| Title: | Download and Visualize Essential Climate Change Data |
|---|---|
| Description: | 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, sea levels, Arctic/Antarctic sea-ice, Hurricanes, 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: <doi:10.3334/CDIAC/ATG.009>. |
| Authors: | Hernando Cortina [aut, cre]
|
| Maintainer: | Hernando Cortina <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.8.5 |
| Built: | 2024-11-25 19:32:24 UTC |
| Source: | CRAN |
Plots a 2x2 grid of carbon, temperature, sea ice, and sea level charts.
climate_grid(print = TRUE)climate_grid(print = TRUE)
print |
(boolean) Display climate grid ggplot2 chart, defaults to TRUE. Use FALSE to not display chart. |
warming_stripes invisibly returns a ggplot2 object with 2x2 grid of carbon, temperature, sea ice, and sea level charts from get_carbon, get_temp, get_seaice, and get_sealevel.
By default the chart is also displayed. Users may further modify the output ggplot2 chart.
Invisibly returns a ggplot2 object with climate grid
Hernando Cortina, [email protected]
# Draw grid grid <- climate_grid()# Draw grid grid <- climate_grid()
Plots a treemap of cumulative co2 emissions by country since 1900.
emissions_map( dataset = get_emissions(), print = TRUE, since = 1900, number = "all", title = substitute(paste(since, "-", to, " Cumulative " * CO[2] * " Emissions by Country"), list(since = since, to = as.character(dataset[nrow(dataset), 2]))) )emissions_map( dataset = get_emissions(), print = TRUE, since = 1900, number = "all", title = substitute(paste(since, "-", to, " Cumulative " * CO[2] * " Emissions by Country"), list(since = since, to = as.character(dataset[nrow(dataset), 2]))) )
dataset |
Name of the tibble generated by |
print |
(boolean) Display emissions treemap, defaults to TRUE. Use FALSE to not display chart. |
since |
(numeric) Start year for cumulative emissions, defaults to 1900 if omitted |
number |
(numeric) Number of countries to display in treemap, defaults to all if omitted |
title |
(string) Manually specify chart title |
emissions_map invisibly returns a ggplot2 object with cumulative emissions treemap by country since 1900 from get_emissions.
By default the chart is also displayed. Users may further modify the output ggplot2 chart.
Invisibly returns a ggplot2 object with emissions treemap
Hernando Cortina, [email protected]
# Draw treemap co2map <- emissions_map() co2map <- emissions_map(since=2000, number=20, title="Top 20 Cumulative Emitters Since 2000")# Draw treemap co2map <- emissions_map() co2map <- emissions_map(since=2000, number=20, title="Top 20 Cumulative Emitters Since 2000")
Internal function
file_info_(x)file_info_(x)
x |
filenames |
Retrieves atmospheric carbon dioxide measurements from National Oceanic and Atmospheric Administration Earth System Research Laboratories monitoring laboratory in Mauna Loa, Hawaii. https://gml.noaa.gov/ccgg/trends/data.html
get_carbon(use_cache = TRUE, write_cache = getOption("hs_write_cache"))get_carbon(use_cache = TRUE, write_cache = getOption("hs_write_cache"))
use_cache |
(boolean) Return cached data if available, defaults to TRUE. Use FALSE to fetch updated data. |
write_cache |
(boolean) Write data to cache, defaults to FALSE. Use TRUE to write data to cache for later use. Can also be set using options(hs_write_cache=TRUE) |
get_carbon invisibly returns a tibble with NOAA's monthly average carbon dioxide measurement.
The returned object includes date, year, month, average, trend, std dev, and uncertainty columns.
Trend is NOAA's published trend. Please refer to above website for details.
Invisibly returns a tibble with the monthly carbon dioxide series
Hernando Cortina, [email protected]
Dr. Pieter Tans, NOAA/GML https://gml.noaa.gov/ccgg/trends/ and Dr. Ralph Keeling, Scripps Institution of Oceanography https://scrippsco2.ucsd.edu/.
C.D. Keeling, R.B. Bacastow, A.E. Bainbridge, C.A. Ekdahl, P.R. Guenther, and L.S. Waterman, (1976), Atmospheric carbon dioxide variations at Mauna Loa Observatory, Hawaii, Tellus, vol. 28, 538-551
# Fetch from cache if available, otherwise download: maunaloa <- get_carbon() # # Force fetch from source: maunaloa <- get_carbon(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_carbon(maunaloa)# Fetch from cache if available, otherwise download: maunaloa <- get_carbon() # # Force fetch from source: maunaloa <- get_carbon(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_carbon(maunaloa)
Retrieves the daily air or sea-surface temperature data since 1940 from ClimateReanalyzer.org Source is University of Maine Climate Change Institute. https://climatereanalyzer.org/clim/t2_daily/
get_dailytemp( use_cache = TRUE, write_cache = getOption("hs_write_cache"), region = "W", mean_start = if (region %in% c("WS", "NS", "ws", "ns")) 1982 else 1979, mean_end = 2000 )get_dailytemp( use_cache = TRUE, write_cache = getOption("hs_write_cache"), region = "W", mean_start = if (region %in% c("WS", "NS", "ws", "ns")) 1982 else 1979, mean_end = 2000 )
use_cache |
(boolean) Return cached data if available, defaults to TRUE. Use FALSE to fetch updated data. |
write_cache |
(boolean) Write data to cache, defaults to FALSE. Use TRUE to write data to cache for later use. Can also be set using options(hs_write_cache=TRUE) |
region |
(string) Region selection, defaults to world air temperature. Options are: World Air "W", Northern Hemisphere Air "NW", Southern Hemisphere Air "SW", Tropics Air "TR", Arctic Air "AR", Antarctica Air "AN", World Sea Surface "WS", and North Atlantic Sea Surface "NS". |
mean_start |
(numeric) Start year for historic mean, defaults to 1979. |
mean_end |
(numeric) End year for historic mean, defaults to 2000. |
Invisibly returns a tibble with the daily 2-meter air or sea surface temperatures since 1940 as well as historic mean by day-of-year and current anomaly versus mean.
get_dailytemp invisibly returns a tibble with the daily temperatures since 1940 as well as mean by day-of-year and anomaly. Default to world data, but region can be selected among six options.
Region options include world air (default), Northern Hemisphere air, Southern Hemisphere air, Tropics air, Arctic air, Antarctic air, World sea surface and North Atlantic sea surface and is stored in attribute of output. The historic daily mean-by-day period defaults to 1979-2000. This range can be optionally modified.
Data are updated daily. For day-of-year mean removes observations from February 29 on leap years.
Hernando Cortina, [email protected]
ClimateReanalyzer.org: https://climatereanalyzer.org/clim/t2_daily/
Notes: daily mean surface air temperature (2-meter height) estimates from the ECMWF Reanalysis version 5 (ERA5) for the period January 1940 to present. ERA5 is a state-of-the-art numerical climate/weather modeling framework that ingests surface, radiosonde, and satellite observations to estimate the state of the atmosphere through time. ERA5 files have a horizontal grid resolution of 0.25° x 0.25° (about 31km x 31km at 45°N). Each daily temperature represents an average across all model gridcells within the defined latitude/longitude bounds for the selected domain. The means are area-weighted to account for the convergence of longitude lines at the poles
# Fetch temp anomaly from cache if available: dailytemps <- get_dailytemp() # # Force cache refresh: dailytemps <- get_dailytemp(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_dailytemp(dailytemps) # Change region to Arctic arctictemp <- get_dailytemp(region='AR', use_cache=FALSE)# Fetch temp anomaly from cache if available: dailytemps <- get_dailytemp() # # Force cache refresh: dailytemps <- get_dailytemp(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_dailytemp(dailytemps) # Change region to Arctic arctictemp <- get_dailytemp(region='AR', use_cache=FALSE)
Retrieves the daily air temperature since 1940 from the EU Copernicus Service https://cds.climate.copernicus.eu/#!/home
get_dailytempcop( use_cache = TRUE, write_cache = getOption("hs_write_cache"), region = "W" )get_dailytempcop( use_cache = TRUE, write_cache = getOption("hs_write_cache"), region = "W" )
use_cache |
(boolean) Return cached data if available, defaults to TRUE. Use FALSE to fetch updated data. |
write_cache |
(boolean) Write data to cache, defaults to FALSE. Use TRUE to write data to cache for later use. Can also be set using options(hs_write_cache=TRUE) |
region |
(string) Region selection, defaults to world air temperature. Options are: World Air "W". |
Invisibly returns a tibble with the daily 2-meter air temperatures since 1940 as well as historic mean by day-of-year and current anomaly versus mean.
get_dailytempcop invisibly returns a tibble with the daily temperatures since 1940 as well as mean by day-of-year and anomaly.
