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-[{"path":"https://adrientaudiere.github.io/greenAlgoR/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc. .","code":" Copyright (C) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://adrientaudiere.github.io/greenAlgoR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Adrien Taudière. Author, maintainer, copyright holder.","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Taudière (2024). greenAlgoR: Compute ecological footprint R. R package version 0.1, https://adrientaudiere.github.io/greenAlgoR/, https://github.com/adrientaudiere/greenAlgoR.","code":"@Manual{, title = {greenAlgoR: Compute ecological footprint in R}, author = {Adrien Taudière}, year = {2024}, note = {R package version 0.1, https://adrientaudiere.github.io/greenAlgoR/}, url = {https://github.com/adrientaudiere/greenAlgoR}, }"},{"path":"https://adrientaudiere.github.io/greenAlgoR/index.html","id":"greenalgor","dir":"","previous_headings":"","what":"Compute ecological footprint in R","title":"Compute ecological footprint in R","text":"package computes ecological footprint R (based green-algorithms, Lannelongue, Grealey, Inouye (2021)). aim facilitate programmatic used green-algorithms using function ga_footprint(). Moreover, greenAlgoR made simple compute ecological footprint {targets} pipelines using function ga_targets(). beta-version. Please submit issues found problems want contribute.","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Compute ecological footprint in R","text":"greenAlgoR available CRAN moment. can install stable development version GitHub :","code":"if (!require(\"devtools\", quietly = TRUE)) { install.packages(\"devtools\") } devtools::install_github(\"adrientaudiere/greenAlgoR\")"},{"path":[]},{"path":"https://adrientaudiere.github.io/greenAlgoR/index.html","id":"the-algorithm-from-lannelongue-grealey-and-inouye-2021","dir":"","previous_headings":"Scientific foundation","what":"The algorithm from Lannelongue, Grealey, and Inouye (2021)","title":"Compute ecological footprint in R","text":"can refer green-algorithms Lannelongue, Grealey, Inouye (2021) understand algorithm. short description green-algorithms carbon footprint calculated estimating energy draw algorithm carbon intensity producing energy given location: energy needed : power draw computing cores depends model number cores, memory power draw depends size memory available. usage factor corrects real core usage (default 1, .e. full usage). PUE (Power Usage Effectiveness) measures much extra energy needed operate data centre (cooling, lighting etc.). PSF (Pragmatic Scaling Factor) used take account multiple identical runs (e.g. testing optimisation). Carbon Intensity depends location technologies used produce electricity.","code":"carbon footprint = energy needed * carbon intensity runtime * (power draw for cores * usage + power draw for memory) * PUE * PSF"},{"path":"https://adrientaudiere.github.io/greenAlgoR/index.html","id":"basic-use","dir":"","previous_headings":"","what":"Basic use","title":"Compute ecological footprint in R","text":"","code":"devtools::load_all() library(\"greenAlgoR\")"},{"path":[]},{"path":"https://adrientaudiere.github.io/greenAlgoR/index.html","id":"using-explicit-input","dir":"","previous_headings":"Basic use > In classical workflow","what":"Using explicit input","title":"Compute ecological footprint in R","text":"","code":"# Your model must match exactly a name in TDP_cpu_internal$model fp_12h <- ga_footprint(runtime_h = 12, cpu_model = \"Core i3-10300\") fp_12h$carbon_footprint_total_gCO2 #> [1] 829.0232 fp_12h$energy_needed_kWh #> [1] 1.745312 ggplot(fp_12h$ref_value, aes( y = variable, x = as.numeric(value), fill = log10(prop_footprint) )) + geom_col() + geom_col( data = data.frame( variable = \"Total\", value = fp_12h$carbon_footprint_total_gCO2 ), fill = \"grey30\" ) + geom_col( data = data.frame( variable = \"Cores\", value = fp_12h$carbon_footprint_cores ), fill = \"darkred\" ) + geom_col( data = data.frame( variable = \"Memory\", value = fp_12h$carbon_footprint_memory ), fill = \"orange\" ) + scale_x_continuous( trans = \"log1p\", breaks = c(0, 10^c(1:max(log1p(as.numeric(fp_12h$ref_value$value))))) ) + geom_vline( xintercept = fp_12h$carbon_footprint_total_gCO2, col = \"grey30\", lwd = 1.2 ) + geom_label( aes(label = round_conditionaly(prop_footprint)), fill = \"grey90\", position = position_stack(vjust = 1.1) ) + labs( title = \"Carbon footprint of the analysis\", subtitle = paste0(\"(\", fp_12h$carbon_footprint_total_gCO2, \" g CO2\", \")\"), caption = \"Please cite Lannelongue et al. 2021 (10.1002/advs.202100707)\" ) + xlab(\"Carbon footprint (g CO2) in log10\") + ylab(\"Modality\") + theme(legend.position = \"none\")"},{"path":"https://adrientaudiere.github.io/greenAlgoR/index.html","id":"based-on-the-r-session","dir":"","previous_headings":"Basic use > In classical workflow","what":"Based on the R session","title":"Compute ecological footprint in R","text":"","code":"fp_session <- ga_footprint(runtime_h = \"session\", add_storage_estimation = TRUE) fp_session$carbon_footprint_total_gCO2 #> user.self #> 0.01989738 fp_session$energy_needed_kWh #> user.self #> 4.188922e-05"},{"path":"https://adrientaudiere.github.io/greenAlgoR/index.html","id":"based-on-a-targets-pipeline","dir":"","previous_headings":"Basic use","what":"Based on a targets pipeline","title":"Compute ecological footprint in R","text":"","code":"ga_targets()"},{"path":"https://adrientaudiere.github.io/greenAlgoR/index.html","id":"roadmap","dir":"","previous_headings":"","what":"Roadmap","title":"Compute ecological footprint in R","text":"Automatically find cpu model using benchmarkme::get_cpu() Submit CRAN?","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/csv_from_url_ga.html","id":null,"dir":"Reference","previous_headings":"","what":"Load csv files from green algo github repositories — csv_from_url_ga","title":"Load csv files from green algo github repositories — csv_from_url_ga","text":"Mainly internal use","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/csv_from_url_ga.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load csv files from green algo github repositories — csv_from_url_ga","text":"","code":"csv_from_url_ga(url, remove_first_line = TRUE)"},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/csv_from_url_ga.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load csv files from green algo github repositories — csv_from_url_ga","text":"url url raw csv file remove_first_line (logical, default TRUE): remove first line csv file.","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/csv_from_url_ga.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load csv files from green algo github repositories — csv_from_url_ga","text":"data.frame","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/csv_from_url_ga.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Load csv files from green algo github repositories — csv_from_url_ga","text":"Adrien Taudière","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/csv_from_url_ga.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Load csv files from green algo github repositories — csv_from_url_ga","text":"","code":"carbon_intensity_internal <- csv_from_url_ga(\"https://raw.githubusercontent.com/GreenAlgorithms/green-algorithms-tool/refs/heads/master/data/v2.2/CI_aggregated.csv\")"},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_footprint.