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.
Global surface temperatures
The data this week comes from the NASA GISS Surface Temperature Analysis (GISTEMP v4). This datasets are tables of global and hemispheric monthly means and zonal annual means. They combine land-surface, air and sea-surface water temperature anomalies (Land-Ocean Temperature Index, L-OTI). The values in the tables are deviations from the corresponding 1951-1980 means.
The GISS Surface Temperature Analysis version 4 (GISTEMP v4) is an estimate of global surface temperature change. Graphs and tables are updated around the middle of every month using current data files from NOAA GHCN v4 (meteorological stations) and ERSST v5 (ocean areas), combined as described in their publications Hansen et al. (2010) and Lenssen et al. (2019). These updated files incorporate reports for the previous month and also late reports and corrections for earlier months.
When comparing seasonal temperatures, it is convenient to use “meteorological seasons” based on temperature and defined as groupings of whole months. Thus, Dec-Jan-Feb (DJF) is the Northern Hemisphere meteorological winter, Mar-Apr-May (MAM) is N.H. meteorological spring, Jun-Jul-Aug (JJA) is N.H. meteorological summer and Sep-Oct-Nov (SON) is N.H. meteorological autumn. String these four seasons together and you have the meteorological year that begins on Dec. 1 and ends on Nov. 30 (D-N). The full year is Jan to Dec (J-D). Brian Bartling
An analysis and more information on the data can be found in Lenssen, N., G. Schmidt, J. Hansen, M. Menne, A. Persin, R. Ruedy, and D. Zyss, 2019: Improvements in the GISTEMP uncertainty model. J. Geophys. Res. Atmos., 124, no. 12, 6307-6326, doi:10.1029/2018JD029522.
There’s also more detail and answers to commonly asked in questions in their FAQ.
Citation: GISTEMP Team, 2023: GISS Surface Temperature Analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies. Dataset accessed 2023-07-09 at https://data.giss.nasa.gov/gistemp/.
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('2023-07-11')
tuesdata <- tidytuesdayR::tt_load(2023, week = 28)
global_temps <- tuesdata$global_temps
nh_temps <- tuesdata$nh_temps
sh_temps <- tuesdata$sh_temps
zonann_temps <- tuesdata$zonann_temps
# Or read in the data manually
global_temps <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-07-11/global_temps.csv')
nh_temps <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-07-11/nh_temps.csv')
sh_temps <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-07-11/sh_temps.csv')
zonann_temps <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-07-11/zonann_temps.csv')Data Dictionary
global_temps.csv
| variable | class | description |
|---|---|---|
| Year | double | Year |
| Jan | double | January |
| Feb | double | February |
| Mar | double | March |
| Apr | double | April |
| May | double | May |
| Jun | double | June |
| Jul | double | July |
| Aug | double | August |
| Sep | double | September |
| Oct | double | October |
| Nov | double | November |
| Dec | double | December |
| J-D | double | January-December |
| D-N | double | Decemeber-November |
| DJF | double | December-January-February |
| MAM | double | March-April-May |
| JJA | double | June-July-August |
| SON | double | September-October-November |
nh_temps.csv
| variable | class | description |
|---|---|---|
| Year | double | Year |
| Jan | double | January |
| Feb | double | February |
| Mar | double | March |
| Apr | double | April |
| May | double | May |
| Jun | double | June |
| Jul | double | July |
| Aug | double | August |
| Sep | double | September |
| Oct | double | October |
| Nov | double | November |
| Dec | double | December |
| J-D | double | January-December |
| D-N | double | Decemeber-November |
| DJF | double | December-January-February |
| MAM | double | March-April-May |
| JJA | double | June-July-August |
| SON | double | September-October-November |
sh_temps.csv
| variable | class | description |
|---|---|---|
| Year | double | Year |
| Jan | double | January |
| Feb | double | February |
| Mar | double | March |
| Apr | double | April |
| May | double | May |
| Jun | double | June |
| Jul | double | July |
| Aug | double | August |
| Sep | double | September |
| Oct | double | October |
| Nov | double | November |
| Dec | double | December |
| J-D | double | January-December |
| D-N | double | Decemeber-November |
| DJF | double | December-January-February |
| MAM | double | March-April-May |
| JJA | double | June-July-August |
| SON | double | September-October-November |
zonann_temps.csv
| variable | class | description |
|---|---|---|
| Year | double | Year |
| Glob | double | Global |
| NHem | double | Northern Hemisphere |
| SHem | double | Southern Hemisphere |
| 24N-90N | double | 24N-90N lattitude |
| 24S-24N | double | 24S-24N lattitude |
| 90S-24S | double | 90S-24S lattitude |
| 64N-90N | double | 64N-90N lattitude |
| 44N-64N | double | 44N-64N lattitude |
| 24N-44N | double | 24N-44N lattitude |
| EQU-24N | double | EQU-24N lattitude |
| 24S-EQU | double | 24S-EQU lattitude |
| 44S-24S | double | 44S-24S lattitude |
| 64S-44S | double | 64S-44S lattitude |
| 90S-64S | double | 90S-64S lattitude |
Cleaning Script
Missing data was indicated by ***. Replaced *** with an empty cell, so these would be NAs.