# 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 = "monthly",
               type = "log") %>%
  ungroup() %>%

set_names(c("asset", "date", "returns"))

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 = 0.01) + 
  facet_wrap(~asset, ncol = 1)

# labeling
labs(title = "Distribution of Monthly Returns 2012=2015",
     y = "frequency",
     x = "rate of returns",
     caption =
"A typical monthly return is higher for SPV and 135 than for AGG, EEM, and EFA")
## $y
## [1] "frequency"
## 
## $x
## [1] "rate of returns"
## 
## $title
## [1] "Distribution of Monthly Returns 2012=2015"
## 
## $caption
## [1] "A typical monthly return is higher for SPV and 135 than for AGG, EEM, and EFA"
## 
## attr(,"class")
## [1] "labels"