Region options include world air (default). The historic daily mean-by-day period defaults to 1991-2020.
Data are updated daily.
Hernando Cortina, [email protected]
Copernicus: https://cds.climate.copernicus.eu/#!/home
Notes: daily mean surface air temperature (2-meter height) estimates from the ECMWF Reanalysis version 5 (ERA5) for the period January 1940 to present. ERA5 is a state-of-the-art numerical climate/weather modeling framework that ingests surface, radiosonde, and satellite observations to estimate the state of the atmosphere through time. ERA5 files have a horizontal grid resolution of 0.25° x 0.25° (about 31km x 31km at 45°N). Each daily temperature represents an average across all model gridcells within the defined latitude/longitude bounds for the selected domain. The means are area-weighted to account for the convergence of longitude lines at the poles
# Fetch temp anomaly from cache if available: dailytemps <- get_dailytempcop() # # Force cache refresh: dailytemps <- get_dailytempcop(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_dailytemp(dailytemps)# Fetch temp anomaly from cache if available: dailytemps <- get_dailytempcop() # # Force cache refresh: dailytemps <- get_dailytempcop(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_dailytemp(dailytemps)
Retrieves Global Carbon Project (GCP) annual global carbon dioxide emissions since 1750 from Our World In Data repository https://github.com/owid/co2-data
get_emissions(use_cache = TRUE, write_cache = getOption("hs_write_cache"))get_emissions(use_cache = TRUE, write_cache = getOption("hs_write_cache"))
use_cache |
(boolean) Return cached data if available, defaults to TRUE. Use FALSE to fetch updated data. |
write_cache |
(boolean) Write data to cache, defaults to FALSE. Use TRUE to write data to cache for later use. Can also be set using options(hs_write_cache=TRUE) |
get_emissions invisibly returns a tibble with GCP's annual carbon dioxide emissions from fossil fuels in aggregate and for every nation.
The returned object includes ISO code, country, year, co2 emissions, growth rates, per capita, and decompositions by industry and gas type.
Please refer to above website for details.
Invisibly returns a tibble with annual carbon dioxide emissions
Hernando Cortina, [email protected]
https://www.globalcarbonproject.org/carbonbudget/
Friedlingstein, P. et al (2020), Global Carbon Budget 2020, Earth System Science Data, vol. 12, 3269-3340 doi:10.5194/essd-12-3269-2020
# Fetch from cache if available: emissions <- get_emissions() # # Force cache refresh: emissions <- get_emissions(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_emissions(emissions)# Fetch from cache if available: emissions <- get_emissions() # # Force cache refresh: emissions <- get_emissions(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_emissions(emissions)
Retrieves Atlantic basin hurricane data since 1851 from National Oceanic and Atmospheric Administration HURDAT Atlantic Hurricane Database Re-analysis Project. https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html
get_hurricanes(use_cache = TRUE, write_cache = getOption("hs_write_cache"))get_hurricanes(use_cache = TRUE, write_cache = getOption("hs_write_cache"))
use_cache |
(boolean) Return cached data if available, defaults to TRUE. Use FALSE to fetch updated data. |
write_cache |
(boolean) Write data to cache, defaults to FALSE. Use TRUE to write data to cache for later use. Can also be set using options(hs_write_cache=TRUE) |
get_hurricanes invisibly returns a tibble with NOAA's annual North Atlantic revised HURDAT hurricane data since 1851.
The returned object includes Year, and number of named storms, hurricanes, major hurricanes (S-S scale 3-5), Accumulated Cyclone Energy (ACE), and U.S. hurricane strikes.
ACE is an index that combines the number of systems, how long they existed and how intense they became. It is calculated by squaring the maximum sustained surface wind in the system every six hours that the cyclone is a Named Storm and summing it up for the season. Please refer to above website for details.
Invisibly returns a tibble with the annual HURDAT hurricane data since 1851
Hernando Cortina, [email protected]
HURDAT North Atlantic Hurricane Database Re-analysis Project, Hurricane Research Division, NOAA https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html.
Data from: https://www.aoml.noaa.gov/hrd/hurdat/comparison_table.html
https://en.wikipedia.org/wiki/Accumulated_cyclone_energy
# Fetch from cache if available: hurricanes <- get_hurricanes() # # Force cache refresh: hurricanes <- get_hurricanes(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_hurricanes(hurricanes)# Fetch from cache if available: hurricanes <- get_hurricanes() # # Force cache refresh: hurricanes <- get_hurricanes(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_hurricanes(hurricanes)
Retrieves Arctic or Antarctic monthly Sea Ice Index time series (in million square km).
Source is the National Snow and Ice Data Center, defaults to Arctic (Northern Hemisphere) monthly sea ice extent since 1979.
https://nsidc.org/data/explore-data
get_icecurves( pole = "N", measure = "extent", use_cache = TRUE, write_cache = getOption("hs_write_cache") )get_icecurves( pole = "N", measure = "extent", use_cache = TRUE, write_cache = getOption("hs_write_cache") )
pole |
'N' for Arctic or 'S' for Antarctic |
measure |
Must be 'extent' or 'area', defaults to 'extent'. Please see terminology link in references for details. |
use_cache |
(boolean) Return cached data if available, defaults to TRUE. Use FALSE to fetch updated data, or to change pole or month in cache. |
write_cache |
(boolean) Write data to cache, defaults to FALSE. Use TRUE to write data to cache for later use. Can also be set using options(hs_write_cache=TRUE) |
Invisibly returns a tibble with the series of monthly Sea Ice Index since 1979 (in million square km).
get_icecurves invisibly returns a tibble with time series of monthly Sea Ice Index since 1979 (in million square km).
User may select monthly Arctic or Antarctic sea ice extent or area (in millions of square kilometers). Defaults to Arctic sea ice extent. https://nsidc.org/sea-ice-today/about-data#area_extent
Hernando Cortina, [email protected]
NSIDC Data Archive: https://nsidc.org/data/explore-data
All About Sea Ice: https://nsidc.org/learn/parts-cryosphere/sea-ice
Sea Ice terminology: https://nsidc.org/learn
# Fetch monthly sea ice history from cache if available: icecurves <- get_icecurves() # # Force cache refresh: icecurves <- get_icecurves(use_cache = FALSE) # change region south_icecurves <- get_icecurves(pole='S', use_cache = FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_icecurves(icecurves)# Fetch monthly sea ice history from cache if available: icecurves <- get_icecurves() # # Force cache refresh: icecurves <- get_icecurves(use_cache = FALSE) # change region south_icecurves <- get_icecurves(pole='S', use_cache = FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_icecurves(icecurves)
Retrieves globally averaged marine surface methane monthly mean data from National Oceanic and Atmospheric Administration. The Global Monitoring Division of NOAA’s Earth System Research Laboratory has measured methane since 1983 at a globally distributed network of air sampling sites. A global average is constructed by first smoothing the data for each site as a function of time, and then smoothed values for each site are plotted as a function of latitude. Global means are calculated from the latitude plot at each time step. https://gml.noaa.gov/ccgg/trends_ch4/ https://gml.noaa.gov/ccgg/about/global_means.html
get_methane(use_cache = TRUE, write_cache = getOption("hs_write_cache"))get_methane(use_cache = TRUE, write_cache = getOption("hs_write_cache"))
use_cache |
(boolean) Return cached data if available, defaults to TRUE. Use FALSE to fetch updated data. |
write_cache |
(boolean) Write data to cache, defaults to FALSE. Use TRUE to write data to cache for later use. Can also be set using options(hs_write_cache=TRUE) |
get_methane invisibly returns a tibble with NOAA's monthly globally averaged methane measurement.