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute footprint in grams of CO2 using Lannelongue et al. 2021 algorithm — ga_footprint","title":"Compute footprint in grams of CO2 using Lannelongue et al. 2021 algorithm — ga_footprint","text":"Please cite Lannelongue, L., Grealey, J., Inouye, M., Green Algorithms: Quantifying Carbon Footprint Computation. Adv. Sci. 2021, 2100707. https://doi.org/10.1002/advs.202100707 Default value https://github.com/GreenAlgorithms/green-algorithms-tool: PUE: https://github.com/GreenAlgorithms/green-algorithms-tool/blob/master/data/v2.2/defaults_PUE.csv TDP_per_core: https://raw.githubusercontent.com/GreenAlgorithms/green-algorithms-tool/refs/heads/master/data/v2.2/TDP_cpu.csv power_draw_per_gb: https://onlinelibrary.wiley.com/doi/10.1002/advs.202100707 Description algorithm green-algorithms website: \"\"\" carbon footprint calculated estimating energy draw algorithm carbon intensity producing energy given location: $$carbon footprint = energy needed * carbon intensity$$ energy needed : $$runtime * (power draw cores * usage + power draw memory) * PUE * PSF$$ power draw computing cores depends model number cores, memory power draw depends size memory available. usage factor corrects real core usage (default 1, .e. full usage). PUE (Power Usage Effectiveness) measures much extra energy needed operate data centre (cooling, lighting etc.). PSF (Pragmatic Scaling Factor) used take account multiple identical runs (e.g. testing optimisation). Carbon Intensity depends location technologies used produce electricity. note \"energy needed\" [...] independent location. \"\"\"","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_footprint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute footprint in grams of CO2 using Lannelongue et al. 2021 algorithm — ga_footprint","text":"","code":"ga_footprint( runtime_h = NULL, location_code = \"WORLD\", PUE = 1.67, TDP_per_core = 12, n_cores = 1, cpu_model = \"Any\", memory_ram = NULL, power_draw_per_gb = 0.3725, PSF = 1, usage_core = 1, add_ref_values = TRUE, add_storage_estimation = FALSE, mass_storage = NULL, carbon_intensity = NULL, TDP_cpu = NULL, ref_value = NULL )"},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_footprint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute footprint in grams of CO2 using Lannelongue et al. 2021 algorithm — ga_footprint","text":"runtime_h Run time hours (int). runtime_h == \"session\", runtime compute using actual R session location_code (character list country region available ) PUE (int) Power usage effectiveness server. See https://github.com/GreenAlgorithms/green-algorithms-tool/blob/master/data/v2.2/defaults_PUE.csv example values. using personal computer, set PUE 1. TDP_per_core (int. Watt, default 12). Find cpu TDP nb cpu https://www.techpowerup.com/cpu-specs/ http://calculator.green-algorithms.org/ available. Owerwrite cpu_model param. n_cores (int, default 1) Number cores. Owerwrite cpu_model param. cpu_model Must present list http://calculator.green-algorithms.org/. CPU set, parameter TPD_per_core n_cores overwriting info cpu_model. \"auto\" modality (find cpu using benchmarkme::get_cpu()$model_name) running moment. memory_ram (int. GB) memory RAM. memory_ram NULL, use benchmarkme::get_ram() get RAM. power_draw_per_gb (int. Watt, default 0.3725) power draw GB RAM PSF (int, default 1) Pragmatic Scaling Factor. Citation Lannelongue et al. 2021: \"Many analyses presented single run particular algorithm software tool; however, computations rarely performed . Algorithms run multiple times, sometimes hundreds, systematically manually, different parameterizations. Statistical models may include number combinations covariates, fitting procedures, etc. important include repeats carbon footprint. take account number times computation performed practice, PSF defined, scaling factor estimated GHG emissions multiplied.\" usage_core (int, default 1). usage factor corrects real core usage (default 1, .e. full usage). add_ref_values (logical, default TRUE) compute return reference values compare footprint ? add_storage_estimation (logical, default FALSE) compute footprint mass storage ? default FALSE far less important cpu memory usage. Note green-algorithms website compute mass storage usage. mass_storage (int. GB, default NULL) size mass_storage. used add_storage_estimation set TRUE. set NULL, use base::gc() function estimate storage used. carbon_intensity (default NULL). Advanced users . dataframe location carbonIntensity columns. Set carbon_intensity_internal NULL. carbon_intensity_internal set using command line csv_from_url_ga(\"https://raw.githubusercontent.com/GreenAlgorithms/ green-algorithms-tool/refs/heads/master/data/v2.2/CI_aggregated.csv\") TDP_cpu (default NULL). Advanced users . dataframe model, n_cores TDP_per_core columns. Set TDP_cpu_internal NULL. TDP_cpu_internal set using command line csv_from_url_ga(\"https://raw.githubusercontent.com/GreenAlgorithms/ green-algorithms-tool/refs/heads/master/data/v2.2/TDP_cpu.csv\") ref_value (default NULL). Advanced users . dataframe variable value columns. Set ref_value_internal NULL. ref_value_internal set using command line csv_from_url_ga(\"https://raw.githubusercontent.com/GreenAlgorithms/ green-algorithms-tool/refs/heads/master/data/v2.2/referenceValues.csv\")","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_footprint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute footprint in grams of CO2 using Lannelongue et al. 2021 algorithm — ga_footprint","text":"list values runtime_h: input run time hours location_code: input location code TDP_per_core: input TDP_per_core (cpu_model set, correspond TDP_per_core cpu) n_cores: input n_cores (cpu_model set, correspond n_cores cpu) cpu_model: input cpu model. set \"\", TDP_per_core ncore used memory_ram: input memory ram GB power_draw_per_gb: input power draw per GB usage_core: input usage core carbon_intensity: input carbon intensity (depend location code) PUE: input PUE PSF: input PUE power_draw_for_cores_kWh: output power draw cores kWh power_draw_for_memory_kWh: output power draw RAM memory kWh energy_needed_kWh: output energy needed kWh carbon_footprint_cores: output carbon footprint grams CO2 cores usage carbon_footprint_memory: output carbon footprint grams CO2 memory usage carbon_footprint_total_gCO2: total output carbon footprint grams CO2 ref_value: (optionnal, return add_ref_values TRUE) : dataframe power_draw_storage_kWh: (optionnal, return add_storage_estimation TRUE) output power draw mass storage kWh","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_footprint.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute footprint in grams of CO2 using Lannelongue et al. 2021 algorithm — ga_footprint","text":"Adrien Taudière","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_footprint.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute footprint in grams of CO2 using Lannelongue et al. 2021 algorithm — ga_footprint","text":"","code":"ga_footprint( runtime_h = 12, n_cores = 6, TDP_per_core = 15.8, location_code = \"FR\", PUE = 1, cpu_model = \"Core i5-9600KF\" ) #> $runtime_h #> [1] 12 #> #> $location_code #> [1] \"FR\" #> #> $TDP_per_core #> [1] 15.8 #> #> $n_cores #> [1] 6 #> #> $cpu_model #> [1] \"Core i5-9600KF\" #> #> $memory_ram #> [1] 16.75734 #> #> $power_draw_per_gb #> [1] 0.3725 #> #> $`usage core` #> [1] 1 #> #> $carbon_intensity #> [1] 51.28 #> #> $PUE #> [1] 1 #> #> $PSF #> [1] 1 #> #> $power_draw_for_cores_kWh #> [1] 1.1376 #> #> $power_draw_for_memory_kWh #> [1] 0.0749053 #> #> $energy_needed_kWh #> [1] 1.212505 #> #> $carbon_footprint_cores #> [1] 58.33613 #> #> $carbon_footprint_memory #> [1] 3.841144 #> #> $carbon_footprint_total_gCO2 #> [1] 62.17727 #> #> $ref_value #> variable value #> 1 Total 62.1772718537736 #> 2 Cores 1.1376 #> 3 Memory 0.07490530136064 #> 4 memoryPower 0.3725 #> 5 google_search 10 #> 6 streaming_netflix_perhour 36 #> 7 train_perkm 41 #> 8 flight_economy_perkm 171 #> 9 passengerCar_EU_perkm 175 #> 10 passengerCar_US_perkm 251 #> 11 tree_month 917 #> 12 treeYear 11000 #> 13 flight_PAR-LON 50000 #> 14 flight_NY-SF 570000 #> 15 flight_NYC-MEL 2310000 #> source #> 1 #> 2 #> 3 #> 4 in W/GB from http://dl.acm.org/citation.cfm?doid=3076113.3076117 and https://www.tomshardware.com/uk/reviews/intel-core-i7-5960x-haswell-e-cpu,3918-13.html #> 5 gCO2 from http://www.janavirgin.com/CO2/CO2GLE_about.html #> 6 gCO2 from https://www.carbonbrief.org/factcheck-what-is-the-carbon-footprint-of-streaming-video-on-netflix (update) #> 7 in gCO2/km, but more like 5-37g, from https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2020 #> 8 in gCO2/km, but more like 139-244g, from https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2020 #> 9 in gCO2/km from http://www.sciencedirect.com/science/article/pii/S1352231018307295 and https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2019 #> 10 in gCO2/km from https://www.epa.gov/greenvehicles/greenhouse-gas-emissions-typical-passenger-vehicle #> 11 gCO2 same source as treeYear #> 12 in gCO2/tree/year from http://www.sciencedirect.com/science/article/pii/S0269749101002640 #> 13 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3 #> 14 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3 #> 15 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3 #> prop_footprint #> 1 1.000000e+00 #> 2 1.829607e-02 #> 3 1.204705e-03 #> 4 