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:
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
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
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
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.
The data this week comes from the European Medicines Agency via Miquel Anglada Girotto on GitHub. We used the source table of all EPARs for human and veterinary medicines, rather than Miquel's scraped data.
Miquel wrote about his exploration of the data.
# 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('2023-03-14')
tuesdata <- tidytuesdayR::tt_load(2023, week = 11)
drugs <- tuesdata$drugs
# Or read in the data manually
drugs <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-03-14/drugs.csv')
| variable | class | description |
|---|---|---|
| category | character | human or veterinary |
| medicine_name | character | brand name of the medicine |
| therapeutic_area | character | semicolon-separated list of therapeutic areas; might benefit from further cleaning |
| common_name | character | international non-proprietary name (INN) or common name |
| active_substance | character | common name of the active chemical in the drug |
| product_number | character | EMEA/H/C/ (human) or EMEA/V/C/ (veterinary) number for the drug |
| patient_safety | logical | has patient safety notices |
| authorisation_status | character | whether the drug was authorised, withdrawn, or refused (or does not have a reported status) |
| atc_code | character | anatomical therapeutic chemical code |
| additional_monitoring | logical | when true, the medicine is under additional monitoring, meaning that it is monitored even more intensively than other medicines |
| generic | logical | whether the drug is a generic medicine, which is developed to be the same as a medicine that has already been authorised, called the reference medicine. A generic medicine contains the same active substance(s) as the reference medicine, and is used at the same dose(s) to treat the same disease(s) |
| biosimilar | logical | whether the drug is a biosimilar medicine, which is a biological medicine highly similar to another already approved biological medicine called the reference medicine. |
| conditional_approval | logical | whether the medicine received a conditional marketing authorisation. This was granted in the interest of public health because the medicine addresses an unmet medical need and the benefit of immediate availability outweighs the risk from less comprehensive data than normally required. |
| exceptional_circumstances | logical | whether the medicine was authorised under exceptional circumstances, because the applicant was unable to provide comprehensive data on the efficacy and safety of the medicine under normal conditions of use. This can happen because the condition to be treated is rare or because collection of full information is not possible or is unethical. |
| accelerated_assessment | logical | whether the medicine had an accelerated assessment. This means that it is a medicine of major interest for public health, so its timeframe for review was 150 evaluation days rather than 210. |
| orphan_medicine | logical | whether the medicine was designated an orphan medicine. This means that it was developed for use against a rare, life-threatening or chronically debilitating condition or, for economic reasons, it would be unlikely to have been developed without incentives. |
| marketing_authorisation_date | date | date on which the drug was authorised |
| date_of_refusal_of_marketing_authorisation | date | date on which the drug was refused |
| marketing_authorisation_holder_company_name | character | name of the company that is authorised to market the drug |
| pharmacotherapeutic_group | character | the target of the drug; might benefit from further cleaning |
| date_of_opinion | date | date on which the opinion was made |
| decision_date | date | date on which the latest decision was made |
| revision_number | integer | integer revision number |
| condition_indication | character | language describing the specific uses of the drug |
| species | character | for veterinary medicines, the target species; might benefit from further cleaning |
| first_published | datetime | datetime when the information was first published |
| revision_date | datetime | datetime of the most recent revision |
| url | character | url for details about the drug and submission |
library(readxl)
library(tidyverse)
library(janitor)
library(here)
# xlsx file downloaded from https://www.ema.europa.eu/en/medicines/download-medicine-data
drugs <- readxl::read_excel(
here::here(
"data",
"2023",
"2023-03-14",
"Medicines_output_european_public_assessment_reports.xlsx"
),
skip = 8
) |>
clean_names() |>
mutate(
category = factor(tolower(category)),
# product_number is based on whether it's human or veterinary, so get rid of
# the extras.
product_number = str_extract(product_number, "\\d+$"),
# These two are often only different in capitalization.
international_non_proprietary_name_inn_common_name = tolower(
international_non_proprietary_name_inn_common_name
),
active_substance = tolower(active_substance),
# These are really just dates that got meaningless hours attached.
marketing_authorisation_date = lubridate::date(
marketing_authorisation_date
),
date_of_refusal_of_marketing_authorisation = lubridate::date(
date_of_refusal_of_marketing_authorisation
),
date_of_opinion = lubridate::date(date_of_opinion),
decision_date = lubridate::date(decision_date),
# Coalesce this in place then later rename and drop the extra column.
human_pharmacotherapeutic_group = coalesce(
human_pharmacotherapeutic_group, vet_pharmacotherapeutic_group
),
atc_code = coalesce(atc_code, at_cvet_code),
# Logicals
patient_safety = patient_safety == "yes",
additional_monitoring = additional_monitoring == "yes",
generic = generic == "yes",
biosimilar = biosimilar == "yes",
conditional_approval = conditional_approval == "yes",
exceptional_circumstances = exceptional_circumstances == "yes",
accelerated_assessment = accelerated_assessment == "yes",
orphan_medicine = orphan_medicine == "yes",
# Other data types
revision_number = as.integer(revision_number),
authorisation_status = factor(tolower(authorisation_status))
) |>
rename(
"common_name" = "international_non_proprietary_name_inn_common_name",
"pharmacotherapeutic_group" = "human_pharmacotherapeutic_group"
) |>
select(
-"vet_pharmacotherapeutic_group",
-"at_cvet_code"
)
write_csv(
drugs,
file = here::here(
"data",
"2023",
"2023-03-14",
"drugs.csv"
)
)