The returned object includes year, month, date, average, average uncertainty, trend, and trend uncertainty columns.
Trend is NOAA's published trend. Please refer to above website for details. CH4 expressed as a mole fraction in dry air, nanomol/mol, abbreviated as ppb.
Invisibly returns a tibble with the monthly methane series
Hernando Cortina, [email protected]
Lan, X., K.W. Thoning, and E.J. Dlugokencky: Trends in globally-averaged CH4, N2O, and SF6 determined from NOAA Global Monitoring Laboratory measurements. Version 2022-11, doi:10.15138/P8XG-AA10
# Fetch from cache if available, otherwise download: ch4 <- get_methane() # # Force fetch from source: ch4 <- get_methane(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_methane(ch4)# Fetch from cache if available, otherwise download: ch4 <- get_methane() # # Force fetch from source: ch4 <- get_methane(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_methane(ch4)
Retrieves historical carbon dioxide and temperature records from the Vostok ice core during the past 420,000 years. Source of data is the U.S. Department of Energy’s (DOE) Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE). https://ess-dive.lbl.gov/ and https://data.ess-dive.lbl.gov/datasets/doi:10.3334/CDIAC/CLI.006
get_paleo(use_cache = TRUE, write_cache = getOption("hs_write_cache"))get_paleo(use_cache = TRUE, write_cache = getOption("hs_write_cache"))
use_cache |
(boolean) Return cached data if available, defaults to TRUE. Use FALSE to fetch updated data. |
write_cache |
(boolean) Write data to cache, defaults to FALSE. Use TRUE to write data to cache for later use. Can also be set using options(hs_write_cache=TRUE) |
Invisibly returns a tibble with the age of the ice (years before C.E.), carbon dioxide (ppm) and temperature (degrees C).
get_paleo invisibly returns a tibble with Vostok ice core data: the age of the ice (years before C.E.), carbon dioxide (ppm) and temperature (degrees C).
Data are from: Barnola J; Raynaud D; Lorius C; Barkov N (2003): Historical Carbon Dioxide Record from the Vostok Ice Core (417,160 - 2,342 years BP) and Petit J R ; Raynaud D ; Lorius C ; Delaygue G ; Jouzel J ; Barkov N I ; Kotlyakov V M (2000): Historical Isotopic Temperature Record from the Vostok Ice Core. CDIAC.
Hernando Cortina, [email protected]
Historical Carbon Dioxide Record from the Vostok Ice Core (US Dept of Energy): https://data.ess-dive.lbl.gov/datasets/doi:10.3334/CDIAC/CLI.006
Petit J R ; Raynaud D ; Lorius C ; Delaygue G ; Jouzel J ; Barkov N I ; Kotlyakov V M (2000): Historical Isotopic Temperature Record from the Vostok Ice Core. CDIAC. doi:10.3334/CDIAC/CLI.006. https://data.ess-dive.lbl.gov/view/doi:10.3334/CDIAC/CLI.006
Barnola J ; Raynaud D ; Lorius C ; Barkov N (2003): Historical Carbon Dioxide Record from the Vostok Ice Core (417,160 - 2,342 years BP). None. doi:10.3334/CDIAC/ATG.009 https://data.ess-dive.lbl.gov/view/doi:10.3334/CDIAC/ATG.009
# Fetch Vostok paleo carbon and temperature data from cache if available: vostok <- get_paleo() # # Force cache refresh: vostok <- get_paleo(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_paleo(vostok)# Fetch Vostok paleo carbon and temperature data from cache if available: vostok <- get_paleo() # # Force cache refresh: vostok <- get_paleo(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_paleo(vostok)
Retrieves Arctic or Antarctic annual Sea Ice Index (in million square km).
Source is the National Snow and Ice Data Center, defaults to Arctic (Northern Hemisphere) July sea ice extent.
https://nsidc.org/data/explore-data
get_seaice( pole = "N", month = "07", measure = "extent", use_cache = TRUE, write_cache = getOption("hs_write_cache") )get_seaice( pole = "N", month = "07", measure = "extent", use_cache = TRUE, write_cache = getOption("hs_write_cache") )
pole |
'N' for Arctic or 'S' for Antarctic |
month |
2-digit month to retrieve sea ice for, defaults to '07' (July) |
measure |
Must be 'extent' or 'area', defaults to 'extent'. Please see terminology link in references for details. |
use_cache |
(boolean) Return cached data if available, defaults to TRUE. Use FALSE to fetch updated data, or to change pole or month in cache. |
write_cache |
(boolean) Write data to cache, defaults to FALSE. Use TRUE to write data to cache for later use. Can also be set using options(hs_write_cache=TRUE) |
Invisibly returns a tibble with the annual series of monthly Sea Ice Index since 1979 (in million square km).
get_seaice invisibly returns a tibble with annual series of monthly Sea Ice Index since 1979 (in million square km).
User may select Arctic or Antarctic sea ice extent or area (in millions of square kilometers) by year for a given month, specified by argument month.
Defaults to Arctic July sea ice extent. https://nsidc.org/sea-ice-today/about-data#area_extent
Hernando Cortina, [email protected]
NSIDC Data Archive: https://nsidc.org/data/explore-data
All About Sea Ice: https://nsidc.org/learn/parts-cryosphere/sea-ice
Sea Ice terminology: https://nsidc.org/learn
# Fetch sea ice from cache if available: seaice <- get_seaice() # # Force cache refresh: seaice <- get_seaice(use_cache = FALSE) # change region and month seaice <- get_seaice(pole='S', month='09', use_cache = FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_seaice(seaice)# Fetch sea ice from cache if available: seaice <- get_seaice() # # Force cache refresh: seaice <- get_seaice(use_cache = FALSE) # change region and month seaice <- get_seaice(pole='S', month='09', use_cache = FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_seaice(seaice)
Retrieves global mean sea level (GMSL) data from historic tide gauge and recent satellite altimeter observations (in mm).
Source for tide gauge data is Commonwealth Scientific and Industrial Research Organisation (CSIRO) as described in Church and White (2011).
https://research.csiro.au/slrwavescoast/sea-level/measurements-and-data/sea-level-data/
Source for satellite data is NOAA Laboratory for Satellite Altimetry:
https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/
get_sealevel(use_cache = TRUE, write_cache = getOption("hs_write_cache"))get_sealevel(use_cache = TRUE, write_cache = getOption("hs_write_cache"))
use_cache |
(boolean) Return cached data if available, defaults to TRUE. Use FALSE to fetch updated data. |
write_cache |
(boolean) Write data to cache, defaults to FALSE. Use TRUE to write data to cache for later use. Can also be set using options(hs_write_cache=TRUE) |
Invisibly returns a tibble with the global mean sea level time series (in mm) over 1880-2013 using tide gauges and since 1993 for satellite measurements.
get_sealevel invisibly returns a tibble with mean sea level in mm time series from tide gauges and satellite observations.
The satellite observations have been releveled so that their mean level in 1993 matches that of the tide gauges.
