1 Loading the data

This week’s tidy Tuesday data set for 2023-02-07 is concerned with tech company stock prices. Let’s load the data:

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library(dygraphs)
library(quantmod)
library(tidyverse)
library(highcharter)

big_tech_companies <-readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-02-07/big_tech_companies.csv',show_col_types = FALSE)

The data gives us an overview of the symbols for a few tech companies:

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big_tech_companies

# A tibble: 14 × 2
   stock_symbol company                                    
   <chr>        <chr>                                      
 1 AAPL         Apple Inc.                                 
 2 ADBE         Adobe Inc.                                 
 3 AMZN         Amazon.com, Inc.                           
 4 CRM          Salesforce, Inc.                           
 5 CSCO         Cisco Systems, Inc.                        
 6 GOOGL        Alphabet Inc.                              
 7 IBM          International Business Machines Corporation
 8 INTC         Intel Corporation                          
 9 META         Meta Platforms, Inc.                       
10 MSFT         Microsoft Corporation                      
11 NFLX         Netflix, Inc.                              
12 NVDA         NVIDIA Corporation                         
13 ORCL         Oracle Corporation                         
14 TSLA         Tesla, Inc.

Although there was corresponding open and closing data for this week’s data, we will take a slightly different approach and will integrate different packages to retrieve that information. Let’s focus on Netflix Inc., NVIDIA Corporation, and Salesforce Inc.. We can get the stock information for these companies by specifying their symbol, when using the getSymbols() function from the quantmod package:

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NVIDIA <- getSymbols("NVDA", auto.assign = FALSE)
NETFLIX <- getSymbols("NFLX", auto.assign = FALSE)
SALESFORCE <- getSymbols("CRM", auto.assign = FALSE)

If you are curious as to how this data may look like, you can call one of the objects:

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NETFLIX %>% 
  head(4)

           NFLX.Open NFLX.High NFLX.Low NFLX.Close NFLX.Volume NFLX.Adjusted
2007-01-03  3.714286  3.824286 3.677143   3.801429    16440900      3.801429
2007-01-04  3.772857  3.828571 3.585714   3.621429    15959300      3.621429
2007-01-05  3.620000  3.620000 3.492857   3.544286    15190700      3.544286
2007-01-08  3.545714  3.555714 3.367143   3.404286    18344900      3.404286

2 Data Visualisation

Now that we have our data, we can proceed to visualise it. We will use the highcharter package, which has some built in functionality, let’s use open-high-low-close chart for the Netflix and Salesforce stocks, for the NVIDIA stocks let’s use a candlestick plot:

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highchart(type = "stock") |> 
  hc_add_series(NVIDIA) |> 
  hc_add_series(NETFLIX, type = "ohlc") %>% 
  hc_add_series(SALESFORCE, type = "ohlc") %>% 
  hc_add_theme(hc_theme_monokai())

The widget offers some interactive elements, play around with it to understand more about the stock prices of the companies over time!

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