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: 922 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     98.7
##  2 2016-01-05 106.  106.  102.  103.  55791000     96.3
##  3 2016-01-06 101.  102.   99.9 101.  68457400     94.4
##  4 2016-01-07  98.7 100.   96.4  96.4 81094400     90.4
##  5 2016-01-08  98.6  99.1  96.8  97.0 70798000     90.9
##  6 2016-01-11  99.0  99.1  97.3  98.5 49739400     92.4
##  7 2016-01-12 101.  101.   98.8 100.0 49154200     93.7
##  8 2016-01-13 100.  101.   97.3  97.4 62439600     91.3
##  9 2016-01-14  98.0 100.   95.7  99.5 63170100     93.3
## 10 2016-01-15  96.2  97.7  95.4  97.1 79833900     91.0
## # … with 912 more rows
## Examine data
glimpse(stocks)
## Observations: 922
## Variables: 7
## $ date     <date> 2016-01-04, 2016-01-05, 2016-01-06, 2016-01-07, 2016-0…
## $ open     <dbl> 102.61, 105.75, 100.56, 98.68, 98.55, 98.97, 100.55, 10…
## $ high     <dbl> 105.37, 105.85, 102.37, 100.13, 99.11, 99.06, 100.69, 1…
## $ low      <dbl> 102.00, 102.41, 99.87, 96.43, 96.76, 97.34, 98.84, 97.3…
## $ close    <dbl> 105.35, 102.71, 100.70, 96.45, 96.96, 98.53, 99.96, 97.…
## $ volume   <dbl> 67649400, 55791000, 68457400, 81094400, 70798000, 49739…
## $ adjusted <dbl> 98.74225, 96.26781, 94.38389, 90.40047, 90.87848, 92.35…
## Visualize
stocks %>%
  ggplot(aes(x = date, y = close)) +
  geom_line()

Q1 Get Microsoft stock prices, instead of Apple.

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

## Import data
stocks <- tq_get("MSFT", get = "stock.prices", from = "2016-01-01")
stocks
## # A tibble: 922 x 7
##    date        open  high   low close   volume adjusted
##    <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 2016-01-04  54.3  54.8  53.4  54.8 53778000     50.7
##  2 2016-01-05  54.9  55.4  54.5  55.0 34079700     50.9
##  3 2016-01-06  54.3  54.4  53.6  54.0 39518900     50.0
##  4 2016-01-07  52.7  53.5  52.1  52.2 56564900     48.3
##  5 2016-01-08  52.4  53.3  52.2  52.3 48754000     48.4
##  6 2016-01-11  52.5  52.8  51.5  52.3 36943800     48.4
##  7 2016-01-12  52.8  53.1  52.1  52.8 36095500     48.8
##  8 2016-01-13  53.8  54.1  51.3  51.6 66883600     47.8
##  9 2016-01-14  52    53.4  51.6  53.1 52381900     49.1
## 10 2016-01-15  51.3  52.0  50.3  51.0 71820700     47.2
## # … with 912 more rows

Q2 How many columns (variables) are there?

## Examine data
glimpse(stocks)
## Observations: 922
## Variables: 7
## $ date     <date> 2016-01-04, 2016-01-05, 2016-01-06, 2016-01-07, 2016-0…
## $ open     <dbl> 54.32, 54.93, 54.32, 52.70, 52.37, 52.51, 52.76, 53.80,…
## $ high     <dbl> 54.80, 55.39, 54.40, 53.49, 53.28, 52.85, 53.10, 54.07,…
## $ low      <dbl> 53.39, 54.54, 53.64, 52.07, 52.15, 51.46, 52.06, 51.30,…
## $ close    <dbl> 54.80, 55.05, 54.05, 52.17, 52.33, 52.30, 52.78, 51.64,…
## $ volume   <dbl> 53778000, 34079700, 39518900, 56564900, 48754000, 36943…
## $ adjusted <dbl> 50.70846, 50.93979, 50.01446, 48.27483, 48.42288, 48.39…

Q3 What are the variables?

The variables are date, open, high, low, close, volume, and adjusted.

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

Variables and data types, the other basic data types are numeric, character, and logical.

Q5 How many rows are there?

922 rows.

Q6 What does the row represent?

Daily information about stock prices.

Q7 Create a line plot for the data.

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

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

Q9 Display the title and your name correctly at the top of the webpage.

Q10 Use the correct slug.