5.990935e-03 #> 5 1.608305e-01 #> 6 5.789897e-01 #> 7 6.594049e-01 #> 8 2.750201e+00 #> 9 2.814533e+00 #> 10 4.036845e+00 #> 11 1.474815e+01 #> 12 1.769135e+02 #> 13 8.041524e+02 #> 14 9.167337e+03 #> 15 3.715184e+04 #> ga_footprint( runtime_h = \"session\", PUE = 1, ) #> $runtime_h #> user.self #> 0.001775833 #> #> $location_code #> [1] \"WORLD\" #> #> $TDP_per_core #> [1] 12 #> #> $n_cores #> [1] 1 #> #> $cpu_model #> [1] \"Any\" #> #> $memory_ram #> [1] 16.75734 #> #> $power_draw_per_gb #> [1] 0.3725 #> #> $`usage core` #> [1] 1 #> #> $carbon_intensity #> [1] 475 #> #> $PUE #> [1] 1 #> #> $PSF #> [1] 1 #> #> $power_draw_for_cores_kWh #> user.self #> 2.131e-05 #> #> $power_draw_for_memory_kWh #> user.self #> 1.108494e-05 #> #> $energy_needed_kWh #> user.self #> 3.239494e-05 #> #> $carbon_footprint_cores #> user.self #> 0.01012225 #> #> $carbon_footprint_memory #> user.self #> 0.005265349 #> #> $carbon_footprint_total_gCO2 #> user.self #> 0.0153876 #> #> $ref_value #> variable value #> 1 Total 0.0153875985187343 #> 2 Cores 2.131e-05 #> 3 Memory 1.10849442499669e-05 #> 4 memoryPower 0.3725 #> 5 google_search 10 #> 6 streaming_netflix_perhour 36 #> 7 train_perkm 41 #> 8 flight_economy_perkm 171 #> 9 passengerCar_EU_perkm 175 #> 10 passengerCar_US_perkm 251 #> 11 tree_month 917 #> 12 treeYear 11000 #> 13 flight_PAR-LON 50000 #> 14 flight_NY-SF 570000 #> 15 flight_NYC-MEL 2310000 #> source #> 1 #> 2 #> 3 #> 4 in W/GB from http://dl.acm.org/citation.cfm?doid=3076113.3076117 and https://www.tomshardware.com/uk/reviews/intel-core-i7-5960x-haswell-e-cpu,3918-13.html #> 5 gCO2 from http://www.janavirgin.com/CO2/CO2GLE_about.html #> 6 gCO2 from https://www.carbonbrief.org/factcheck-what-is-the-carbon-footprint-of-streaming-video-on-netflix (update) #> 7 in gCO2/km, but more like 5-37g, from https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2020 #> 8 in gCO2/km, but more like 139-244g, from https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2020 #> 9 in gCO2/km from http://www.sciencedirect.com/science/article/pii/S1352231018307295 and https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2019 #> 10 in gCO2/km from https://www.epa.gov/greenvehicles/greenhouse-gas-emissions-typical-passenger-vehicle #> 11 gCO2 same source as treeYear #> 12 in gCO2/tree/year from http://www.sciencedirect.com/science/article/pii/S0269749101002640 #> 13 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3 #> 14 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3 #> 15 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3 #> prop_footprint #> 1 1.000000e+00 #> 2 1.384881e-03 #> 3 7.203817e-04 #> 4 2.420781e+01 #> 5 6.498740e+02 #> 6 2.339546e+03 #> 7 2.664483e+03 #> 8 1.111285e+04 #> 9 1.137279e+04 #> 10 1.631184e+04 #> 11 5.959344e+04 #> 12 7.148614e+05 #> 13 3.249370e+06 #> 14 3.704282e+07 #> 15 1.501209e+08 #> res_ga <- ga_footprint( runtime_h = 12, n_cores = 6, memory_ram = 64, PUE = 1, add_storage_estimation = TRUE, mass_storage = 1 ) ggplot(res_ga$ref_value, aes(y = variable, x = as.numeric(value), fill = log10(prop_footprint))) + geom_col() + geom_col(data = data.frame( variable = \"Total\", value = res_ga$carbon_footprint_total_gCO2 ), fill = \"grey30\") + geom_col(data = data.frame( variable = \"Cores\", value = res_ga$carbon_footprint_cores ), fill = \"darkred\") + geom_col(data = data.frame( variable = \"Memory\", value = res_ga$carbon_footprint_memory ), fill = \"orange\") + geom_col(data = data.frame( variable = \"Mass storage\", value = res_ga$carbon_footprint_storage ), fill = \"violet\") + scale_x_continuous( trans = \"log1p\", breaks = c(0, 10^c(1:max(log1p(as.numeric(res_ga$ref_value$value))))) ) + geom_vline( xintercept = res_ga$carbon_footprint_total_gCO2, col = \"grey30\", lwd = 1.2 ) + geom_label(aes(label = round_conditionaly(prop_footprint)), fill = \"grey90\", position = position_stack(vjust = 1.1) ) + labs( title = \"Carbon footprint of the analysis\", subtitle = paste0( \"(\", res_ga$carbon_footprint_total_gCO2, \" g CO2\", \")\" ), caption = \"Please cite Lannelongue et al. 2021 (10.1002/advs.202100707)\" ) + xlab(\"Carbon footprint (g CO2) in log10\") + ylab(\"Modality\") + theme(legend.position = \"none\")"},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_targets.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute footprint in grams of CO2 for {targets} pipelines — ga_targets","title":"Compute footprint in grams of CO2 for {targets} pipelines — ga_targets","text":"mainly wrapper function ga_footprint() compute run time mass_storage (used add_storage_estimation = TRUE) using targets::tar_meta().","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_targets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute footprint in grams of CO2 for {targets} pipelines — ga_targets","text":"","code":"ga_targets( names = NULL, fields = NULL, targets_only = TRUE, complete_only = FALSE, store = targets::tar_config_get(\"store\"), tar_meta_raw = NULL, ... )"},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_targets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute footprint in grams of CO2 for {targets} pipelines — ga_targets","text":"names Optional, names targets. See ?targets::tar_meta() fields Optional, names columns/fields select. See ?targets::tar_meta() targets_only Logical, whether just show information targets also return metadata functions global objects. complete_only Logical, whether return complete rows (NA values). store Character length 1, path targets data store. See ?targets::tar_meta() tar_meta_raw Optional, NULL, listed options (params targets::tar_meta() used. ... args passed ga_footprint()","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_targets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute footprint in grams of CO2 for {targets} pipelines — ga_targets","text":"list value. See ?ga_footprint details.","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_targets.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute footprint in grams of CO2 for {targets} pipelines — ga_targets","text":"Adrien Taudière","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_targets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute footprint in grams of CO2 for {targets} pipelines — ga_targets","text":"","code":"# In a targets folder, just run function ga_targets() # with the options you want # The next exemple emulate a mini-targets before to ask for tar_meta tar_dir({ # tar_dir() runs code from a temp dir for CRAN. tar_script( { list( tar_target( name = waiting, command = Sys.sleep(2), description = \"Sleep 2 seconds\" ), tar_target(x, writeLines( targets::tar_option_get(\"error\"), \"error.txt\" )) ) }, ask = FALSE ) tar_make() tm <- tar_meta() res_gat <- ga_targets( tar_meta_raw = tm, n_cores = 6, TDP_per_core = 15.8, location_code = \"FR\", PUE = 2, add_storage_estimation = TRUE ) ggplot(res_gat$ref_value, aes( y = reorder(variable, as.numeric(value)), x = as.numeric(value), fill = log10(prop_footprint) )) + geom_col() + geom_col(data = data.frame( variable = \"Total \", value = res_gat$carbon_footprint_total_gCO2 ), fill = \"grey30\") + geom_col( data = data.frame( variable = \"Cores\", value = res_gat$carbon_intensity * res_gat$power_draw_for_cores_kWh ), fill = \"darkred\" ) + geom_col( data = data.frame( variable = \"Memory\", value = res_gat$carbon_intensity * res_gat$power_draw_for_memory_kWh ), fill = \"orange\" ) + geom_col( data = data.frame( variable = \"Storage\", value = res_gat$carbon_intensity * res_gat$power_draw_per_gb ), fill = \"violet\" ) + scale_x_continuous(trans = \"log1p\") + geom_vline( xintercept = res_gat$carbon_footprint_total_gCO2, col = \"grey30\", lwd = 1.2 ) + geom_label(aes(label = round(prop_footprint, 1)), fill = \"grey90\") + xlab(\"g CO^2\") + ylab(\"Modality\") }) #> ▶ dispatched target x #> ● completed target x [0 seconds, 44 bytes] #> ▶ dispatched target waiting #> ● completed target waiting [2.002 seconds, 44 bytes] #> ▶ ended pipeline [2.064 seconds] #>"},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/greenAlgoR-package.html","id":null,"dir":"Reference","previous_headings":"","what":"greenAlgoR package — greenAlgoR-package","title":"greenAlgoR package — greenAlgoR-package","text":"package computes ecological footprint R (based green-algorithms. greenAlgoR also made simple compute ecological footprint targets pipelines..","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/round_conditionaly.html","id":null,"dir":"Reference","previous_headings":"","what":"Round numeric vector conditionaly — round_conditionaly","title":"Round numeric vector conditionaly — round_conditionaly","text":"Round numeric vector conditionaly","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/round_conditionaly.