# Load packages
library(tidyquant) 
library(tidyverse) 
library(dplyr)

Q1 economic data Import the U.S. Industrial Production Index since 2010.

Production <- tq_get("INDPRO", get = "economic.data", from = "2010-01-01")
Production
## # A tibble: 122 x 2
##    date       price
##    <date>     <dbl>
##  1 2010-01-01  91.7
##  2 2010-02-01  92.0
##  3 2010-03-01  92.6
##  4 2010-04-01  92.9
##  5 2010-05-01  94.3
##  6 2010-06-01  94.4
##  7 2010-07-01  94.9
##  8 2010-08-01  95.1
##  9 2010-09-01  95.4
## 10 2010-10-01  95.1
## # … with 112 more rows

Hint: Find the symbol in FRED.

Q2 stock prices Import stock prices of NASDAQ and S&P500 since 2019.

stocks <- tq_get(c("^GSPC","^IXIC"), get = "stock.prices", from = "2019-01-01")
## Warning: `cols` is now required.
## Please use `cols = c(stock.prices)`
stocks
## # A tibble: 620 x 8
##    symbol date        open  high   low close     volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>      <dbl>    <dbl>
##  1 ^GSPC  2019-01-02 2477. 2519. 2467. 2510. 3733160000    2510.
##  2 ^GSPC  2019-01-03 2492. 2493. 2444. 2448. 3822860000    2448.
##  3 ^GSPC  2019-01-04 2474. 2538. 2474. 2532. 4213410000    2532.
##  4 ^GSPC  2019-01-07 2536. 2566. 2525. 2550. 4104710000    2550.
##  5 ^GSPC  2019-01-08 2568. 2580. 2548. 2574. 4083030000    2574.
##  6 ^GSPC  2019-01-09 2580  2595. 2569. 2585. 4052480000    2585.
##  7 ^GSPC  2019-01-10 2574. 2598. 2562. 2597. 3704500000    2597.
##  8 ^GSPC  2019-01-11 2588. 2596. 2577. 2596. 3434490000    2596.
##  9 ^GSPC  2019-01-14 2580. 2589. 2570. 2583. 3664450000    2583.
## 10 ^GSPC  2019-01-15 2585. 2613. 2585. 2610. 3572330000    2610.
## # … with 610 more rows

Q3 filter Select S&P500 and save it under stocks_filtered.

Hint: See the code in 1.2.2 Selecting observations

stocks_filtered <- filter(stocks,
                         symbol == "^GSPC")
stocks_filtered
## # A tibble: 310 x 8
##    symbol date        open  high   low close     volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>      <dbl>    <dbl>
##  1 ^GSPC  2019-01-02 2477. 2519. 2467. 2510. 3733160000    2510.
##  2 ^GSPC  2019-01-03 2492. 2493. 2444. 2448. 3822860000    2448.
##  3 ^GSPC  2019-01-04 2474. 2538. 2474. 2532. 4213410000    2532.
##  4 ^GSPC  2019-01-07 2536. 2566. 2525. 2550. 4104710000    2550.
##  5 ^GSPC  2019-01-08 2568. 2580. 2548. 2574. 4083030000    2574.
##  6 ^GSPC  2019-01-09 2580  2595. 2569. 2585. 4052480000    2585.
##  7 ^GSPC  2019-01-10 2574. 2598. 2562. 2597. 3704500000    2597.
##  8 ^GSPC  2019-01-11 2588. 2596. 2577. 2596. 3434490000    2596.
##  9 ^GSPC  2019-01-14 2580. 2589. 2570. 2583. 3664450000    2583.
## 10 ^GSPC  2019-01-15 2585. 2613. 2585. 2610. 3572330000    2610.
## # … with 300 more rows

Q4 select Select two variables (date and closing price) from stocks_filtered, and save it under stocks_selected.

Hint: See the code in 1.2.2 Selecting observations

stocks_selected <- select(stocks_filtered, symbol, date, close)
stocks_selected
## # A tibble: 310 x 3
##    symbol date       close
##    <chr>  <date>     <dbl>
##  1 ^GSPC  2019-01-02 2510.
##  2 ^GSPC  2019-01-03 2448.
##  3 ^GSPC  2019-01-04 2532.
##  4 ^GSPC  2019-01-07 2550.
##  5 ^GSPC  2019-01-08 2574.
##  6 ^GSPC  2019-01-09 2585.
##  7 ^GSPC  2019-01-10 2597.
##  8 ^GSPC  2019-01-11 2596.
##  9 ^GSPC  2019-01-14 2583.
## 10 ^GSPC  2019-01-15 2610.
## # … with 300 more rows

Q5 exchange rates Import the exchange rate between the U.S. dollar and the Chinese Yuan Renminbi, and save it under FX.

Hint: Find the symbol in oanda.com.

FX <- tq_get("CNY/USD",
       get  = "exchange.rates")
## Warning: Oanda only provides historical data for the past 180 days. Symbol: CNY/
## USD
FX
## # A tibble: 180 x 2
##    date       exchange.rate
##    <date>             <dbl>
##  1 2019-09-28         0.140
##  2 2019-09-29         0.140
##  3 2019-09-30         0.140
##  4 2019-10-01         0.140
##  5 2019-10-02         0.140
##  6 2019-10-03         0.140
##  7 2019-10-04         0.140
##  8 2019-10-05         0.140
##  9 2019-10-06         0.140
## 10 2019-10-07         0.140
## # … with 170 more rows

Q6 left_join Merge FX and stocks_selected.

Hint: For left_join, refer to our other textbook, R for Data Scince, Ch13.4 Mutating joins. Or Google dplyr::left_join() to find example codes.

data_merged <-
  stocks_selected %>%
  left_join(FX)
data_merged
## # A tibble: 310 x 4
##    symbol date       close exchange.rate
##    <chr>  <date>     <dbl>         <dbl>
##  1 ^GSPC  2019-01-02 2510.            NA
##  2 ^GSPC  2019-01-03 2448.            NA
##  3 ^GSPC  2019-01-04 2532.            NA
##  4 ^GSPC  2019-01-07 2550.            NA
##  5 ^GSPC  2019-01-08 2574.            NA
##  6 ^GSPC  2019-01-09 2585.            NA
##  7 ^GSPC  2019-01-10 2597.            NA
##  8 ^GSPC  2019-01-11 2596.            NA
##  9 ^GSPC  2019-01-14 2583.            NA
## 10 ^GSPC  2019-01-15 2610.            NA
## # … with 300 more rows

Q7 Scatterplot Plot the relationship between the stock market and the exchange rate. Add the best fit line.

Hint: See the code in 4.2.1 Scatterplot

data_merged %>%
  ggplot(aes(close, exchange.rate))+
  geom_point()+
  geom_smooth(method = "lm")
## Warning: Removed 187 rows containing non-finite values (stat_smooth).
## Warning: Removed 187 rows containing missing values (geom_point).

Q7.a Describe the relationship between the stock market and the exchange rate.

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

There is a positively correlated relationship between the stock market and exchange rate according to the graph above.

Q8 Hide the messages, but display the code and its results on 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.