| Title: | Bits and pieces of code to use with quitte-style data frames |
|---|---|
| Description: | A collection of functions for easily dealing with quitte-style data frames, doing multi-model comparisons and plots. |
| Authors: | Michaja Pehl [aut, cre], Nico Bauer [aut], Jérôme Hilaire [aut], Antoine Levesque [aut], Gunnar Luderer [aut], Anselm Schultes [aut], Jan Philipp Dietrich [aut], Oliver Richters [aut] |
| Maintainer: | Michaja Pehl <[email protected]> |
| License: | GPL-2 |
| Version: | 0.3139.0 |
| Built: | 2024-12-05 11:21:00 UTC |
| Source: | https://github.com/pik-piam/quitte |
Bits and pieces for easier handling of quitte-style data frames.
Michaja Pehl
Useful links:
Wrapper function for countrycode::countrycode() enabling piped
execution.
add_countrycode(data, ..., warn = TRUE, na.rm = FALSE) add_countrycode_(data, origin, destination, warn = TRUE, na.rm = FALSE)add_countrycode(data, ..., warn = TRUE, na.rm = FALSE) add_countrycode_(data, origin, destination, warn = TRUE, na.rm = FALSE)
data |
A data frame. |
... |
Key-value pairs for NSE of |
warn |
Prints unique elements from sourcevar for which no match was found. |
na.rm |
If |
origin |
Named scalar linking source column to source coding scheme. See
|
destination |
Named scalar linking destination column name to
destination coding scheme. See |
A data frame.
Michaja Pehl
library(dplyr) data <- tibble( country = c('Belgium', 'Narnia', 'Russia', 'Botswana'), data = 1:4) data %>% add_countrycode(country = country.name, m49.code = un) data %>% add_countrycode_(c('country' = 'country.name'), 'iso3c', warn = FALSE, na.rm = TRUE)library(dplyr) data <- tibble( country = c('Belgium', 'Narnia', 'Russia', 'Botswana'), data = 1:4) data %>% add_countrycode(country = country.name, m49.code = un) data %>% add_countrycode_(c('country' = 'country.name'), 'iso3c', warn = FALSE, na.rm = TRUE)
Utility functions for plotting stacked bars with variable widths for displaying time-series data with variable time steps (like REMIND data).
add_timesteps_columns( data, timesteps, periods = "period", gaps = 0, interval_shift = c(-0.5, 0.5), timesteps_period = "period", timesteps_interval = "year" ) add_remind_timesteps_columns(data, periods = "period", gaps = 0) ggplot_bar_vts( data, timesteps, mapping = aes(x = !!sym("period"), y = !!sym("value"), fill = !!sym("variable")), gaps = 0.1, position_fill = FALSE, interval_shift = c(-0.5, 0.5), timesteps_period = "period", timesteps_interval = "year" ) ggplot_bar_remind_vts( data, mapping = aes(x = !!sym("period"), y = !!sym("value"), fill = !!sym("variable")), gaps = 0.1, position_fill = FALSE )add_timesteps_columns( data, timesteps, periods = "period", gaps = 0, interval_shift = c(-0.5, 0.5), timesteps_period = "period", timesteps_interval = "year" ) add_remind_timesteps_columns(data, periods = "period", gaps = 0) ggplot_bar_vts( data, timesteps, mapping = aes(x = !!sym("period"), y = !!sym("value"), fill = !!sym("variable")), gaps = 0.1, position_fill = FALSE, interval_shift = c(-0.5, 0.5), timesteps_period = "period", timesteps_interval = "year" ) ggplot_bar_remind_vts( data, mapping = aes(x = !!sym("period"), y = !!sym("value"), fill = !!sym("variable")), gaps = 0.1, position_fill = FALSE )
data |
A data frame. |
timesteps |
A data frame like |
periods |
The column holding the period information in |
gaps |
Gaps between bars as a fraction of the smallest bar width. Defaults to 0.1 (e.g. 0.1 * 5 years = 0.5 years). |
interval_shift |
numeric of length 2. Shifts added to the interval fix
point to obtain the beginning and end of time interval.
If the interval for period 1 should be |
timesteps_period |
character string giving the column name of the
|
timesteps_interval |
character string giving the column name of the time
interval in the |
mapping |
|
position_fill |
If |
add_timesteps_columns() calculates the x-axis position and width of bars
based on the information in timesteps and joins it to data.
add_remind_timesteps_columns() uses the remind_timesteps data frame.
ggplot_bar_vts() produces a bar plot with bars positioned according to
timesteps. ggplot_bar_remind_vts() uses the remind_timesteps data
frame.
add_timesteps_columns() and add_remind_timesteps_columns() return
a data frame.
ggplot_bar_vts() and ggplot_bar_remind_vts() return a
ggplot()-like object.
Michaja Pehl
require(tidyverse) # some example data (data <- quitte_example_data %>% filter(first(scenario) == scenario, last(region) == region, first(variable) == variable)) # adding individual timesteps add_timesteps_columns(data, remind_timesteps) # adding remind timesteps with gaps add_remind_timesteps_columns(data, gaps = 0.1) # plotting individual timesteps without gaps ggplot_bar_vts(data, remind_timesteps, gaps = 0) # plotting remind timegaps, using further ggplot2 functions ggplot_bar_remind_vts( data = quitte_example_data %>% filter(scenario %in% levels(quitte_example_data$scenario)[1:3], last(region) == region, grepl('PE\\|', variable), 2100 >= period)) + scale_fill_manual( values = mip::plotstyle(grep('^PE\\|', levels(quitte_example_data$variable), value = TRUE))) + facet_wrap(~ scenario) # another data set with a different time column data2 <- data.frame(variable = c('Wind', 'Solar', 'Wind', 'Solar'), tau = c(1,1,2,2), value = 1:4) # some timesteps dataframe with hourly data timesteps <- data.frame(tau = c(rep(1,2),rep(2,4)), hour = 1:6, weight = 1) # plotting with different timesteps than periods and years ggplot_bar_vts(data2, timesteps, mapping = aes(tau, value, group = variable, fill = variable), timesteps_period = 'tau', timesteps_interval = 'hour', interval_shift = c(-1,0))require(tidyverse) # some example data (data <- quitte_example_data %>% filter(first(scenario) == scenario, last(region) == region, first(variable) == variable)) # adding individual timesteps add_timesteps_columns(data, remind_timesteps) # adding remind timesteps with gaps add_remind_timesteps_columns(data, gaps = 0.1) # plotting individual timesteps without gaps ggplot_bar_vts(data, remind_timesteps, gaps = 0) # plotting remind timegaps, using further ggplot2 functions ggplot_bar_remind_vts( data = quitte_example_data %>% filter(scenario %in% levels(quitte_example_data$scenario)[1:3], last(region) == region, grepl('PE\\|', variable), 2100 >= period)) + scale_fill_manual( values = mip::plotstyle(grep('^PE\\|', levels(quitte_example_data$variable), value = TRUE))) + facet_wrap(~ scenario) # another data set with a different time column data2 <- data.frame(variable = c('Wind', 'Solar', 'Wind', 'Solar'), tau = c(1,1,2,2), value = 1:4) # some timesteps dataframe with hourly data timesteps <- data.frame(tau = c(rep(1,2),rep(2,4)), hour = 1:6, weight = 1) # plotting with different timesteps than periods and years ggplot_bar_vts(data2, timesteps, mapping = aes(tau, value, group = variable, fill = variable), timesteps_period = 'tau', timesteps_interval = 'hour', interval_shift = c(-1,0))
Aggregates or disaggregates the values of a data frame according to a mapping
aggregate_map( data, mapping, by, subset2agg = NULL, only.new = TRUE, na.rm = TRUE, weights = NULL, forceAggregation = FALSE, autodetect = "auto", scaleWeights = TRUE, variable = "variable", value = "value", unit = "unit", weight_val_col = "weight_val_col", weight_item_col = NULL, fun = sum )aggregate_map( data, mapping, by, subset2agg = NULL, only.new = TRUE, na.rm = TRUE, weights = NULL, forceAggregation = FALSE, autodetect = "auto", scaleWeights = TRUE, variable = "variable", value = "value", unit = "unit", weight_val_col = "weight_val_col", weight_item_col = NULL, fun = sum )
data |
a data frame. |
mapping |
a data frame connecting the resolution in |
by |
(named) vector giving the correspondence between the column name
of |
subset2agg |
subset of variables for which the (dis)aggregation is
applied. If |
only.new |
If |
na.rm |
If |
weights |
a data frame, a variable name as a character vector, or
|
forceAggregation |
binary. If |
autodetect |
this parameter takes the values |
scaleWeights |
logical. If |
variable |
Column name of variables. Defaults to |
value |
Column name of values. Defaults to |
unit |
Column name of units. Defaults to |
weight_val_col |
name of the value column in the |
weight_item_col |
name of the item column in the |
fun |
aggregation function to use. Defaults to |
By default "weights" is set to NULL. For aggregations, this means that
values will be summed as they are. For disaggregations, each component of the
larger category will take the same value as the larger category, or
"scaleWeights" is TRUE, each component will be given an even weight. For
aggregations, "weights" can also be the name of a variable contained in
"data". "weights" may also be a data frame.
