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: 123 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 113 more rows

Q1 Interpret Row 2 of stock_returns.

Hint: In your interpretation, make sure to use all variables. Row 2 of the data set is, the Groups and symbols for the variables which there are only 3 variables, being AAPL, MSFT and AMZN which all represent a different company, Apple, Microsfot and Amazon

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 price per share that Apple was traded at on January 28, 2021 was $141.99 per share

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: 41 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 31 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()

Q5 Describe the performance of Apple stock this year.

Hint: Interpret the line plot you created in Q4. Apple stock prices hit a high for the year in late January and then began to slow fall over the next month to the lowest point in the year in late February. The in March, Apple began to rapidly increase and then shortly after that increase, it began to fall a similar amount that it just increased.

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).

library(dplyr)
plotdata <- stock_returns %>%
  group_by(symbol) %>%
  summarize(mean_dialy.returns = mean(daily.returns))

ggplot(plotdata, 
       aes(x = symbol, 
           y = mean_dialy.returns)) +
  geom_bar(stat = "identity")

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. I would except Microsoft to have the highest daily return because its data plot is positive. This means that it is stock daily return is increasing while compared to Apples and Amazons stocks that daily returns are going negitive from the stte of the year.

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.