Quiz 2
Luke Wieland
3/3/2021
- Q1 Interpret Row 2 of stock_returns.
- Q2 How much was the highest price per share, at which Apple was traded on January 28, 2021?
- Q3
filterSelect Apple stock prices and save it under plotdata. - Q4 Create a simple line plot with date on the x-axis and opening price on the y-axis.
- Q5 Describe the performance of Apple stock this year.
- Q6 Calculate mean daily returns for each stock and save it under plotdata.
- 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.
- Q8 Hide the messages and warnings, but display the code and its results on the webpage.
- Q9 Display the title and your name correctly at the top of the webpage.
- Q10 Use the correct slug.
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")
stock_prices## # A tibble: 120 x 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL 2021-01-04 134. 134. 127. 129. 143301900 129.
## 2 AAPL 2021-01-05 129. 132. 128. 131. 97664900 131.
## 3 AAPL 2021-01-06 128. 131. 126. 127. 155088000 126.
## 4 AAPL 2021-01-07 128. 132. 128. 131. 109578200 131.
## 5 AAPL 2021-01-08 132. 133. 130. 132. 105158200 132.
## 6 AAPL 2021-01-11 129. 130. 128. 129. 100620900 129.
## 7 AAPL 2021-01-12 128. 130. 127. 129. 91951100 129.
## 8 AAPL 2021-01-13 129. 131. 128. 131. 88636800 131.
## 9 AAPL 2021-01-14 131. 131 129. 129. 90221800 129.
## 10 AAPL 2021-01-15 129. 130. 127 127. 111598500 127.
## # ... with 110 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: 120 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 110 more rowsQ1 Interpret Row 2 of stock_returns.
Hint: In your interpretation, make sure to use all variables.
Row two of stock_returns includes: symbol, date, open, high, low, close, volume, and adjusted.
Q3 filter Select Apple stock prices and save it under plotdata.
Hint: See the code in 4.2.2 Line plot.
plotdata <- filter(stock_prices,
symbol == "AAPL")
plotdata## # A tibble: 40 x 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL 2021-01-04 134. 134. 127. 129. 143301900 129.
## 2 AAPL 2021-01-05 129. 132. 128. 131. 97664900 131.
## 3 AAPL 2021-01-06 128. 131. 126. 127. 155088000 126.
## 4 AAPL 2021-01-07 128. 132. 128. 131. 109578200 131.
## 5 AAPL 2021-01-08 132. 133. 130. 132. 105158200 132.
## 6 AAPL 2021-01-11 129. 130. 128. 129. 100620900 129.
## 7 AAPL 2021-01-12 128. 130. 127. 129. 91951100 129.
## 8 AAPL 2021-01-13 129. 131. 128. 131. 88636800 131.
## 9 AAPL 2021-01-14 131. 131 129. 129. 90221800 129.
## 10 AAPL 2021-01-15 129. 130. 127 127. 111598500 127.
## # ... with 30 more rowsQ4 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.
This year, Apple stock began with a sell off. After some volatility, it surged to its peak toward the middle to end of January. It decreased into the beginning of February, then started to recover. In the middle of February, the stock had its biggest sell off taking it to the lowest level this year. At the beginning of March, Apple began to increase again, but is still at a lower level than the start of the year.
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) %>%
summarize(mean_daily.returns = mean(daily.returns))
plotdata## # A tibble: 3 x 2
## symbol mean_daily.returns
## * <chr> <dbl>
## 1 AAPL -0.000592
## 2 AMZN -0.000600
## 3 MSFT 0.00197Q7 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 expect the highest daily return from MFST as it has the highest average daily return.
ggplot(plotdata,
aes(x = symbol,
y = mean_daily.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.