# 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 = "2017-01-01",
                 to = "2021-01-01")

2 Convert prices to returns

asset_returns_tbl <- prices %>%
  
  group_by(symbol) %>%
  tq_transmute(select = adjusted,
               mutate_fun = periodReturn, 
               period = "daily",
               type = "log") %>%
  ungroup() %>% 
rename(asset = symbol,
       returns =daily.returns)
    set_names(c("asset", "date", "returns"))
##     asset      date   returns 
##   "asset"    "date" "returns"
asset_returns_tbl
## # A tibble: 5,035 × 3
##    asset date         returns
##    <chr> <date>         <dbl>
##  1 SPY   2017-01-03  0       
##  2 SPY   2017-01-04  0.00593 
##  3 SPY   2017-01-05 -0.000795
##  4 SPY   2017-01-06  0.00357 
##  5 SPY   2017-01-09 -0.00331 
##  6 SPY   2017-01-10  0       
##  7 SPY   2017-01-11  0.00282 
##  8 SPY   2017-01-12 -0.00251 
##  9 SPY   2017-01-13  0.00229 
## 10 SPY   2017-01-17 -0.00353 
## # ℹ 5,025 more rows

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, 2012-2016",
     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, 2012-2016"
## 
## $caption
## [1] "A typical monthly return is higher for SPY and IJS than for AGG, EEM, and EFA."
## 
## attr(,"class")
## [1] "labels"