Apply it to Your Data 2

Using dplyr Verbs on Stock Prices

knitr::opts_chunk$set(
  echo = TRUE,
  message = FALSE,
  warning = FALSE
)

library(tidyverse)
library(tidyquant)

Get Stock Data

symbols <- c("V", "AMZN", "NFLX", "WMT", "KO")

stocks <- tq_get(
  x = symbols,
  get = "stock.prices",
  from = "2016-01-01",
  to   = "2016-12-31"
)

Plot: Stock Price Over Time

# Line plot of adjusted closing prices
ggplot(data = stocks, aes(x = date, y = adjusted, color = symbol)) +
  geom_line(size = 1) +
  labs(title = "Adjusted Closing Prices for Selected Stocks (2016)",
     x = "Date",
     y = "Adjusted Price",
     color = "Ticker") +
  theme_minimal()

This chart shows how the adjusted closing prices of V, AMZN, NFLX, WMT, and KO varied throughout 2016. We can see that Amazon and Netflix had more price volatility, while Coca-Cola (KO) showed a steadier pattern. This reflects the difference between growth and defensive stocks during that time period.

Summary

• AMZN and NFLX showed more price volatility during 2016, likely reflecting their growth stock profiles.
• KO and WMT were more stable, indicating their role as defensive or consumer staples.
• V showed steady growth, which aligns with its position as a financial services leader.
• This plot highlights the performance differences between high-growth and low-volatility stocks.

Apply the dplyr Verbs

Filter: Keep only rows where adjusted price is above $100

filter(stocks, adjusted > 100)

## # A tibble: 102 × 8
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 NFLX   2016-01-04  109   110   105.  110. 20794800     110.
##  2 NFLX   2016-01-05  110.  111.  106.  108. 17664600     108.
##  3 NFLX   2016-01-06  105.  118.  105.  118. 33045700     118.
##  4 NFLX   2016-01-07  116.  122.  112.  115. 33636700     115.
##  5 NFLX   2016-01-08  116.  118.  111.  111. 18067100     111.
##  6 NFLX   2016-01-11  112.  117.  111.  115. 21920400     115.
##  7 NFLX   2016-01-12  116.  118.  115.  117. 15133500     117.
##  8 NFLX   2016-01-13  114.  114.  105.  107. 24921600     107.
##  9 NFLX   2016-01-14  106.  109.  101.  107. 23664800     107.
## 10 NFLX   2016-01-15  102.  106.  102.  104. 19775100     104.
## # ℹ 92 more rows

Arrange: Sort by adjusted price, descending

arrange(stocks, desc(adjusted))

## # A tibble: 1,260 × 8
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 NFLX   2016-12-27  126.  129.  126.  128.  5288200     128.
##  2 NFLX   2016-10-21  122.  128.  122.  128. 18832400     128.
##  3 NFLX   2016-10-24  127.  129.  126.  127. 15980700     127.
##  4 NFLX   2016-10-26  126.  129.  126.  127.  8602400     127.
##  5 NFLX   2016-10-28  126.  129.  126.  127.  7077300     127.
##  6 NFLX   2016-10-25  127.  128.  126.  127.  8253900     127.
##  7 NFLX   2016-12-21  126.  128.  125   126.  6514800     126.
##  8 NFLX   2016-10-27  127.  128.  126.  126.  6914200     126.
##  9 NFLX   2016-12-28  129.  129.  126.  126.  4389200     126.
## 10 NFLX   2016-12-23  126.  126.  125.  126.  2010400     126.
## # ℹ 1,250 more rows

Select: Keep only symbol, date, and adjusted price

select(stocks, symbol, date, adjusted)

## # A tibble: 1,260 × 3
##    symbol date       adjusted
##    <chr>  <date>        <dbl>
##  1 V      2016-01-04     70.8
##  2 V      2016-01-05     71.4
##  3 V      2016-01-06     70.4
##  4 V      2016-01-07     69.1
##  5 V      2016-01-08     68.2
##  6 V      2016-01-11     69.2
##  7 V      2016-01-12     70.0
##  8 V      2016-01-13     68.4
##  9 V      2016-01-14     69.1
## 10 V      2016-01-15     67.2
## # ℹ 1,250 more rows

Mutate: Add daily range column (high - low)

mutate(stocks, range = high - low)

## # A tibble: 1,260 × 9
##    symbol date        open  high   low close   volume adjusted range
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl> <dbl>
##  1 V      2016-01-04  76.1  76.4  75.0  75.7 13476800     70.8 1.37 
##  2 V      2016-01-05  76.2  76.5  75.5  76.3  8972700     71.4 0.970
##  3 V      2016-01-06  75    75.8  74.8  75.3 16092000     70.4 1    
##  4 V      2016-01-07  73.8  75.1  73.3  73.8 12470800     69.1 1.74 
##  5 V      2016-01-08  74.1  74.5  72.8  72.9  9977600     68.2 1.76 
##  6 V      2016-01-11  73.1  74.1  72.7  73.9 13069500     69.2 1.42 
##  7 V      2016-01-12  74.5  75    74.0  74.8 10236900     70.0 0.980
##  8 V      2016-01-13  75.3  75.4  73.0  73.1 12659600     68.4 2.40 
##  9 V      2016-01-14  73.2  74.5  72.7  73.8 10426200     69.1 1.86 
## 10 V      2016-01-15  71.9  72.9  70.7  71.8 18470500     67.2 2.29 
## # ℹ 1,250 more rows

Summarize: Average adjusted price by stock

stocks %>%
  group_by(symbol) %>%
  summarize(avg_price = mean(adjusted, na.rm = TRUE))

## # A tibble: 5 × 2
##   symbol avg_price
##   <chr>      <dbl>
## 1 AMZN        35.0
## 2 KO          32.9
## 3 NFLX       102. 
## 4 V           73.2
## 5 WMT         19.7