# 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")

# Using tq_get() ----
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"))

asset_returns_tbl
## # A tibble: 305 × 3
##    asset date       returns
##    <chr> <date>       <dbl>
##  1 SPY   2012-12-31  0     
##  2 SPY   2013-01-31  0.0499
##  3 SPY   2013-02-28  0.0127
##  4 SPY   2013-03-28  0.0373
##  5 SPY   2013-04-30  0.0190
##  6 SPY   2013-05-31  0.0233
##  7 SPY   2013-06-28 -0.0134
##  8 SPY   2013-07-31  0.0504
##  9 SPY   2013-08-30 -0.0305
## 10 SPY   2013-09-30  0.0312
## # ℹ 295 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) +

    labs(title = "distribution of monthly returns, 2012-2016",
         x = "frequency",
         y = "rate of returns") +
    theme_update(plot.title = element_text(hjust = 0.5))