# Load packages

# Core
library(tidyverse)
library(tidyquant)

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 = "2012-12-31",
                 to = "2017-12-31")

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) +

    labs(title = "Distribution of monthly returns 2012-2016",
         y = "freqeuncy",
         x = "rate of returns",
         caption = "A typic montly return is higher for SPY and IJS than for AGG, EEM and EFA")