Apply it to your data 4

# Load packages

# Core
library(tidyverse)
library(tidyquant)

1 Import stock prices

# Choose stocks
symbols <- c("LULU", "UA", "NKE", "AMZN", "GOOG")

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 = "quarterly",
               type = "log") %>%
  ungroup() %>% 
rename(asset = symbol,
       returns = quarterly.returns)
    set_names(c("asset", "date", "returns"))

##     asset      date   returns 
##   "asset"    "date" "returns"

asset_returns_tbl

## # A tibble: 80 × 3
##    asset date       returns
##    <chr> <date>       <dbl>
##  1 LULU  2017-03-31 -0.254 
##  2 LULU  2017-06-30  0.140 
##  3 LULU  2017-09-29  0.0423
##  4 LULU  2017-12-29  0.233 
##  5 LULU  2018-03-29  0.126 
##  6 LULU  2018-06-29  0.337 
##  7 LULU  2018-09-28  0.264 
##  8 LULU  2018-12-31 -0.290 
##  9 LULU  2019-03-29  0.298 
## 10 LULU  2019-06-28  0.0950
## # ℹ 70 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 AMZN, GOOG, and NKE than for LULU and UA.")

## $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 AMZN, GOOG, and NKE than for LULU and UA."
## 
## attr(,"class")
## [1] "labels"