# 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 = "2012-01-01", 
                 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") %>%
    
    set_names(c("asset", "date", "returns"))

3 Make plot

asset_returns_tbl %>%
    
    ggplot(aes(x = returns)) +
    geom_density(aes(color = asset), 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, 2012-2017",
         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] "Distribution of monthly returns, 2012-2017"
## 
## $caption
## [1] "A typical monthly return is higher for SPY and IJS than for AGG, EEM, and EFA"
## 
## attr(,"class")
## [1] "labels"