# Load packages
library(tidyquant)
library(tidyverse)

# Import stock prices
stock_prices <- tq_get(c("WMT", "TGT", "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: 258 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     118.       0      
##  2 WMT    2020-01-03  118.  119.  118.  118. 5399200     117.      -0.00883
##  3 WMT    2020-01-06  117.  118.  117.  118. 6445500     117.      -0.00204
##  4 WMT    2020-01-07  117.  118.  116.  117. 6846900     116.      -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     115.      -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 248 more rows

Q1 filter select stock returns of March 31, 2020.

Hint: See the code in 1.2.2 Selecting observations.

filter(stock_returns, date == "2020-03-31")
## # 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    2020-03-31 114.  116.  113.  114.   8752800    114.        -0.0136
## 2 TGT    2020-03-31  95.3  96.8  92.2  93.0  6388400     93.0       -0.0317
## 3 MSFT   2020-03-31 159.  165.  157.  158.  77927200    158.        -0.0157

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

Hint: Answer the question by comparing daily returns of each stock on March 31, 2020.

WMT had the best preforming stocks on March 31,2020. they had the lowest amount of returns at -0.0136.

Q3 Plot the distribution of daily returns by stock using boxplots.

Hint: See the code in 4.3.3 Box plots. Add an appropriate title and labels for both axes.

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

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

Hint: Answer the question by comparing median and outliers of each stock.

Based on the boxplot MSFT was doing the best because their median for daily returns was the lowest and below 0. They also had the least amount of outliers meaning they have a lower amount of stocks that are returned. This shows that at that time they were doing better than TGT and WMT.

Q5 Calculate mean daily returns for each stock.

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

plotdata <- stock_returns %>%
  group_by(symbol) %>%
  summarize(mean_return = mean(daily.returns))
plotdata
## # A tibble: 3 x 2
##   symbol mean_return
##   <chr>        <dbl>
## 1 MSFT      0.00220 
## 2 TGT      -0.000778
## 3 WMT       0.00101

Q6 Plot mean daily returns using bar charts.

Hint: See the code in 4.3.1 Bar chart (on summary statistics). Add an appropriate title and labels for both axes.

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

labs(title = "Daily Returns", subtitle = "March 31 stock returns")
## $title
## [1] "Daily Returns"
## 
## $subtitle
## [1] "March 31 stock returns"
## 
## attr(,"class")
## [1] "labels"

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

Hint: Google search something like “ggplot2 multiple lines”.

ggplot(stock_prices, 
       aes(x = date, 
           y = close,
           color = symbol)) +
  geom_line()

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

Note: Insert a new code chunk below, copy and paste the code in Q7, and revise it using the dplyr::filter function. This is an extra credit question worth 10 points. However, the total number of points you could earn for this quiz is capped at 100 points. In other words, the extra credit can only offset any one question you missed in the first seven questions.

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.