Use the given code below to answer the questions.

## Load package
library(tidyverse) # for cleaning, plotting, etc
library(tidyquant) # for financial analysis

## Import data
stocks <- tq_get("AAPL", get = "stock.prices", from = "2016-01-01")
stocks
## # A tibble: 1,025 x 7
##    date        open  high   low close   volume adjusted
##    <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 2016-01-04 103.  105.  102   105.  67649400     98.4
##  2 2016-01-05 106.  106.  102.  103.  55791000     96.0
##  3 2016-01-06 101.  102.   99.9 101.  68457400     94.1
##  4 2016-01-07  98.7 100.   96.4  96.4 81094400     90.1
##  5 2016-01-08  98.6  99.1  96.8  97.0 70798000     90.6
##  6 2016-01-11  99.0  99.1  97.3  98.5 49739400     92.1
##  7 2016-01-12 101.  101.   98.8 100.  49154200     93.4
##  8 2016-01-13 100.  101.   97.3  97.4 62439600     91.0
##  9 2016-01-14  98.0 100.   95.7  99.5 63170100     93.0
## 10 2016-01-15  96.2  97.7  95.4  97.1 79833900     90.8
## # … with 1,015 more rows
## Examine data
glimpse(stocks)
## Observations: 1,025
## Variables: 7
## $ date     <date> 2016-01-04, 2016-01-05, 2016-01-06, 2016-01-07, 2016-01-08,…
## $ open     <dbl> 102.61, 105.75, 100.56, 98.68, 98.55, 98.97, 100.55, 100.32,…
## $ high     <dbl> 105.37, 105.85, 102.37, 100.13, 99.11, 99.06, 100.69, 101.19…
## $ low      <dbl> 102.00, 102.41, 99.87, 96.43, 96.76, 97.34, 98.84, 97.30, 95…
## $ close    <dbl> 105.35, 102.71, 100.70, 96.45, 96.96, 98.53, 99.96, 97.39, 9…
## $ volume   <dbl> 67649400, 55791000, 68457400, 81094400, 70798000, 49739400, …
## $ adjusted <dbl> 98.44666, 95.97968, 94.10139, 90.12987, 90.60644, 92.07356, …
## Visualize
stocks %>%
  ggplot(aes(x = date, y = close)) +
  geom_line()

Q1 Get Amazon stock prices, instead of Apple.

Hint: Insert a new code chunk below and type in the code, using the tq_get() function above. Replace the ticker symbol for Microsoft. You may find the ticker symbol for Microsoft from Yahoo Finance.

## Load package
library(tidyverse) # for cleaning, plotting, etc
library(tidyquant) # for financial analysis

stocks <- tq_get("AMZN", get = "stock.prices", from = "2016-01-01")
stocks
## # A tibble: 1,025 x 7
##    date        open  high   low close  volume adjusted
##    <date>     <dbl> <dbl> <dbl> <dbl>   <dbl>    <dbl>
##  1 2016-01-04  656.  658.  628.  637. 9314500     637.
##  2 2016-01-05  647.  647.  628.  634. 5822600     634.
##  3 2016-01-06  622   640.  620.  633. 5329200     633.
##  4 2016-01-07  622.  630   605.  608. 7074900     608.
##  5 2016-01-08  620.  624.  606   607. 5512900     607.
##  6 2016-01-11  612.  620.  599.  618. 4891600     618.
##  7 2016-01-12  625.  626.  612.  618. 4724100     618.
##  8 2016-01-13  621.  621.  579.  582. 7655200     582.
##  9 2016-01-14  580.  602.  570.  593  7238000     593 
## 10 2016-01-15  572.  585.  565.  570. 7784500     570.
## # … with 1,015 more rows

Q2 How many columns (variables) are there?

glimpse(stocks)
## Observations: 1,025
## Variables: 7
## $ date     <date> 2016-01-04, 2016-01-05, 2016-01-06, 2016-01-07, 2016-01-08,…
## $ open     <dbl> 656.29, 646.86, 622.00, 621.80, 619.66, 612.48, 625.25, 620.…
## $ high     <dbl> 657.72, 646.91, 639.79, 630.00, 624.14, 619.85, 625.99, 620.…
## $ low      <dbl> 627.51, 627.65, 620.31, 605.21, 606.00, 598.57, 612.24, 579.…
## $ close    <dbl> 636.99, 633.79, 632.65, 607.94, 607.05, 617.74, 617.89, 581.…
## $ volume   <dbl> 9314500, 5822600, 5329200, 7074900, 5512900, 4891600, 472410…
## $ adjusted <dbl> 636.99, 633.79, 632.65, 607.94, 607.05, 617.74, 617.89, 581.…

Q3 What are the variables?

Date, Open, High, Low, Close, Volume, Adjusted

Q4 What type of data are they? What are other basic data types?

Hint: Watch the video, “Basic Data Types”, in DataCamp: Introduction to R for Finance: Ch1 The Basics.

Q5 How many rows are there?

There are 7 rows

Q6 What does the row represent?

The rows represent each of the variables

Q7 Create a line plot for the data.

Hint: Insert a new code chunk below and type in the code, using the ggplot() function above. For more information on the ggplot() function, refer to Ch2 Introduction to ggplot2 in one of our e-textbooks, Data Visualization with R.

## Visualize
stocks %>%
  ggplot(aes(x = date, y = close)) +
  geom_line()

Q8 Hide the messages and warings but display the code and its results on the webpage.

Hint: Change message, warning, collapse, 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.