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.

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

plotdata
## # A tibble: 3 x 2
##   symbol mean_yearly.returns
##   <chr>                <dbl>
## 1 AAPL                 0.366
## 2 IBM                  0.116
## 3 MSFT                 0.283

Q2 Plot mean yearly returns using bar charts.

ggplot(plotdata, 
       aes(x = symbol, 
           y = mean_yearly.returns)) +
  geom_bar(stat = "identity")

Q3 Label the bars with mean yearly returns.

library(scales)
ggplot(plotdata, 
       aes(x = factor(symbol,
                      labels = c("AAPL",
                                 "IBM",
                                 "MSFT")), 
                      y = mean_yearly.returns)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = percent(mean_yearly.returns)), 
            vjust = -0.25)

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

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

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

The stock that has the highest chance of losing big when things go wrong is Apple becuse they are the only stock that hasn’t crashed yet. Microsoft and IBM hit their peak and crashed right after.

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

ggplot(stock_returns, 
       aes(x = symbol, 
           y = yearly.returns)) +
  geom_boxplot() +
  labs(title = "Yearly returns distribution 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 three stocks would you choose? Discuss your reason.

If I had to choose between the three stocks, I would choose Apple because although after it hit its peak it dropped, it didn’t crash like Microsoft and IBM did. Apple may be low in stocks but I believe they will rise back up. IBM and Microsoft are a scary investment because as shown on the density graph, they reached their all time high and flat-lined right after.

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

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

Q10 Use the correct slug.