A data frame.
Antoine Levesque
library(tidyr) library(dplyr) data <- inline.data.frame(c( "model; scenario; region; variable; unit; period; value", "REMIND; Baseline; USA; GDP per Capita|MER; US$2005/yr; 2010; 40000", "REMIND; Baseline; USA; GDP per Capita|MER; US$2005/yr; 2020; 50000", "REMIND; Baseline; USA; Population; million; 2010; 300", "REMIND; Baseline; USA; Population; million; 2020; 350", "REMIND; Baseline; CHN; GDP per Capita|MER; US$2005/yr; 2010; 7000", "REMIND; Baseline; CHN; GDP per Capita|MER; US$2005/yr; 2020; 8000", "REMIND; Baseline; CHN; Population; million; 2010; 1300", "REMIND; Baseline; CHN; Population; million; 2020; 1400")) mapping = inline.data.frame(c( "region; New_region", "USA; GLO", "CHN; GLO" )) mapping2 = inline.data.frame(c( "Item ; Item_new", "Population; Urban Population ", "Population; Rural Population" )) weights = inline.data.frame(c( "region; itemI ; weight", "USA ; Urban Population; 0.5", "USA ; Rural Population; 0.2", "CHN ; Urban Population; 2", "CHN ; Rural Population; 1" )) #Regional Aggregation aggregate_map(data,mapping, by = "region", subset2agg = c("Population")) #Regional Weighted Aggregation aggregate_map(data,mapping, by = "region", subset2agg = "GDP per Capita|MER", weights = "Population") #Variable Weigthed Disaggregation aggregate_map(data,mapping2, by = c("variable" = "Item"), subset2agg = c("Population"),weights = weights, weight_val_col = "weight", weight_item_col = "itemI")library(tidyr) library(dplyr) data <- inline.data.frame(c( "model; scenario; region; variable; unit; period; value", "REMIND; Baseline; USA; GDP per Capita|MER; US$2005/yr; 2010; 40000", "REMIND; Baseline; USA; GDP per Capita|MER; US$2005/yr; 2020; 50000", "REMIND; Baseline; USA; Population; million; 2010; 300", "REMIND; Baseline; USA; Population; million; 2020; 350", "REMIND; Baseline; CHN; GDP per Capita|MER; US$2005/yr; 2010; 7000", "REMIND; Baseline; CHN; GDP per Capita|MER; US$2005/yr; 2020; 8000", "REMIND; Baseline; CHN; Population; million; 2010; 1300", "REMIND; Baseline; CHN; Population; million; 2020; 1400")) mapping = inline.data.frame(c( "region; New_region", "USA; GLO", "CHN; GLO" )) mapping2 = inline.data.frame(c( "Item ; Item_new", "Population; Urban Population ", "Population; Rural Population" )) weights = inline.data.frame(c( "region; itemI ; weight", "USA ; Urban Population; 0.5", "USA ; Rural Population; 0.2", "CHN ; Urban Population; 2", "CHN ; Rural Population; 1" )) #Regional Aggregation aggregate_map(data,mapping, by = "region", subset2agg = c("Population")) #Regional Weighted Aggregation aggregate_map(data,mapping, by = "region", subset2agg = "GDP per Capita|MER", weights = "Population") #Variable Weigthed Disaggregation aggregate_map(data,mapping2, by = c("variable" = "Item"), subset2agg = c("Population"),weights = weights, weight_val_col = "weight", weight_item_col = "itemI")
bin_distribute(x, binsize) distributes the items in x into the minimum
number of bins whose sizes differ at maximum by one and do not exceed
binsize.
bin_distribute_sizes(count, binsize) calculates the sizes of such bins for
count items.
bin_distribute(x, binsize) bin_distribute_sizes(count, binsize)bin_distribute(x, binsize) bin_distribute_sizes(count, binsize)
x |
A character vector to be distributed into bins. |
binsize |
The maximum bin size. |
count |
The number of items to be binned. |
bin_distribute() returns a list with the sub-vectors of x.
bin_distribute_sizes() returns a vector of sizes.
Michaja Pehl
regions <- c('CAZ', 'CHA', 'EUR', 'IND', 'JPN', 'LAM', 'MEA', 'NEU', 'OAS', 'REF', 'SSA', 'USA', 'World') bin_distribute(regions, 5) bin_distribute_sizes(length(regions), 5) bin_distribute(regions, 6) bin_distribute_sizes(length(regions), 6) bin_distribute(regions, 7) bin_distribute_sizes(length(regions), 7)regions <- c('CAZ', 'CHA', 'EUR', 'IND', 'JPN', 'LAM', 'MEA', 'NEU', 'OAS', 'REF', 'SSA', 'USA', 'World') bin_distribute(regions, 5) bin_distribute_sizes(length(regions), 5) bin_distribute(regions, 6) bin_distribute_sizes(length(regions), 6) bin_distribute(regions, 7) bin_distribute_sizes(length(regions), 7)
Calculate new variables from existing ones, using generic formulas.
