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: 555 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 545 more rows

Q1 Interpret Row 2 of stock_returns.

The 2nd row represents Apple’s stock returns for January 3rd, 2020.They opened at 74.3 and closed at 74.4 with a high of 75.1 and a low of 74.1. They had a volume of 146322800, an adjusted price of 73.8, and the daily return was -0.00972.

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

Microsoft’s closing price was 203.90 on July 30, 2020.

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

library(dplyr)
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.

ggplot(plotdata, 
       aes(x = date, 
           y = close)) +
  geom_line()

Q5 Describe the performance of Microsoft stock this year.

Microsoft had a huge dip starting in early February before gradually increasing again in April.

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

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

Q7 Which of the stocks would you expect the highest daily return? Plot mean daily returns using bar chart.

ggplot(plotdata, 
       aes(x = symbol, 
           y = mean_return)) +
  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.