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Carbon and Temperature","monthly_downloads":860,"total_dependent_repos":3,"total_dependent_packages":0,"readme":"# heatwaveR\n\n\u003cp\u003e\u003cstrong\u003eheatwaveR\u003c/strong\u003e provides fast, research-grade tools to detect, classify, and visualise heatwaves and cold-spells in daily or hourly temperature data.\u003c/p\u003e\u003cp\u003eUse it to build climatologies, identify events, summarise metrics, make publication-ready figures.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eheatwaveR\u003c/strong\u003e is the modern, faster successor to \u003ca href=\"https://github.com/ajsmit/RmarineHeatWaves\"\u003e\u003cstrong\u003eRmarineHeatWaves\u003c/strong\u003e\u003c/a\u003e, which in turn was the original R translation of \u003ca href=\"https://github.com/ecjoliver/marineHeatWaves\"\u003e\u003cstrong\u003emarineHeatWaves\u003c/strong\u003e\u003c/a\u003e. This package implements the Hobday et al. conventions that are used by many marine heatwave researchers around the world.\u003c/p\u003e\n\n\u003cdiv class=\"pkgdown-badges\"\u003e\n  \u003ca href=\"https://cran.r-project.org/package=heatwaveR\"\u003e\u003cimg src=\"https://www.r-pkg.org/badges/version/heatwaveR\" alt=\"CRAN version badge\" /\u003e\u003c/a\u003e\n  \u003ca href=\"https://github.com/robwschlegel/heatwaveR/actions\"\u003e\u003cimg src=\"https://github.com/robwschlegel/heatwaveR/workflows/R-CMD-check/badge.svg\" alt=\"R CMD check badge\" /\u003e\u003c/a\u003e\n  \u003ca href=\"https://app.codecov.io/gh/robwschlegel/heatwaveR?branch=master\"\u003e\u003cimg src=\"https://codecov.io/gh/robwschlegel/heatwaveR/branch/master/graph/badge.svg\" alt=\"Codecov badge\" /\u003e\u003c/a\u003e\n  \u003ca href=\"https://doi.org/10.21105/joss.00821\"\u003e\u003cimg src=\"https://joss.theoj.org/papers/10.21105/joss.00821/status.svg\" alt=\"JOSS badge\" /\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n## Install\n\nInstall the CRAN release:\n\n```r\ninstall.packages(\"heatwaveR\")\n```\n\nInstall the development version:\n\n```r\nremotes::install_github(\"robwschlegel/heatwaveR\")\n```\n\n## Start here\n\n\u003cdiv class=\"hw-grid\"\u003e\u003cdiv class=\"hw-card\"\u003e\u003ch3\u003eDetect events\u003c/h3\u003e\u003cp\u003eBuild seasonal climatologies and identify heatwaves or cold-spells with \u003ccode\u003ets2clm()\u003c/code\u003e and \u003ccode\u003edetect_event()\u003c/code\u003e.\u003c/p\u003e\u003cp\u003e\u003ca href=\"https://robwschlegel.github.io/heatwaveR/articles/detection_and_visualisation.html\"\u003eBasic detection and visualisation\u003c/a\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv class=\"hw-card\"\u003e\u003ch3\u003eClassify severity\u003c/h3\u003e\u003cp\u003eAdd Hobday et al. event categories, summarise metrics, and compare events across years or locations.\u003c/p\u003e\u003cp\u003e\u003ca href=\"https://robwschlegel.github.io/heatwaveR/articles/event_categories.html\"\u003eEvent category workflow\u003c/a\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv class=\"hw-card\"\u003e\u003ch3\u003eScale to gridded data\u003c/h3\u003e\u003cp\u003ePrepare OISST or other gridded inputs, then move into larger workflows with \u003cstrong\u003eheatwave3\u003c/strong\u003e.\u003c/p\u003e\u003cp\u003e\u003ca href=\"https://robwschlegel.github.io/heatwaveR/articles/gridded_event_detection.html\"\u003eGridded event detection\u003c/a\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\n## Where to go next\n\n- Browse the [reference index](https://robwschlegel.github.io/heatwaveR/reference/index.html) for function-level details.\n- Work through the [articles index](https://robwschlegel.github.io/heatwaveR/articles/index.html) for practical workflows.\n- Explore the [interactive demo](https://robert-schlegel.shinyapps.io/demoMHW/) for a quick hands-on overview.\n- See the [Marine Heatwave Tracker](https://www.marineheatwaves.org/tracker.html) for a live global application.\n- Check the [citations page](https://robwschlegel.github.io/heatwaveR/CITATIONS.html) for published uses of the package.