calc_addVariable( data, ..., units = NA, na.rm = TRUE, completeMissing = FALSE, only.new = FALSE, variable = variable, unit = unit, value = value, overwrite = TRUE, skip.missing.rhs = FALSE ) calc_addVariable_( data, .dots, na.rm = TRUE, completeMissing = FALSE, only.new = FALSE, variable = "variable", unit = "unit", value = "value", overwrite = TRUE, skip.missing.rhs = FALSE )calc_addVariable( data, ..., units = NA, na.rm = TRUE, completeMissing = FALSE, only.new = FALSE, variable = variable, unit = unit, value = value, overwrite = TRUE, skip.missing.rhs = FALSE ) calc_addVariable_( data, .dots, na.rm = TRUE, completeMissing = FALSE, only.new = FALSE, variable = "variable", unit = "unit", value = "value", overwrite = TRUE, skip.missing.rhs = FALSE )
data |
A data frame. |
... |
Formulas to calculate new variables. Either name-value pairs, the path to a .csv file, the content of a .csv file as a string, or a data frame. See details. |
units |
Character vector of units corresponding to new variables. Must
be of length equal to |
na.rm |
If |
completeMissing |
If |
only.new |
If |
variable |
Column name of variables. Defaults to |
unit |
Column name of units. Defaults to |
value |
Column name of values. Defaults to |
overwrite |
If |
skip.missing.rhs |
If |
.dots |
Used to work around non-standard evaluation. See details. If "lhs" = "rhs + calculations - formula", "`lhs 2`" = "lhs / `rhs 2`" where If the new variables should have units, set
list("`lhs 1`" = "`rhs` / `calculation`",
"`lhs 2`" = "sin(`rhs 2`)")
Units are optionally included with the formulas in a vector like list("`lhs w/ unit`" = c("`rhs 1` + `rhs 2`", "rhs unit")
Units do not require quoting. As an alternative, the variable, unit and formula can be specified as a .csv file in this format: variable; unit; formula Carbon Intensity|Cement; Mt CO2/Mt; `Emi|CO2|Cement` / `Production|Cement` or as a single string containing the .csv content (joined by paste(c(
"variable; unit; formula",
"Consumption|pCap; US$2005/cap; 0.001 * `Consumption`/`Population`"),
collapse = '\n')
or as a data frame with the same columns: data.frame(
variable = 'Consumption|pCap',
unit = 'US$2005/cap',
formula = '0.001 * `Consumption`/`Population`')
|
A data frame.
Michaja Pehl
data <- inline.data.frame(c( "model; scenario; region; variable; unit; period; value", "REMIND; Baseline; USA; GDP|MER; billion US$2005/yr; 2010; 12990", "REMIND; Baseline; USA; Population; million; 2010; 310.4", "REMIND; Baseline; USA; PE; EJ/yr; 2010; 91.62", "REMIND; Baseline; CHN; GDP|MER; billion US$2005/yr; 2020; 8882", "REMIND; Baseline; CHN; GDP|MER; billion US$2005/yr; 2010; 4119", "REMIND; Baseline; CHN; Population; million; 2020; 1387", "REMIND; Baseline; CHN; Population; million; 2010; 1349")) calc_addVariable(data, "GDPpC" = "`GDP|MER` / Population * 1e3", "`ln GDPpC`" = "log(GDPpC)", units = c("US$2005/cap", NA)) calc_addVariable_( data, list("`GDPpC`" = c("`GDP|MER` / `Population` * 1e3", "US$/cap"), "`ln GDPpC`" = "log(`GDPpC`)") )data <- inline.data.frame(c( "model; scenario; region; variable; unit; period; value", "REMIND; Baseline; USA; GDP|MER; billion US$2005/yr; 2010; 12990", "REMIND; Baseline; USA; Population; million; 2010; 310.4", "REMIND; Baseline; USA; PE; EJ/yr; 2010; 91.62", "REMIND; Baseline; CHN; GDP|MER; billion US$2005/yr; 2020; 8882", "REMIND; Baseline; CHN; GDP|MER; billion US$2005/yr; 2010; 4119", "REMIND; Baseline; CHN; Population; million; 2020; 1387", "REMIND; Baseline; CHN; Population; million; 2010; 1349")) calc_addVariable(data, "GDPpC" = "`GDP|MER` / Population * 1e3", "`ln GDPpC`" = "log(GDPpC)", units = c("US$2005/cap", NA)) calc_addVariable_( data, list("`GDPpC`" = c("`GDP|MER` / `Population` * 1e3", "US$/cap"), "`ln GDPpC`" = "log(`GDPpC`)") )
Calculates the growth rate in '%/yr' for variables
calc_growthrate(x, only.new = FALSE, filter.function = identity)calc_growthrate(x, only.new = FALSE, filter.function = identity)
x |
anything with an as.quitte method (data.frame, quitte or magclass object, mif file, ...) |
only.new |
If |
filter.function |
A function used to filter data before calculating growth rates. If instead a character vector is passed, only variables matching this vector are used. |
If, for example, your data contains the data in 2070 and 2060, the growth rate returned for 2070 is calculated as 100 * ((d2070/d2060)^(1/10) - 1). No growth rate can be calculated for the first year of the data. Infinite or undefined values (for example if d2060 = 0) are dropped.
data as a quitte object
Oliver Richters
## Not run: GDPgrowth <- calc_growthrate(quitte_example_data, only.new = TRUE, filter.function = "GDP|PPP") alldata <- calc_growthrate(quitte_example_data, only.new = FALSE, filter.function = function(x) filter(x, grepl("GDP", .data$variable))) ## End(Not run)## Not run: GDPgrowth <- calc_growthrate(quitte_example_data, only.new = TRUE, filter.function = "GDP|PPP") alldata <- calc_growthrate(quitte_example_data, only.new = FALSE, filter.function = function(x) filter(x, grepl("GDP", .data$variable))) ## End(Not run)
Calculate the mode of a sample
calc_mode(v)calc_mode(v)
v |
A vector. |
The mode, or a vector of modes if the sample is multi-modal.
Michaja Pehl
calc_mode(c(1, 1, 100)) calc_mode(c('a', 'a', 'b', 'c', 'c'))calc_mode(c(1, 1, 100)) calc_mode(c('a', 'a', 'b', 'c', 'c'))
This is a wrapper function for quantile for easy use with data frames.
calc_quantiles( .data, value = NA, probs = c(q0 = 0, q25 = 0.25, q50 = 0.5, q75 = 0.75, q100 = 1), na.rm = TRUE, type = 7 ) calc_quantiles_( .data, value = "value", probs = c(q0 = 0, q25 = 0.25, q50 = 0.5, q75 = 0.75, q100 = 1), na.rm = TRUE, type = 7 )calc_quantiles( .data, value = NA, probs = c(q0 = 0, q25 = 0.25, q50 = 0.5, q75 = 0.75, q100 = 1), na.rm = TRUE, type = 7 ) calc_quantiles_( .data, value = "value", probs = c(q0 = 0, q25 = 0.25, q50 = 0.5, q75 = 0.75, q100 = 1), na.rm = TRUE, type = 7 )
.data |
a data frame, possibly grouped |
value |
column name for which sample quantiles should be calculated |
probs |
named numeric vector of probabilities with values in
|
na.rm |
logical; if |
type |
an integer between 1 and 9 select one of the nine quantile algorithms detailed in quantile to be used. |
A data frame.
Michaja Pehl
require(dplyr) require(tidyr) tibble(group = rep(c("A", "B"), 10), value = 1:20) %>% group_by(group) %>% calc_quantiles() %>% pivot_wider(names_from = 'quantile')require(dplyr) require(tidyr) tibble(group = rep(c("A", "B"), 10), value = 1:20) %>% group_by(group) %>% calc_quantiles() %>% pivot_wider(names_from = 'quantile')
Calculate new variable from existing ones, using a generic formula.
calcAddVariable(data, formula, newUnit = "None", na.act = "no")calcAddVariable(data, formula, newUnit = "None", na.act = "no")
data |
A data frame with columns |
formula |
An object of class formula, as returned by
|
newUnit |
Character vector with the unit for the newly calculated variable. |
na.act |
Indicates how NAs in the wide data frame should be handled. Default "no" indicates no action is takenl. |
Obsolete. This function will be removed in the near future. Use
calc_addVariable() instead.
