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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.

The Portal Project

The data this week comes from the Portal Project. This is a long-term ecological research site studying the dynamics of desert rodents, plants, ants and weather in Arizona.

The Portal Project is a long-term ecological study being conducted near Portal, AZ. Since 1977, the site has been used to study the interactions among rodents, ants and plants and their respective responses to climate. To study the interactions among organisms, they experimentally manipulate access to 24 study plots. This study has produced over 100 scientific papers and is one of the longest running ecological studies in the U.S.

The Weecology research group monitors rodents, plants, ants, and weather. All data from the Portal Project are made openly available in near real-time so that they can provide the maximum benefit to scientific research and outreach. The core dataset is managed using an automated living data workflow run using GitHub and Continuous Analysis.

This dataset focuses on the rodent data. Full data is available through these resources:

The Portal Project data can also be accessed through the Data Retriever, a package manager for data.

Data Retriever

A teaching focused version of the dataset is also maintained with some of the complexities of the data removed to make it easy to use for computational training purposes. This dataset serves as the core dataset for the Data Carpentry Ecology material and has been downloaded almost 50,000 times.

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('2023-05-02')
tuesdata <- tidytuesdayR::tt_load(2023, week = 18)

plots <- tuesdata$plots
species <- tuesdata$species
surveys <- tuesdata$surveys


# Or read in the data manually

plots <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-05-02/plots.csv')
species <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-05-02/species.csv')
surveys <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-05-02/surveys.csv')

Data Dictionary

plots.csv

variable class description
plot double Plot number
treatment character Treatment type

species.csv

variable class description
species character Species
scientificname character Scientific Name
taxa character Taxa
commonname character Common Name
censustarget double Target species (0 or 1)
unidentified double Unidentified (0 or 1)
rodent double Rodent (0 or 1)
granivore double Granivore (0 or 1)
minhfl double Minimum hindfoot length
meanhfl double Mean hindfoot length
maxhfl double Maximum hindfoot length
minwgt double Minimum weight
meanwgt double Mean weight
maxwgt double Maximum weight
juvwgt double Juvenile weight

surveys.csv

variable class description
censusdate double Census date
month double Month
day double Day
year double Year
treatment character Treatment type
plot double Plot number
stake double Stake number
species character Species code
sex character Sex
reprod character Reproductive condition
age character Age
testes character Testes (Scrotal, Recent, or Minor)
vagina character Vagina (Swollen, Plugged, or Both)
pregnant character Pregnant
nipples character Nipples (Enlarged, Swollen, or Both)
lactation character Lactating
hfl double Hindfoot length
wgt double Weight
tag character Primary individual identifier
note2 character Newly tagged individual for 'tag'
ltag character Secondary tag information when ear tags were used in both ears
note3 character Newly tagged individual for 'ltag'

Cleaning Script

Thanks to @ethanwhite for the data cleaning script. This script downloads the data using the {portalr} package. It filters for the species and plot data, and years greater than 1977.

# All packages used in this script:
library(portalr)
library(dplyr)

download_observations(".")
data_tables <- load_rodent_data()

species_data <- data_tables[["species_table"]]
plots_data <- data_tables[["plots_table"]]

plot_treatments <- plots_data %>%
  filter(year > 1977) |>
  mutate(iso_date = as.Date(paste0(year, "-", month, "-", "01")), 
         plot = as.factor(plot)) %>%
  select(iso_date, plot, treatment)

plots_data_longterm <- plot_treatments |>
  group_by(plot) |>
  summarize(treatment = case_when(
              all(treatment == "control") ~ "control",
              all(treatment == "exclosure") ~ "exclosure")) |>
  filter(!is.na(treatment))

species_data <- species_data |>
  filter(censustarget == 1, unidentified == 0)

survey_data <- summarize_individual_rodents(
  time = "date",
  length = "Longterm") |>
  filter(year > 1977) |>
  filter(species %in% unique(species_data$species))

write.csv(survey_data, "surveys.csv", row.names = FALSE, na = "")
write.csv(plots_data_longterm, "plots.csv", row.names = FALSE, na = "")
write.csv(species_data, "species.csv", row.names = FALSE, na = "")