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
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':
## 
##     as.Date, as.Date.numeric
## 
## Attaching package: 'PerformanceAnalytics'
## The following object is masked from 'package:graphics':
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## 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("AAPL", "MSFT"), 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: 72 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   2020-01-02  296.  301.  295.  300. 33870100     300.       0      
##  2 AAPL   2020-01-03  297.  301.  296.  297. 36580700     297.      -0.00972
##  3 AAPL   2020-01-06  294.  300.  293.  300. 29596800     299.       0.00797
##  4 AAPL   2020-01-07  300.  301.  297.  298. 27218000     298.      -0.00470
##  5 AAPL   2020-01-08  297.  304.  297.  303. 33019800     302.       0.0161 
##  6 AAPL   2020-01-09  307.  310.  306.  310. 42527100     309.       0.0212 
##  7 AAPL   2020-01-10  311.  313.  308.  310. 35161200     310.       0.00226
##  8 AAPL   2020-01-13  312.  317.  311.  317. 30383000     316.       0.0214 
##  9 AAPL   2020-01-14  317.  318.  312.  313. 40488600     312.      -0.0135 
## 10 AAPL   2020-01-15  312.  316.  310.  311. 30480900     311.      -0.00429
## # … with 62 more rows

Q1 Plot the distribution of daily returns by stock using kernel density plots.

ggplot(stock_returns, 
       aes(x = daily.returns, 
           fill = symbol)) +
  geom_density(alpha = 0.4) +
  labs(title = "Daily return distribution by stock")

Q2 Plot the distribution of daily returns by stock using boxplots.

ggplot(stock_returns, 
       aes(x = symbol, 
           y = daily.returns)) +
  geom_boxplot() +
  labs(title = "Daily return distribution by stock")

Q3 Based on the boxplot above, which of the two stocks would you invest in?

I would invest in Microsoft because apples height was higher and had a thicker tail, showing a bigger loss. The median was also higher in Microsoft.

Q4 Calculate mean daily returns for each stock.

library(dplyr)
plotdata <- stock_returns %>%
  group_by(symbol) %>%
  summarize(mean_return = mean(daily.returns))
plotdata
## # A tibble: 2 x 2
##   symbol mean_return
##   <chr>        <dbl>
## 1 AAPL     0.0000264
## 2 MSFT     0.00194

Q5 Plot mean daily returns using bar charts.

ggplot(plotdata, 
       aes(x = symbol, 
           y = mean_return)) +
  geom_bar(stat = "identity")

Q6 Add the color (cornflowerblue) to the bar chart.

ggplot(plotdata, 
       aes(x = symbol, 
           y = mean_return)) +
  geom_bar(stat = "identity", 
           fill = "cornflowerblue")

Q7 Label the bars with mean daily returns.

library(scales)
## 
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
## 
##     discard
## The following object is masked from 'package:readr':
## 
##     col_factor
ggplot(plotdata, 
       aes(x = symbol, 
           y = mean_return)) +
  geom_bar(stat = "identity", 
           fill = "cornflowerblue")+
  geom_text(aes(label = (mean_return)), 
            vjust = -0.25)

Q8 Hide the messages, 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.