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Round numeric vector conditionaly — round_conditionaly","text":"","code":"round_conditionaly( vec, cond = cbind(c(1e-05, 5), c(0.001, 3), c(0.01, 3), c(1, 2), c(10, 1), c(100, 0)) )"},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/round_conditionaly.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Round numeric vector conditionaly — round_conditionaly","text":"vec numeric vector cond : matrix 2 row n column first row defining condition second row defining number round. cond order decreasing order 1 row internally. Thus order cond rows important","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/round_conditionaly.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Round numeric vector conditionaly — round_conditionaly","text":"numeric vector length vec","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/round_conditionaly.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Round numeric vector conditionaly — round_conditionaly","text":"Adrien Taudière","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/round_conditionaly.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Round numeric vector conditionaly — round_conditionaly","text":"","code":"round_conditionaly(vec = c(1000.27890, 10.87988, 1.769869, 0.99796, 0.000179)) #> [1] 1000.27890 10.87988 1.76987 0.99796 0.00018 round_conditionaly( vec = c(1000.27890, 0.000179, 10e-11), cond = cbind(c(10e-5, 5), c(10, 2)) ) #> [1] 1.000279e+03 1.800000e-04 1.000000e-10"},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/session_runtime.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute session run time and mass storage use (based on gc()) — session_runtime","title":"Compute session run time and mass storage use (based on gc()) — session_runtime","text":"Compute cpu times using base::proc.time() mass storage using base::gc()","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/session_runtime.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute session run time and mass storage use (based on gc()) — session_runtime","text":"","code":"session_runtime(compute_mass_storage = TRUE)"},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/session_runtime.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute session run time and mass storage use (based on gc()) — session_runtime","text":"compute_mass_storage (logical, default TRUE) mass storage computed base::gc() function","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/session_runtime.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute session run time and mass storage use (based on gc()) — session_runtime","text":"list values","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/session_runtime.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute session run time and mass storage use (based on gc()) — session_runtime","text":"Adrien Taudière","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/session_runtime.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute session run time and mass storage use (based on gc()) — session_runtime","text":"","code":"session_runtime() #> $cpu_times_users #> user.self #> 7.666 #> #> $cpu_times_system #> user.child #> 1.929 #> #> $time_elapsed #> elapsed #> 13.789 #> #> $cpu_times #> user.self #> 9.595 #> #> $mass_storage_used #> [1] 236 #> #> $mass_storage_max #> [1] 357.8 #> session_runtime(compute_mass_storage = FALSE) #> $cpu_times_users #> user.self #> 8.122 #> #> $cpu_times_system #> user.child #> 1.929 #> #> $time_elapsed #> elapsed #> 14.245 #> #> $cpu_times #> user.self #> 10.051 #>"},{"path":"https://adrientaudiere.github.io/greenAlgoR/news/index.html","id":"greenalgor-01","dir":"Changelog","previous_headings":"","what":"greenAlgoR 0.1","title":"greenAlgoR 0.1","text":"Initial github submission. Creation function ga_footprint() ga_targets()","code":""}]
+[{"path":"https://adrientaudiere.github.io/greenAlgoR/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc. .","code":" Copyright (C) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://adrientaudiere.github.io/greenAlgoR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Adrien Taudière. Author, maintainer, copyright holder.","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Taudière (2024). greenAlgoR: Compute ecological footprint R. R package version 0.1, https://adrientaudiere.github.io/greenAlgoR/, https://github.com/adrientaudiere/greenAlgoR.","code":"@Manual{, title = {greenAlgoR: Compute ecological footprint in R}, author = {Adrien Taudière}, year = {2024}, note = {R package version 0.1, https://adrientaudiere.github.io/greenAlgoR/}, url = {https://github.com/adrientaudiere/greenAlgoR}, }"},{"path":"https://adrientaudiere.github.io/greenAlgoR/index.html","id":"greenalgor","dir":"","previous_headings":"","what":"Compute ecological footprint in R","title":"Compute ecological footprint in R","text":"package computes ecological footprint R (based green-algorithms, Lannelongue, Grealey, Inouye (2021)). aim facilitate programmatic used green-algorithms using function ga_footprint(). Moreover, greenAlgoR made simple compute ecological footprint {targets} pipelines using function ga_targets(). beta-version. Please submit issues found problems want contribute.","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Compute ecological footprint in R","text":"greenAlgoR available CRAN moment. can install stable development version GitHub :","code":"if (!require(\"devtools\", quietly = TRUE)) { install.packages(\"devtools\") } devtools::install_github(\"adrientaudiere/greenAlgoR\")"},{"path":[]},{"path":"https://adrientaudiere.github.io/greenAlgoR/index.html","id":"the-algorithm-from-lannelongue-grealey-and-inouye-2021","dir":"","previous_headings":"Scientific foundation","what":"The algorithm from Lannelongue, Grealey, and Inouye (2021)","title":"Compute ecological footprint in R","text":"can refer green-algorithms Lannelongue, Grealey, Inouye (2021) understand algorithm. short description green-algorithms carbon footprint calculated estimating energy draw algorithm carbon intensity producing energy given location: energy needed : power draw computing cores depends model number cores, memory power draw depends size memory available. usage factor corrects real core usage (default 1, .e. full usage). PUE (Power Usage Effectiveness) measures much extra energy needed operate data centre (cooling, lighting etc.). PSF (Pragmatic Scaling Factor) used take account multiple identical runs (e.g. testing optimisation). Carbon Intensity depends location technologies used produce electricity.","code":"carbon footprint = energy needed * carbon intensity runtime * (power draw for cores * usage + power draw for memory) * PUE * PSF"},{"path":"https://adrientaudiere.github.io/greenAlgoR/index.html","id":"basic-use","dir":"","previous_headings":"","what":"Basic use","title":"Compute ecological footprint in R","text":"","code":"devtools::load_all() library(\"greenAlgoR\")"},{"path":[]},{"path":"https://adrientaudiere.github.io/greenAlgoR/index.html","id":"using-explicit-input","dir":"","previous_headings":"Basic use > In classical workflow","what":"Using explicit input","title":"Compute ecological footprint in R","text":"","code":"# Your model must match exactly a name in TDP_cpu_internal$model fp_12h <- ga_footprint(runtime_h = 12, cpu_model = \"Core i3-10300\") fp_12h$carbon_footprint_total_gCO2 #> [1] 829.0232 fp_12h$energy_needed_kWh #> [1] 1.745312 ggplot(fp_12h$ref_value, aes( y = variable, x = as.numeric(value), fill = log10(prop_footprint) )) + geom_col() + geom_col( data = data.frame( variable = \"Total\", value = fp_12h$carbon_footprint_total_gCO2 ), fill = \"grey30\" ) + geom_col( data = data.frame( variable = \"Cores\", value = fp_12h$carbon_footprint_cores ), fill = \"darkred\" ) + geom_col( data = data.frame( variable = \"Memory\", value = fp_12h$carbon_footprint_memory ), fill = \"orange\" ) + scale_x_continuous( trans = \"log1p\", breaks = c(0, 10^c(1:max(log1p(as.numeric(fp_12h$ref_value$value))))) ) + geom_vline( xintercept = fp_12h$carbon_footprint_total_gCO2, col = \"grey30\", lwd = 1.2 ) + geom_label( aes(label = round_conditionaly(prop_footprint)), fill = \"grey90\", position = position_stack(vjust = 1.1) ) + labs( title = \"Carbon footprint of the analysis\", subtitle = paste0(\"(\", fp_12h$carbon_footprint_total_gCO2, \" g CO2\", \")\"), caption = \"Please cite Lannelongue et al. 2021 (10.1002/advs.202100707)\" ) + xlab(\"Carbon footprint (g CO2) in log10\") + ylab(\"Modality\") + theme(legend.position = \"none\")"},{"path":"https://adrientaudiere.github.io/greenAlgoR/index.html","id":"based-on-the-r-session","dir":"","previous_headings":"Basic use > In classical workflow","what":"Based on the R session","title":"Compute ecological footprint in R","text":"","code":"fp_session <- ga_footprint(runtime_h = \"session\", add_storage_estimation = TRUE) fp_session$carbon_footprint_total_gCO2 #> user.self #> 0.01989738 fp_session$energy_needed_kWh #> user.self #> 4.188922e-05"},{"path":"https://adrientaudiere.github.io/greenAlgoR/index.html","id":"based-on-a-targets-pipeline","dir":"","previous_headings":"Basic use","what":"Based on a targets pipeline","title":"Compute ecological footprint in R","text":"","code":"ga_targets()"},{"path":"https://adrientaudiere.github.io/greenAlgoR/index.html","id":"roadmap","dir":"","previous_headings":"","what":"Roadmap","title":"Compute ecological footprint in R","text":"Automatically find cpu model using benchmarkme::get_cpu() Submit CRAN?","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/csv_from_url_ga.html","id":null,"dir":"Reference","previous_headings":"","what":"Load csv files from green algo github repositories — csv_from_url_ga","title":"Load csv files from green algo github repositories — csv_from_url_ga","text":"Mainly internal use","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/csv_from_url_ga.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load csv files from green algo github repositories — csv_from_url_ga","text":"","code":"csv_from_url_ga(url, remove_first_line = TRUE)"},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/csv_from_url_ga.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load csv files from green algo github repositories — csv_from_url_ga","text":"url url raw csv file remove_first_line (logical, default TRUE): remove first line csv file.","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/csv_from_url_ga.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load csv files from green algo github repositories — csv_from_url_ga","text":"data.frame","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/csv_from_url_ga.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Load csv files from green algo github repositories — csv_from_url_ga","text":"Adrien Taudière","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/csv_from_url_ga.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Load csv files from green algo github repositories — csv_from_url_ga","text":"","code":"carbon_intensity_internal <- csv_from_url_ga(\"https://raw.githubusercontent.com/GreenAlgorithms/green-algorithms-tool/refs/heads/master/data/v2.2/CI_aggregated.csv\")"},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_footprint.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute footprint in grams of CO2 using Lannelongue et al. 2021 algorithm — ga_footprint","title":"Compute footprint in grams of CO2 using Lannelongue et al. 2021 algorithm — ga_footprint","text":"Please cite Lannelongue, L., Grealey, J., Inouye, M., Green Algorithms: Quantifying Carbon Footprint Computation. Adv. Sci. 2021, 2100707. https://doi.org/10.1002/advs.202100707 Default value https://github.com/GreenAlgorithms/green-algorithms-tool: PUE: https://github.com/GreenAlgorithms/green-algorithms-tool/blob/master/data/v2.2/defaults_PUE.csv TDP_per_core: https://raw.githubusercontent.com/GreenAlgorithms/green-algorithms-tool/refs/heads/master/data/v2.2/TDP_cpu.csv power_draw_per_gb: https://onlinelibrary.wiley.com/doi/10.1002/advs.202100707 Description algorithm green-algorithms website: \"\"\" carbon footprint calculated estimating energy draw algorithm carbon intensity producing energy given location: $$carbon footprint = energy needed * carbon intensity$$ energy needed : $$runtime * (power draw cores * usage + power draw memory) * PUE * PSF$$ power draw computing cores depends model number cores, memory power draw depends size memory available. usage factor corrects real core usage (default 1, .e. full usage). PUE (Power Usage Effectiveness) measures much extra energy needed operate data centre (cooling, lighting etc.). PSF (Pragmatic Scaling Factor) used take account multiple identical runs (e.g. testing optimisation). Carbon Intensity depends location technologies used produce electricity. note \"energy needed\" [...] independent location. \"\"\"","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_footprint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute footprint in grams of CO2 using Lannelongue et al. 2021 algorithm — ga_footprint","text":"","code":"ga_footprint( runtime_h = NULL, location_code = \"WORLD\", PUE = 1.67, TDP_per_core = 12, n_cores = 1, cpu_model = \"Any\", memory_ram = NULL, power_draw_per_gb = 0.3725, PSF = 1, usage_core = 1, add_ref_values = TRUE, add_storage_estimation = FALSE, mass_storage = NULL, carbon_intensity = NULL, TDP_cpu = NULL, ref_value = NULL )"},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_footprint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute footprint in grams of CO2 using Lannelongue et al. 2021 algorithm — ga_footprint","text":"runtime_h Run time hours (int). runtime_h == \"session\", runtime compute using actual R session location_code (character list country region available ) PUE (int) Power usage effectiveness server. See https://github.com/GreenAlgorithms/green-algorithms-tool/blob/master/data/v2.2/defaults_PUE.csv example values. using personal computer, set PUE 1. TDP_per_core (int. Watt, default 12). Find cpu TDP nb cpu https://www.techpowerup.com/cpu-specs/ http://calculator.green-algorithms.org/ available. Owerwrite cpu_model param. n_cores (int, default 1) Number cores. Owerwrite cpu_model param. cpu_model Must present list http://calculator.green-algorithms.org/. CPU set, parameter TPD_per_core n_cores overwriting info cpu_model. \"auto\" modality (find cpu using benchmarkme::get_cpu()$model_name) running moment. memory_ram (int. GB) memory RAM. memory_ram NULL, use benchmarkme::get_ram() get RAM. power_draw_per_gb (int. Watt, default 0.3725) power draw GB RAM PSF (int, default 1) Pragmatic Scaling Factor. Citation Lannelongue et al. 2021: \"Many analyses presented single run particular algorithm software tool; however, computations rarely performed . Algorithms run multiple times, sometimes hundreds, systematically manually, different parameterizations. Statistical models may include number combinations covariates, fitting procedures, etc. important include repeats carbon footprint. take account number times computation performed practice, PSF defined, scaling factor estimated GHG emissions multiplied.\" usage_core (int, default 1). usage factor corrects real core usage (default 1, .e. full usage). add_ref_values (logical, default TRUE) compute return reference values compare footprint ? add_storage_estimation (logical, default FALSE) compute footprint mass storage ? default FALSE far less important cpu memory usage. Note green-algorithms website compute mass storage usage. mass_storage (int. GB, default NULL) size mass_storage. used add_storage_estimation set TRUE. set NULL, use base::gc() function estimate storage used. carbon_intensity (default NULL). Advanced users . dataframe location carbonIntensity columns. Set carbon_intensity_internal NULL. carbon_intensity_internal set using command line csv_from_url_ga(\"https://raw.githubusercontent.com/GreenAlgorithms/ green-algorithms-tool/refs/heads/master/data/v2.2/CI_aggregated.csv\") TDP_cpu (default NULL). Advanced users . dataframe model, n_cores TDP_per_core columns. Set TDP_cpu_internal NULL. TDP_cpu_internal set using command line csv_from_url_ga(\"https://raw.githubusercontent.com/GreenAlgorithms/ green-algorithms-tool/refs/heads/master/data/v2.2/TDP_cpu.csv\") ref_value (default NULL). Advanced users . dataframe variable value columns. Set ref_value_internal NULL. ref_value_internal set using command line csv_from_url_ga(\"https://raw.githubusercontent.com/GreenAlgorithms/ green-algorithms-tool/refs/heads/master/data/v2.2/referenceValues.csv\")","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_footprint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute footprint in grams of CO2 using Lannelongue et al. 2021 algorithm — ga_footprint","text":"list values runtime_h: input run time hours location_code: input location code TDP_per_core: input TDP_per_core (cpu_model set, correspond TDP_per_core cpu) n_cores: input n_cores (cpu_model set, correspond n_cores cpu) cpu_model: input cpu model. set \"\", TDP_per_core ncore used memory_ram: input memory ram GB power_draw_per_gb: input power draw per GB usage_core: input usage core carbon_intensity: input carbon intensity (depend location code) PUE: input PUE PSF: input PUE power_draw_for_cores_kWh: output power draw cores kWh power_draw_for_memory_kWh: output power draw RAM memory kWh energy_needed_kWh: output energy needed kWh carbon_footprint_cores: output carbon footprint grams CO2 cores usage carbon_footprint_memory: output carbon footprint grams CO2 memory usage carbon_footprint_total_gCO2: total output carbon footprint grams CO2 ref_value: (optionnal, return add_ref_values TRUE) : dataframe power_draw_storage_kWh: (optionnal, return add_storage_estimation TRUE) output power draw mass storage kWh","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_footprint.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute footprint in grams of CO2 using Lannelongue et al. 2021 algorithm — ga_footprint","text":"Adrien Taudière","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_footprint.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute footprint in grams of CO2 using Lannelongue et al. 2021 algorithm — ga_footprint","text":"","code":"ga_footprint( runtime_h = 12, n_cores = 6, TDP_per_core = 15.8, location_code = \"FR\", PUE = 1, cpu_model = \"Core i5-9600KF\" ) #> $runtime_h #> [1] 12 #> #> $location_code #> [1] \"FR\" #> #> $TDP_per_core #> [1] 15.8 #> #> $n_cores #> [1] 6 #> #> $cpu_model #> [1] \"Core i5-9600KF\" #> #> $memory_ram #> [1] 16.75734 #> #> $power_draw_per_gb #> [1] 0.3725 #> #> $`usage core` #> [1] 1 #> #> $carbon_intensity #> [1] 51.28 #> #> $PUE #> [1] 1 #> #> $PSF #> [1] 1 #> #> $power_draw_for_cores_kWh #> [1] 1.1376 #> #> $power_draw_for_memory_kWh #> [1] 0.07490532 #> #> $energy_needed_kWh #> [1] 1.212505 #> #> $carbon_footprint_cores #> [1] 58.33613 #> #> $carbon_footprint_memory #> [1] 3.841145 #> #> $carbon_footprint_total_gCO2 #> [1] 62.17727 #> #> $ref_value #> variable value #> 1 Total 62.1772727926653 #> 2 Cores 1.1376 #> 3 Memory 0.07490531966976 #> 4 memoryPower 0.3725 #> 5 google_search 10 #> 6 streaming_netflix_perhour 36 #> 7 train_perkm 41 #> 8 flight_economy_perkm 171 #> 9 passengerCar_EU_perkm 175 #> 10 passengerCar_US_perkm 251 #> 11 tree_month 917 #> 12 treeYear 11000 #> 13 flight_PAR-LON 50000 #> 14 flight_NY-SF 570000 #> 15 flight_NYC-MEL 2310000 #> source #> 1 #> 2 #> 3 #> 4 in W/GB from http://dl.acm.org/citation.cfm?doid=3076113.3076117 and https://www.tomshardware.com/uk/reviews/intel-core-i7-5960x-haswell-e-cpu,3918-13.html #> 5 gCO2 from http://www.janavirgin.com/CO2/CO2GLE_about.html #> 6 gCO2 from https://www.carbonbrief.org/factcheck-what-is-the-carbon-footprint-of-streaming-video-on-netflix (update) #> 7 in gCO2/km, but more like 5-37g, from https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2020 #> 8 in gCO2/km, but more like 139-244g, from https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2020 #> 9 in gCO2/km from http://www.sciencedirect.com/science/article/pii/S1352231018307295 and https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2019 #> 10 in gCO2/km from https://www.epa.gov/greenvehicles/greenhouse-gas-emissions-typical-passenger-vehicle #> 11 gCO2 same source as treeYear #> 12 in gCO2/tree/year from http://www.sciencedirect.com/science/article/pii/S0269749101002640 #> 13 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3 #> 14 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3 #> 15 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3 #> prop_footprint #> 1 1.000000e+00 #> 2 1.829607e-02 #> 3 1.204706e-03 #> 4 5.990935e-03 #> 5 1.608305e-01 #> 6 5.789897e-01 #> 7 6.594049e-01 #> 8 2.750201e+00 #> 9 2.814533e+00 #> 10 4.036845e+00 #> 11 1.474815e+01 #> 12 1.769135e+02 #> 13 8.041523e+02 #> 14 9.167337e+03 #> 15 3.715184e+04 #> ga_footprint( runtime_h = \"session\", PUE = 1, ) #> $runtime_h #> user.self #> 0.0018375 #> #> $location_code #> [1] \"WORLD\" #> #> $TDP_per_core #> [1] 12 #> #> $n_cores #> [1] 1 #> #> $cpu_model #> [1] \"Any\" #> #> $memory_ram #> [1] 16.75734 #> #> $power_draw_per_gb #> [1] 0.3725 #> #> $`usage core` #> [1] 1 #> #> $carbon_intensity #> [1] 475 #> #> $PUE #> [1] 1 #> #> $PSF #> [1] 1 #> #> $power_draw_for_cores_kWh #> user.self #> 2.205e-05 #> #> $power_draw_for_memory_kWh #> user.self #> 1.146988e-05 #> #> $energy_needed_kWh #> user.self #> 3.351988e-05 #> #> $carbon_footprint_cores #> user.self #> 0.01047375 #> #> $carbon_footprint_memory #> user.self #> 0.005448192 #> #> $carbon_footprint_total_gCO2 #> user.self #> 0.01592194 #> #> $ref_value #> variable value #> 1 Total 0.0159219416103552 #> 2 Cores 2.205e-05 #> 3 Memory 1.1469877074432e-05 #> 4 memoryPower 0.3725 #> 5 google_search 10 #> 6 streaming_netflix_perhour 36 #> 7 train_perkm 41 #> 8 flight_economy_perkm 171 #> 9 passengerCar_EU_perkm 175 #> 10 passengerCar_US_perkm 251 #> 11 tree_month 917 #> 12 treeYear 11000 #> 13 flight_PAR-LON 50000 #> 14 flight_NY-SF 570000 #> 15 flight_NYC-MEL 2310000 #> source #> 1 #> 2 #> 3 #> 4 in W/GB from http://dl.acm.org/citation.cfm?doid=3076113.3076117 and https://www.tomshardware.com/uk/reviews/intel-core-i7-5960x-haswell-e-cpu,3918-13.html #> 5 gCO2 from http://www.janavirgin.com/CO2/CO2GLE_about.html #> 6 gCO2 from https://www.carbonbrief.org/factcheck-what-is-the-carbon-footprint-of-streaming-video-on-netflix (update) #> 7 in gCO2/km, but more like 5-37g, from https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2020 #> 8 in gCO2/km, but more like 139-244g, from https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2020 #> 9 in gCO2/km from http://www.sciencedirect.com/science/article/pii/S1352231018307295 and https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2019 #> 10 in gCO2/km from https://www.epa.gov/greenvehicles/greenhouse-gas-emissions-typical-passenger-vehicle #> 11 gCO2 same source as treeYear #> 12 in gCO2/tree/year from http://www.sciencedirect.com/science/article/pii/S0269749101002640 #> 13 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3 #> 14 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3 #> 15 gCO2e from https://calculator.carbonfootprint.com/calculator.aspx?tab=3 #> prop_footprint #> 1 1.000000e+00 #> 2 1.384881e-03 #> 3 7.203818e-04 #> 4 2.339539e+01 #> 5 6.280641e+02 #> 6 2.261031e+03 #> 7 2.575063e+03 #> 8 1.073990e+04 #> 9 1.099112e+04 #> 10 1.576441e+04 #> 11 5.759348e+04 #> 12 6.908705e+05 #> 13 3.140321e+06 #> 14 3.579965e+07 #> 15 1.450828e+08 #> res_ga <- ga_footprint( runtime_h = 12, n_cores = 6, memory_ram = 64, PUE = 1, add_storage_estimation = TRUE, mass_storage = 1 ) ggplot(res_ga$ref_value, aes(y = variable, x = as.numeric(value), fill = log10(prop_footprint))) + geom_col() + geom_col(data = data.frame( variable = \"Total\", value = res_ga$carbon_footprint_total_gCO2 ), fill = \"grey30\") + geom_col(data = data.frame( variable = \"Cores\", value = res_ga$carbon_footprint_cores ), fill = \"darkred\") + geom_col(data = data.frame( variable = \"Memory\", value = res_ga$carbon_footprint_memory ), fill = \"orange\") + geom_col(data = data.frame( variable = \"Mass storage\", value = res_ga$carbon_footprint_storage ), fill = \"violet\") + scale_x_continuous( trans = \"log1p\", breaks = c(0, 10^c(1:max(log1p(as.numeric(res_ga$ref_value$value))))) ) + geom_vline( xintercept = res_ga$carbon_footprint_total_gCO2, col = \"grey30\", lwd = 1.2 ) + geom_label(aes(label = round_conditionaly(prop_footprint)), fill = \"grey90\", position = position_stack(vjust = 1.1) ) + labs( title = \"Carbon footprint of the analysis\", subtitle = paste0( \"(\", res_ga$carbon_footprint_total_gCO2, \" g CO2\", \")\" ), caption = \"Please cite Lannelongue et al. 2021 (10.1002/advs.202100707)\" ) + xlab(\"Carbon footprint (g CO2) in log10\") + ylab(\"Modality\") + theme(legend.position = \"none\")"},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_targets.