Quiz 2
Noah Albion
9/23/2020
- Q1 Interpret Row 2 of stock_returns.
- Q2 How much was Microsoft per share at closing on July 30, 2020?
- Q3
filterSelect Microsoft stock prices and save it under plotdata. - Q4 Create a simple line plot with date on the x-axis and closing price on the y-axis.
- Q5 Describe the performance of Microsoft stock this year.
- Q6 Calculate mean daily returns for each stock and save it under plotdata.
- Q7 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)## Loading required package: lubridate##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
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## date, intersect, setdiff, union## Loading required package: PerformanceAnalytics## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
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## as.Date, as.Date.numeric##
## Attaching package: 'PerformanceAnalytics'## The following object is masked from 'package:graphics':
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## legend## Loading required package: quantmod## Loading required package: TTR## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo## Version 0.4-0 included new data defaults. See ?getSymbols.## == Need to Learn tidyquant? =============================================
## Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
## </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>library(tidyverse)## -- Attaching packages -------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.3 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## -- Conflicts ----------------------------------- tidyverse_conflicts() --
## x lubridate::as.difftime() masks base::as.difftime()
## x lubridate::date() masks base::date()
## x dplyr::filter() masks stats::filter()
## x dplyr::first() masks xts::first()
## x lubridate::intersect() masks base::intersect()
## x dplyr::lag() masks stats::lag()
## x dplyr::last() masks xts::last()
## x lubridate::setdiff() masks base::setdiff()
## x lubridate::union() masks base::union()# 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: 564 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 554 more rowsQ1 Interpret Row 2 of stock_returns.
Hint: In your interpretation, make sure to use all variables.
Row 2 of stock_returns describes Apple stock returns on march 1st of 2020.
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 == "MSFT")
plotdata## # A tibble: 188 x 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 MSFT 2020-01-02 159. 161. 158. 161. 22622100 159.
## 2 MSFT 2020-01-03 158. 160. 158. 159. 21116200 157.
## 3 MSFT 2020-01-06 157. 159. 157. 159. 20813700 158.
## 4 MSFT 2020-01-07 159. 160. 157. 158. 21634100 156.
## 5 MSFT 2020-01-08 159. 161. 158. 160. 27746500 159.
## 6 MSFT 2020-01-09 162. 162. 161. 162. 21385000 161.
## 7 MSFT 2020-01-10 163. 163. 161. 161. 20725900 160.
## 8 MSFT 2020-01-13 162. 163. 161. 163. 21626500 162.
## 9 MSFT 2020-01-14 163. 164. 162. 162. 23477400 161.
## 10 MSFT 2020-01-15 163. 164. 163. 163. 21417900 162.
## # ... with 178 more rowsQ4 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 did relatively well, Fluctuated a little bit but still had an overall increase in shareholder equity.
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 0.00276
## 2 AMZN 0.00301
## 3 MSFT 0.00187Q7 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.