In JGCRI/metis: Multi-Scale Multi-Sector Analysis

Key Links

• Github: https://github.com/JGCRI/metis
• Webpage: https://jgcri.github.io/metis/
• Cheatsheet: https://github.com/JGCRI/metis/blob/master/metisCheatsheet.pdf

Folder Structure

metis.mapsProcess() Folder structure

Read GCAM Data

For a full list of available params for this function please see colors, maps and params. metis.readGCAM will return a list containing:

• data: a dataframe with the original data
• dataAggParam: a dataframe with the data aggregated to the parameter
• dataAggClass1: a dataframe with the data aggregated to class 1
• dataAggclass2: a dataframe with the data aggregated to class 2
• scenarios: A list of the scenarios
• queries: A list of the queries used to extract the data

library(metis)


dataGCAM <- metis.readgcam (
       #gcamdatabase = “Path_to_GCAMdatabase”,
       dataProjFile = metis::exampleGCAMproj)

df <- dataGCAM$data; df
dfParam <- dataGCAM$dataAggParam; dfParam
dfClass1 <- dataGCAM$dataAggClass1; dfClass1
dfClass2 <- dataGCAM$dataAggClass1; dfClass2

Read & Subset GCAM data

library(metis)


dataGCAMsub <- metis.readgcam (
                 #gcamdatabase = “Path_to_GCAMdatabase”,
                 dataProjFile = metis::exampleGCAMproj,
                 scenOrigNames = c("GCAM_SSP5","GCAM_SSP3"),
                 scenNewNames = c("SSP5","SSP3"),
                 paramsSelect = c("socioecon", "elecByTechTWh","watWithdrawBySec"),  # From Param list ?metis.readgcam
                 regionsSelect = c("China","India","Pakistan"))

df <- dataGCAMsub$data; df
dfParam <- dataGCAMsub$dataAggParam; dfParam
dfClass1 <- dataGCAMsub$dataAggClass1; dfClass1
dfClass2 <- dataGCAMsub$dataAggClass1; dfClass2

JGCRI/metis documentation built on April 12, 2021, 12:07 a.m.