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 = "2020-01-01")

# Calculate daily returns
stock_returns <-
  stock_prices  %>%
    group_by(symbol) %>%
    tq_mutate(select = adjusted, mutate_fun = periodReturn, period = "daily") 
stock_returns
## # A tibble: 552 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   2020-01-02  74.1  75.2  73.8  75.1 135480400     74.6       0      
##  2 AAPL   2020-01-03  74.3  75.1  74.1  74.4 146322800     73.8      -0.00972
##  3 AAPL   2020-01-06  73.4  75.0  73.2  74.9 118387200     74.4       0.00797
##  4 AAPL   2020-01-07  75.0  75.2  74.4  74.6 108872000     74.1      -0.00470
##  5 AAPL   2020-01-08  74.3  76.1  74.3  75.8 132079200     75.3       0.0161 
##  6 AAPL   2020-01-09  76.8  77.6  76.6  77.4 170108400     76.9       0.0212 
##  7 AAPL   2020-01-10  77.7  78.2  77.1  77.6 140644800     77.1       0.00226
##  8 AAPL   2020-01-13  77.9  79.3  77.8  79.2 121532000     78.7       0.0214 
##  9 AAPL   2020-01-14  79.2  79.4  78.0  78.2 161954400     77.6      -0.0135 
## 10 AAPL   2020-01-15  78.0  78.9  77.4  77.8 121923600     77.3      -0.00429
## # ... with 542 more rows

Q1 Interpret Row 2 of stock_returns.

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

Row 2 of stock returns is how the stocks for Apple did on January 3, 2020.When the market opened the price of the stock was at 74.2875, but when it closed it was at 74.3575. The data set also shows the highest price of the stock that day, and the lowest price with them being 75.1450 and 74.1250. The volume of 146322800 told how many stocks were used that day, with the adjusted stock price of 73.84803. The final column is the dailey returns of apple on that specific day.

Q2 How much was Microsoft per share at closing on July 30, 2020?

Hint: Examine the data in the spreadsheet view.

The Microsoft per share at closing on July 30, 2020 was 203.90.

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

Hint: See the code in 4.2.2 Line plot.

plotdata <- filter(stock_prices, 
                   symbol == "MSFT")

Q4 Create a simple line plot with date on the x-axis and closing 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 = close)) +
  geom_line()

Q5 Describe the performance of Microsoft stock this year.

Hint: Interpret the line plot you created in Q4.

Microsoft stock dropped significantly when COVID first happened, but has been rising until about September, then it has been declining.

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

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

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

Q7 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_returns)) +
  geom_bar(stat = "identity")

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.