\n\n## Citation\n\nIf you use **`heatwaveR`** in research or reporting, please cite:\n\nSchlegel, R. W., \u0026 Smit, A. J. (2018). *heatwaveR: A central algorithm for the detection of heatwaves and cold-spells*. Journal of Open Source Software, 3(27), 821. https://doi.org/10.21105/joss.00821\n\nIn R:\n\n```r\ncitation(\"heatwaveR\")\n```\n\n## Core functions\n\n| Function | Role |\n|----|----|\n| `ts2clm()` / `ts2clm3()` | Construct seasonal climatologies and thresholds from a time series. |\n| `detect_event()` / `detect_event3()` | Detect heatwaves or cold-spells and return full event metrics. |\n| `category()` | Apply Hobday et al. (2018) event categories. |\n| `exceedance()` | Detect consecutive exceedances relative to a static threshold. |\n| `block_average()` | Summarise event metrics at annual or block scale. |\n| `event_line()`, `lolli_plot()`, `geom_flame()`, `geom_lolli()` | Create publication-ready visualisations of detected events. |\n\n## Output and metrics\n\nThe `detect_event()` function will return a list of two tibbles (see the **`tidyverse`**), `climatology` and `event`, which are the time series climatology and MHW (or MCS) events, respectively. The climatology contains the full time series of daily temperatures, as well as the the seasonal climatology, the threshold and various aspects of the events that were detected. The software was designed for detecting extreme thermal events, and the units specified below reflect that intended purpose. However, various other kinds of extreme events (e.g. rainfall) may be detected according to the ‘heatwave’ specifications, and if that\nis the case, the appropriate `minDuration` etc. and units of measurement need to be determined by the user.\n\n| Climatology metric | Description |\n|----|----|\n| `doy` | Julian day (day-of-year). For non-leap years it runs 1…59 and 61…366, while leap years run 1…366. This column will be named differently if another name was specified to the `doy` argument. |\n| `t` | The date of the temperature measurement. This column will be named differently if another name was specified to the `x` argument. |\n| `temp` | If the software was used for the purpose for which it was designed, seawater temperature (deg. C) on the specified date will be returned. This column will of course be named differently if another kind of measurement was specified to the `y` argument. |\n| `seas` | Climatological seasonal cycle (deg. C). |\n| `thresh` | Seasonally varying threshold (e.g., 90th percentile) (deg. C). |\n| `var` | Variance (standard deviation) per `doy` of `temp` (deg. C). (not returned by default as of v0.3.5) |\n| `threshCriterion` | Boolean indicating if `temp` exceeds `thresh`. |\n| `durationCriterion` | Boolean indicating whether periods of consecutive `threshCriterion` are \\\u003e= `minDuration`. |\n| `event` | Boolean indicating if all criteria that define a MHW or MCS are met. |\n| `event_no` | A sequential number indicating the ID and order of occurrence of the MHWs or MCSs. |\n\nThe events are summarised using a range of event metrics:\n\n| Event metric | Description |\n|----|----|\n| `event_no` | A sequential number indicating the ID and order of the events. This allows one to match/join results between the `climatology` and `event` outputs. |\n| `index_start` | Row number from the given time series where the event starts. |\n| `index_peak` | Row number from the given time series where the event peaks. |\n| `index_end` | Row number from the given time series where the event ends. |\n| `duration` | Duration of event (days). |\n| `date_start` | Start date of event (date). |\n| `date_peak` | Date of event peak (date). |\n| `date_end` | End date of event (date). |\n| `intensity_mean` | Mean intensity (deg. C). |\n| `intensity_max` | Maximum (peak) intensity (deg. C). |\n| `intensity_var` | Intensity variability (standard deviation) (deg. C). |\n| `intensity_cumulative` | Cumulative intensity (deg. C x days). |\n| `rate_onset` | Onset rate of event (deg. C / day). |\n| `rate_decline` | Decline rate of event (deg. C / day). |\n\n`intensity_max_relThresh`, `intensity_mean_relThresh`, `intensity_var_relThresh`, and `intensity_cumulative_relThresh` are as above except relative to the threshold (e.g., 90th percentile) rather than the seasonal climatology.