A data frame with the original and the new variables.
Anselm Schultes, Michaja Pehl
Discount and cumulated a times series - gives the time series of the net present value (NPV). Baseyear for the NPV is the first period.
calcCumulatedDiscount( data, nameVar = "Consumption", nameDisrate = "Interest Rate t/(t-1)|Real", discount = 0.05, fixYear = "none" )calcCumulatedDiscount( data, nameVar = "Consumption", nameDisrate = "Interest Rate t/(t-1)|Real", discount = 0.05, fixYear = "none" )
data |
a quitte object containing consumption values - consumption has to be named "Consumption" |
nameVar |
name of the variable to be cumulated (and discounted) |
nameDisrate |
Name of the variable containing the discount rate |
discount |
The discount rate: either a numeric value, or 'BAU' to choose the discount rate supplied in nameDisrate |
fixYear |
From the discounted time series, substract the value in year fixYear, if fixYear is not 'none' |
cumulated discounted values for each scenario, model, region (quitte object)
Anselm Schultes
## Not run: erg <- calcCumulatedDiscount(data, disRate=0.03) ## End(Not run)## Not run: erg <- calcCumulatedDiscount(data, disRate=0.03) ## End(Not run)
Computes decomposition for a change in time or policy of a variable. The decomposition follows the methodology of the paper: "Some properties of an exact energy decomposition model", Sun and Ang, 2000, Energy
calcDecompEff(df, x, bau = NULL, pol = NULL, gap = "policy") calcDecompEff_scen(df, x, bau)calcDecompEff(df, x, bau = NULL, pol = NULL, gap = "policy") calcDecompEff_scen(df, x, bau)
df |
a quitte object (with coluns model, scenario, region, variable, unit, period, value) |
x |
a character vector detailing the explained variable from the decomposition as well as the factors in the decomposition. The explained variable should be named "explained" in the character vector. |
bau |
the name of the reference scenario, as a character string. It is
|
pol |
the name of the policy scenario, as a character string. It is
|
gap |
either |
A data frame with the effects of each component of the decomposition. The data frame contains new columns:
explained gives the name of the explained variable
factors gives the name of the factor considered (from the decomposition
chain)
type gives the parameters:
eff is the result of the decomposition: how much of the change is to
be attributed to the factor
value is the value of the factor
lag is the value of the factor in the reference scenario or in the
previous period
delta is the difference in the factor's value between the policy and
the reference, or between one period and another
Antoine Levesque
# In this example, emissions = ue * fe_ue * emi_fe testdf = inline.data.frame( c("scenario ;period; variable; value", "reference; 2015 ; emissions; 10", "reference; 2015 ; ue ; 4", "reference; 2015 ; fe_ue ; 2", "reference; 2015 ; emi_fe ; 1.25", "reference; 2050 ; emissions; 50", "reference; 2050 ; ue ; 25", "reference; 2050 ; fe_ue ; 1.25", "reference; 2050 ; emi_fe ; 1.6", "policy1; 2015 ; emissions; 10", "policy1; 2015 ; ue ; 4", "policy1; 2015 ; fe_ue ; 2", "policy1; 2015 ; emi_fe ; 1.25", "policy1; 2050 ; emissions; 20", "policy1; 2050 ; ue ; 25", "policy1; 2050 ; fe_ue ; 1.25", "policy1; 2050 ; emi_fe ; 0.64", "policy2; 2015 ; emissions; 10", "policy2; 2015 ; ue ; 4", "policy2; 2015 ; fe_ue ; 2", "policy2; 2015 ; emi_fe ; 1.25", "policy2; 2050 ; emissions; 10", "policy2; 2050 ; ue ; 25", "policy2; 2050 ; fe_ue ; 1.25", "policy2; 2050 ; emi_fe ; 0.32") ) testdf = as.quitte(testdf) decomposition_chain = c(explained = "emissions", "ue","fe_ue","emi_fe") result = calcDecompEff(testdf,x = decomposition_chain,gap = "time") result2 = calcDecompEff(testdf,x = decomposition_chain,bau = "reference",pol = "policy1") result3 = calcDecompEff_scen(testdf, x = decomposition_chain, bau = "reference")# In this example, emissions = ue * fe_ue * emi_fe testdf = inline.data.frame( c("scenario ;period; variable; value", "reference; 2015 ; emissions; 10", "reference; 2015 ; ue ; 4", "reference; 2015 ; fe_ue ; 2", "reference; 2015 ; emi_fe ; 1.25", "reference; 2050 ; emissions; 50", "reference; 2050 ; ue ; 25", "reference; 2050 ; fe_ue ; 1.25", "reference; 2050 ; emi_fe ; 1.6", "policy1; 2015 ; emissions; 10", "policy1; 2015 ; ue ; 4", "policy1; 2015 ; fe_ue ; 2", "policy1; 2015 ; emi_fe ; 1.25", "policy1; 2050 ; emissions; 20", "policy1; 2050 ; ue ; 25", "policy1; 2050 ; fe_ue ; 1.25", "policy1; 2050 ; emi_fe ; 0.64", "policy2; 2015 ; emissions; 10", "policy2; 2015 ; ue ; 4", "policy2; 2015 ; fe_ue ; 2", "policy2; 2015 ; emi_fe ; 1.25", "policy2; 2050 ; emissions; 10", "policy2; 2050 ; ue ; 25", "policy2; 2050 ; fe_ue ; 1.25", "policy2; 2050 ; emi_fe ; 0.32") ) testdf = as.quitte(testdf) decomposition_chain = c(explained = "emissions", "ue","fe_ue","emi_fe") result = calcDecompEff(testdf,x = decomposition_chain,gap = "time") result2 = calcDecompEff(testdf,x = decomposition_chain,bau = "reference",pol = "policy1") result3 = calcDecompEff_scen(testdf, x = decomposition_chain, bau = "reference")
Calculate mitigation costs
calcMitigationCost( data, scenBau, scenPol, yearFrom = 2010, yearTo = 2100, nameVar = "Consumption", nameDisrate = "Interest Rate t/(t-1)|Real", discount = 0.05 )calcMitigationCost( data, scenBau, scenPol, yearFrom = 2010, yearTo = 2100, nameVar = "Consumption", nameDisrate = "Interest Rate t/(t-1)|Real", discount = 0.05 )
data |
quitte object |
scenBau |
baseline scenario name |
scenPol |
policy scenario name |
yearFrom |
the startyear |
yearTo |
the endyear |
nameVar |
Name of the variable containing consumption. Defaults to "Consumption" |
nameDisrate |
Name of the variable for the discount rate, only needed if discount=endo. Defaults to "Interest Rate t/(t-1)|Real" |
discount |
discount rate - exogenous only for now |
regional mitigation costs (quitte object)
Anselm Schultes
## Not run: calcMitigationCost(qd,"BAU","POL") ## End(Not run)## Not run: calcMitigationCost(qd,"BAU","POL") ## End(Not run)
Generate Cartesian Product from Vectors
cartesian(..., sep = ".")cartesian(..., sep = ".")
