Quiz 2
S. Aidan Brooks
3/04/2020
- Q1
filterSelect stock returns of January 31, 2020. - Q2 Which of the three stocks performed best on January 31, 2020?
- Q3 Plot the distribution of daily returns by stock using boxplots.
- Q4 Based on the boxplot above, which of the three stocks performed best this year?
- Q5 Calculate mean daily returns for each stock.
- Q6 Plot mean daily returns using bar charts.
- Q7 Create the line plot of stock prices for all three stocks in one graph.
- Q7.a
filterCreate the same line plot as in Q7, but without Amazon. - Q8 Hide the messages, 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.
# Load packages
library(tidyquant)## Loading required package: lubridate##
## Attaching package: 'lubridate'## The following object is masked from 'package:base':
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## date## 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 ──## ✓ ggplot2 3.2.1 ✓ purrr 0.3.3
## ✓ tibble 2.1.3 ✓ dplyr 0.8.4
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.4.0## ── 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("WMT", "TGT", "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: 126 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 WMT 2020-01-02 119. 120. 119. 119. 6764900 119. 0
## 2 WMT 2020-01-03 118. 119. 118. 118. 5399200 118. -0.00883
## 3 WMT 2020-01-06 117. 118. 117. 118. 6445500 118. -0.00204
## 4 WMT 2020-01-07 117. 118. 116. 117. 6846900 117. -0.00926
## 5 WMT 2020-01-08 116. 117. 116. 116. 5875800 116. -0.00343
## 6 WMT 2020-01-09 116. 117. 116. 117. 5563700 117. 0.0103
## 7 WMT 2020-01-10 117. 117. 116. 116. 6054800 116. -0.00835
## 8 WMT 2020-01-13 116. 117. 115. 116. 6112600 116. -0.00430
## 9 WMT 2020-01-14 115. 116. 115. 116. 6585800 116. 0.00259
## 10 WMT 2020-01-15 115. 116. 115. 115. 7454200 115. -0.00775
## # … with 116 more rowsQ1 filter Select stock returns of January 31, 2020.
Hint: See the code in 1.2.2 Selecting observations.
# Load packages
library(tidyquant)
library(tidyverse)
# Import stock prices
stock_prices <- tq_get(c("WMT", "TGT", "AMZN"), get = "stock.prices")
# Calculate daily returns
stock_returns <-
stock_prices %>%
group_by(symbol) %>%
tq_mutate(select = adjusted, mutate_fun = periodReturn, period = "daily")
stock_returns.2020.01.31 <- filter(stock_returns,
date == "2020-01-31")
stock_returns.2020.01.31## # A tibble: 3 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 WMT 2020-01-31 116. 116. 114. 114. 7775800 114. -0.0179
## 2 TGT 2020-01-31 113. 114. 110. 111. 6961900 110. -0.0343
## 3 AMZN 2020-01-31 2051. 2056. 2002. 2009. 15567300 2009. 0.0738Q2 Which of the three stocks performed best on January 31, 2020?
Hint: Answer the question by comparing daily returns of each stock on January 31, 2020.
Amazon did the best with a .0738 increase.
Q3 Plot the distribution of daily returns by stock using boxplots.
Hint: See the code in 4.3.3 Box plots. Add an appropriate title and labels for both axes.
ggplot(data = stock_returns, mapping = aes(x = symbol, y = daily.returns)) +
geom_boxplot()
Q4 Based on the boxplot above, which of the three stocks performed best this year?
Hint: Answer the question by comparing median and outliers of each stock.
Amazon performed the best.
Q5 Calculate mean daily returns for each stock.
Hint: See the code in 4.3.1 Bar chart (on summary statistics).
# Load packages
library(tidyquant)
library(tidyverse)
# Import stock prices
stock_prices <- tq_get(c("WMT", "TGT", "AMZN"), get = "stock.prices")
# Calculate mean daily returns
mean_stock_returns <-
stock_prices %>%
group_by(symbol) %>%
tq_mutate(select = adjusted, mutate_fun = periodReturn, period = "daily") Q6 Plot mean daily returns using bar charts.
Hint: See the code in 4.3.1 Bar chart (on summary statistics). Add an appropriate title and labels for both axes.
ggplot(data = mean_stock_returns) +
geom_bar(mapping = aes(x = daily.returns, fill = symbol), position = "dodge")
Q7 Create the line plot of stock prices for all three stocks in one graph.
Hint: Google search something like “ggplot2 multiple lines”.
# Load packages
library(tidyquant)
library(tidyverse)
# Import stock prices
stock_prices <- tq_get(c("WMT", "TGT", "AMZN"), get = "stock.prices")
stock_prices## # A tibble: 7,674 x 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WMT 2010-01-04 53.7 54.7 53.7 54.2 20753100 42.3
## 2 WMT 2010-01-05 54.1 54.2 53.6 53.7 15648400 41.9
## 3 WMT 2010-01-06 53.5 53.8 53.4 53.6 12517200 41.8
## 4 WMT 2010-01-07 53.7 53.8 53.3 53.6 10662700 41.8
## 5 WMT 2010-01-08 53.4 53.5 53.0 53.3 11363200 41.6
## 6 WMT 2010-01-11 53.3 54.4 53.1 54.2 13987700 42.3
## 7 WMT 2010-01-12 54 54.8 53.9 54.7 15117000 42.7
## 8 WMT 2010-01-13 54.8 55.2 54.4 55.0 13290700 42.9
## 9 WMT 2010-01-14 54.7 54.8 54.2 54.2 13772000 42.3
## 10 WMT 2010-01-15 54.3 54.5 53.6 53.7 19087500 41.9
## # … with 7,664 more rowsQ7.a filter Create the same line plot as in Q7, but without Amazon.
Note: Insert a new code chunk below, copy and paste the code in Q7, and revise it using the dplyr::filter function. This is an extra credit question worth 10 points. However, the total number of points you could earn for this quiz is capped at 100 points. In other words, the extra credit can only offset any one question you missed in the first seven questions.
Q8 Hide the messages, but display the code and its results on the webpage.
Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.