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute footprint in grams of CO2 for {targets} pipelines — ga_targets","title":"Compute footprint in grams of CO2 for {targets} pipelines — ga_targets","text":"mainly wrapper function ga_footprint() compute run time mass_storage (used add_storage_estimation = TRUE) using targets::tar_meta().","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_targets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute footprint in grams of CO2 for {targets} pipelines — ga_targets","text":"","code":"ga_targets( names = NULL, fields = NULL, targets_only = TRUE, complete_only = FALSE, store = targets::tar_config_get(\"store\"), tar_meta_raw = NULL, ... )"},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_targets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute footprint in grams of CO2 for {targets} pipelines — ga_targets","text":"names Optional, names targets. See ?targets::tar_meta() fields Optional, names columns/fields select. See ?targets::tar_meta() targets_only Logical, whether just show information targets also return metadata functions global objects. complete_only Logical, whether return complete rows (NA values). store Character length 1, path targets data store. See ?targets::tar_meta() tar_meta_raw Optional, NULL, listed options (params targets::tar_meta() used. ... args passed ga_footprint()","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_targets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute footprint in grams of CO2 for {targets} pipelines — ga_targets","text":"list value. See ?ga_footprint details.","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_targets.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute footprint in grams of CO2 for {targets} pipelines — ga_targets","text":"Adrien Taudière","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/ga_targets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute footprint in grams of CO2 for {targets} pipelines — ga_targets","text":"","code":"# In a targets folder, just run function ga_targets() # with the options you want # The next exemple emulate a mini-targets before to ask for tar_meta tar_dir({ # tar_dir() runs code from a temp dir for CRAN. tar_script( { list( tar_target( name = waiting, command = Sys.sleep(2), description = \"Sleep 2 seconds\" ), tar_target(x, writeLines( targets::tar_option_get(\"error\"), \"error.txt\" )) ) }, ask = FALSE ) tar_make() tm <- tar_meta() res_gat <- ga_targets( tar_meta_raw = tm, n_cores = 6, TDP_per_core = 15.8, location_code = \"FR\", PUE = 2, add_storage_estimation = TRUE ) ggplot(res_gat$ref_value, aes( y = reorder(variable, as.numeric(value)), x = as.numeric(value), fill = log10(prop_footprint) )) + geom_col() + geom_col(data = data.frame( variable = \"Total \", value = res_gat$carbon_footprint_total_gCO2 ), fill = \"grey30\") + geom_col( data = data.frame( variable = \"Cores\", value = res_gat$carbon_intensity * res_gat$power_draw_for_cores_kWh ), fill = \"darkred\" ) + geom_col( data = data.frame( variable = \"Memory\", value = res_gat$carbon_intensity * res_gat$power_draw_for_memory_kWh ), fill = \"orange\" ) + geom_col( data = data.frame( variable = \"Storage\", value = res_gat$carbon_intensity * res_gat$power_draw_per_gb ), fill = \"violet\" ) + scale_x_continuous(trans = \"log1p\") + geom_vline( xintercept = res_gat$carbon_footprint_total_gCO2, col = \"grey30\", lwd = 1.2 ) + geom_label(aes(label = round(prop_footprint, 1)), fill = \"grey90\") + xlab(\"g CO^2\") + ylab(\"Modality\") }) #> ▶ dispatched target x #> ● completed target x [0.001 seconds, 44 bytes] #> ▶ dispatched target waiting #> ● completed target waiting [2.002 seconds, 44 bytes] #> ▶ ended pipeline [2.067 seconds] #>"},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/greenAlgoR-package.html","id":null,"dir":"Reference","previous_headings":"","what":"greenAlgoR package — greenAlgoR-package","title":"greenAlgoR package — greenAlgoR-package","text":"package computes ecological footprint R (based green-algorithms. greenAlgoR also made simple compute ecological footprint targets pipelines..","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/round_conditionaly.html","id":null,"dir":"Reference","previous_headings":"","what":"Round numeric vector conditionaly — round_conditionaly","title":"Round numeric vector conditionaly — round_conditionaly","text":"Round numeric vector conditionaly","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/round_conditionaly.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Round numeric vector conditionaly — round_conditionaly","text":"","code":"round_conditionaly( vec, cond = cbind(c(1e-05, 5), c(0.001, 3), c(0.01, 3), c(1, 2), c(10, 1), c(100, 0)) )"},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/round_conditionaly.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Round numeric vector conditionaly — round_conditionaly","text":"vec numeric vector cond : matrix 2 row n column first row defining condition second row defining number round. cond order decreasing order 1 row internally. Thus order cond rows important","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/round_conditionaly.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Round numeric vector conditionaly — round_conditionaly","text":"numeric vector length vec","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/round_conditionaly.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Round numeric vector conditionaly — round_conditionaly","text":"Adrien Taudière","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/round_conditionaly.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Round numeric vector conditionaly — round_conditionaly","text":"","code":"round_conditionaly(vec = c(1000.27890, 10.87988, 1.769869, 0.99796, 0.000179)) #> [1] 1000.27890 10.87988 1.76987 0.99796 0.00018 round_conditionaly( vec = c(1000.27890, 0.000179, 10e-11), cond = cbind(c(10e-5, 5), c(10, 2)) ) #> [1] 1.000279e+03 1.800000e-04 1.000000e-10"},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/session_runtime.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute session run time and mass storage use (based on gc()) — session_runtime","title":"Compute session run time and mass storage use (based on gc()) — session_runtime","text":"Compute cpu times using base::proc.time() mass storage using base::gc()","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/session_runtime.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute session run time and mass storage use (based on gc()) — session_runtime","text":"","code":"session_runtime(compute_mass_storage = TRUE)"},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/session_runtime.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute session run time and mass storage use (based on gc()) — session_runtime","text":"compute_mass_storage (logical, default TRUE) mass storage computed base::gc() function","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/session_runtime.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute session run time and mass storage use (based on gc()) — session_runtime","text":"list values","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/session_runtime.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute session run time and mass storage use (based on gc()) — session_runtime","text":"Adrien Taudière","code":""},{"path":"https://adrientaudiere.github.io/greenAlgoR/reference/session_runtime.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute session run time and mass storage use (based on gc()) — session_runtime","text":"","code":"session_runtime() #> $cpu_times_users #> user.self #> 7.9 #> #> $cpu_times_system #> user.child #> 2.054 #> #> $time_elapsed #> elapsed #> 13.381 #> #> $cpu_times #> user.self #> 9.954 #> #> $mass_storage_used #> [1] 236 #> #> $mass_storage_max #> [1] 358.4 #> session_runtime(compute_mass_storage = FALSE) #> $cpu_times_users #> user.self #> 8.39 #> #> $cpu_times_system #> user.child #> 2.054 #> #> $time_elapsed #> elapsed #> 13.871 #> #> $cpu_times #> user.self #> 10.444 #>"},{"path":"https://adrientaudiere.github.io/greenAlgoR/news/index.html","id":"greenalgor-01","dir":"Changelog","previous_headings":"","what":"greenAlgoR 0.1","title":"greenAlgoR 0.1","text":"Initial github submission. Creation function ga_footprint() ga_targets()","code":""}]
Authors
Citation
-Source: DESCRIPTION
Source: DESCRIPTION
Taudière A (2024).
greenAlgoR: Compute ecological footprint in R.