The tide gauge data are no longer updated and cover the period from 1880 to 2013, per Church, J. A. and N.J. White (2011) https://research.csiro.au/slrwavescoast/sea-level/measurements-and-data/sea-level-data/
Satellite data are updated monthly or more frequently from the NOAA Laboratory for Satellite Altimetry. TOPEX and Jason-1,-2,-3 satellites dataset, with seasonal signals removed. https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/
Hernando Cortina, [email protected]
CSIRO reconstructed tide gauge GMSL for 1880 to 2009: https://research.csiro.au/slrwavescoast/sea-level/measurements-and-data/sea-level-data/
Church, J. A. and N.J. White (2011), Sea-level rise from the late 19th to the early 21st Century. Surveys in Geophysics, doi:10.1007/s10712-011-9119-1. https://link.springer.com/article/10.1007/s10712-011-9119-1
NOAA Laboratory for Satellite Altimetry https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/
# Fetch sea level from cache if available: gmsl <- get_sealevel() # # Force cache refresh: gmsl <- get_sealevel(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_sealevel(gmsl)# Fetch sea level from cache if available: gmsl <- get_sealevel() # # Force cache refresh: gmsl <- get_sealevel(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_sealevel(gmsl)
Retrieves the combined global land- and sea-surface temperature anomaly (Land-Ocean Temperature Index, LOTI). Source is NASA/GISS Surface Temperature Analysis (GISTEMP v4), an estimate of global surface temperature change. https://data.giss.nasa.gov/gistemp/
get_temp(use_cache = TRUE, write_cache = getOption("hs_write_cache"))get_temp(use_cache = TRUE, write_cache = getOption("hs_write_cache"))
use_cache |
(boolean) Return cached data if available, defaults to TRUE. Use FALSE to fetch updated data. |
write_cache |
(boolean) Write data to cache, defaults to FALSE. Use TRUE to write data to cache for later use. Can also be set using options(hs_write_cache=TRUE) |
Invisibly returns a tibble with the annual mean and monthly Combined Land-Surface Air and Sea-Surface Water Temperature Anomalies.
get_temp invisibly returns a tibble with the NASA/GISS annual mean and monthly global temperature anomaly. Data are global from 1880 to present, and represent the deviations from the 1951-1980 mean.
The returned object includes monthly and annual average anomalies, as well as seasonal anomalies. GISS Surface Temperature Analysis (GISTEMP v4) is an estimate of global surface temperature change.
Data are updated around the middle of every month using current data files from NOAA GHCN v4 (meteorological stations) and ERSST v5 (ocean areas). Station data are combined as described in Hansen et al. (2010) https://data.giss.nasa.gov/gistemp/references.html and Lenssen et al. (2019) https://pubs.giss.nasa.gov/abs/le05800h.html
Hernando Cortina, [email protected]
GISS Surface Temperature Analysis (GISTEMP v4): https://data.giss.nasa.gov/gistemp/
GISTEMP Team, 2020: GISS Surface Temperature Analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies.
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. https://pubs.giss.nasa.gov/abs/le05800h.html
# Fetch temp anomaly from cache if available: anomaly <- get_temp() # # Force cache refresh: anomaly <- get_temp(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_temp(anomaly)# Fetch temp anomaly from cache if available: anomaly <- get_temp() # # Force cache refresh: anomaly <- get_temp(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_temp(anomaly)
Retrieves the Common Era Global Surface Temperature Reconstructions. Source is PAGES2k Consortium and NOAA National Centers for Environmental Information. https://www.ncei.noaa.gov/access/paleo-search/study/26872
get_temp2k(use_cache = TRUE, write_cache = getOption("hs_write_cache"))get_temp2k(use_cache = TRUE, write_cache = getOption("hs_write_cache"))
use_cache |
(boolean) Return cached data if available, defaults to TRUE. Use FALSE to fetch updated data. |
write_cache |
(boolean) Write data to cache, defaults to FALSE. Use TRUE to write data to cache for later use. Can also be set using options(hs_write_cache=TRUE) |
Invisibly returns a tibble with filtered and unfiltered temperature reconstructions and Cowtan & Way instrumental temperatures.
get_temp2k invisibly returns a tibble with the PAGES2k Consortium temperature reconstruction (years 1-2000 CE) and instrumental record (years 1850-2017 CE). Temperatures represent deviations from the 1961-1990 mean.
The returned object includes annual average temperature anomalies as well as filtered anomalies using a 31-year Butterworth filter. Reconstructions use seven different statistical methods that draw from a global collection of temperature-sensitive palaeoclimate records.
Methodology described in PAGES2k (2019) https://www.nature.com/articles/s41561-019-0400-0
Hernando Cortina, [email protected]
PAGES2k Common Era Surface Temperature Reconstructions. https://www.ncei.noaa.gov/access/paleo-search/study/26872
PAGES 2k Consortium., Neukom, R., Barboza, L.A. et al. Consistent multidecadal variability in global temperature reconstructions and simulations over the Common Era. Nat. Geosci. 12, 643–649 (2019). doi:10.1038/s41561-019-0400-0
# Fetch temp anomaly from cache if available: anomaly <- get_temp2k() # # Force cache refresh: anomaly <- get_temp2k(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_temp2k(anomaly)# Fetch temp anomaly from cache if available: anomaly <- get_temp2k() # # Force cache refresh: anomaly <- get_temp2k(use_cache=FALSE) # # Review cache contents and last update dates: hockeystick_cache_details() # # Plot output using package's built-in ggplot2 settings plot_temp2k(anomaly)
Internal function
getsize(x)getsize(x)
x |
filenames |
Manage cached datasets
hockeystick_cache_list() hockeystick_cache_delete(files, force = TRUE) hockeystick_cache_delete_all(force = TRUE) hockeystick_cache_details(files = NULL) hockeystick_update_all()hockeystick_cache_list() hockeystick_cache_delete(files, force = TRUE) hockeystick_cache_delete_all(force = TRUE) hockeystick_cache_details(files = NULL) hockeystick_update_all()
files |
(character) one or more complete file names |
force |
(logical) Should files be force deleted? Default: |
cache_delete only accepts 1 file name, while cache_delete_all
doesn't accept any names, but deletes all files. For deleting many
specific files, use cache_delete in a lapply() type call
We cache using tools::R_user_dir(), find your cache
folder by executing tools::R_user_dir("hockeystick","cache")
hockeystick_cache_list() returns a character vector of full path filenames in cache.
hockeystick_cache_delete() no return value, called for side effect.
hockeystick_cache_delete_all() no return value, called for side effect.
hockeystick_cache_details() returns list of filenames and sizes of cached files.
hockeystick_update_all() updates all datasets and caches them. No return value, called for side effect.
hockeystick_cache_list() returns a character vector of full path
file names in cache
hockeystick_cache_delete() deletes one or more files, returns nothing
hockeystick_cache_delete_all() delete all files, returns nothing
hockeystick_cache_details() prints file name and file size of each file, supply with one or more files, or no files (and get details for all available)
hockeystick_update_all() updates the cache with the latest co2, temperature, sea level, and sea ice data.
