Import stock prices

stocks <- tq_get(c("TSLA", "AMZN", "HD", "NVDA", "LLY"),
                 get = "stock.prices",
                 from = "2016-01-01",)
stocks
## # A tibble: 12,175 × 8
##    symbol date        open  high   low close    volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
##  1 TSLA   2016-01-04  15.4  15.4  14.6  14.9 102406500     14.9
##  2 TSLA   2016-01-05  15.1  15.1  14.7  14.9  47802000     14.9
##  3 TSLA   2016-01-06  14.7  14.7  14.4  14.6  56686500     14.6
##  4 TSLA   2016-01-07  14.3  14.6  14.2  14.4  53314500     14.4
##  5 TSLA   2016-01-08  14.5  14.7  14.1  14.1  54421500     14.1
##  6 TSLA   2016-01-11  14.3  14.3  13.5  13.9  61371000     13.9
##  7 TSLA   2016-01-12  14.1  14.2  13.7  14.0  46378500     14.0
##  8 TSLA   2016-01-13  14.1  14.2  13.3  13.4  61896000     13.4
##  9 TSLA   2016-01-14  13.5  14    12.9  13.7  97360500     13.7
## 10 TSLA   2016-01-15  13.3  13.7  13.1  13.7  83679000     13.7
## # ℹ 12,165 more rows

Plot stock prices

stocks %>%
    
    ggplot(aes(x = date, y = adjusted, color = symbol)) +
    geom_line()

Apply the dyplr verbs you learned in Chapter 5

Filter Rows

stocks %>% filter(adjusted > 24)
## # A tibble: 9,502 × 8
##    symbol date        open  high   low close    volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
##  1 TSLA   2017-06-08  24.2  24.8  24.0  24.7 135922500     24.7
##  2 TSLA   2017-06-13  24.5  25.1  24.4  25.1 177118500     25.1
##  3 TSLA   2017-06-14  25.4  25.6  25.1  25.4 192276000     25.4
##  4 TSLA   2017-06-15  24.8  25.0  24.4  25.0 156397500     25.0
##  5 TSLA   2017-06-16  25.2  25.2  24.7  24.8 100965000     24.8
##  6 TSLA   2017-06-19  25    25.1  24.5  24.7  98239500     24.7
##  7 TSLA   2017-06-20  25.1  25.3  24.6  24.8 111580500     24.8
##  8 TSLA   2017-06-21  25.0  25.1  24.5  25.1  73848000     25.1
##  9 TSLA   2017-06-22  25.2  25.7  24.9  25.5 112947000     25.5
## 10 TSLA   2017-06-23  25.5  25.8  25.3  25.6  96687000     25.6
## # ℹ 9,492 more rows

Arrange Rows

arrange(stocks, desc(date), desc(high))
## # A tibble: 12,175 × 8
##    symbol date        open  high   low close    volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
##  1 LLY    2025-09-09  734.  753.  734.  751.   2986900     751.
##  2 HD     2025-09-09  418.  419.  413.  415.   3516000     415.
##  3 TSLA   2025-09-09  348.  351.  344.  347.  53816000     347.
##  4 AMZN   2025-09-09  236.  239.  235.  238.  27033800     238.
##  5 NVDA   2025-09-09  169.  171.  167.  171. 157548400     171.
##  6 LLY    2025-09-08  730.  743.  719.  739.   4534600     739.
##  7 HD     2025-09-08  418.  421.  416.  420.   3742600     420.
##  8 TSLA   2025-09-08  355.  358.  345.  346.  75208300     346.
##  9 AMZN   2025-09-08  235.  238.  234.  236.  33947100     236.
## 10 NVDA   2025-09-08  168.  171.  167.  168. 163769100     168.
## # ℹ 12,165 more rows

Select Columns

select(stocks, date, symbol, adjusted)
## # A tibble: 12,175 × 3
##    date       symbol adjusted
##    <date>     <chr>     <dbl>
##  1 2016-01-04 TSLA       14.9
##  2 2016-01-05 TSLA       14.9
##  3 2016-01-06 TSLA       14.6
##  4 2016-01-07 TSLA       14.4
##  5 2016-01-08 TSLA       14.1
##  6 2016-01-11 TSLA       13.9
##  7 2016-01-12 TSLA       14.0
##  8 2016-01-13 TSLA       13.4
##  9 2016-01-14 TSLA       13.7
## 10 2016-01-15 TSLA       13.7
## # ℹ 12,165 more rows

Add Columns

mutate(stocks, 
       daily_returns = (adjusted - lag(adjusted))/lag(adjusted)) %>%
    
    # Select symbol, date, daily_returns
    select(symbol, date, daily_returns)
## # A tibble: 12,175 × 3
##    symbol date       daily_returns
##    <chr>  <date>             <dbl>
##  1 TSLA   2016-01-04    NA        
##  2 TSLA   2016-01-05     0.0000895
##  3 TSLA   2016-01-06    -0.0196   
##  4 TSLA   2016-01-07    -0.0155   
##  5 TSLA   2016-01-08    -0.0216   
##  6 TSLA   2016-01-11    -0.0149   
##  7 TSLA   2016-01-12     0.0102   
##  8 TSLA   2016-01-13    -0.0460   
##  9 TSLA   2016-01-14     0.0293   
## 10 TSLA   2016-01-15    -0.00577  
## # ℹ 12,165 more rows
# Using transmute() to only show daily_returns
transmute(stocks, daily_returns = (adjusted - lag(adjusted))/lag(adjusted))
## # A tibble: 12,175 × 1
##    daily_returns
##            <dbl>
##  1    NA        
##  2     0.0000895
##  3    -0.0196   
##  4    -0.0155   
##  5    -0.0216   
##  6    -0.0149   
##  7     0.0102   
##  8    -0.0460   
##  9     0.0293   
## 10    -0.00577  
## # ℹ 12,165 more rows

Summarize with groups

stocks %>%
    group_by(symbol) %>%
    summarise(count = n(), avg_monthly_return = mean((adjusted - lag(adjusted))/lag(adjusted), na.rm = TRUE))
## # A tibble: 5 × 3
##   symbol count avg_monthly_return
##   <chr>  <int>              <dbl>
## 1 AMZN    2435           0.00104 
## 2 HD      2435           0.000692
## 3 LLY     2435           0.00115 
## 4 NVDA    2435           0.00271 
## 5 TSLA    2435           0.00200