Package 'rmndt'

Title: Tools for data.table objects in the REMIND context
Description: Helper functions for REMIND-related tasks with data.table objects, e.g., interpolation and (dis-)aggregation.
Authors: Alois Dirnaichner [aut, cre]
Maintainer: Alois Dirnaichner <[email protected]>
License: GPL-3
Version: 0.6.0
Built: 2024-11-14 03:43:08 UTC
Source: https://github.com/pik-piam/rmndt

Help Index

rmndt: Tools for data.table objects in the REMIND context

Description

Helper functions for REMIND-related tasks with data.table objects, e.g., interpolation and (dis-)aggregation.

Author(s)

Maintainer: Alois Dirnaichner [email protected]

Aggregate values in a data.table object using a mapping. If no weight is given, the value for the aggregated categories is the sum of the parts. Otherwise, the weight is used to calculate a weighted average accross the parts.

Description

Aggregate values in a data.table object using a mapping. If no weight is given, the value for the aggregated categories is the sum of the parts. Otherwise, the weight is used to calculate a weighted average accross the parts.

Usage

aggregate_dt(
  data,
  mapping,
  fewcol = "region",
  yearcol = "year",
  manycol = "iso",
  datacols = "data",
  valuecol = "value",
  weights = NULL,
  weightcol = "weight"
)

Arguments

data

a magpie object.
mapping

a mapping between the aggregated categories in the data and ISO3 countrycodes. *All* regions in 'data' have to be part of the mapping.
fewcol

name of the column containing aggregated categories. Default is "region".
yearcol

name of the column containing time step info. Default is "year".
manycol

name of the column containing dis-aggregated categories. Default is "iso".
datacols

index columns that label categories which have to be treated seperately when aggregating with a weight.
valuecol

name of the column with the value to aggregate, default is 'value'.
weights

table with weights for a (weighted average) aggregation, the name of the column with the aggregated categories has to be 'manycol'. If columns (other than the column with the aggregated category) of the 'weights' coincide with columns of the data, the respective columns are considered when joining.
weightcol

column with the weights for aggregation, default is 'weight'.

Internal function to apply the weights and perform some checks.

Description

Internal function to apply the weights and perform some checks.

Usage

apply_weights(
  data,
  mapping,
  weights,
  fewcol,
  manycol,
  valuecol,
  datacols,
  weightcol
)

Arguments

data

a data.table.
mapping

a mapping between the aggregated categories and their parts. *All* aggregated categories in 'data' have to be part of the mapping.
weights

table with weights for disaggregation, the name of the column with the aggregated categories has to be 'manycol'. If columns (other than the column with the aggregated category) of the 'weights' coincide with columns of the data, the respective columns are considered when joining.
fewcol

name of the column containing aggregated categories. Default is "region".
manycol

name of the column containing dis-aggregated categories. Default is "iso".
valuecol

name of the column with the actual value to disaggregate, default is 'value'.
datacols

index columns that label categories which have to be treated seperately when dis-aggregating with a weight.
weightcol

column with the weights for the dis-aggregation, default is 'weight'.

Approximate missing values in a data.table.

Description

Similar to, but not quite like, 'stats::approx'. Does only support constant extrapolation and linear interpolation. The resulting 'data.table' only contains the range provided by 'xdata' along 'xcol'. Without extrapolation, 'xcol' in the resulting 'data.table' may not cover the range given by 'xdata'.

Usage

approx_dt(
  dt,
  xdata,
  xcol,
  ycol,
  idxcols = NULL,
  keepna = FALSE,
  extrapolate = FALSE
)

Arguments

dt

a data.table.
xdata

the range to interpolate to. This is the range the result will have along the dimension 'xcol'.
xcol

name of the column for interpolation.
ycol

name of the column that contains the value to be interpolated.
idxcols

columns that identify a row (besides xcol), i.e., the remaining index dimensions.
keepna

keep NA values for rows that can not be interpolated (since they are outside of [min(xcol), max(xcol)]), default is FALSE.
extrapolate

use the closest values to fill 'ycol' outside of the interpolation domain, default is FALSE. This will also work if there is only one value along 'ycol', i.e., no interpolation is taking place.

Value

a data.table with the range given by 'xdata' along 'xcol'. Columns not given in 'idxcols' will be kept but NAs will appear on extrapolated and interpolated rows.

Examples

dt <- as.data.table(ChickWeight)
## delete all values but 1
dt[Chick == 1 & Time > 0, weight := NA]
## delete all values but 2
dt[Chick == 2 & Time > 2, weight := NA]

## extrapolation from 1 value
approx_dt(dt, 0:21, "Time", "weight", idxcols=c("Chick", "Diet"), extrapolate = TRUE)[Chick == 1]
## extrapolation and interpolation
approx_dt(dt, 0:21, "Time", "weight", idxcols=c("Chick", "Diet"), extrapolate = TRUE)[Chick == 2]
## column not in idxcols
approx_dt(dt, 0:21, "Time", "weight", idxcols="Chick", extrapolate = TRUE)[Chick == 2]

dt <- as.data.table(ChickWeight)
## interpolation only
approx_dt(dt, 0:21, "Time", "weight", idxcols=c("Chick", "Diet"))[Chick == 2]

Disaggregate data in a data.table object using a mapping. If no weights are given, the value for the aggregated categories is used on the disaggregated ones. If a weight is given, the values from the aggregated categories are distributed according to the weights.

