QUIZ ON INTRO TO TIDYQUANT

library(tidyquant)

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library(tidyverse) 

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

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stocks %>% View()

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

## Q1. How many columns (variables) are there? There are seven columns

Q2. What are the variables?

date open high low close volume adjusted

Q3. Add Microsoft stock prices, in addition to Apple.

stocks <- tq_get(c("AAPL", "MSFT"), 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

Q4. How many variables are there now? Any new variable?

Yes there are 8 variables and the new variable is symbol

Q5. On how many days either of the two stocks closed higher than $200 per share?

Hint: Use dplyr::filter. 72 days they closed over 200

filter(stocks, close > 200)

## # A tibble: 72 x 8
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 AAPL   2018-08-01  199.  202.  197.  202. 67935700     199.
##  2 AAPL   2018-08-02  201.  208.  200.  207. 62404000     205.
##  3 AAPL   2018-08-03  207.  209.  205.  208. 33447400     206.
##  4 AAPL   2018-08-06  208   209.  207.  209. 25425400     207.
##  5 AAPL   2018-08-07  209.  210.  207.  207. 25587400     205.
##  6 AAPL   2018-08-08  206.  208.  205.  207. 22525500     205.
##  7 AAPL   2018-08-09  207.  210.  207.  209. 23469200     207.
##  8 AAPL   2018-08-10  207.  209.  207.  208. 24611200     206.
##  9 AAPL   2018-08-13  208.  211.  208.  209. 25869100     207.
## 10 AAPL   2018-08-14  210.  211.  208.  210. 20748000     208.
## # ... with 62 more rows

stocks %>% filter(close > 200)

## # A tibble: 72 x 8
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 AAPL   2018-08-01  199.  202.  197.  202. 67935700     199.
##  2 AAPL   2018-08-02  201.  208.  200.  207. 62404000     205.
##  3 AAPL   2018-08-03  207.  209.  205.  208. 33447400     206.
##  4 AAPL   2018-08-06  208   209.  207.  209. 25425400     207.
##  5 AAPL   2018-08-07  209.  210.  207.  207. 25587400     205.
##  6 AAPL   2018-08-08  206.  208.  205.  207. 22525500     205.
##  7 AAPL   2018-08-09  207.  210.  207.  209. 23469200     207.
##  8 AAPL   2018-08-10  207.  209.  207.  208. 24611200     206.
##  9 AAPL   2018-08-13  208.  211.  208.  209. 25869100     207.
## 10 AAPL   2018-08-14  210.  211.  208.  210. 20748000     208.
## # ... with 62 more rows

Q6. On how many days Apple stock closed higher than $200 per share?

Hint: Use dplyr::filter.

filter(stocks, close > 200, symbol == "MSFT")

## # A tibble: 0 x 8
## # ... with 8 variables: symbol <chr>, date <date>, open <dbl>, high <dbl>,
## #   low <dbl>, close <dbl>, volume <dbl>, adjusted <dbl>

stocks %>% filter(close > 200, symbol == "MFST")

## # A tibble: 0 x 8
## # ... with 8 variables: symbol <chr>, date <date>, open <dbl>, high <dbl>,
## #   low <dbl>, close <dbl>, volume <dbl>, adjusted <dbl>

Q7. Create a new variable, MC, market capitalization.

Hint: Use dplyr::mutate. Market cap is given by the formula, MC = N × P, where MC is the market capitalization, N is the number of shares outstanding, and P is the closing price per share.

stocks %>%
  mutate(MC = volume * close)

## # A tibble: 1,568 x 9
##    symbol date        open  high   low close   volume adjusted          MC
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>       <dbl>
##  1 AAPL   2016-01-04 103.  105.  102   105.  67649400     99.5 7126864155.
##  2 AAPL   2016-01-05 106.  106.  102.  103.  55791000     97.0 5730293554.
##  3 AAPL   2016-01-06 101.  102.   99.9 101.  68457400     95.1 6893659975.
##  4 AAPL   2016-01-07  98.7 100.   96.4  96.4 81094400     91.1 7821554637.
##  5 AAPL   2016-01-08  98.6  99.1  96.8  97.0 70798000     91.6 6864574009.
##  6 AAPL   2016-01-11  99.0  99.1  97.3  98.5 49739400     93.1 4900823032.
##  7 AAPL   2016-01-12 101.  101.   98.8 100.0 49154200     94.4 4913453783.
##  8 AAPL   2016-01-13 100.  101.   97.3  97.4 62439600     92.0 6080992582.
##  9 AAPL   2016-01-14  98.0 100.   95.7  99.5 63170100     94.0 6286688162.
## 10 AAPL   2016-01-15  96.2  97.7  95.4  97.1 79010000     91.7 7674241063.
## # ... with 1,558 more rows

Q8. Keep only three variables symbol, date, close and market capitalization and drop the other variables.

Hint: Use dplyr::select.

stocks %>% select(symbol, date,)

## # A tibble: 1,568 x 2
##    symbol date      
##    <chr>  <date>    
##  1 AAPL   2016-01-04
##  2 AAPL   2016-01-05
##  3 AAPL   2016-01-06
##  4 AAPL   2016-01-07
##  5 AAPL   2016-01-08
##  6 AAPL   2016-01-11
##  7 AAPL   2016-01-12
##  8 AAPL   2016-01-13
##  9 AAPL   2016-01-14
## 10 AAPL   2016-01-15
## # ... with 1,558 more rows

Q9 Plot daily closing stock prices for both stocks by mapping symbol to color.

Hint: Use ggplot2::ggplot.

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