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

#labeling
labs(title= "Distribution of Monthly Returns,2012-2016", 
     y     = "Frequency" , 
     x     = "Rate of Returns" ,
     captions = "A typically return is higher for SPY and IJS than for AGG, EEM EFA." )
## Warning in geom_histogram(aes(fill = asset), show.legend = FALSE, alpha = 0.3,
## : Ignoring unknown parameters: `bindwith`
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.