Q1 dividends Import dividends of Walmart and Target since 2010.

## # A tibble: 80 x 3
##    symbol date       dividends
##    <chr>  <date>         <dbl>
##  1 WMT    2010-03-10     0.303
##  2 WMT    2010-05-12     0.303
##  3 WMT    2010-08-11     0.303
##  4 WMT    2010-12-08     0.303
##  5 WMT    2011-03-09     0.365
##  6 WMT    2011-05-11     0.365
##  7 WMT    2011-08-10     0.365
##  8 WMT    2011-12-07     0.365
##  9 WMT    2012-03-08     0.398
## 10 WMT    2012-05-09     0.398
## # … with 70 more rows

Q2 economic.data Import real U.S. GDP growth since 2000.

Hint: Find the symbol in FRED. Select in the list of related variables, Percent Change from Preceding Period, Annual, Not Seasonally Adjusted.

## Warning: x = 'GDP1', get = 'economic.data': Error in getSymbols.FRED(Symbols = "GDP1", env = <environment>, verbose = FALSE, : Unable to import "GDP1".
## Failed to download file. Error message:
## cannot open URL 'https://fred.stlouisfed.org/series/GDP1/downloaddata/GDP1.csv'
## If this is related to https, possible solutions are:
## 1. Explicitly pass method= via the getSymbols call (or via setDefaults)
## 2. Install downloader, which may be able to automagically determine a method
## 3. Set the download.file.method global option
## [1] NA

Q3 exchange rates Import exchange rate between the U.S. dollar and the Japanese yen.

Hint: Find the symbol in oanda.com.

## Warning: Oanda only provides historical data for the past 180 days. Symbol:
## USD/JPY
## # A tibble: 180 x 2
##    date       exchange.rate
##    <date>             <dbl>
##  1 2019-06-16          109.
##  2 2019-06-17          109.
##  3 2019-06-18          108.
##  4 2019-06-19          108.
##  5 2019-06-20          108.
##  6 2019-06-21          107.
##  7 2019-06-22          107.
##  8 2019-06-23          107.
##  9 2019-06-24          107.
## 10 2019-06-25          107.
## # … with 170 more rows

Q4 stock prices Import stock price of Google, Apple and Facebook since 2010.

## # A tibble: 6,913 x 8
##    symbol date        open  high   low close    volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
##  1 AAPL   2010-01-04  30.5  30.6  30.3  30.6 123432400     26.6
##  2 AAPL   2010-01-05  30.7  30.8  30.5  30.6 150476200     26.6
##  3 AAPL   2010-01-06  30.6  30.7  30.1  30.1 138040000     26.2
##  4 AAPL   2010-01-07  30.2  30.3  29.9  30.1 119282800     26.2
##  5 AAPL   2010-01-08  30.0  30.3  29.9  30.3 111902700     26.3
##  6 AAPL   2010-01-11  30.4  30.4  29.8  30.0 115557400     26.1
##  7 AAPL   2010-01-12  29.9  30.0  29.5  29.7 148614900     25.8
##  8 AAPL   2010-01-13  29.7  30.1  29.2  30.1 151473000     26.2
##  9 AAPL   2010-01-14  30.0  30.1  29.9  29.9 108223500     26.0
## 10 AAPL   2010-01-15  30.1  30.2  29.4  29.4 148516900     25.6
## # … with 6,903 more rows

Q5 filter Select Apple and Facebook.

Hint: See the code in 1.2.2 Selecting observations

## # A tibble: 4,409 x 8
##    symbol date        open  high   low close    volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
##  1 AAPL   2010-01-04  30.5  30.6  30.3  30.6 123432400     26.6
##  2 AAPL   2010-01-05  30.7  30.8  30.5  30.6 150476200     26.6
##  3 AAPL   2010-01-06  30.6  30.7  30.1  30.1 138040000     26.2
##  4 AAPL   2010-01-07  30.2  30.3  29.9  30.1 119282800     26.2
##  5 AAPL   2010-01-08  30.0  30.3  29.9  30.3 111902700     26.3
##  6 AAPL   2010-01-11  30.4  30.4  29.8  30.0 115557400     26.1
##  7 AAPL   2010-01-12  29.9  30.0  29.5  29.7 148614900     25.8
##  8 AAPL   2010-01-13  29.7  30.1  29.2  30.1 151473000     26.2
##  9 AAPL   2010-01-14  30.0  30.1  29.9  29.9 108223500     26.0
## 10 AAPL   2010-01-15  30.1  30.2  29.4  29.4 148516900     25.6
## # … with 4,399 more rows

Q6 Scatterplot Plot relationships between volume and closing price for Google, Apple and Facebook, using the facet_wrap function. Add the best fit line.

Hint: See the code in 4.2.1 Scatterplot 

## # A tibble: 6,913 x 8
##    symbol date        open  high   low close    volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
##  1 AAPL   2010-01-04  30.5  30.6  30.3  30.6 123432400     26.6
##  2 AAPL   2010-01-05  30.7  30.8  30.5  30.6 150476200     26.6
##  3 AAPL   2010-01-06  30.6  30.7  30.1  30.1 138040000     26.2
##  4 AAPL   2010-01-07  30.2  30.3  29.9  30.1 119282800     26.2
##  5 AAPL   2010-01-08  30.0  30.3  29.9  30.3 111902700     26.3
##  6 AAPL   2010-01-11  30.4  30.4  29.8  30.0 115557400     26.1
##  7 AAPL   2010-01-12  29.9  30.0  29.5  29.7 148614900     25.8
##  8 AAPL   2010-01-13  29.7  30.1  29.2  30.1 151473000     26.2
##  9 AAPL   2010-01-14  30.0  30.1  29.9  29.9 108223500     26.0
## 10 AAPL   2010-01-15  30.1  30.2  29.4  29.4 148516900     25.6
## # … with 6,903 more rows

Q7 Describe the relationship between volume and closing price. For which of the three stocks, the trade closing price appears to be relatively more sensitive to the volume?

Hint: See the scatterplot you created in the previous question.

When greater volume of stock gets sold the price increases. Both the trading volume and closing prices are negativley assoiated, the more shares sold the lower the closing price ## Q8 Hide the messages and the code, but display results of the code from the webpage. Hint: Use message, 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.