns.

Hint: In your interpretation, make sure to use all variables.

Interpreting Row 2 shows Apples stock data for 1/5/2021. It shows apple opened at $128.89, closed at $131.01, high of the day was $131.74, and low of day was $128.43. Also, it shows the volume traded on the 5th was 97664900 and the adjusted price was $130.8145. Lastly, it states the daily return was 0.0123636673 or 1.23%.

Q2 How much was the highest price per share, at which Apple was traded on January 28, 2021?

Hint: Examine the data in the spreadsheet view.

The highest Apple traded on the 28th of 2021 was $141.99.

Q3 filter Select Apple stock prices and save it under plotdata.

Hint: See the code in 4.2.2 Line plot.

plotdata <- filter(stock_returns, symbol == "AAPL")

plotdata
## # A tibble: 43 x 9
## # Groups:   symbol [1]
##    symbol date        open  high   low close    volume adjusted daily.returns
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>         <dbl>
##  1 AAPL   2021-01-04  134.  134.  127.  129. 143301900     129.       0      
##  2 AAPL   2021-01-05  129.  132.  128.  131.  97664900     131.       0.0124 
##  3 AAPL   2021-01-06  128.  131.  126.  127. 155088000     126.      -0.0337 
##  4 AAPL   2021-01-07  128.  132.  128.  131. 109578200     131.       0.0341 
##  5 AAPL   2021-01-08  132.  133.  130.  132. 105158200     132.       0.00863
##  6 AAPL   2021-01-11  129.  130.  128.  129. 100620900     129.      -0.0232 
##  7 AAPL   2021-01-12  128.  130.  127.  129.  91951100     129.      -0.00140
##  8 AAPL   2021-01-13  129.  131.  128.  131.  88636800     131.       0.0162 
##  9 AAPL   2021-01-14  131.  131   129.  129.  90221800     129.      -0.0151 
## 10 AAPL   2021-01-15  129.  130.  127   127. 111598500     127.      -0.0137 
## # ... with 33 more rows

Q4 Create a simple line plot with date on the x-axis and opening price on the y-axis.

Hint: See the code in 4.2.2 Line plot. Use plotdata you created in Q3.

ggplot(plotdata,
       aes(x = date,
           y = open)) +
  geom_line()+
  labs(y = "Opening Price",
       x = "Date")

Q5 Describe the performance of Apple stock this year.

Hint: Interpret the line plot you created in Q4.

Apples stock performance this year has been asymmetric. It technically was trading horizontally with a bullish run in mid January and then continued to hold at $135 area. On around Feb 16th Apple began to show a bearish pattern lowering too the $120 area.

Q6 Calculate mean daily returns for each stock and save it under plotdata.

Hint: See the code in 4.3.1 Bar chart (on summary statistics).

plotdata <- stock_returns %>%
  group_by(symbol) %>%
  summarise(mean_DR = mean(daily.returns))

plotdata
## # A tibble: 3 x 2
##   symbol  mean_DR
## * <chr>     <dbl>
## 1 AAPL   -0.00124
## 2 AMZN   -0.00126
## 3 MSFT    0.00162

Q7 If the stock’s performance this year is any indication, which of the stocks would you expect the highest daily return? Plot mean daily returns using bar chart.

Hint: See the code in 4.3.1 Bar chart (on summary statistics). Use plotdata you created in Q5.

ggplot(plotdata,
       aes(x = symbol,
           y = mean_DR)) +
  geom_bar(stat = "identity")

I would personally believe Microsoft to show the highest daily return. As seen on the graph it has the highest mean DR, as well as the only positive daily return. Although the sector APPL, MSFT, and AMZN are inhave began showinga bearish pattern as a whole.

Q8 Hide the messages and warnings, but display the code and its results on the webpage.

Hint: Refer to the RMarkdown Reference Guide.

Q9 Display the title and your name correctly at the top of the webpage.

Q10 Use the correct slug.