# Load packages

# Core
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(tidyquant)
## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo 
## ── Attaching core tidyquant packages ──────────────────────── tidyquant 1.0.9 ──
## ✔ PerformanceAnalytics 2.0.4      ✔ TTR                  0.24.4
## ✔ quantmod             0.4.26     ✔ xts                  0.14.0── Conflicts ────────────────────────────────────────── tidyquant_conflicts() ──
## ✖ zoo::as.Date()                 masks base::as.Date()
## ✖ zoo::as.Date.numeric()         masks base::as.Date.numeric()
## ✖ dplyr::filter()                masks stats::filter()
## ✖ xts::first()                   masks dplyr::first()
## ✖ dplyr::lag()                   masks stats::lag()
## ✖ xts::last()                    masks dplyr::last()
## ✖ PerformanceAnalytics::legend() masks graphics::legend()
## ✖ quantmod::summary()            masks base::summary()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(dplR)
## This is dplR version 1.7.7.
## dplR is part of openDendro https://opendendro.org.
## New users can visit https://opendendro.github.io/dplR-workshop/ to get started.
## 
## Attaching package: 'dplR'
## 
## The following object is masked from 'package:zoo':
## 
##     time<-

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, 2021-2016",
     y = "Frequency",
     x = "Rate of Returns",
     caption = "A typic 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, 2021-2016"
## 
## $caption
## [1] "A typic monthly return is higher for SPY and IJS than for AGG, EEM, and EFA."
## 
## attr(,"class")
## [1] "labels"