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# 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("T", "DIS", "NVDA", "PG", "PSX")

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") %>% 
    ungroup() %>%
    set_names(c("asset", "date", "returns"))

asset_returns_tbl
## # A tibble: 297 × 3
##    asset date       returns
##    <chr> <date>       <dbl>
##  1 T     2012-01-31 -0.0131
##  2 T     2012-02-29  0.0393
##  3 T     2012-03-30  0.0207
##  4 T     2012-04-30  0.0710
##  5 T     2012-05-31  0.0376
##  6 T     2012-06-29  0.0427
##  7 T     2012-07-31  0.0779
##  8 T     2012-08-31 -0.0343
##  9 T     2012-09-28  0.0285
## 10 T     2012-10-31 -0.0708
## # … with 287 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 = .3, binwidth = .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 T, PSX and DIS than for NVDA, and PG")