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")
stock_prices
## # A tibble: 552 x 8
##    symbol date        open  high   low close    volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
##  1 AAPL   2020-01-02  74.1  75.2  73.8  75.1 135480400     74.6
##  2 AAPL   2020-01-03  74.3  75.1  74.1  74.4 146322800     73.8
##  3 AAPL   2020-01-06  73.4  75.0  73.2  74.9 118387200     74.4
##  4 AAPL   2020-01-07  75.0  75.2  74.4  74.6 108872000     74.1
##  5 AAPL   2020-01-08  74.3  76.1  74.3  75.8 132079200     75.3
##  6 AAPL   2020-01-09  76.8  77.6  76.6  77.4 170108400     76.9
##  7 AAPL   2020-01-10  77.7  78.2  77.1  77.6 140644800     77.1
##  8 AAPL   2020-01-13  77.9  79.3  77.8  79.2 121532000     78.7
##  9 AAPL   2020-01-14  79.2  79.4  78.0  78.2 161954400     77.6
## 10 AAPL   2020-01-15  78.0  78.9  77.4  77.8 121923600     77.3
## # ... with 542 more rows
# 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. Apples stock returns on January 3, 2020 had an open return of $74.26 and a high of $75.14 with a low of $74.12. They also had a close of $74.35

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

Hint: Examine the data in the spreadsheet view. $203.90

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

Hint: See the code in 4.2.2 Line plot.

library(dplyr)
plotdata <- filter(stock_prices, 
                   symbol == "AAPL")
plotdata
## # A tibble: 184 x 8
##    symbol date        open  high   low close    volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
##  1 AAPL   2020-01-02  74.1  75.2  73.8  75.1 135480400     74.6
##  2 AAPL   2020-01-03  74.3  75.1  74.1  74.4 146322800     73.8
##  3 AAPL   2020-01-06  73.4  75.0  73.2  74.9 118387200     74.4
##  4 AAPL   2020-01-07  75.0  75.2  74.4  74.6 108872000     74.1
##  5 AAPL   2020-01-08  74.3  76.1  74.3  75.8 132079200     75.3
##  6 AAPL   2020-01-09  76.8  77.6  76.6  77.4 170108400     76.9
##  7 AAPL   2020-01-10  77.7  78.2  77.1  77.6 140644800     77.1
##  8 AAPL   2020-01-13  77.9  79.3  77.8  79.2 121532000     78.7
##  9 AAPL   2020-01-14  79.2  79.4  78.0  78.2 161954400     77.6
## 10 AAPL   2020-01-15  78.0  78.9  77.4  77.8 121923600     77.3
## # ... with 174 more rows

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 very steep around March when Covid happened. However, it has risin significantly around July to September.

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).

avgreturns <- stock_returns %>%
  group_by(symbol) %>%
  summarize(mean_returns = mean(daily.returns))
## `summarise()` ungrouping output (override with `.groups` argument)
avgreturns
## # A tibble: 3 x 2
##   symbol mean_returns
##   <chr>         <dbl>
## 1 AAPL        0.00248
## 2 AMZN        0.00281
## 3 MSFT        0.00173

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(avgreturns,
       aes(x = symbol, 
           y = mean_returns)) +
  geom_bar(stat = "identity") +
  labs(title = "Mean Daily Returns", x = "Symbol", y = "Mean Returns")

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.