Using the given code, answer the questions below.

library(tidyquant) 
library(tidyverse) 

stocks <- tq_get("AAPL", get = "stock.prices", from = "2018-08-18")
stocks
## # A tibble: 122 x 7
##    date        open  high   low close   volume adjusted
##    <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 2018-08-20  218.  219.  215.  215. 30287700     214.
##  2 2018-08-21  217.  217.  214.  215. 26159800     213.
##  3 2018-08-22  214.  216.  214.  215. 19018100     213.
##  4 2018-08-23  215.  217.  215.  215. 18883200     214.
##  5 2018-08-24  217.  217.  215.  216. 18476400     214.
##  6 2018-08-27  217.  219.  216.  218. 20525100     216.
##  7 2018-08-28  219.  221.  219.  220. 22776800     218.
##  8 2018-08-29  220.  223.  219.  223. 27254800     221.
##  9 2018-08-30  223.  228.  222.  225. 48793800     223.
## 10 2018-08-31  227.  229.  226   228. 43340100     226.
## # ... with 112 more rows

Q1. How many columns (variables) are there?

There are seven columns.

Q2. What are the variables?

date, open, high, low, close, volume, adjusted

Q3. What does the row represent?

Daily Apple stock data.

Q4. Download Facebook, in addition to Apple.

stocks <- tq_get(c("AAPL", "FB"), get = "stock.prices", from = "2016-01-01")

stocks
## # A tibble: 1,568 x 8
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 AAPL   2016-01-04 103.  105.  102   105.  67649400     99.5
##  2 AAPL   2016-01-05 106.  106.  102.  103.  55791000     97.0
##  3 AAPL   2016-01-06 101.  102.   99.9 101.  68457400     95.1
##  4 AAPL   2016-01-07  98.7 100.   96.4  96.4 81094400     91.1
##  5 AAPL   2016-01-08  98.6  99.1  96.8  97.0 70798000     91.6
##  6 AAPL   2016-01-11  99.0  99.1  97.3  98.5 49739400     93.1
##  7 AAPL   2016-01-12 101.  101.   98.8 100.0 49154200     94.4
##  8 AAPL   2016-01-13 100.  101.   97.3  97.4 62439600     92.0
##  9 AAPL   2016-01-14  98.0 100.   95.7  99.5 63170100     94.0
## 10 AAPL   2016-01-15  96.2  97.7  95.4  97.1 79010000     91.7
## # ... with 1,558 more rows

Q5. On how many days did Facebook close higher than $200 per share?

stocks %>% filter(symbol == "FB", close > 200)
## # A tibble: 17 x 8
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 FB     2018-06-20  199.  204.  199.  202  28230900     202 
##  2 FB     2018-06-21  203.  203.  200.  202. 19045700     202.
##  3 FB     2018-06-22  201.  202.  199.  202. 17420200     202.
##  4 FB     2018-07-06  198.  204.  198.  203. 19740100     203.
##  5 FB     2018-07-09  205.  206.  202.  205. 18149400     205.
##  6 FB     2018-07-10  204.  205.  202.  204. 13190100     204.
##  7 FB     2018-07-11  202.  204.  202.  203. 12927400     203.
##  8 FB     2018-07-12  203.  207.  203.  207. 15454700     207.
##  9 FB     2018-07-13  208.  208.  206.  207. 11486800     207.
## 10 FB     2018-07-16  208.  209.  207.  207. 11078200     207.
## 11 FB     2018-07-17  205.  210.  205.  210. 15349900     210.
## 12 FB     2018-07-18  210.  211.  208.  209. 15334900     209.
## 13 FB     2018-07-19  209.  210.  208.  208. 11350400     208.
## 14 FB     2018-07-20  209.  212.  208.  210. 16163900     210.
## 15 FB     2018-07-23  211.  212.  209.  211. 16732000     211.
## 16 FB     2018-07-24  215.  216.  213.  215. 28468700     215.
## 17 FB     2018-07-25  216.  219.  214.  218. 58954200     218.

Q6. What was the highest closing price of Facebook.

stocks %>% 
  filter(symbol == "FB") %>% 
  arrange(desc(close))
## # A tibble: 784 x 8
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 FB     2018-07-25  216.  219.  214.  218. 58954200     218.
##  2 FB     2018-07-24  215.  216.  213.  215. 28468700     215.
##  3 FB     2018-07-23  211.  212.  209.  211. 16732000     211.
##  4 FB     2018-07-17  205.  210.  205.  210. 15349900     210.
##  5 FB     2018-07-20  209.  212.  208.  210. 16163900     210.
##  6 FB     2018-07-18  210.  211.  208.  209. 15334900     209.
##  7 FB     2018-07-19  209.  210.  208.  208. 11350400     208.
##  8 FB     2018-07-13  208.  208.  206.  207. 11486800     207.
##  9 FB     2018-07-16  208.  209.  207.  207. 11078200     207.
## 10 FB     2018-07-12  203.  207.  203.  207. 15454700     207.
## # ... with 774 more rows

Q7. Download exchange rate between the U.S. dollar and the Japanese yen, save the retrieved data under the name, “FX”, instead of “stocks”, and print the data.

library(tidyquant) 
library(tidyverse) 

FX <- tq_get("USD/JPY", get = "exchange.rate", from = "2016-01-01")

FX
## # A tibble: 180 x 2
##    date       exchange.rate
##    <date>             <dbl>
##  1 2018-08-18          111.
##  2 2018-08-19          111.
##  3 2018-08-20          110.
##  4 2018-08-21          110.
##  5 2018-08-22          110.
##  6 2018-08-23          111.
##  7 2018-08-24          111.
##  8 2018-08-25          111.
##  9 2018-08-26          111.
## 10 2018-08-27          111.
## # ... with 170 more rows