Caching data sets: ROpenSci guide to persistent config and data for R packages: https://blog.r-hub.io/2020/03/12/user-preferences/
# list files in cache hockeystick_cache_list() # List info for single files hockeystick_cache_details(files = hockeystick_cache_list()[1]) hockeystick_cache_details(files = hockeystick_cache_list()[2]) # List info for all files hockeystick_cache_details() # Delete cached files by name hockeystick_cache_delete(files = hockeystick_cache_list()[1]) # Update all hockeystick data and place in cache hockeystick_update_all() # Delete all cached data hockeystick_cache_delete_all()# list files in cache hockeystick_cache_list() # List info for single files hockeystick_cache_details(files = hockeystick_cache_list()[1]) hockeystick_cache_details(files = hockeystick_cache_list()[2]) # List info for all files hockeystick_cache_details() # Delete cached files by name hockeystick_cache_delete(files = hockeystick_cache_list()[1]) # Update all hockeystick data and place in cache hockeystick_update_all() # Delete all cached data hockeystick_cache_delete_all()
Internal Function
hscache_path()hscache_path()
Return path of data cache directory
Merge NOAA carbon and NASA temperature datasets on common dates.
merge_carbontemp(carbon = get_carbon(), temp = get_temp())merge_carbontemp(carbon = get_carbon(), temp = get_temp())
carbon |
Name of the tibble generated by |
temp |
Name of the tibble generated by |
merge_carbontemp invisibly returns a tibble with the merged data from from get_carbon and get_temp functions.
Tibble only includes data from dates when both datasets are available, essentially from 1960.
Invisibly returns a tibble with merged datasets from get_carbon and get_temp functions.
Hernando Cortina, [email protected]
# Create merged tibble mergedcarbontemp <- merge_carbontemp()# Create merged tibble mergedcarbontemp <- merge_carbontemp()
Plots carbon dioxide data retrieved using get_carbon() with ggplot2. The output ggplot2 object may be modified.
plot_carbon(dataset = get_carbon(), print = TRUE, annot = TRUE)plot_carbon(dataset = get_carbon(), print = TRUE, annot = TRUE)
dataset |
Name of the tibble generated by |
print |
(boolean) Display carbon dioxide ggplot2 chart, defaults to TRUE. Use FALSE to not display chart. |
annot |
(boolean) Include chart annotation with latest date and value, defaults to TRUE. |
plot_carbon invisibly returns a ggplot2 object with a pre-defined carbon dioxide chart using data from get_carbon.
By default the chart is also displayed. Users may further modify the output ggplot2 chart.
Invisibly returns a ggplot2 object with carbon dioxide chart
Hernando Cortina, [email protected]
# Fetch carbon dioxide data: maunaloa <- get_carbon() # # Plot output using package's built-in ggplot2 defaults plot_carbon(maunaloa) # Or just call plot_carbon(), which defaults to get_carbon() dataset plot_carbon() p <- plot_carbon(maunaloa, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='The Keeling Curve')# Fetch carbon dioxide data: maunaloa <- get_carbon() # # Plot output using package's built-in ggplot2 defaults plot_carbon(maunaloa) # Or just call plot_carbon(), which defaults to get_carbon() dataset plot_carbon() p <- plot_carbon(maunaloa, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='The Keeling Curve')
Plots the global monthly mean temperature anomaly vs atmospheric carbon with ggplot2. The output ggplot2 object may be further modified.
plot_carbontemp(dataset = merge_carbontemp(), print = TRUE)plot_carbontemp(dataset = merge_carbontemp(), print = TRUE)
dataset |
Name of the tibble generated by |
print |
(boolean) Display temperature anomaly ggplot2 chart, defaults to TRUE. Use FALSE to not display chart. |
plot_carbontemp invisibly returns a ggplot2 object with a pre-defined temperature anomaly vs carbon chart using data from merge_carbontemp.
By default the chart is also displayed. Users may further modify the output ggplot2 chart.
Invisibly returns a ggplot2 object with temperature anomaly vs carbon chart
Hernando Cortina, [email protected]
# Fetch temperature anomaly: mergedtemp <- merge_carbontemp() # # Plot output using package's built-in ggplot2 defaults plot_carbontemp(mergedtemp) # Or just call plot_carbontemp(), which defaults to merge_carbontemp() dataset plot_carbontemp() p <- plot_carbontemp(mergedtemp, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='The Signature of Climate Change')# Fetch temperature anomaly: mergedtemp <- merge_carbontemp() # # Plot output using package's built-in ggplot2 defaults plot_carbontemp(mergedtemp) # Or just call plot_carbontemp(), which defaults to merge_carbontemp() dataset plot_carbontemp() p <- plot_carbontemp(mergedtemp, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='The Signature of Climate Change')
Plots the daily temperatures since 1940 and current anomaly data retrieved using get_dailytempcop() with ggplot2. The output ggplot2 object may be further modified.
plot_dailytemp( dataset = get_dailytempcop(), print = TRUE, anomaly = FALSE, maxtemp = FALSE, current_year = as.numeric(substr(Sys.Date(), 1, 4)), title_lab = "Daily Average Air Temperature", cop = TRUE )plot_dailytemp( dataset = get_dailytempcop(), print = TRUE, anomaly = FALSE, maxtemp = FALSE, current_year = as.numeric(substr(Sys.Date(), 1, 4)), title_lab = "Daily Average Air Temperature", cop = TRUE )
dataset |
Name of the tibble generated by |
print |
(boolean) Display daily temperature ggplot2 chart, defaults to TRUE. Use FALSE to not display chart. |
anomaly |
(boolean) Display current anomaly versus historic mean, defaults to TRUE. |
maxtemp |
(boolean) Display current deviation versus historic max, defaults to FALSE. |
current_year |
(numeric) Year to highlight in alternate color, defaults to current year. |
title_lab |
(string) Title to override default chart title. Default title pulls region name from dataset attributes. |
cop |
(boolean) Flag for chart caption, TRUE = Copernicus, FALSE =. ClimateReanalyzer.org |
plot_temp invisibly returns a ggplot2 object with a pre-defined daily temperature anomaly chart using data from get_dailytemp.
By default the chart is also displayed. Plots one line per year, as well as mean and anomaly (which may be disabled). Users may further modify the output ggplot2 chart.
Invisibly returns a ggplot2 object with daily temperature anomaly chart
Hernando Cortina, [email protected]
# Fetch temperature anomaly: dailydata <- get_dailytempcop() # # Plot output using package's built-in ggplot2 defaults plot_dailytemp(dailydata) # Don't plot anomaly shading and highight specific year plot_dailytemp(anomaly = FALSE, current_year = 2012) # Or just call plot_temp(), which defaults to get_dailytempcop() dataset plot_dailytemp() p <- plot_dailytemp(dailydata, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Record Temperatures in 2023')# Fetch temperature anomaly: dailydata <- get_dailytempcop() # # Plot output using package's built-in ggplot2 defaults plot_dailytemp(dailydata) # Don't plot anomaly shading and highight specific year plot_dailytemp(anomaly = FALSE, current_year = 2012) # Or just call plot_temp(), which defaults to get_dailytempcop() dataset plot_dailytemp() p <- plot_dailytemp(dailydata, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Record Temperatures in 2023')
Plots carbon dioxide emissions retrieved using get_emissions() with ggplot2. The output ggplot2 object may be modified. Alternative columns from the dataset may also be plotted.
plot_emissions( dataset = get_emissions(), start_year = 1900, region = "World", field = "co2", print = TRUE, annot = TRUE, title_expression = expression("Fossil Combustion " * CO[2] * " Emissions"), yaxis_expression = expression("Gt " * CO[2] * " per year") )plot_emissions( dataset = get_emissions(), start_year = 1900, region = "World", field = "co2", print = TRUE, annot = TRUE, title_expression = expression("Fossil Combustion " * CO[2] * " Emissions"), yaxis_expression = expression("Gt " * CO[2] * " per year") )
dataset |
Name of the tibble generated by |
start_year |
Year to start plot at. Defaults to 1900. Data is available since 1750. |
region |
ISO code of region to plot. Defaults to 'OWID_WRL' which signifies entire world. |
field |
Field from GCP dataset to be plotted, defaults to 'co2' |
print |
(boolean) Display carbon dioxide emissions ggplot2 chart, defaults to TRUE. Use FALSE to not display chart. |
annot |
(boolean) Include chart annotation with latest date and value, defaults to TRUE. |
title_expression |
Chart title, defaults to CO2 emissions |
yaxis_expression |
y-axis label, defaults to Gt CO2 emissions |
plot_emissions invisibly returns a ggplot2 object with a pre-defined carbon dioxide emissions chart using data from get_emissions. Use the field parameter to select alternative columns from the data set such as co2_per_capita.