... |
Vectors to be combined |
sep |
If a character string, that string will separate the elements of
|
If sep is a character string, then a character vector of
concatenated elements. If sep is NULL, then a list of concatenated
elements.
cartesian(c('a', 'b'), 1:3, c('X', 'Y', 'Z')) # [1] "a.1.X" "a.1.Y" "a.1.Z" "a.2.X" "a.2.Y" "a.2.Z" "a.3.X" "a.3.Y" # [9] "a.3.Z" "b.1.X" "b.1.Y" "b.1.Z" "b.2.X" "b.2.Y" "b.2.Z" "b.3.X" # [17] "b.3.Y" "b.3.Z" str(cartesian(c('a', 'b'), 17:19, sep = NULL)) # List of 6 # $ :List of 2 # ..$ : chr "a" # ..$ : int 17 # $ :List of 2 # ..$ : chr "a" # ..$ : int 18 # $ :List of 2 # ..$ : chr "a" # ..$ : int 19 # $ :List of 2 # ..$ : chr "b" # ..$ : int 17 # $ :List of 2 # ..$ : chr "b" # ..$ : int 18 # $ :List of 2 # ..$ : chr "b" # ..$ : int 19cartesian(c('a', 'b'), 1:3, c('X', 'Y', 'Z')) # [1] "a.1.X" "a.1.Y" "a.1.Z" "a.2.X" "a.2.Y" "a.2.Z" "a.3.X" "a.3.Y" # [9] "a.3.Z" "b.1.X" "b.1.Y" "b.1.Z" "b.2.X" "b.2.Y" "b.2.Z" "b.3.X" # [17] "b.3.Y" "b.3.Z" str(cartesian(c('a', 'b'), 17:19, sep = NULL)) # List of 6 # $ :List of 2 # ..$ : chr "a" # ..$ : int 17 # $ :List of 2 # ..$ : chr "a" # ..$ : int 18 # $ :List of 2 # ..$ : chr "a" # ..$ : int 19 # $ :List of 2 # ..$ : chr "b" # ..$ : int 17 # $ :List of 2 # ..$ : chr "b" # ..$ : int 18 # $ :List of 2 # ..$ : chr "b" # ..$ : int 19
character.data.frame() turns factor columns of a data frame into
character columns.
character.data.frame(df, ...) character.data.frame_(df, .dots)character.data.frame(df, ...) character.data.frame_(df, .dots)
df |
a data frame |
... |
Column names to convert to characters. |
.dots |
Character vector of column names to turn into characters. |
A data frame.
Antoine Levesque
require(dplyr) (df <- tibble( char = letters[1:3], fact = factor(LETTERS[24:26], levels = LETTERS[c(1:3, 24:26)]), num = (1:3) ^ 2)) character.data.frame(df) character.data.frame_(df, 'num')require(dplyr) (df <- tibble( char = letters[1:3], fact = factor(LETTERS[24:26], levels = LETTERS[c(1:3, 24:26)]), num = (1:3) ^ 2)) character.data.frame(df) character.data.frame_(df, 'num')
Check an IAMC-style data frame to see if values across variables and regions sum up to the totals specified within the data frame.
check_quitte(quitte, check_variables, check_regions = NULL)check_quitte(quitte, check_variables, check_regions = NULL)
quitte |
IAMC-style data frame. |
check_variables |
List, string or file of variables to check. |
check_regions |
List, string or file of regions to check. |
Checking is performed for all variables and regions in
check_variables and check_regions, which can be passed as a list of
format
list("sum" = c("summand1", "summand2", ...))
a character string of format
sum1 summand1a summand1b sum2 summand2a ...
or as the path to a text file with this format.
If checking should be performed for variables or regions that are neither sum
nor summand (e.g., the variable 'GDP' should be summed across regions, but is
itself not a sum of other variables), include them as sum and their only
summand in the respective list (i.e., list("GDP" = "GDP") or as a double
line in the character string or file.
If check_regions is NULL, variables are check across all regions in
quitte.
A data frame of all entries that did not match.
Michaja Pehl
require(dplyr, quietly = TRUE, warn.conflicts = FALSE) quitte <- rbind( data.frame( model = "REMIND", scenario = "Baseline", region = c("World", "USA", "EUR"), variable = "GDP", unit = "US$2005", period = 2005, value = c(3, 1, 1) ), data.frame( model = "REMIND", scenario = "Baseline", region = "ROW", variable = c("FE|Total", "FE|Solids", "FE|Electricity"), unit = "EJ/a", period = 2005, value = c(3, 1, 1) ) ) check_variables <- list( "FE|Total" = c("FE|Solids", "FE|Electricity"), "GDP" = "GDP") check_regions <- paste0("World\nUSA\nEUR\n\nROW\nROW") print(quitte) print(check_variables) cat(check_regions) check_quitte(quitte, check_variables, check_regions)require(dplyr, quietly = TRUE, warn.conflicts = FALSE) quitte <- rbind( data.frame( model = "REMIND", scenario = "Baseline", region = c("World", "USA", "EUR"), variable = "GDP", unit = "US$2005", period = 2005, value = c(3, 1, 1) ), data.frame( model = "REMIND", scenario = "Baseline", region = "ROW", variable = c("FE|Total", "FE|Solids", "FE|Electricity"), unit = "EJ/a", period = 2005, value = c(3, 1, 1) ) ) check_variables <- list( "FE|Total" = c("FE|Solids", "FE|Electricity"), "GDP" = "GDP") check_regions <- paste0("World\nUSA\nEUR\n\nROW\nROW") print(quitte) print(check_variables) cat(check_regions) check_quitte(quitte, check_variables, check_regions)
Allows to interactively filter data from quitte object
chooseFilter( data, types = c("model", "scenario", "region", "variable", "period"), keep = list() )chooseFilter( data, types = c("model", "scenario", "region", "variable", "period"), keep = list() )
data |
A quitte object or something that can be transformed into one by as.quitte |
types |
vector of quitte columns for user to select data if more than one option available |
keep |
list with quitte columns as names and data points that should always be kept. If the column is not also in types, only the elements in that list are kept |
Oliver Richters
## Not run: qe <- chooseFilter(quitte_example_dataAR6, types = c("model"), keep = list(region = "World")) ## End(Not run)## Not run: qe <- chooseFilter(quitte_example_dataAR6, types = c("model"), keep = list(region = "World")) ## End(Not run)
Generate sequences of n equidistant data points for a column in a data frame.
df_populate_range(df, column, n = 100)df_populate_range(df, column, n = 100)
df |
A data frame. |
column |
<tidy-select> The column with data to populate. |
n |
Length of the sequence to return. Defaults to 100. |
A data frame.
require(dplyr, warn.conflicts = FALSE, quietly = TRUE) require(tidyr, warn.conflicts = FALSE, quietly = TRUE) tibble(A = (1:3) ^ 2, B = exp(0:2)) %>% pivot_longer(everything()) %>% arrange(name, value) %>% print() %>% df_populate_range(value, n = 6)require(dplyr, warn.conflicts = FALSE, quietly = TRUE) require(tidyr, warn.conflicts = FALSE, quietly = TRUE) tibble(A = (1:3) ^ 2, B = exp(0:2)) %>% pivot_longer(everything()) %>% arrange(name, value) %>% print() %>% df_populate_range(value, n = 6)
Removes all columns from a data frame which have only identical data, to facilitate a quick overview.
df_variation(x)df_variation(x)
x |
A data frame. |
A data frame.
