Using the given code, answer the questions below.

library(tidyquant) 
library(tidyverse) 

GDP <- tq_get("GDPC1", get = "economic.data", from = "1980-01-01", to ="2019-01-01")
GDP
## # A tibble: 157 x 2
##    date       price
##    <date>     <dbl>
##  1 1980-01-01 6838.
##  2 1980-04-01 6697.
##  3 1980-07-01 6689.
##  4 1980-10-01 6814.
##  5 1981-01-01 6947.
##  6 1981-04-01 6896.
##  7 1981-07-01 6978.
##  8 1981-10-01 6902.
##  9 1982-01-01 6795.
## 10 1982-04-01 6826.
## # ... with 147 more rows

GDP %>%
  ggplot(aes(x = date, y = price)) +
  geom_line()

Hear is the GDP over the past 39 years. You can see each recession.

UERATE <- tq_get("UNRATE", get = "economic.data", from = "1980-01-01", to ="2019-01-01")
UERATE
## # A tibble: 469 x 2
##    date       price
##    <date>     <dbl>
##  1 1980-01-01   6.3
##  2 1980-02-01   6.3
##  3 1980-03-01   6.3
##  4 1980-04-01   6.9
##  5 1980-05-01   7.5
##  6 1980-06-01   7.6
##  7 1980-07-01   7.8
##  8 1980-08-01   7.7
##  9 1980-09-01   7.5
## 10 1980-10-01   7.5
## # ... with 459 more rows

UERATE %>%
  ggplot(aes(x = date, y = price)) +
  geom_line()

Hear is the unemployment rate over the past 39 years. When unemployment rate hits lowest, recession seems to follow.

TREAS <- c("DGS10", "DGS20", "DGS30") %>% 
  tq_get( get = "economic.data", from = "1980-01-01", to ="2019-01-01")
TREAS
## # A tibble: 26,940 x 3
##    symbol date       price
##    <chr>  <date>     <dbl>
##  1 DGS10  1980-01-01  NA  
##  2 DGS10  1980-01-02  10.5
##  3 DGS10  1980-01-03  10.6
##  4 DGS10  1980-01-04  10.7
##  5 DGS10  1980-01-07  10.6
##  6 DGS10  1980-01-08  10.6
##  7 DGS10  1980-01-09  10.6
##  8 DGS10  1980-01-10  10.5
##  9 DGS10  1980-01-11  10.7
## 10 DGS10  1980-01-14  10.7
## # ... with 26,930 more rows

TREAS %>%
  ggplot(aes(x = date, y = price, col = symbol)) +
  geom_line()

Hear are the 10,20,30 year treasury bonds over the pasy 39 years. When 10 year bond at same or higher rate than 30, a recession seems to most likely follow.

TREAS <- c("DGS10", "DGS20", "DGS30") %>% 
  tq_get( get = "economic.data", from = "2007-01-01", to ="2010-01-01")
TREAS
## # A tibble: 2,355 x 3
##    symbol date       price
##    <chr>  <date>     <dbl>
##  1 DGS10  2007-01-01 NA   
##  2 DGS10  2007-01-02  4.68
##  3 DGS10  2007-01-03  4.67
##  4 DGS10  2007-01-04  4.62
##  5 DGS10  2007-01-05  4.65
##  6 DGS10  2007-01-08  4.66
##  7 DGS10  2007-01-09  4.66
##  8 DGS10  2007-01-10  4.69
##  9 DGS10  2007-01-11  4.74
## 10 DGS10  2007-01-12  4.77
## # ... with 2,345 more rows

TREAS %>%
  ggplot(aes(x = date, y = price, col = symbol)) +
  geom_line()

Same as graph before, just smaller time frame.

INDEX <- tq_get("USSLIND", get = "economic.data", from = "1980-01-01", to ="2019-01-01")
INDEX
## # A tibble: 445 x 2
##    date        price
##    <date>      <dbl>
##  1 1982-01-01 -0.92 
##  2 1982-02-01 -0.43 
##  3 1982-03-01 -0.2  
##  4 1982-04-01 -0.17 
##  5 1982-05-01 -0.11 
##  6 1982-06-01 -0.1  
##  7 1982-07-01 -0.13 
##  8 1982-08-01 -0.3  
##  9 1982-09-01 -0.290
## 10 1982-10-01 -0.1  
## # ... with 435 more rows

INDEX %>%
  ggplot(aes(x = date, y = price)) +
  geom_line()

10 different economic numbers combined into 1 index When graph is going towards 0, a recession is most likely around the corner