In this exercise you will learn to plot data using the ggplot2 package. To answer the questions below, use 4.1 Categorical vs. Categorical from Data Visualization with R.

# Load packages
library(tidyquant)
library(tidyverse)
library(lubridate) #for year()

# Pick stocks
stocks <- c("AAPL", "MSFT", "IBM")

# Import stock prices
stock_prices <- stocks %>%
    tq_get(get  = "stock.prices",
           from = "1990-01-01",
           to   = "2019-05-31") %>%
    group_by(symbol)
stock_prices
## # A tibble: 22,230 x 8
## # Groups:   symbol [3]
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 AAPL   1990-01-02  1.26  1.34  1.25  1.33 45799600    1.08 
##  2 AAPL   1990-01-03  1.36  1.36  1.34  1.34 51998800    1.09 
##  3 AAPL   1990-01-04  1.37  1.38  1.33  1.34 55378400    1.10 
##  4 AAPL   1990-01-05  1.35  1.37  1.32  1.35 30828000    1.10 
##  5 AAPL   1990-01-08  1.34  1.36  1.32  1.36 25393200    1.11 
##  6 AAPL   1990-01-09  1.36  1.36  1.32  1.34 21534800    1.10 
##  7 AAPL   1990-01-10  1.34  1.34  1.28  1.29 49929600    1.05 
##  8 AAPL   1990-01-11  1.29  1.29  1.23  1.23 52763200    1.00 
##  9 AAPL   1990-01-12  1.22  1.24  1.21  1.23 42974400    1.00 
## 10 AAPL   1990-01-15  1.23  1.28  1.22  1.22 40434800    0.997
## # … with 22,220 more rows
# Process stock_prices and save it under stock_returns
stock_returns <-
  stock_prices %>%
  # Calculate yearly returns
  tq_transmute(select = adjusted, mutate_fun = periodReturn, period = "yearly") %>%
  # create a new variable, year
  mutate(year = year(date)) %>%
  # drop date 
  select(-date)
stock_returns
## # A tibble: 90 x 3
## # Groups:   symbol [3]
##    symbol yearly.returns  year
##    <chr>           <dbl> <dbl>
##  1 AAPL           0.169   1990
##  2 AAPL           0.323   1991
##  3 AAPL           0.0691  1992
##  4 AAPL          -0.504   1993
##  5 AAPL           0.352   1994
##  6 AAPL          -0.173   1995
##  7 AAPL          -0.345   1996
##  8 AAPL          -0.371   1997
##  9 AAPL           2.12    1998
## 10 AAPL           1.51    1999
## # … with 80 more rows

Q1 Calculate mean yearly 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(yearly.returns))
plotdata
## # A tibble: 3 x 2
##   symbol mean_returns
##   <chr>         <dbl>
## 1 AAPL          0.366
## 2 IBM           0.141
## 3 MSFT          0.283

Q2 Plot mean yearly 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 Label the bars with mean yearly returns.

Hint: See the code in 4.3.1 Bar chart (on summary statistics).

library(scales)
ggplot(plotdata, 
       aes(x = symbol, 
           y = mean_returns)) +
  geom_bar(stat = "identity") + 
  geom_text(aes(label = percent(mean_returns)), 
            vjust = -0.25)

Q4 Plot the distribution of yearly returns by stock using kernel density plots.

Hint: See the code in 4.3.2 Grouped kernel density plots.

ggplot(stock_returns, 
       aes(x = yearly.returns, 
           fill = symbol)) +
  geom_density(alpha = 0.4) +
  labs(title = "Distribution of Yearly Returns by Stock")

Q5 Which of the three stocks has highest chance of losing big when things go wrong? Discuss your reason.

Hint: Google how to interpret density plots.

IBM would have the highest chance of losing big when things go wrong because they have the steepest decline and shortest range of variation.

Q6 Plot the distribution of yearly returns by stock using boxplots.

Hint: See the code in 4.3.3 Box plots.

ggplot(stock_returns, 
       aes(x = symbol, 
           y = yearly.returns)) +
  geom_boxplot() +
  labs(title = "Distribution of Yearly Returns by Stock")

Q7 If you were a risk-loving investor (defined as one chasing after the greatest returns even at the risk of losing big), which of the two stocks would you choose? Discuss your reason.

I would choose Apple because they have the greatest upside of return by far especially with some outliers much higher than anything else, even though they also have the biggest downside it could be worth the risk.

Q8 Hide the messages, but display the code and their results from the webpage.

Hint: Use message, echo and results in the global 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.