Chapter 4 Bivariate Graphs
Rebecca Stockley
9/24/2019
- Q1 Calculate mean daily returns for each stock.
- Q2 Plot mean daily returns using bar charts.
- Q3 Rename the stocks as Apple and Microsoft.
- Q4 Add the color (cornflowerblue) to the bar chart.
- Q5 Label the bars with mean daily returns.
- Q6 Add labels to the axes.
- Q7 Plot the distribution of daily returns by stock using kernel density plots.
- Q8 Plot the distribution of daily returns by stock using boxplots.
- Q8 Hide the messages and the code, but display results of the code from the webpage.
- Q9 Display the title and your name correctly at the top of the webpage.
- Q10 Use the correct slug.
In this exercise you will learn to plot data using the ggplot2 package. To answer the questions below, use Chapter 4.3 Categorical vs. Quantitative Data Visualization with R.
# Load packages
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# Pick stocks
stocks <- c("AAPL", "MSFT")
# Import stock prices
stock_prices <- stocks %>%
tq_get(get = "stock.prices",
from = "2019-01-01",
to = "2019-05-31") %>%
group_by(symbol)
stock_prices## # A tibble: 206 x 8
## # Groups: symbol [2]
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL 2019-01-02 155. 159. 154. 158. 37039700 156.
## 2 AAPL 2019-01-03 144. 146. 142 142. 91312200 141.
## 3 AAPL 2019-01-04 145. 149. 144. 148. 58607100 147.
## 4 AAPL 2019-01-07 149. 149. 146. 148. 54777800 146.
## 5 AAPL 2019-01-08 150. 152. 149. 151. 41025300 149.
## 6 AAPL 2019-01-09 151. 155. 150. 153. 45099100 151.
## 7 AAPL 2019-01-10 152. 154. 151. 154. 35780700 152.
## 8 AAPL 2019-01-11 153. 154. 152. 152. 27023200 150.
## 9 AAPL 2019-01-14 151. 151. 149. 150 32439200 148.
## 10 AAPL 2019-01-15 150. 153. 150. 153. 28710900 151.
## # … with 196 more rows# Calculate daily returns
stock_returns <-
stock_prices %>%
tq_mutate(select = adjusted, mutate_fun = periodReturn, period = "daily")
stock_returns## # A tibble: 206 x 9
## # Groups: symbol [2]
## symbol date open high low close volume adjusted daily.returns
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL 2019-01-02 155. 159. 154. 158. 3.70e7 156. 0
## 2 AAPL 2019-01-03 144. 146. 142 142. 9.13e7 141. -0.0996
## 3 AAPL 2019-01-04 145. 149. 144. 148. 5.86e7 147. 0.0427
## 4 AAPL 2019-01-07 149. 149. 146. 148. 5.48e7 146. -0.00223
## 5 AAPL 2019-01-08 150. 152. 149. 151. 4.10e7 149. 0.0191
## 6 AAPL 2019-01-09 151. 155. 150. 153. 4.51e7 151. 0.0170
## 7 AAPL 2019-01-10 152. 154. 151. 154. 3.58e7 152. 0.00320
## 8 AAPL 2019-01-11 153. 154. 152. 152. 2.70e7 150. -0.00982
## 9 AAPL 2019-01-14 151. 151. 149. 150 3.24e7 148. -0.0150
## 10 AAPL 2019-01-15 150. 153. 150. 153. 2.87e7 151. 0.0205
## # … with 196 more rowsQ1 Calculate mean daily returns for each stock.
Hint: See the code in 4.3.1 Bar chart (on summary statistics).
library(dplyr)
plotdata <- stock_returns %>%
group_by(symbol) %>%
summarize(mean_returns = mean(daily.returns))
plotdata## # A tibble: 2 x 2
## symbol mean_returns
## <chr> <dbl>
## 1 AAPL 0.00144
## 2 MSFT 0.00228Q2 Plot mean daily returns using bar charts.
Hint: See the code in 4.3.1 Bar chart (on summary statistics).
ggplot(plotdata,
aes(x = symbol,
y = mean_returns)) +
geom_bar(stat = "identity")
Q3 Rename the stocks as Apple and Microsoft.
Hint: See the code in 4.3.1 Bar chart (on summary statistics).
ggplot(plotdata,
aes(x = factor(symbol,
labels = c("Apple",
"Microsoft")),
y = mean_returns)) +
geom_bar(stat = "identity")
Q4 Add the color (cornflowerblue) to the bar chart.
Hint: See the code in 4.3.1 Bar chart (on summary statistics).
ggplot(plotdata,
aes(x = factor(symbol,
labels = c("Apple",
"Microsoft")),
y = mean_returns)) +
geom_bar(stat = "identity") + geom_bar(stat = "identity",
fill = "cornflowerblue")
Q5 Label the bars with mean daily returns.
Hint: See the code in 4.3.1 Bar chart (on summary statistics).
library(scales)##
## Attaching package: 'scales'## The following object is masked from 'package:purrr':
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## discard## The following object is masked from 'package:readr':
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## col_factorggplot(plotdata,
aes(x = factor(symbol,
labels = c("Apple",
"Microsoft")),
y = mean_returns)) +
geom_bar(stat = "identity") + geom_bar(stat = "identity",
fill = "cornflowerblue") +
geom_text(aes(label = percent(mean_returns)),
vjust = -0.25)
Q6 Add labels to the axes.
Hint: See the code in 4.3.1 Bar chart (on summary statistics).
library(scales)
ggplot(plotdata,
aes(x = factor(symbol,
labels = c("Apple",
"Microsoft")),
y = mean_returns)) +
geom_bar(stat = "identity") + geom_bar(stat = "identity",
fill = "cornflowerblue") +
geom_text(aes(label = percent(mean_returns)),
vjust = -0.25) + labs(title = "Mean Daily Returns",
x = "Stocks",
y = "Mean Daily Returns")
Q7 Plot the distribution of daily returns by stock using kernel density plots.
Hint: See the code in 4.3.2 Grouped kernel density plots.
ggplot(stock_returns,
aes(x = daily.returns,
fill = symbol)) +
geom_density(alpha = 0.4) +
labs(title = "Salary distribution by rank")
Q8 Plot the distribution of daily returns by stock using boxplots.
Hint: See the code in 4.3.3 Box plots.
ggplot(stock_returns,
aes(x = symbol,
y = daily.returns)) +
geom_boxplot() +
labs(title = "Salary distribution by rank")
Q8 Hide the messages and the code, but display results of the code from the webpage.
Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.