Using the given code, answer the questions below.

library(tidyquant) 
library(tidyverse) 

stocks <- tq_get("AAPL", get = "stock.prices", from = "2016-01-01")
stocks
## # A tibble: 776 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     99.9
##  2 2016-01-05 106.  106.  102.  103.  55791000     97.4
##  3 2016-01-06 101.  102.   99.9 101.  68457400     95.5
##  4 2016-01-07  98.7 100.   96.4  96.4 81094400     91.5
##  5 2016-01-08  98.6  99.1  96.8  97.0 70798000     92.0
##  6 2016-01-11  99.0  99.1  97.3  98.5 49739400     93.5
##  7 2016-01-12 101.  101.   98.8 100.0 49154200     94.8
##  8 2016-01-13 100.  101.   97.3  97.4 62439600     92.4
##  9 2016-01-14  98.0 100.   95.7  99.5 63170100     94.4
## 10 2016-01-15  96.2  97.7  95.4  97.1 79010000     92.1
## # ... with 766 more rows

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

Q1. How many columns (variables) are there?

      -There are 7 variables

Q2. What are the variables?

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

Q3. How many rows are there?

      -There are 776 rows

Q4. What does the row represent?

      -The row represents the date the data event occurred

Q5. Get Microsoft stock prices, instead of Apple.

stocks <- tq_get("MSFT", get = "stock.prices", from = "2016-01-01")
stocks
## # A tibble: 776 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     51.3
##  2 2016-01-05  54.9  55.4  54.5  55.0 34079700     51.5
##  3 2016-01-06  54.3  54.4  53.6  54.0 39518900     50.6
##  4 2016-01-07  52.7  53.5  52.1  52.2 56564900     48.8
##  5 2016-01-08  52.4  53.3  52.2  52.3 48754000     49.0
##  6 2016-01-11  52.5  52.8  51.5  52.3 36663600     48.9
##  7 2016-01-12  52.8  53.1  52.1  52.8 36095500     49.4
##  8 2016-01-13  53.8  54.1  51.3  51.6 66883600     48.3
##  9 2016-01-14  52    53.4  51.6  53.1 52381900     49.7
## 10 2016-01-15  51.3  52.0  50.3  51.0 71820700     47.7
## # ... with 766 more rows

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

Q6. Get the stock prices from 2017-01-15 to 2018-12-15.

stocks <- tq_get("MSFT", get = "stock.prices", from = "2017-01-01", to = "
                 2018-12-15")
stocks
## [1] NA

Q7. Get economic data, the unemployment rate for the U.S., instead of stock prices.

stocks <- tq_get("UNRATE", get = "economic.data", from = "2016-01-01")
stocks
## # A tibble: 37 x 2
##    date       price
##    <date>     <dbl>
##  1 2016-01-01   4.9
##  2 2016-02-01   4.9
##  3 2016-03-01   5  
##  4 2016-04-01   5  
##  5 2016-05-01   4.8
##  6 2016-06-01   4.9
##  7 2016-07-01   4.8
##  8 2016-08-01   4.9
##  9 2016-09-01   5  
## 10 2016-10-01   4.9
## # ... with 27 more rows

stocks %>%
  ggplot(aes(x = date, y = price)) +
  geom_line()

Q8. Get exchange rate between the U.S. dollar and the euro, instead of stock prices.

stocks <- tq_get("USD/EUR", get = "exchange.rate", from = "2016-01-01")
stocks
## # A tibble: 180 x 2
##    date       exchange.rate
##    <date>             <dbl>
##  1 2018-08-09         0.864
##  2 2018-08-10         0.873
##  3 2018-08-11         0.876
##  4 2018-08-12         0.877
##  5 2018-08-13         0.878
##  6 2018-08-14         0.879
##  7 2018-08-15         0.882
##  8 2018-08-16         0.879
##  9 2018-08-17         0.877
## 10 2018-08-18         0.874
## # ... with 170 more rows

stocks %>%
  ggplot(aes(x = date, y = exchange.rate)) +
  geom_line()