By default the chart is also displayed. Users may further modify the output ggplot2 chart.
Invisibly returns a ggplot2 object with carbon dioxide emissions chart
Hernando Cortina, [email protected]
# Fetch carbon dioxide emissions: emissions <- get_emissions() # Plot output using package's built-in ggplot2 defaults plot_emissions(emissions) # Or just call plot_emissions(), which defaults to get_emissions() dataset plot_emissions() # You can also select a region by name and start year plot_emissions(region='United States', start_year=1950) p <- plot_emissions(emissions, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Anthropogenic Carbon Emissions') # Plot a different field from GCP dataset plot_emissions(field='co2_per_capita', yaxis_expression=expression(CO[2]*' per capita' ))# Fetch carbon dioxide emissions: emissions <- get_emissions() # Plot output using package's built-in ggplot2 defaults plot_emissions(emissions) # Or just call plot_emissions(), which defaults to get_emissions() dataset plot_emissions() # You can also select a region by name and start year plot_emissions(region='United States', start_year=1950) p <- plot_emissions(emissions, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Anthropogenic Carbon Emissions') # Plot a different field from GCP dataset plot_emissions(field='co2_per_capita', yaxis_expression=expression(CO[2]*' per capita' ))
Plots carbon dioxide emissions (including land use change) retrieved using get_emissions() with ggplot2. The output ggplot2 object may be modified. Alternative columns from the dataset may also be plotted.
plot_emissions_with_land( dataset = get_emissions(), start_year = 1900, region = "World", print = TRUE, annot = TRUE, title_expression = expression("Fossil + Land Use Change " * CO[2] * " Emissions"), yaxis_expression = expression("Gt " * CO[2] * " per year") )plot_emissions_with_land( dataset = get_emissions(), start_year = 1900, region = "World", print = TRUE, annot = TRUE, title_expression = expression("Fossil + Land Use Change " * CO[2] * " Emissions"), yaxis_expression = expression("Gt " * CO[2] * " per year") )
dataset |
Name of the tibble generated by |
start_year |
Year to start plot at. Defaults to 1900. Data is available since 1750. |
region |
ISO code of region to plot. Defaults to 'OWID_WRL' which signifies entire world. |
print |
(boolean) Display carbon dioxide emissions ggplot2 chart, defaults to TRUE. Use FALSE to not display chart. |
annot |
(boolean) Include chart annotation with latest date and value, defaults to TRUE. |
title_expression |
Chart title, defaults to CO2 emissions |
yaxis_expression |
y-axis label, defaults to Gt CO2 emissions |
plot_emissions_with_land invisibly returns a ggplot2 object with a pre-defined carbon dioxide emissions (including land use change) chart using data from get_emissions.
By default the chart is also displayed. Users may further modify the output ggplot2 chart.
Invisibly returns a ggplot2 object with carbon dioxide emissions chart
Hernando Cortina, [email protected]
# Fetch carbon dioxide emissions: emissions <- get_emissions() # Plot output (including land use change) using package's built-in ggplot2 defaults plot_emissions_with_land(emissions) # Or just call plot_emissions_with_land(), which defaults to get_emissions() dataset plot_emissions_with_land() # You can also select a region by name and starting year plot_emissions_with_land(region='United States', start_year=1950) p <- plot_emissions_with_land(emissions, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Anthropogenic Carbon Emissions')# Fetch carbon dioxide emissions: emissions <- get_emissions() # Plot output (including land use change) using package's built-in ggplot2 defaults plot_emissions_with_land(emissions) # Or just call plot_emissions_with_land(), which defaults to get_emissions() dataset plot_emissions_with_land() # You can also select a region by name and starting year plot_emissions_with_land(region='United States', start_year=1950) p <- plot_emissions_with_land(emissions, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Anthropogenic Carbon Emissions')
Plots the hurricane energy data (ACE) retrieved using get_hurricanes() with ggplot2. The output ggplot2 object may be further modified.
plot_hurricane_nrg(dataset = get_hurricanes(), print = TRUE)plot_hurricane_nrg(dataset = get_hurricanes(), print = TRUE)
dataset |
Name of the tibble generated by |
print |
(boolean) Display hurricane ggplot2 chart, defaults to TRUE. Use FALSE to not display chart. |
plot_hurricane_nrg invisibly returns a ggplot2 object with a pre-defined hurricane energy data chart using data from get_hurricanes.
By default the chart is also displayed. Smooths using ggplot2's built-in loess smoother. Users may further modify the output ggplot2 chart.
ACE is an index that combines the number of systems, how long they existed and how intense they became. It is calculated by squaring the maximum sustained surface wind in the system every six hours that the cyclone is a Named Storm and summing it up for the season. Please refer to above website for details.
Invisibly returns a ggplot2 object with hurricane energy chart
Hernando Cortina, [email protected]
# Fetch hurricane data: hurricanes <- get_hurricanes() # # Plot output using package's built-in ggplot2 defaults plot_hurricane_nrg(hurricanes) # Or just call plot_hurricane_nrg(), which defaults to get_hurricanes() dataset plot_hurricane_nrg() p <- plot_hurricane_nrg(hurricanes, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Accumulated Cyclone Energy')# Fetch hurricane data: hurricanes <- get_hurricanes() # # Plot output using package's built-in ggplot2 defaults plot_hurricane_nrg(hurricanes) # Or just call plot_hurricane_nrg(), which defaults to get_hurricanes() dataset plot_hurricane_nrg() p <- plot_hurricane_nrg(hurricanes, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Accumulated Cyclone Energy')
Plots the hurricane data retrieved using get_hurricanes() with ggplot2. The output ggplot2 object may be further modified.
plot_hurricanes(dataset = get_hurricanes(), cat = "major", print = TRUE)plot_hurricanes(dataset = get_hurricanes(), cat = "major", print = TRUE)
dataset |
Name of the tibble generated by |
cat |
(string) Select which category of hurricane to plot. May be "major", "hurricane", or "storm". Defaults to "major". |
print |
(boolean) Display hurricane ggplot2 chart, defaults to TRUE. Use FALSE to not display chart. |
plot_hurricanes invisibly returns a ggplot2 object with a pre-defined hurricane data chart using data from get_hurricanes.
By default the chart is also displayed. Smooths using ggplot2's built-in loess smoother. Users may further modify the output ggplot2 chart.
Categories may be "major" (category 3-5), "hurricane" (category 1-5), or "storm" (named storm).
Invisibly returns a ggplot2 object with hurricanes chart
Hernando Cortina, [email protected]
# Fetch hurricane data: hurricanes <- get_hurricanes() # # Plot output using package's built-in ggplot2 defaults plot_hurricanes(hurricanes) # Or just call plot_hurricanes(), which defaults to get_hurricanes() dataset plot_hurricanes() p <- plot_hurricanes(hurricanes, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Growing number of North Atlantic named storms')# Fetch hurricane data: hurricanes <- get_hurricanes() # # Plot output using package's built-in ggplot2 defaults plot_hurricanes(hurricanes) # Or just call plot_hurricanes(), which defaults to get_hurricanes() dataset plot_hurricanes() p <- plot_hurricanes(hurricanes, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Growing number of North Atlantic named storms')
Plots the monthly Sea Ice Index data retrieved using get_icecurves() with ggplot2. The output ggplot2 object may be further modified.
plot_icecurves(dataset = get_icecurves(), print = TRUE)plot_icecurves(dataset = get_icecurves(), print = TRUE)
dataset |
Name of the tibble generated by |
print |
(boolean) Display sea ice ggplot2 chart, defaults to TRUE. Use FALSE to not display chart. |
plot_icecurves invisibly returns a ggplot2 object with a pre-defined Sea Ice Index chart using data from get_icecurves.
