# 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 
## # … with 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 = 0.01) +
    facet_wrap(~asset, ncol = 1) + 

# Labeling
labs(title = "Distribution of Monthly Returns, 2012-2016", 
     y = "Frequuency",
     x = "Rate of Returns",
     caption = "A typical monthly return is higher for SPY and IJS than for AGG, EEM, and EFA")