# Load packages
library(tidyquant) 
library(tidyverse)

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

USINDPRO <- tq_get("INDPRO",
       get  = "economic.data",
       from = "2010-01-01")
USINDPRO
## # 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

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

stocks <- tq_get(c("^IXIC", "^GSPC"), get = "stock.prices", from = "2019-01-01")
stocks
## # A tibble: 620 x 8
##    symbol date        open  high   low close     volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>      <dbl>    <dbl>
##  1 ^IXIC  2019-01-02 6507. 6694. 6507. 6666. 2261800000    6666.
##  2 ^IXIC  2019-01-03 6585. 6600. 6457. 6464. 2607290000    6464.
##  3 ^IXIC  2019-01-04 6567. 6761. 6554. 6739. 2579550000    6739.
##  4 ^IXIC  2019-01-07 6758. 6856. 6741. 6823. 2507550000    6823.
##  5 ^IXIC  2019-01-08 6893. 6910. 6796. 6897  2380290000    6897 
##  6 ^IXIC  2019-01-09 6923. 6985. 6900. 6957. 2422590000    6957.
##  7 ^IXIC  2019-01-10 6909. 6991. 6877. 6986. 2179080000    6986.
##  8 ^IXIC  2019-01-11 6947. 6976. 6934. 6971. 2066500000    6971.
##  9 ^IXIC  2019-01-14 6908. 6936. 6887. 6906. 1942210000    6906.
## 10 ^IXIC  2019-01-15 6931. 7026. 6928. 7024. 2038090000    7024.
## # … 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, c("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("CNH/USD",
       get  = "exchange.rates")
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         0.141
##  9 2019-10-06         0.141
## 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.

Merged <- stocks_selected %>%
  select(-symbol) %>%
  left_join(FX, by = "date")
Merged
## # A tibble: 310 x 3
##    date       close exchange.rate
##    <date>     <dbl>         <dbl>
##  1 2019-01-02 2510.            NA
##  2 2019-01-03 2448.            NA
##  3 2019-01-04 2532.            NA
##  4 2019-01-07 2550.            NA
##  5 2019-01-08 2574.            NA
##  6 2019-01-09 2585.            NA
##  7 2019-01-10 2597.            NA
##  8 2019-01-11 2596.            NA
##  9 2019-01-14 2583.            NA
## 10 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

library(ggplot2)
ggplot(Merged,
       aes(close, exchange.rate)) +
  geom_point() +
  geom_smooth(method = "lm")

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

There is a weak, positive correlation between the stock market and the exchange rate. As the stock market climbs, the exchange rate should climb with it.