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)
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: 114 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 104 more rows

Q1 filter Select stock returns of January 31, 2020.

library(dplyr)

filter(stock_returns, date == "2020-01-31")
## # 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    2020-01-31  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

Hint: See the code in 1.2.2 Selecting observations.

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

Amazon was the only company to go positive so they performed the best on January 31st 2020. Hint: Answer the question by comparing daily returns of each stock on January 31, 2020.

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

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

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

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

The stock that performed the best this year was Amazon.

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

Q5 Calculate mean daily returns for each stock.

library(dplyr)
plotdata <- stock_returns %>%
  group_by(symbol) %>%
  summarize(mean_return = mean(daily.returns))
plotdata
## # A tibble: 3 x 2
##   symbol mean_return
##   <chr>        <dbl>
## 1 AMZN       0.00126
## 2 TGT       -0.00315
## 3 WMT       -0.00113

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

Q6 Plot mean daily returns using bar charts.

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

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

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

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

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

Q7.a Remove Amazon from the line plot you created in Q7.

# Import stock prices
stock_prices <- tq_get(c("WMT", "TGT"), get  = "stock.prices", from = "2020-01-01")

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

Note: 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.