In this exercise, you will learn to create line plot using the ggplot2 package. To answer the questions below, use Chapter 4.2 Quantitative vs. Quantitative Data Visualization with R.

# Load packages
library(tidyquant)
library(tidyverse)

# Import stock prices
stock_prices <- tq_get(c("AAPL", "MSFT"), get  = "stock.prices", from = "2020-01-01")
stock_prices
## # A tibble: 582 x 8
##    symbol date        open  high   low close    volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
##  1 AAPL   2020-01-02  74.1  75.2  73.8  75.1 135480400     74.3
##  2 AAPL   2020-01-03  74.3  75.1  74.1  74.4 146322800     73.6
##  3 AAPL   2020-01-06  73.4  75.0  73.2  74.9 118387200     74.2
##  4 AAPL   2020-01-07  75.0  75.2  74.4  74.6 108872000     73.8
##  5 AAPL   2020-01-08  74.3  76.1  74.3  75.8 132079200     75.0
##  6 AAPL   2020-01-09  76.8  77.6  76.6  77.4 170108400     76.6
##  7 AAPL   2020-01-10  77.7  78.2  77.1  77.6 140644800     76.8
##  8 AAPL   2020-01-13  77.9  79.3  77.8  79.2 121532000     78.4
##  9 AAPL   2020-01-14  79.2  79.4  78.0  78.2 161954400     77.4
## 10 AAPL   2020-01-15  78.0  78.9  77.4  77.8 121923600     77.1
## # … with 572 more rows

Q1 Select Apple stock prices and save it under plotdata.

Hint: See the code in 4.2.2 Line plot.

plotdata <- filter(stock_prices, symbol == "AAPL")
plotdata
## # A tibble: 291 x 8
##    symbol date        open  high   low close    volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
##  1 AAPL   2020-01-02  74.1  75.2  73.8  75.1 135480400     74.3
##  2 AAPL   2020-01-03  74.3  75.1  74.1  74.4 146322800     73.6
##  3 AAPL   2020-01-06  73.4  75.0  73.2  74.9 118387200     74.2
##  4 AAPL   2020-01-07  75.0  75.2  74.4  74.6 108872000     73.8
##  5 AAPL   2020-01-08  74.3  76.1  74.3  75.8 132079200     75.0
##  6 AAPL   2020-01-09  76.8  77.6  76.6  77.4 170108400     76.6
##  7 AAPL   2020-01-10  77.7  78.2  77.1  77.6 140644800     76.8
##  8 AAPL   2020-01-13  77.9  79.3  77.8  79.2 121532000     78.4
##  9 AAPL   2020-01-14  79.2  79.4  78.0  78.2 161954400     77.4
## 10 AAPL   2020-01-15  78.0  78.9  77.4  77.8 121923600     77.1
## # … with 281 more rows

Q2 Create a simple line plot with date on the x-axis and closing price on the y-axis.

Hint: See the code in 4.2.2 Line plot.

ggplot(plotdata, 
       aes(x = date, 
           y = close)) +
  geom_line()

Q3 Add the color (cornflowerblue) to the line plot.

Hint: See the code in 4.2.2 Line plot.

ggplot(plotdata, 
       aes(x = date, 
           y = close)) +
  geom_line(color = "cornflowerblue")

Q4 Make the line thicker in the line plot.

Hint: See the code in 4.2.2 Line plot.

ggplot(plotdata, 
       aes(x = date, 
           y = close)) +
  geom_line(color = "cornflowerblue", size = 2)

Q5 Label the y-axis as “Closing Price”.

Hint: See the code in 4.2.2 Line plot.

ggplot(plotdata, 
       aes(x = date, 
           y = close)) +
  geom_line(color = "cornflowerblue", size = 2) +
  labs(y = "Closing Price")

Q6 Remove the label of the x-axis.

Hint: See the code in 4.2.2 Line plot.

ggplot(plotdata, 
       aes(x = date, 
           y = close)) +
  geom_line(color = "cornflowerblue", size = 2) +
  labs(y = "Closing Price", x = "")

Q7 Create the line plot for both Apple and Microsoft.

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

ggplot(stock_prices, 
       aes(x = date, 
           y = close, color = symbol)) +
  geom_line(size = 2) +
  labs(y = "Closing Price", x = "")

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.