By default the chart is also displayed. Users may further modify the output ggplot2 chart.
Chart consists of one line per year showing monthly sea ice from January through December. Current year is highlighted.
Invisibly returns a ggplot2 object with Sea Ice Index chart
Hernando Cortina, [email protected]
# Fetch historic monthly sea ice data since 1979: icecurves <- get_icecurves() # # Plot output using package's built-in ggplot2 defaults plot_icecurves(icecurves) # Or just call plot_icecurves(), which defaults to get_icecurves() dataset plot_icecurves() p <- plot_icecurves(icecurves, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Shrinking Arctic Sea Ice')# Fetch historic monthly sea ice data since 1979: icecurves <- get_icecurves() # # Plot output using package's built-in ggplot2 defaults plot_icecurves(icecurves) # Or just call plot_icecurves(), which defaults to get_icecurves() dataset plot_icecurves() p <- plot_icecurves(icecurves, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Shrinking Arctic Sea Ice')
Plots atmospheric methane data retrieved using get_methane() with ggplot2. The output ggplot2 object may be modified.
plot_methane(dataset = get_methane(), print = TRUE, annot = TRUE)plot_methane(dataset = get_methane(), print = TRUE, annot = TRUE)
dataset |
Name of the tibble generated by |
print |
(boolean) Display methane ggplot2 chart, defaults to TRUE. Use FALSE to not display chart. |
annot |
(boolean) Include chart annotation with latest date and value, defaults to TRUE. |
plot_methane invisibly returns a ggplot2 object with a pre-defined methane chart using data from get_methane.
By default the chart is also displayed. Users may further modify the output ggplot2 chart.
Invisibly returns a ggplot2 object with methane chart
Hernando Cortina, [email protected]
# Fetch methane data: ch4 <- get_methane() # # Plot output using package's built-in ggplot2 defaults plot_methane(ch4) # Or just call plot_methane(), which defaults to get_methane() dataset plot_methane() p <- plot_methane(ch4, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Trend in Atmospheric Methane')# Fetch methane data: ch4 <- get_methane() # # Plot output using package's built-in ggplot2 defaults plot_methane(ch4) # Or just call plot_methane(), which defaults to get_methane() dataset plot_methane() p <- plot_methane(ch4, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Trend in Atmospheric Methane')
Plots the Vostok ice core data retrieved using get_paleo() with ggplot2. The output ggplot2 object may be further modified.
plot_paleo(dataset = get_paleo(), print = TRUE)plot_paleo(dataset = get_paleo(), print = TRUE)
dataset |
Name of the tibble generated by |
print |
(boolean) Display Vostok ice core ggplot2 chart, defaults to TRUE. Use FALSE to not display chart. |
plot_paleo invisibly returns a ggplot2 object with a pre-defined Vostok ice core chart using data from get_paleo.
The returned chart stacks carbon dioxide concentration over temperature over 420,000 years.
By default the chart is also displayed. Users may further modify the output ggplot2 chart.
Invisibly returns a ggplot2 object with the Vostok chart
Hernando Cortina, [email protected]
Barnola, J.-M., D. Raynaud, C. Lorius, and N.I. Barkov. 2003. Historical CO2 record from the Vostok ice core. In Trends: A Compendium of Data on Global Change. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A
# Fetch Vostok data: vostok <- get_paleo() # # Plot output using package's built-in ggplot2 defaults plot_paleo(vostok) # Or just call plot_paleo(), which defaults to get_paleo() dataset plot_paleo() p <- plot_paleo(vostok, print = FALSE) # Modify plot such as: p + patchwork::plot_annotation(title='A Long History of Carbon')# Fetch Vostok data: vostok <- get_paleo() # # Plot output using package's built-in ggplot2 defaults plot_paleo(vostok) # Or just call plot_paleo(), which defaults to get_paleo() dataset plot_paleo() p <- plot_paleo(vostok, print = FALSE) # Modify plot such as: p + patchwork::plot_annotation(title='A Long History of Carbon')
Plots the Sea Ice Index data retrieved using get_seaice() with ggplot2. The output ggplot2 object may be further modified.
plot_seaice(dataset = get_seaice(), title = "Arctic Sea Ice", print = TRUE)plot_seaice(dataset = get_seaice(), title = "Arctic Sea Ice", print = TRUE)
dataset |
Name of the tibble generated by |
title |
chart title, defaults to Arctic Sea Ice |
print |
(boolean) Display sea ice ggplot2 chart, defaults to TRUE. Use FALSE to not display chart. |
plot_seaice invisibly returns a ggplot2 object with a pre-defined Sea Ice Index chart using data from get_seaice.
By default the chart is also displayed. Users may further modify the output ggplot2 chart.
Invisibly returns a ggplot2 object with Sea Ice Index chart
Hernando Cortina, [email protected]
# Fetch sea ice data: seaice <- get_seaice() # # Plot output using package's built-in ggplot2 defaults plot_seaice(seaice) # Or just call plot_seaice(), which defaults to get_seaice() dataset plot_seaice() p <- plot_seaice(seaice, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Shrinking Arctic Sea Ice')# Fetch sea ice data: seaice <- get_seaice() # # Plot output using package's built-in ggplot2 defaults plot_seaice(seaice) # Or just call plot_seaice(), which defaults to get_seaice() dataset plot_seaice() p <- plot_seaice(seaice, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Shrinking Arctic Sea Ice')
Plots the global mean sea level data retrieved using get_sealevel() with ggplot2. The output ggplot2 object may be further modified.
plot_sealevel(dataset = get_sealevel(), print = TRUE)plot_sealevel(dataset = get_sealevel(), print = TRUE)
dataset |
Name of the tibble generated by |
print |
(boolean) Display sealevel ggplot2 chart, defaults to TRUE. Use FALSE to not display chart. |
plot_sealevel invisibly returns a ggplot2 object with a pre-defined sealevel change chart using data from get_sealevel.
By default the chart is also displayed. Users may further modify the output ggplot2 chart.
Invisibly returns a ggplot2 object with sealevel chart
Hernando Cortina, [email protected]
# Fetch sealevel data: gmsl <- get_sealevel() # # Plot output using package's built-in ggplot2 defaults plot_sealevel(gmsl) # Or just call plot_sealevel(), which defaults to get_sealevel() dataset plot_sealevel() p <- plot_sealevel(gmsl, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Rising Waters')# Fetch sealevel data: gmsl <- get_sealevel() # # Plot output using package's built-in ggplot2 defaults plot_sealevel(gmsl) # Or just call plot_sealevel(), which defaults to get_sealevel() dataset plot_sealevel() p <- plot_sealevel(gmsl, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Rising Waters')
Plots the global annual mean temperature anomaly retrieved using get_temp() with ggplot2. The output ggplot2 object may be further modified.
plot_temp(dataset = get_temp(), print = TRUE)plot_temp(dataset = get_temp(), print = TRUE)
dataset |
Name of the tibble generated by |
print |
(boolean) Display temperature anomaly ggplot2 chart, defaults to TRUE. Use FALSE to not display chart. |
plot_temp invisibly returns a ggplot2 object with a pre-defined temperature annual mean anomaly chart using data from get_temp.
By default the chart is also displayed. Smooths using ggplot2's built-in loess smoother. Users may further modify the output ggplot2 chart.