(quitte_example_data['Consumption' == quitte_example_data$variable,] -> x) df_variation(x)(quitte_example_data['Consumption' == quitte_example_data$variable,] -> x) df_variation(x)
Turns the two first columns of a data frame into a named vector, taking the values from the second and the names from the first column.
df.2.named.vector(.data)df.2.named.vector(.data)
.data |
A data frame with at least two columns. |
A named vector.
Michaja Pehl
data <- data.frame(names = c("one", "two", "three"), values = 1:3) data df.2.named.vector(data)data <- data.frame(names = c("one", "two", "three"), values = 1:3) data df.2.named.vector(data)
Data Frame as List
df.as.list(df, names = 1, x = 2)df.as.list(df, names = 1, x = 2)
df |
A data frame. |
names |
Index used for naming list items. Integer or character, must
work with |
x |
Index used for list items. Integer or character, must work with
|
A list.
(df <- data.frame( modules = c('power', 'macro', 'welfare', 'PE_FE_parameters', 'initialCap', 'aerosols'), `*` = c('IntC', 'singleSectorGr', 'utilitarian', 'iea2014', 'on', 'exoGAINS'), check.names = FALSE )) df.as.list(df, 'modules', '*')(df <- data.frame( modules = c('power', 'macro', 'welfare', 'PE_FE_parameters', 'initialCap', 'aerosols'), `*` = c('IntC', 'singleSectorGr', 'utilitarian', 'iea2014', 'on', 'exoGAINS'), check.names = FALSE )) df.as.list(df, 'modules', '*')
Duplicate rows in a data frame, modifying a specified column.
duplicate(data, ...) duplicate_(data, column)duplicate(data, ...) duplicate_(data, column)
data |
A |
..., column
|
A key-value pair of the column to modify. |
A data frame or quitte object, same as input.
require(dplyr) (data <- tibble(region = rep(c('AFR', 'CHN'), 2), variable = paste('Var', c(1, 1, 2, 2)), value = 1:4)) data %>% duplicate(region = 'World')require(dplyr) (data <- tibble(region = rep(c('AFR', 'CHN'), 2), variable = paste('Var', c(1, 1, 2, 2)), value = 1:4)) data %>% duplicate(region = 'World')
factor.data.frame() turns character columns in a data frame into
factor columns and refactorises factor columns, silently dropping unused
levels.
factor.data.frame(df, ...) factor.data.frame_(df, .dots)factor.data.frame(df, ...) factor.data.frame_(df, .dots)
df |
A data frame. |
... |
Column names to factorise. |
.dots |
Character vector of column names to factorise. |
A data frame.
Michaja Pehl
require(dplyr) (df <- tibble( char = letters[1:3], fact = factor(LETTERS[24:26], levels = LETTERS[c(1:3, 24:26)]), num = (1:3) ^ 2)) str(factor.data.frame(df)) str(factor.data.frame_(df, 'num'))require(dplyr) (df <- tibble( char = letters[1:3], fact = factor(LETTERS[24:26], levels = LETTERS[c(1:3, 24:26)]), num = (1:3) ^ 2)) str(factor.data.frame(df)) str(factor.data.frame_(df, 'num'))
Generate a factor with levels in prescribed order.
factorise(x)factorise(x)
x |
A character vector. |
A factor from x, with levels in the same order as they appear
in within x.
Michaja Pehl
factor(c('a', 'c', 'b')) factorise(c('a', 'c', 'b'))factor(c('a', 'c', 'b')) factorise(c('a', 'c', 'b'))
Retrieves values from one column of a quitte object
getColValues(df, colVar)getColValues(df, colVar)
df |
quitte object |
colVar |
name of the column of interest |
a vector containing the values of the column without duplicates. The class of the returned vector is either numeric or character
Antoine Levesque
data(mtcars) getColValues(mtcars,"mpg")data(mtcars) getColValues(mtcars,"mpg")
Retrieves models from a quitte object
getModels(df)getModels(df)
df |
quitte object |
a character vector containing the values of the column without duplicates
Antoine Levesque
## Not run: getPeriods(dataframe) ## End(Not run)## Not run: getPeriods(dataframe) ## End(Not run)
Retrieves periods from a quitte object
getPeriods(df)getPeriods(df)
df |
quitte object |
a numeric vector containing the values of the column without duplicates
Antoine Levesque
## Not run: getPeriods(dataframe) ## End(Not run)## Not run: getPeriods(dataframe) ## End(Not run)
Retrieves regions from a quitte object
getRegs(df)getRegs(df)
df |
quitte object |
a character vector containing the values of the column without duplicates
Antoine Levesque
## Not run: getRegs(dataframe) ## End(Not run)## Not run: getRegs(dataframe) ## End(Not run)
Retrieves scenarios from a quitte object
getScenarios(df)getScenarios(df)
df |
quitte object |
a character vector containing the values of the column without duplicates
Antoine Levesque
## Not run: getPeriods(dataframe) ## End(Not run)## Not run: getPeriods(dataframe) ## End(Not run)
Retrieves variable names from a quitte object
getVars(df)getVars(df)
df |
quitte object |
a character vector containing the values of the column without duplicates
Antoine Levesque
## Not run: getVars(dataframe) ## End(Not run)## Not run: getVars(dataframe) ## End(Not run)
Get n colours, evenly spaced along the colour wheel. Just like the ones
ggplot2::scale_colour_hue() is using.
gg_colour_hue(n)gg_colour_hue(n)
n |
Either the number of colours to generate, or a character vector which will be used as names for the returned colours. |
A vector of character strings which can be used as color specifications by R graphics functions. The vector is named if n is a character vector.
gg_colour_hue(5) gg_colour_hue(letters[1:3])gg_colour_hue(5) gg_colour_hue(letters[1:3])
Utility functions for plotting stacked (on top of each other) and dodged (next to each other) bars in the same figure.
ggplot_bar_stacked_dodged(data, mapping, gap = 1) add_stacked_dodged_xpos(data, ..., gap = 1) calc_stacked_dodged_xlabels(data, ..., gap = 1)ggplot_bar_stacked_dodged(data, mapping, gap = 1) add_stacked_dodged_xpos(data, ..., gap = 1) calc_stacked_dodged_xlabels(data, ..., gap = 1)
data |
A data frame |
mapping |
An aesthetic mapping generated by ggplot2:aes,
containing the aesthetics |
gap |
The width of the gap between bars, relative to the width of the
bars themselves (default: |
... |
A selection of two columns. Both will be combined to form x-axis
coordinates. The first will form the outer iteration (groups), the second
the inner iteration (bars within a group). If unnamed, the column with
calculated positions will be called |
add_stacked_dodged_xpos() adds x-axis positions to a data frame for
plotting two categorical variables within a bar plot.
calc_stacked_dodged_xlabels() calculates matching label positions on
the x-axis.
ggplot_bar_stacked_dodged() uses both functions to generate a plot.
add_stacked_dodged_xpos() returns the input data frame with an
additional column. Row and column order are preserved.
calc_stacked_dodged_xlabels() returns a named character vector for use
with ggplot2:scale_x_continuous.
ggplot_bar_stacked_dodged() returns a ggplot2:ggplot object.
