# Load packages

# Core
library(tidyverse)
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library(tidyquant)
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Goal

Take raw prices of five individual stocks and transform them into monthly returns five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG”

1 Import stock prices

# Choose stocks
symbols <- c("Spy", "EFA", "IJS", "EEM", "AGG")

prices <- tq_get(x  = symbols,
                  get = "stock.prices",
                  from = "2012-01-01",
                  to = "2017-01-01",)

2 Convert prices to returns

asset_returns_tbl <- prices %>%
    
  group_by(symbol) %>%
  tq_transmute(select = adjusted,
               mutate_fun = periodReturn,
               period = "quarterly",
               type = "log") %>%
  ungroup() %>%
  
  set_names(c("asset", "date", "returns"))

asset_returns_tbl
## # A tibble: 100 × 3
##    asset date        returns
##    <chr> <date>        <dbl>
##  1 Spy   2012-03-30  0.104  
##  2 Spy   2012-06-29 -0.0289 
##  3 Spy   2012-09-28  0.0615 
##  4 Spy   2012-12-31 -0.00383
##  5 Spy   2013-03-28  0.0999 
##  6 Spy   2013-06-28  0.0289 
##  7 Spy   2013-09-30  0.0511 
##  8 Spy   2013-12-31  0.100  
##  9 Spy   2014-03-31  0.0169 
## 10 Spy   2014-06-30  0.0503 
## # ℹ 90 more rows

3 Make plot

asset_returns_tbl %>%
  
  ggplot(aes(x = returns)) +
  geom_density(aes(color = asset), show.legend = FALSE, alpha = 1) +
  geom_histogram(aes(fill = asset), show.legend = FALSE, alpha = 0.3, binwidth = 0.1) +
  facet_wrap(~asset, ncol = 1)

  # labeling
  labs(title = "Distrubution of Monthly Returns, 2012 - 2016",
       y = "Frequency",
       x = "Rate of Returns",
       caption = "A typical monthly return is higher for SPY and IJS than for AGG, EEM, and EFA.")
## $y
## [1] "Frequency"
## 
## $x
## [1] "Rate of Returns"
## 
## $title
## [1] "Distrubution of Monthly Returns, 2012 - 2016"
## 
## $caption
## [1] "A typical monthly return is higher for SPY and IJS than for AGG, EEM, and EFA."
## 
## attr(,"class")
## [1] "labels"