diff --git a/news/index.html b/news/index.html
index 711307c..b4ca229 100644
--- a/news/index.html
+++ b/news/index.html
@@ -29,7 +29,7 @@
diff --git a/pkgdown.yml b/pkgdown.yml
index d7b4148..cddfca8 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -2,7 +2,7 @@ pandoc: 3.1.11
pkgdown: 2.1.1
pkgdown_sha: ~
articles: {}
-last_built: 2024-11-19T06:37Z
+last_built: 2024-11-19T06:47Z
urls:
reference: https://adrientaudiere.github.io/greenAlgoR/reference
article: https://adrientaudiere.github.io/greenAlgoR/articles
diff --git a/reference/csv_from_url_ga.html b/reference/csv_from_url_ga.html
index a1f6d65..6da6ec4 100644
--- a/reference/csv_from_url_ga.html
+++ b/reference/csv_from_url_ga.html
@@ -35,7 +35,7 @@
diff --git a/reference/ga_footprint.html b/reference/ga_footprint.html
index 3f4c68c..156ef08 100644
--- a/reference/ga_footprint.html
+++ b/reference/ga_footprint.html
@@ -93,7 +93,7 @@
Load csv files from green algo github repositories
- Source: R/utils.R
+ Source: R/utils.R
csv_from_url_ga.Rd
@@ -345,7 +345,7 @@ Compute footprint in grams of CO2 using Lannelongue et al. 2021 algorithm
- Source: R/ga_footprint.R
+ Source: R/ga_footprint.R
ga_footprint.RdExamples#> [1] 1.1376
#>
#> $power_draw_for_memory_kWh
-#> [1] 0.0749053
+#> [1] 0.07490532
#>
#> $energy_needed_kWh
#> [1] 1.212505
@@ -354,16 +354,16 @@
Examples#> [1] 58.33613
#>
#> $carbon_footprint_memory
-#> [1] 3.841144
+#> [1] 3.841145
#>
#> $carbon_footprint_total_gCO2
#> [1] 62.17727
#>
#> $ref_value
#> variable value
-#> 1 Total 62.1772718537736
+#> 1 Total 62.1772727926653
#> 2 Cores 1.1376
-#> 3 Memory 0.07490530136064
+#> 3 Memory 0.07490531966976
#> 4 memoryPower 0.3725
#> 5 google_search 10
#> 6 streaming_netflix_perhour 36
@@ -395,7 +395,7 @@
Examples#> prop_footprint
#> 1 1.000000e+00
#> 2 1.829607e-02
-#> 3 1.204705e-03
+#> 3 1.204706e-03
#> 4 5.990935e-03
#> 5 1.608305e-01
#> 6 5.789897e-01
@@ -405,7 +405,7 @@
Examples#> 10 4.036845e+00
#> 11 1.474815e+01
#> 12 1.769135e+02
-#> 13 8.041524e+02
+#> 13 8.041523e+02
#> 14 9.167337e+03
#> 15 3.715184e+04
#>
@@ -415,8 +415,8 @@
Examples PUE = 1,
)
#> $runtime_h
-#> user.self
-#> 0.001775833
+#> user.self
+#> 0.0018375
#>
#> $location_code
#> [1] "WORLD"
@@ -450,45 +450,45 @@
Examples#>
#> $power_draw_for_cores_kWh
#> user.self
-#> 2.131e-05
+#> 2.205e-05
#>
#> $power_draw_for_memory_kWh
#> user.self
-#> 1.108494e-05
+#> 1.146988e-05
#>
#> $energy_needed_kWh
#> user.self
-#> 3.239494e-05
+#> 3.351988e-05
#>
#> $carbon_footprint_cores
#> user.self
-#> 0.01012225
+#> 0.01047375
#>
#> $carbon_footprint_memory
#> user.self
-#> 0.005265349
+#> 0.005448192
#>
#> $carbon_footprint_total_gCO2
-#> user.self
-#> 0.0153876
+#> user.self
+#> 0.01592194
#>
#> $ref_value
-#> variable value
-#> 1 Total 0.0153875985187343
-#> 2 Cores 2.131e-05
-#> 3 Memory 1.10849442499669e-05
-#> 4 memoryPower 0.3725
-#> 5 google_search 10
-#> 6 streaming_netflix_perhour 36
-#> 7 train_perkm 41
-#> 8 flight_economy_perkm 171
-#> 9 passengerCar_EU_perkm 175
-#> 10 passengerCar_US_perkm 251
-#> 11 tree_month 917
-#> 12 treeYear 11000
-#> 13 flight_PAR-LON 50000
-#> 14 flight_NY-SF 570000
-#> 15 flight_NYC-MEL 2310000
+#> variable value
+#> 1 Total 0.0159219416103552
+#> 2 Cores 2.205e-05
+#> 3 Memory 1.1469877074432e-05
+#> 4 memoryPower 0.3725
+#> 5 google_search 10
+#> 6 streaming_netflix_perhour 36
+#> 7 train_perkm 41
+#> 8 flight_economy_perkm 171
+#> 9 passengerCar_EU_perkm 175
+#> 10 passengerCar_US_perkm 251
+#> 11 tree_month 917
+#> 12 treeYear 11000
+#> 13 flight_PAR-LON 50000
+#> 14 flight_NY-SF 570000
+#> 15 flight_NYC-MEL 2310000
#> source
#> 1 <NA>
#> 2 <NA>
@@ -508,19 +508,19 @@
Examples#> prop_footprint
#> 1 1.000000e+00
#> 2 1.384881e-03
-#> 3 7.203817e-04
-#> 4 2.420781e+01
-#> 5 6.498740e+02
-#> 6 2.339546e+03
-#> 7 2.664483e+03
-#> 8 1.111285e+04
-#> 9 1.137279e+04
-#> 10 1.631184e+04
-#> 11 5.959344e+04
-#> 12 7.148614e+05
-#> 13 3.249370e+06
-#> 14 3.704282e+07
-#> 15 1.501209e+08
+#> 3 7.203818e-04
+#> 4 2.339539e+01
+#> 5 6.280641e+02
+#> 6 2.261031e+03
+#> 7 2.575063e+03
+#> 8 1.073990e+04
+#> 9 1.099112e+04
+#> 10 1.576441e+04
+#> 11 5.759348e+04
+#> 12 6.908705e+05
+#> 13 3.140321e+06
+#> 14 3.579965e+07
+#> 15 1.450828e+08
#>
res_ga <- ga_footprint(
diff --git a/reference/ga_targets-1.png b/reference/ga_targets-1.png
index cdbf646..b77628d 100644
Binary files a/reference/ga_targets-1.png and b/reference/ga_targets-1.png differ
diff --git a/reference/ga_targets.html b/reference/ga_targets.html
index fad1ce7..1250135 100644
--- a/reference/ga_targets.html
+++ b/reference/ga_targets.html
@@ -39,7 +39,7 @@
@@ -189,10 +189,10 @@ Compute footprint in grams of CO2 for {targets} pipelines
- Source: R/ga_targets.R
+ Source: R/ga_targets.R
ga_targets.RdExamples ylab("Modality")
})
#> ▶ dispatched target x
-#> ● completed target x [0 seconds, 44 bytes]
+#> ● completed target x [0.001 seconds, 44 bytes]
#> ▶ dispatched target waiting
#> ● completed target waiting [2.002 seconds, 44 bytes]
-#> ▶ ended pipeline [2.064 seconds]
+#> ▶ ended pipeline [2.067 seconds]
#>
diff --git a/reference/greenAlgoR-package.html b/reference/greenAlgoR-package.html
index 59ac73d..f72bc7c 100644
--- a/reference/greenAlgoR-package.html
+++ b/reference/greenAlgoR-package.html
@@ -31,7 +31,7 @@
diff --git a/reference/round_conditionaly.html b/reference/round_conditionaly.html
index 7597e81..4c32641 100644
--- a/reference/round_conditionaly.html
+++ b/reference/round_conditionaly.html
@@ -29,7 +29,7 @@
- Source: greenAlgoR packageR/greenAlgo-package.R
+ Source: R/greenAlgo-package.R
greenAlgoR-package.Rd
@@ -77,42 +77,42 @@ Compute session run time and mass storage use (based on
- Source: gc())R/utils.R
+ Source: R/utils.R
session_runtime.RdExamples
session_runtime()
#> $cpu_times_users
#> user.self
-#> 7.666
+#> 7.9
#>
#> $cpu_times_system
#> user.child
-#> 1.929
+#> 2.054
#>
#> $time_elapsed
#> elapsed
-#> 13.789
+#> 13.381
#>
#> $cpu_times
#> user.self
-#> 9.595
+#> 9.954
#>
#> $mass_storage_used
#> [1] 236
#>
#> $mass_storage_max
-#> [1] 357.8
+#> [1] 358.4
#>
session_runtime(compute_mass_storage = FALSE)
#> $cpu_times_users
#> user.self
-#> 8.122
+#> 8.39
#>
#> $cpu_times_system
#> user.child
-#> 1.929
+#> 2.054
#>
#> $time_elapsed
#> elapsed
-#> 14.245
+#> 13.871
#>
#> $cpu_times
#> user.self
-#> 10.051
+#> 10.444
#>