Provides easy access to essential climate change datasets to non-climate experts. Users can download the latest raw data from authoritative sources and view it via pre-defined 'ggplot2' charts. Datasets include atmospheric CO2, methane, emissions, instrumental and proxy temperature records, CMIP6 projections, sea levels, Arctic/Antarctic sea-ice, Hurricanes, Wildfires, and Paleoclimate data. Sources include: NOAA Mauna Loa Laboratory < https://gml.noaa.gov/ccgg/trends/data.html>, Global Carbon Project < https://www.globalcarbonproject.org/carbonbudget/>, NASA GISTEMP < https://data.giss.nasa.gov/gistemp/>, National Snow and Sea Ice Data Center < https://nsidc.org/home>, CSIRO < https://research.csiro.au/slrwavescoast/sea-level/measurements-and-data/sea-level-data/>, NOAA Laboratory for Satellite Altimetry < https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/> and HURDAT Atlantic Hurricane Database < https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html>, Vostok Paleo carbon dioxide and temperature data:

The goal of hockeystick is to make essential Global Heating datasets
easily available in R to non-climate experts. hockeystick users can
download the latest raw data from authoritative sources as well as view
it via pre-defined ggplot2 charts. Datasets include climate projects
through 2100, atmospheric CO2 and CH4, carbon
emissions, instrumental, reconstructed, and paleo ice-core temperature
records, sea levels, hurricanes, and Arctic/Antarctic sea-ice.
The choice of data was originally based on Professor Stefan Rahmstorf’s presentation on The 5 Most Important Data Sets of Climate Science. I came across this on a post on the Open Mind blog. I wrote my own post on obtaining and visualizing this data (now out of date), which is the basis for this package. Additional datasets and visualizations have been added over The name of the package stems from the well known hockeystick temperature chart.
hockeystick was highlighted on the RStudio
RViews
blog by Joseph Rickert as one of the “Top 40” new packages on CRAN in
February 2021.
New in version 1.0.0: Retrieve and plot global maps of future cmip6 projections from WorldClim.
New in version 0.9.0: Global wildfire area burnt and emissions data from GWIS.
To install the latest hockeystick release from CRAN type:
install.packages("hockeystick")
You may alternatively install the development version from https://github.com/cortinah/hockeystick with:
pak::pak("cortinah/hockeystick@testing")
Plot daily global temperatures since 1940 and current anomaly:
library(hockeystick)
globaldaily <- get_dailytempcop()
plot_dailytemp(globaldaily)
Retrieve NOAA/ESRL Mauna Loa CO2 Observatory concentration data and plot:
ml_co2 <- get_carbon()
plot_carbon(ml_co2)
Retrieve 2081-2100 temperature projections and map:
proj <- get_cmip6()
plot_cmip6(proj)
Retrieve 2081-2100 precipation anomaly and map:
anom <- get_cmip6_anom(var = 'prec')
plot_cmip6_anom(anom)
Retrieve GCP global CO2 emissions and plot:
emissions <- get_emissions()
plot_emissions(emissions)
plot_emissions_with_land(emissions)
Visualize cumulative emissions by country:
emissions_map()
Retrieve NASA/GISS global surface temperature anomaly data and plot:
anomaly <- get_temp()
plot_temp(anomaly)
Plot relationship between temperature anomaly and carbon:
plot_carbontemp()
Visualize warming using Ed Hawkins styled “warming stripes”:
warming_stripes()
warming_stripes(stripe_only = TRUE, col_strip = viridisLite::viridis(11))
Retrieve tide gauge and satellite sea level data and plot:
gmsl <- get_sealevel()
plot_sealevel(gmsl)
Retrieve July annual Arctic Sea Ice Index and plot:
seaice <- get_seaice()
plot_seaice(seaice)
get_seaice() arguments can be modified to download Antarctic sea ice,
and allow any month.
You can also visualize sea ice by month and year:
arcticice <- get_icecurves()
plot_icecurves(arcticice)
Retrieve Common Era temperature reconstruction and plot it with instrumental record:
anomaly2k <- get_temp2k()
plot_temp2k(anomaly2k)
Retrieve NOAA HURDAT2 hurricane data and plot:
hurricanes <- get_hurricanes()
plot_hurricanes(hurricanes)
plot_hurricane_nrg(hurricanes)
Retrieve GWIS wildfire data and plot:
usfires <- get_fires_area(place='USA', year = 2026)
plot_fires_area(usfires)
plot_fires_area(usfires, style = 'weekly')
Retrieve NOAA/ESRL CH4 Globally averaged mean data and plot:
ch4 <- get_methane()
plot_methane(ch4)
Retrieve Vostok paleo ice core data and plot:
vostok <- get_paleo()
plot_paleo(vostok)
By default, no climate data is cached, and all data is downloaded every
time any of the get_ functions is called. To cache data for future
use, use the write_cache = TRUE option, available in all of the get_
functions. To download and cache all data use
hockeystick_update_all(). To view the files, date, and size of cached
data use hockeystick_cache_details(). To re-download data from the
source use the use_cache = FALSE argument in any of the get_
functions, for example:
get_carbon(use_cache = FALSE, write_cache = TRUE). To delete all
cached data use hockeystick_cache_delete_all().
Users may also cache data by default by adding
options(hs_write_cache = TRUE)to their script or .Rprofile file.
climate_grid()
warming_stripes() is based on.
https://dominicroye.github.io/blog/how-to-create-warming-stripes-in-r/hockeystick is driven by my interest in tracking climate data and
making it easily available to the community.