Michaja Pehl
require(tidyverse) set.seed(0) (data <- crossing(a = factorise(c('left', 'center', 'right')), b = factorise(c('top', 'middle', 'bottom')), c = letters[1:4], d = LETTERS[25:26]) %>% mutate(value = abs(rnorm(n())) + 0.2)) (plot.data <- add_stacked_dodged_xpos(data, c('c', 'a'))) (xlabels <- calc_stacked_dodged_xlabels(data, c('c', 'a'))) ggplot(data = plot.data) + scale_x_continuous(breaks = xlabels) + facet_wrap(~ d, ncol = 1, scales = 'free_x') ggplot_bar_stacked_dodged(data, aes(x = a, y = value, fill = b, dodge = c), gap = 1/3) + facet_wrap(~ d, ncol = 1, scales = 'free_x')require(tidyverse) set.seed(0) (data <- crossing(a = factorise(c('left', 'center', 'right')), b = factorise(c('top', 'middle', 'bottom')), c = letters[1:4], d = LETTERS[25:26]) %>% mutate(value = abs(rnorm(n())) + 0.2)) (plot.data <- add_stacked_dodged_xpos(data, c('c', 'a'))) (xlabels <- calc_stacked_dodged_xlabels(data, c('c', 'a'))) ggplot(data = plot.data) + scale_x_continuous(breaks = xlabels) + facet_wrap(~ d, ncol = 1, scales = 'free_x') ggplot_bar_stacked_dodged(data, aes(x = a, y = value, fill = b, dodge = c), gap = 1/3) + facet_wrap(~ d, ncol = 1, scales = 'free_x')
inline.data.frame() converts a vector of strings that contain
separated items into a data frame.
inline.data.frame(..., sep = ";", quote = "")inline.data.frame(..., sep = ";", quote = "")
... |
string, or a vector of strings |
sep |
Item separator within strings, defaults to ";" |
quote |
Quote character for masking separators, empty by default |
a data frame
Michaja Pehl
inline.data.frame( "letters; numbers", "A; 1", "B; 2", NULL) # this last line allows for easy switching of line order inline.data.frame(c("letters; numbers", "A; 1", "B; 2"))inline.data.frame( "letters; numbers", "A; 1", "B; 2", NULL) # this last line allows for easy switching of line order inline.data.frame(c("letters; numbers", "A; 1", "B; 2"))
Adds missing periods to data frame and interpolates missing values linearly or using splines from adjacent existing ones. Values for periods smaller/bigger than the existing ones can be filled with the values for the first/last available period in the case of linear interpolation.
interpolate_missing_periods( data, ..., value = "value", expand.values = FALSE, method = "linear", combinations = "nesting" ) interpolate_missing_periods_( data, periods, value = "value", expand.values = FALSE, method = "linear", combinations = "nesting" )interpolate_missing_periods( data, ..., value = "value", expand.values = FALSE, method = "linear", combinations = "nesting" ) interpolate_missing_periods_( data, periods, value = "value", expand.values = FALSE, method = "linear", combinations = "nesting" )
data |
A data frame or a quitte object. |
... |
A name-value pair of periods to fill. If unnamed, defaults to
|
value |
Name of the column to fill, defaults to |
expand.values |
If |
method |
Specifies the interpolation method. Either |
combinations |
Specifies the method with which other columns are
treated. They are either preserved as-is ( |
periods |
A named list of periods to fill. |
A data frame or a quitte object, the same as data.
Michaja Pehl
require(dplyr) # generate some test data with explicit (A-y-2025) and implicit (B-x-2030) # missing values (data <- tibble( group = rep(c('A', 'B'), c(8, 4)), item = c(rep('x', 4), rep('y', 4), rep('x', 4)), period = rep(c(2015, 2025, 2030, 2035), 3), value = c(2, 4, 5, 6, 20, NA, 50, 60, NA, 400, 500, NA))) # fill values for already existing periods interpolate_missing_periods(data) # fill values for existing periods, with full combinations of other columns interpolate_missing_periods(data, combinations = 'crossing') # add additional periods and fill values interpolate_missing_periods(data, period = seq(2010, 2035, 5)) # also fill values outside the original data range interpolate_missing_periods(data, seq(2010, 2035, 5), expand.values = TRUE) # works on data frames with different column names (data <- data %>% rename(year = period, coeff = value)) interpolate_missing_periods(data, year, value = 'coeff') # works on quitte objects too (quitte <- data %>% rename(model = group, scenario = item, period = year, value = coeff) %>% mutate(variable = 'Var 1', unit = 'u1') %>% as.quitte()) interpolate_missing_periods(quitte, expand.values = TRUE) # and works with POSIXct periods (quitte <- quitte %>% mutate(period = ISOyear(period))) interpolate_missing_periods(quitte, period = ISOyear(seq(2010, 2035, 5))) # standard evaluation example interpolate_missing_periods_(data, periods = list(year = seq(2010, 2035, 5)), value = 'coeff', expand.values = TRUE)require(dplyr) # generate some test data with explicit (A-y-2025) and implicit (B-x-2030) # missing values (data <- tibble( group = rep(c('A', 'B'), c(8, 4)), item = c(rep('x', 4), rep('y', 4), rep('x', 4)), period = rep(c(2015, 2025, 2030, 2035), 3), value = c(2, 4, 5, 6, 20, NA, 50, 60, NA, 400, 500, NA))) # fill values for already existing periods interpolate_missing_periods(data) # fill values for existing periods, with full combinations of other columns interpolate_missing_periods(data, combinations = 'crossing') # add additional periods and fill values interpolate_missing_periods(data, period = seq(2010, 2035, 5)) # also fill values outside the original data range interpolate_missing_periods(data, seq(2010, 2035, 5), expand.values = TRUE) # works on data frames with different column names (data <- data %>% rename(year = period, coeff = value)) interpolate_missing_periods(data, year, value = 'coeff') # works on quitte objects too (quitte <- data %>% rename(model = group, scenario = item, period = year, value = coeff) %>% mutate(variable = 'Var 1', unit = 'u1') %>% as.quitte()) interpolate_missing_periods(quitte, expand.values = TRUE) # and works with POSIXct periods (quitte <- quitte %>% mutate(period = ISOyear(period))) interpolate_missing_periods(quitte, period = ISOyear(seq(2010, 2035, 5))) # standard evaluation example interpolate_missing_periods_(data, periods = list(year = seq(2010, 2035, 5)), value = 'coeff', expand.values = TRUE)
Converts integer years (e.g. 2023) to POSIXct date/time values (e.g.
2023-07-02 12:00:00 GMT) corresponding to July 2, noon, which is the middle
of the (non-leap) year. The function keeps a cache of already converted
values, as the underlying function ISOdate() is rather slow.
ISOyear(year)ISOyear(year)
year |
Vector of years to convert to POSIXct. |
A vector of POSIXct values.
Michaja Pehl
ISOyear(c(2005, 2010, 2100, 1900))ISOyear(c(2005, 2010, 2100, 1900))
Convert mapping list to data frame
list_to_data_frame(l, ...) list_to_data_frame_(l, category = "category", item = "item")list_to_data_frame(l, ...) list_to_data_frame_(l, category = "category", item = "item")
l |
A named list of character vectors. |
... |
Unquoted names of category and item columns. Defaults to 'category' and 'item'. |
category |
Name of category column. Defaults to 'category'. |
item |
Name of item column. Defaults to 'item'. |
A data frame.
