readme.md

Please add alt text to your posts

Please add alt text (alternative text) to all of your posted graphics for #TidyTuesday.

Twitter provides guidelines for how to add alt text to your images.

The DataViz Society/Nightingale by way of Amy Cesal has an article on writing good alt text for plots/graphs.

Here’s a simple formula for writing alt text for data visualization:

Chart type

It’s helpful for people with partial sight to know what chart type it is and gives context for understanding the rest of the visual. Example: Line graph

Type of data

What data is included in the chart? The x and y axis labels may help you figure this out. Example: number of bananas sold per day in the last year

Reason for including the chart

Think about why you’re including this visual. What does it show that’s meaningful. There should be a point to every visual and you should tell people what to look for. Example: the winter months have more banana sales

Link to data or source

Don’t include this in your alt text, but it should be included somewhere in the surrounding text. People should be able to click on a link to view the source data or dig further into the visual. This provides transparency about your source and lets people explore the data. Example: Data from the USDA

Penn State has an article on writing alt text descriptions for charts and tables.

Charts, graphs and maps use visuals to convey complex images to users. But since they are images, these media provide serious accessibility issues to colorblind users and users of screen readers. See the examples on this page for details on how to make charts more accessible.

The {rtweet} package includes the ability to post tweets with alt text programatically.

Need a reminder? There are extensions that force you to remember to add Alt Text to Tweets with media.

Big Tech Stock Prices

The data this week comes from Yahoo Finance via Kaggle (by Evan Gower).

This dataset consists of the daily stock prices and volume of 14 different tech companies, including Apple (AAPL), Amazon (AMZN), Alphabet (GOOGL), and Meta Platforms (META) and more!

A number of articles have examined the collapse of "Big Tech" stock prices, including this article from morningstar.com.

Note: All stock_symbols have 3271 prices, except META (2688) and TSLA (3148) because they were not publicly traded for part of the period examined.

Get the data here

# Get the Data

# Read in with tidytuesdayR package 
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest

# Either ISO-8601 date or year/week works!

tuesdata <- tidytuesdayR::tt_load('2023-02-07')
tuesdata <- tidytuesdayR::tt_load(2023, week = 6)

big_tech_stock_prices <- tuesdata$big_tech_stock_prices
big_tech_companies <- tuesdata$big_tech_companies

# Or read in the data manually

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

Data Dictionary

big_tech_stock_prices.csv

variable	class	description
stock_symbol	character	stock_symbol
date	double	date
open	double	The price at market open.
high	double	The highest price for that day.
low	double	The lowest price for that day.
close	double	The price at market close, adjusted for splits.
adj_close	double	The closing price after adjustments for all applicable splits and dividend distributions. Data is adjusted using appropriate split and dividend multipliers, adhering to Center for Research in Security Prices (CRSP) standards.
volume	double	The number of shares traded on that day.

big_tech_companies.csv

variable	class	description
stock_symbol	character	stock_symbol
company	character	Full name of the company.

Cleaning Script

library(fs)
library(tidyverse)
library(here)
library(janitor)

# Source for datasets. The datasets were downloaded and extracted to an
# "archive" folder within the working directory for processing, but they are not
# included in this repo.
"https://www.kaggle.com/datasets/evangower/big-tech-stock-prices"

# This is mostly equivalent to fs::dir_map, but we need to keep the info from
# the filename.
big_tech_stock_prices_list <- purrr::map(
  fs::dir_ls(
    here::here("data", "2023", "2023-02-07", "archive"),
    glob = "*.csv"
  ),
  \(path) {
    ticker <- fs::path_file(path) |> fs::path_ext_remove()
    readr::read_csv(
      file = path,
      col_types = cols(
        Date = col_date(format = ""),
        Open = col_double(),
        High = col_double(),
        Low = col_double(),
        Close = col_double(),
        `Adj Close` = col_double(),
        Volume = col_double()
      )
    ) |> 
      dplyr::mutate(stock_symbol = ticker, .before = 1)
  }
)

big_tech_stock_prices <- purrr::list_rbind(big_tech_stock_prices_list) |> 
  janitor::clean_names()
dplyr::glimpse(big_tech_stock_prices)

readr::write_csv(
  big_tech_stock_prices,
  here::here(
    "data", "2023", "2023-02-07",
    "big_tech_stock_prices.csv"
  )
)

big_tech_stock_prices |> 
  dplyr::count(stock_symbol, sort = TRUE)


# Make a lookup for the symbols.
tibble::tibble(
  stock_symbol = c(
    "AAPL",
    "ADBE",
    "AMZN",
    "CRM",
    "CSCO",
    "GOOGL",
    "IBM",
    "INTC",
    "META",
    "MSFT",
    "NFLX",
    "NVDA",
    "ORCL",
    "TSLA"
  ),
  company = c(
    "Apple Inc.",
    "Adobe Inc.",
    "Amazon.com, Inc.",
    "Salesforce, Inc.",
    "Cisco Systems, Inc.",
    "Alphabet Inc.",
    "International Business Machines Corporation",
    "Intel Corporation",
    "Meta Platforms, Inc.",
    "Microsoft Corporation",
    "Netflix, Inc.",
    "NVIDIA Corporation",
    "Oracle Corporation",
    "Tesla, Inc."
  )
) |> 
  readr::write_csv(
    here::here(
      "data", "2023", "2023-02-07",
      "big_tech_companies.csv"
    )
  )