# Load packages
library(tidyverse)
library(tidyquant)

1 Import stock prices of your choice

symbols <-c("VOOG", "NVDA", "TSLA")
stocks <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2019-01-01",
                 to = "2022-01-01")
stocks
## # A tibble: 2,271 × 8
##    symbol date        open  high   low close volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl>
##  1 VOOG   2019-01-02  133.  135.  133.  135. 239200     129.
##  2 VOOG   2019-01-03  134.  134.  131.  131. 177400     126.
##  3 VOOG   2019-01-04  133.  137.  133.  136. 198400     130.
##  4 VOOG   2019-01-07  136.  138.  136.  137. 262000     131.
##  5 VOOG   2019-01-08  139.  139.  137.  139. 178600     133.
##  6 VOOG   2019-01-09  139.  140.  138.  139. 207000     133.
##  7 VOOG   2019-01-10  139.  140.  138.  140. 156200     134.
##  8 VOOG   2019-01-11  139.  140.  139.  140. 123400     134.
##  9 VOOG   2019-01-14  139.  139.  138.  139.  75500     133.
## 10 VOOG   2019-01-15  139.  141.  139.  141. 128500     135.
## # ℹ 2,261 more rows

2 Convert prices to returns by quarterly

asset_returns_tbl <- stocks %>%

    
    group_by(symbol) %>%
    tq_transmute(select = adjusted,
                 mutate_fun = periodReturn,
                 period = "quarterly",
                 type = "log") %>%
   
    ungroup() %>%

 
    set_names(c("asset", "date", "returns"))

asset_returns_tbl
## # A tibble: 36 × 3
##    asset date        returns
##    <chr> <date>        <dbl>
##  1 VOOG  2019-03-29  0.140  
##  2 VOOG  2019-06-28  0.0444 
##  3 VOOG  2019-09-30  0.00714
##  4 VOOG  2019-12-31  0.0793 
##  5 VOOG  2020-03-31 -0.155  
##  6 VOOG  2020-06-30  0.231  
##  7 VOOG  2020-09-30  0.111  
##  8 VOOG  2020-12-31  0.102  
##  9 VOOG  2021-03-31  0.0225 
## 10 VOOG  2021-06-30  0.112  
## # ℹ 26 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",
         y = "freqeuncy",
         x = "rate of returns",
         caption = "")

4 Interpret the plot

Tsla was my riskiest stock as in some quarters it had the highest return, but it also had the biggest decrease in quarters. As voog was a lot differnce with a constent increase in rate or return.

5 Change the global chunck options

Hide the code, messages, and warnings