# Load packages
library(tidyquant)
library(tidyverse)

# Import stock prices
stock_prices <- tq_get(c("WMT", "TGT", "AMZN"), 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: 126 x 9
## # Groups:   symbol [3]
##    symbol date        open  high   low close  volume adjusted daily.returns
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>   <dbl>    <dbl>         <dbl>
##  1 WMT    2020-01-02  119.  120.  119.  119. 6764900     119.       0      
##  2 WMT    2020-01-03  118.  119.  118.  118. 5399200     118.      -0.00883
##  3 WMT    2020-01-06  117.  118.  117.  118. 6445500     118.      -0.00204
##  4 WMT    2020-01-07  117.  118.  116.  117. 6846900     117.      -0.00926
##  5 WMT    2020-01-08  116.  117.  116.  116. 5875800     116.      -0.00343
##  6 WMT    2020-01-09  116.  117.  116.  117. 5563700     117.       0.0103 
##  7 WMT    2020-01-10  117.  117.  116.  116. 6054800     116.      -0.00835
##  8 WMT    2020-01-13  116.  117.  115.  116. 6112600     116.      -0.00430
##  9 WMT    2020-01-14  115.  116.  115.  116. 6585800     116.       0.00259
## 10 WMT    2020-01-15  115.  116.  115.  115. 7454200     115.      -0.00775
## # … with 116 more rows

Q1 filter Select stock returns of January 31, 2020.

filtered_stock <- filter(stock_returns, 
                  date == "2020-01-31")
filtered_stock
## # A tibble: 3 x 9
## # Groups:   symbol [3]
##   symbol date        open  high   low close   volume adjusted daily.returns
##   <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>         <dbl>
## 1 WMT    2020-01-31  116.  116.  114.  114.  7775800     114.       -0.0179
## 2 TGT    2020-01-31  113.  114.  110.  111.  6961900     110.       -0.0343
## 3 AMZN   2020-01-31 2051. 2056. 2002. 2009. 15567300    2009.        0.0738

Q2 Which of the three stocks performed best on January 31, 2020?

Amazon stock perfomed the best because it was the only positive return.

Q3 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 By Stock",
       y="Daily Returns",
       x="Stock Tickers")

Q4 Based on the boxplot above, which of the three stocks performed best this year?

Amazon’s median is the closest out of the three stocks to zero, the other two fall further below. Along with this, it’s outliers are closer to it’s main set of data than the other two.

Q5 Calculate mean daily returns for each stock.

mean_returndata <- stock_returns %>%
  group_by(symbol) %>%
  summarize(mean_return = mean(daily.returns))
mean_returndata
## # A tibble: 3 x 2
##   symbol mean_return
##   <chr>        <dbl>
## 1 AMZN      0.000326
## 2 TGT      -0.00382 
## 3 WMT      -0.00111

Q6 Plot mean daily returns using bar charts.

ggplot(mean_returndata, 
       aes(x = symbol, 
           y = mean_return)) +
  geom_bar(stat = "identity") +
  labs(title = "Mean Daily Returns of Stocks",
       x = "Stock Tickers",
       y = "Mean Daily Return")

Q7 Create the line plot of stock prices for all three stocks in one graph.

library(ggplot2)
ggplot(stock_returns, 
       aes(x = date, y = daily.returns, 
           group = symbol, color = symbol)) +
  geom_line()

Q7.a filter Create the same line plot as in Q7, but without Amazon.

newstocks <- filter(stock_returns,
                    symbol != "AMZN")
newstocks
## # A tibble: 84 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 WMT    2020-01-02  119.  120.  119.  119. 6764900     119.       0      
##  2 WMT    2020-01-03  118.  119.  118.  118. 5399200     118.      -0.00883
##  3 WMT    2020-01-06  117.  118.  117.  118. 6445500     118.      -0.00204
##  4 WMT    2020-01-07  117.  118.  116.  117. 6846900     117.      -0.00926
##  5 WMT    2020-01-08  116.  117.  116.  116. 5875800     116.      -0.00343
##  6 WMT    2020-01-09  116.  117.  116.  117. 5563700     117.       0.0103 
##  7 WMT    2020-01-10  117.  117.  116.  116. 6054800     116.      -0.00835
##  8 WMT    2020-01-13  116.  117.  115.  116. 6112600     116.      -0.00430
##  9 WMT    2020-01-14  115.  116.  115.  116. 6585800     116.       0.00259
## 10 WMT    2020-01-15  115.  116.  115.  115. 7454200     115.      -0.00775
## # … with 74 more rows
library(ggplot2)
ggplot(newstocks, 
       aes(x = date, y = daily.returns, 
           group = symbol, color = symbol)) +
  geom_line()

Q8 Hide the messages, but display 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.