\n\n`intensity_max_abs`, `intensity_mean_abs`, `intensity_var_abs`, and `intensity_cumulative_abs` are as above except as absolute magnitudes rather than relative to the seasonal climatology or threshold.\n\nNote that `rate_onset` and `rate_decline` will return `NA` when the event begins/ends on the first/last day of the time series. This may be particularly evident when the function is applied to large gridded datasets. Although the other metrics do not contain any errors and provide sensible values, please take this into account in the interpretation of the output. It must also be noted that events whose `date_peak` occur on the same day as the `date_start` or `date_end` of the event will return small negative values. This tends to only occur in areas with persistent ice cover.\n\n## Technical note\n\nThis package does what [RmarineHeatWaves](https://github.com/ajsmit/RmarineHeatWaves) does, but faster. The entire package has been deconstructed, modularised, and we are continuing to implement slow portions of the code in C++. This has alleviated the bottlenecks that slowed down the climatology creation portions of the code as well as generally creating an overall increase in the speed of the calculations. Currently the R code runs about as fast as the original python functions, at least in as far as applying it to single time series of temperatures.\n\nReaders familiar with both languages will know about the ongoing debate around the relative speed of the two. In our experience, R can be as fast as python, provided that attention is paid to finding ways to reduce the computational inefficiencies that stem from i) the liberal use of complex and inefficient non-atomic data structures, such as data frames; ii) the reliance on non-vectorised calculations such as loops; and iii) lazy (but convenient) coding that comes from drawing too heavily on the __`tidyverse`__ suite of packages. We will continue to ensure that heatwaveR becomes more-and-more efficient so that it can be applied to large gridded data products with ease. To that end, the extension package __`heatwave3`__ has been developed. This helps the user to apply the code from __`heatwaveR`__ directly onto their NetCDF and other 3D gridded data files.\n\n__`heatwaveR`__ was also developed and released in order to better accommodate the inclusion of the definitions of atmospheric heatwaves. Additionally, __`heatwaveR`__ also provides the first implementation of a definition for a ‘compound heatwave’. There are currently multiple different definitions for this type of event and each of which has arguments provided for it within the `ts2clm()` and `detect_event()` functions.\n\n## Contributing and support\n\n- Report bugs or request features on [GitHub issues](https://github.com/robwschlegel/heatwaveR/issues).\n- See the [contribution guide](https://robwschlegel.github.io/heatwaveR/CONTRIBUTING.html) for development workflow details.\n- Use the [news page](https://robwschlegel.github.io/heatwaveR/news/index.html) to review recent package changes.\n\n## Acknowledgements\n\nThe [Python code](https://github.com/ecjoliver/marineHeatWaves) was written by Eric C. J. Oliver.\n\nContributors to the Marine Heatwaves definition and its numerical implementation include Alistair J. Hobday, Lisa V. Alexander, Sarah E. Perkins, Dan A. Smale, Sandra C. Straub, Jessica Benthuysen, Michael T. Burrows, Markus G. Donat, Ming Feng, Neil J. Holbrook, Pippa J. Moore, Hillary A. Scannell, Alex Sen Gupta, and Thomas Wernberg.\n\nThe original translation from Python to R was done by A. J. Smit and the graphing functions were contributed by Robert W. Schlegel.\n\n## Contact\n\nRobert W. Schlegel : \u003crobwschlegel@gmail.com\u003e\n\n## References\n\n- Hobday, A. J., et al. (2016). A hierarchical approach to defining marine heatwaves. *Progress in Oceanography, 141*, 227-238.\n- Hobday, A. J., Oliver, E. C. J., Sen Gupta, A., Benthuysen, J. A., Burrows, M. T., Donat, M. G., Holbrook, N. J., Moore, P. J., Thomsen, M. S., Wernberg, T., \u0026 Smale, D. A. (2018). 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