Description

Disaggregate data in a data.table object using a mapping. If no weights are given, the value for the aggregated categories is used on the disaggregated ones. If a weight is given, the values from the aggregated categories are distributed according to the weights.

Usage

disaggregate_dt(
  data,
  mapping,
  fewcol = "region",
  manycol = "iso",
  valuecol = "value",
  datacols = "data",
  weights = NULL,
  weightcol = "weight"
)

Arguments

data

a data.table.
mapping

a mapping between the aggregated categories and their parts. *All* aggregated categories in 'data' have to be part of the mapping.
fewcol

name of the column containing aggregated categories. Default is "region".
manycol

name of the column containing dis-aggregated categories. Default is "iso".
valuecol

name of the column with the actual value to disaggregate, default is 'value'.
datacols

index columns that label categories which have to be treated seperately when dis-aggregating with a weight.
weights

table with weights for disaggregation, the name of the column with the aggregated categories has to be 'manycol'. If columns (other than the column with the aggregated category) of the 'weights' coincide with columns of the data, the respective columns are considered when joining.
weightcol

column with the weights for the dis-aggregation, default is 'weight'.

Load a magpie object as data.table object with given colnames. Replaces years by numeric values, removing the leading y.

Description

Load a magpie object as data.table object with given colnames. Replaces years by numeric values, removing the leading y.

Usage

magpie2dt(
  data,
  regioncol = NULL,
  yearcol = NULL,
  datacols = NULL,
  valcol = "value"
)

Arguments

data

a magpie object.
regioncol

name of the column containing REMIND regions, default is "region".
yearcol

name of the column containing the year, default is "year".
datacols

the names of the data dimension(s) of the magpie object. If no value is given, the name provided in the magpie object is used.
valcol

column to host actual value, default is "value"

Examples

## Not run: 
require(magpie)
dt <- magpie2dt(population_magpie)

## End(Not run)

Read a REMIND output (MIF) file.

Description

REMIND style output files are semi-colon separated CSVs with a trailing semi-colon at the end of each row. The following structure is assumed: Columns "Model", "Scenario", "Region", "Variable", "Unit" and an arbitrary number of year colums (convertable to numeric).

Usage

readMIF(mif)

Arguments

mif

A REMIND output file (.MIF)

Examples

## Not run: 
dt <- readMIF("REMIND_generic_default.mif")

## End(Not run)

A random REMIND FE trajectory.

Description

A random REMIND FE trajectory.

Usage

REMIND_FinalEnergy

Format

A data frame with 2011 rows and 6 columns:

year

REMIND time step

region

REMIND-12 region

se

Secondary energy identifier

fe

Final energy identifier

te

Conversion technology identifier

value

The data column, EJ/yr

REMIND GDP Trajectories for ISO countries

Description

REMIND GDP Trajectories for ISO countries

Usage

REMIND_GDP

Format

A data frame with 9462 rows and 4 columns:

iso

ISO3 country code

variable

SSP GDP variant, this is gdp_SSP2 in this case

weight

GDP in US$2010

year

Year

The mapping between REMIND-12 regions and ISO countries

Description

The mapping between REMIND-12 regions and ISO countries

Usage

REMIND_RegionMap

Format

A data frame with 249 rows and 3 columns:

name

Name of the country

iso

ISO3 country code

region

REMIND region name

Execute *vertical* calculations along a given column.

Description

This assumes a *long* format with a single value column, dcasts the data.table to wide format, executes the calulation(s), melts back to long format and returns the resulting data.table with the additional column(s).

Usage

varcalc_dt(dt, varcol, valcol, expr, ...)

Arguments

dt

data.table, long format
varcol

name of the column with the variable
valcol

name of the column with the value
expr

vector of expressions to be handed to j in data.table, as strings, e.g., "a := b/c"
...

other arguments are passed on to the data.table call where 'expr' is evaluated. Most likely you want to pass the 'by=' parameter for group-by calls, see examples.

Details

Note that the data.table should have at least three columns, i.e., the variable, the value and one id column.

Examples

mt_dt <- as.data.table(mtcars, keep.rownames = TRUE)
## to long
mt1 <- melt(mt_dt, id.vars=c("rn", "cyl"))

varcalc_dt(mt1, "variable", "value", c("`spec. hp` := wt/hp", "wsum := sum(wt)"), by="cyl")

Write a REMIND output (MIF) file.

Description

Note that these files are semi-colon separated CSVs with a trailing semi-colon at the end of each entry. Required columns are "Model", "Scenario", "Region", "Variable", "Unit" and an arbitrary number of year colums (should be convertable to numeric).

Usage

writeMIF(dt, destination, append = FALSE, ...)

Arguments

dt

a data.table in the correct format.
destination

path to the resulting MIF file
append

append to an existing MIF file?
...

other parameters are passed on to data.table::fwrite

Details

NAs are written to the file as "N/A".

Examples

## Not run: 
writeMIF(dt, "REMIND_generic_default.mif")

## End(Not run)