Invisibly returns a ggplot2 object with temperature anomaly chart
Hernando Cortina, [email protected]
# Fetch temperature anomaly: anomaly <- get_temp() # # Plot output using package's built-in ggplot2 defaults plot_temp(anomaly) # Or just call plot_temp(), which defaults to get_temp() dataset plot_temp() p <- plot_temp(anomaly, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='The Signature of Climate Change')# Fetch temperature anomaly: anomaly <- get_temp() # # Plot output using package's built-in ggplot2 defaults plot_temp(anomaly) # Or just call plot_temp(), which defaults to get_temp() dataset plot_temp() p <- plot_temp(anomaly, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='The Signature of Climate Change')
Plots the monthly mean temperature anomaly retrieved using get_temp() with ggplot2. The output ggplot2 object may be further modified.
plot_temp_monthly(dataset = get_temp(), print = TRUE)plot_temp_monthly(dataset = get_temp(), print = TRUE)
dataset |
Name of the tibble generated by |
print |
(boolean) Display temperature anomaly ggplot2 chart, defaults to TRUE. Use FALSE to not display chart. |
plot_temp_monthly invisibly returns a ggplot2 object with a pre-defined temperature monthly mean anomaly chart using data from get_temp.
By default the chart is also displayed. Smooths using ggplot2's built-in loess smoother. Users may further modify the output ggplot2 chart.
Invisibly returns a ggplot2 object with temperature anomaly chart
Hernando Cortina, [email protected]
# Fetch temperature anomaly: anomaly <- get_temp() # # Plot output using package's built-in ggplot2 defaults plot_temp_monthly(anomaly) # Or just call plot_temp_monthly(), which defaults to get_temp() dataset plot_temp_monthly() p <- plot_temp_monthly(anomaly, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='The Signature of Climate Change')# Fetch temperature anomaly: anomaly <- get_temp() # # Plot output using package's built-in ggplot2 defaults plot_temp_monthly(anomaly) # Or just call plot_temp_monthly(), which defaults to get_temp() dataset plot_temp_monthly() p <- plot_temp_monthly(anomaly, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='The Signature of Climate Change')
Plots scatter of monthly temperature anomaly retrieved using get_temp() with ggplot2. The output ggplot2 object may be further modified.
plot_temp_scatter( dataset = get_temp(), print = TRUE, labelmax = FALSE, labellatest = TRUE )plot_temp_scatter( dataset = get_temp(), print = TRUE, labelmax = FALSE, labellatest = TRUE )
dataset |
Name of the tibble generated by |
print |
(boolean) Display temperature anomaly ggplot2 chart, defaults to TRUE. Use FALSE to not display chart. |
labelmax |
(boolean) Display date of max value, default = FALSE |
labellatest |
(boolean) Display date of latest value, default = TRUE |
plot_temp_scatter invisibly returns a ggplot2 object with a pre-defined temperature monthly mean anomaly chart using data from get_temp.
By default the chart is also displayed. Smooths using ggplot2's built-in loess smoother. Users may further modify the output ggplot2 chart.
Invisibly returns a ggplot2 object with temperature anomaly chart
Hernando Cortina, [email protected]
# Fetch temperature anomaly: anomaly <- get_temp() # # Plot output using package's built-in ggplot2 defaults plot_temp_scatter(anomaly) # Or just call plot_temp_scatter(), which defaults to get_temp() dataset plot_temp_scatter() p <- plot_temp_scatter(anomaly, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='The Signature of Climate Change')# Fetch temperature anomaly: anomaly <- get_temp() # # Plot output using package's built-in ggplot2 defaults plot_temp_scatter(anomaly) # Or just call plot_temp_scatter(), which defaults to get_temp() dataset plot_temp_scatter() p <- plot_temp_scatter(anomaly, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='The Signature of Climate Change')
Plots the Common Era 2000-year global temperature anomaly retrieved using get_temp2k() with ggplot2. The output ggplot2 object may be further modified.
plot_temp2k( dataset = get_temp2k(), instrumental = TRUE, filtered = TRUE, print = TRUE )plot_temp2k( dataset = get_temp2k(), instrumental = TRUE, filtered = TRUE, print = TRUE )
dataset |
Name of the tibble generated by |
instrumental |
(boolean) Include the Cowtan & Way instrumental temperatures through 2017. Defaults to TRUE. |
filtered |
(boolean) Use the filtered temperatures provided by PAGES2k Consortium. Temperatures filtered using a 31-year Butterworth filter. Defaults to TRUE. |
print |
(boolean) Display temperature anomaly ggplot2 chart, defaults to TRUE. Use FALSE to not display chart. |
plot_temp2k invisibly returns a ggplot2 object with a pre-defined temperature anomaly chart using data from get_temp2k.
By default the chart is also displayed. Users may further modify the output ggplot2 chart.
Invisibly returns a ggplot2 object with temperature anomaly chart
Hernando Cortina, [email protected]
# Fetch temperature anomaly: anomaly <- get_temp2k() # # Plot output using package's built-in ggplot2 defaults plot_temp2k(anomaly) # Or just call plot_temp2k(), which defaults to get_temp2k() dataset plot_temp2k() p <- plot_temp2k(anomaly, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Temperature Increase in the Common Era')# Fetch temperature anomaly: anomaly <- get_temp2k() # # Plot output using package's built-in ggplot2 defaults plot_temp2k(anomaly) # Or just call plot_temp2k(), which defaults to get_temp2k() dataset plot_temp2k() p <- plot_temp2k(anomaly, print = FALSE) # Modify plot such as: p + ggplot2::labs(title='Temperature Increase in the Common Era')
Display data cache info Shows filenames and cache file sizes
## S3 method for class 'hockeystick_cache_info' print(x, ...)## S3 method for class 'hockeystick_cache_info' print(x, ...)
x |
filenames |
... |
Additional parameters |
Display data cache info. No return value, called for side effect.
Plots global "warming stripes" graph in the style popularized by Ed Hawkins, based on temperature anomaly retrieved using get_temp().
Function can output stripes chart with legend or a minimal chart. The output ggplot2 object may be further modified.
warming_stripes( dataset = get_temp(), stripe_only = FALSE, col_strip = RColorBrewer::brewer.pal(11, "RdBu"), print = TRUE )warming_stripes( dataset = get_temp(), stripe_only = FALSE, col_strip = RColorBrewer::brewer.pal(11, "RdBu"), print = TRUE )
dataset |
Name of the tibble generated by |
stripe_only |
Display legend and axes, defaults to TRUE |
col_strip |
Color palette to use. Defaults to Red-Blue RColorBrewer palette. |
print |
(boolean) Display warming stripe ggplot2 chart, defaults to TRUE. Use FALSE to not display chart. |
warming_stripes invisibly returns a ggplot2 object with warming stripes chart using data from get_temp.
By default the chart is also displayed. User may modify color palette or remove axes and legend. Users may further modify the output ggplot2 chart.
Invisibly returns a ggplot2 object with warming stripes
Hernando Cortina, [email protected]
Climate Lab. 2018. https://www.climate-lab-book.ac.uk/2018/warming-stripes/
GISS Surface Temperature Analysis (GISTEMP v4): https://data.giss.nasa.gov/gistemp/
GISTEMP Team, 2020: GISS Surface Temperature Analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies.
Dr. Dominic Roye blog post "How to Create Warming Stripes in R": https://dominicroye.github.io/en/2018/how-to-create-warming-stripes-in-r/
# Draw with axes and legend stripes <- warming_stripes() # Draw stripes only stripes <- warming_stripes(stripe_only = TRUE) # Don't display, store for further modifications stripes <- warming_stripes(print = FALSE) # Change color palette stripes <- warming_stripes(stripe_only = TRUE, col_strip = viridisLite::viridis(11))# Draw with axes and legend stripes <- warming_stripes() # Draw stripes only stripes <- warming_stripes(stripe_only = TRUE) # Don't display, store for further modifications stripes <- warming_stripes(print = FALSE) # Change color palette stripes <- warming_stripes(stripe_only = TRUE, col_strip = viridisLite::viridis(11))