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':
## 
##     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: 74 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 64 more rows

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

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

## Q3 Based on the boxplot above, which of the two stocks would you invest in? I would invest in Microsoft because the median is higher then Apples.

Q4 Calculate mean daily returns for each stock.

library(dplyr)
plotdata <- stock_returns %>%
  group_by(symbol) %>%
  summarize(mean_return = mean(daily.returns))

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(label = "mean returns")

Q8 Hide the messages, the code, and its results on the webpage.

Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.

Q9 Display the title and your name correctly at the top of the webpage.

Q10 Use the correct slug.