Michaja Pehl
l <- list(Africa = c('Egypt', 'Tanzania'), Europe = c('Portugal', 'Ukraine', 'Denmark')) list_to_data_frame(l, region, country)l <- list(Africa = c('Egypt', 'Tanzania'), Europe = c('Portugal', 'Ukraine', 'Denmark')) list_to_data_frame(l, region, country)
Convert anything to a magpie object and back to
traffic data across madrat borders.
madrat_mule(x)madrat_mule(x)
x |
Anything. |
A magpie object containing x (in unusable
form), or the original x if a magpie object was
passed.
Michaja Pehl
str(x <- madrat_mule(quitte_example_data)) madrat_mule(x)str(x <- madrat_mule(quitte_example_data)) madrat_mule(x)
Sensible magclass to tibble conversion.
magclass_to_tibble(m, colnames = NULL)magclass_to_tibble(m, colnames = NULL)
m |
A |
colnames |
Column names for the returned |
A tibble.
magclass_to_tibble(magclass::maxample('pop'))magclass_to_tibble(magclass::maxample('pop'))
Converts mif file into more memory efficient rds file. Additionally, removes NA and duplicate entries.
mif2rds(input, output = "output.rds")mif2rds(input, output = "output.rds")
input |
Path to the MIF file to be converted |
output |
File name of the output file |
Jan Philipp Dietrich
Uses mutate based on a character vector#'
mutate_text(df, s)mutate_text(df, s)
df |
a data frame |
s |
a character string containing the formula to be applied in mutate |
A data frame transformed with the mutate function
Antoine Levesque
df = data.frame(x = c(1,2), y = c(3,4)) form = "z = x + y" mutate_text(df,form)df = data.frame(x = c(1,2), y = c(3,4)) form = "z = x + y" mutate_text(df,form)
Arranges the levels of data frame columns in a given order. Non-factor columns are silently converted.
order.levels(df, ..., drop.extra.levels = TRUE) order.levels_(df, dots, drop.extra.levels = TRUE)order.levels(df, ..., drop.extra.levels = TRUE) order.levels_(df, dots, drop.extra.levels = TRUE)
df |
A data frame (or quitte object). |
... |
Name-value pairs assigning level order to factor columns. |
drop.extra.levels |
If |
dots |
A named list of factor columns and corresponding levels. |
A data frame (or quitte object, same as data).
Michaja Pehl
require(dplyr) str(df <- tibble(UPPER = LETTERS[3:1], lower = factor(letters[24:26]), value = 1:3)) str(order.levels(df, UPPER = LETTERS[1:3], lower = letters[26:20])) str(order.levels_(df, list(UPPER = LETTERS[1:3], lower = letters[26:23]), drop.extra.levels = FALSE))require(dplyr) str(df <- tibble(UPPER = LETTERS[3:1], lower = factor(letters[24:26]), value = 1:3)) str(order.levels(df, UPPER = LETTERS[1:3], lower = letters[26:20])) str(order.levels_(df, list(UPPER = LETTERS[1:3], lower = letters[26:23]), drop.extra.levels = FALSE))
overwrite() rbind()s the data frames lhs and rhs,
removing any duplicate lines, which are determined without regard to the
columns in except.
overwrite(lhs, rhs, except = "value")overwrite(lhs, rhs, except = "value")
lhs |
data frame with values that will replace others |
rhs |
data frame with values that will be replaced |
except |
names of columns that will not be considered in determining which columns to replace; defaults to "value" |
data frame in which rows from rhs have been replaced with rows from lhs
Michaja Pehl
require(dplyr) data <- data.frame(expand.grid(UPPER = LETTERS[1:2], lower = letters[24:26]), value = 1:6) data data %>% filter(lower == "y") %>% mutate(value = value * 10) %>% overwrite(data)require(dplyr) data <- data.frame(expand.grid(UPPER = LETTERS[1:2], lower = letters[24:26]), value = 1:6) data data %>% filter(lower == "y") %>% mutate(value = value * 10) %>% overwrite(data)
QuitteIn contains two variables containedin varNames that should be plotted in a scatter plot. The functions forms the new QuitteOut with the variables x and y. QuitteOut can be used in ggplot with plotting x and y. The unit needs to be replaced by None.
prepQuitteForScatter(quitteIn, varNames)prepQuitteForScatter(quitteIn, varNames)
quitteIn |
Quitte with original data |
varNames |
Vector with two variable names that must be contained in QuitteIn$variable |
quitte object
Nico Bauer, Anselm Schultes, Jerome Hilaire
## Not run: quitteOut <- prepQuitteForScatter(quitteIn, c('Emissions|CO2', 'Price|Carbon')) ## End(Not run)## Not run: quitteOut <- prepQuitteForScatter(quitteIn, c('Emissions|CO2', 'Price|Carbon')) ## End(Not run)
A quitte object with some example data from the REMIND model.
quitte_example_dataquitte_example_data
Michaja Pehl
A quitte object with some example data from NGFS Phase 3.
quitte_example_dataAR6quitte_example_dataAR6
Oliver Richters
The quitte class is a more standardized data.frame format. is.quitte
tests if x is an quitte-object, as.quitte transforms x
to an quitte-object (if possible).
as.quitte(x, periodClass = "integer", addNA = FALSE, na.rm = FALSE) is.quitte(x, warn = TRUE)as.quitte(x, periodClass = "integer", addNA = FALSE, na.rm = FALSE) is.quitte(x, warn = TRUE)
x |
An object that should be either tested or transformed as/to an quitte-object. |
periodClass |
integer or POSIXct |
addNA |
modifies a factor by turning NA into an extra level (so that NA values are counted in tables, for instance). |
na.rm |
if set to TRUE entries with value NA will be removed |
warn |
display warnings or not |
is.quitte(): quitte check
Jan Philipp Dietrich
calculate quantiles
quitte2quantiles( x, probs = c(q0 = 0, q25 = 0.25, q50 = 0.5, q75 = 0.75, q100 = 1), grouping = c("region", "variable", "period", "scenario") )quitte2quantiles( x, probs = c(q0 = 0, q25 = 0.25, q50 = 0.5, q75 = 0.75, q100 = 1), grouping = c("region", "variable", "period", "scenario") )
x |
dataframe to add quantiles |
probs |
default=c(q0=0,q25=0.25,q50=0.5,q75=0.75,q100=1) |
grouping |
default=c("region", "variable", "period", "scenario") |
Gunnar Luderer, Lavinia Baumstark
## Not run: p <- x.minmax = quitte2quantiles(x,probs=c("min"=0,"max"=1)) ## End(Not run)## Not run: p <- x.minmax = quitte2quantiles(x,probs=c("min"=0,"max"=1)) ## End(Not run)
Sorts a quitte object in a standardized way: model -> scenario -> variable -> unit -> region -> period
quitteSort(x)quitteSort(x)
x |
anything with an as.quitte method (data.frame, quitte or magclass object, mif file, ...) |
the sorted quitte object
Oliver Richters
Read .mif Header
read_mif_header(file, sep = ";", comment = "#")read_mif_header(file, sep = ";", comment = "#")
file |
A path to a |
sep |
Column separator, defaults to ";". |
comment |
A character which at line start signifies the optional comment
header with metadata at the head of |
A list with elements header, comment_header, and
useless.last.column.
Michaja Pehl
.gdx file as quitte data frameread.gdx() is a wrapper function for either gdxrrw::rgdx() or
gamstransfer::readGDX() that returns a quitte data frame.
read.gdx( gdxName, requestList.name, fields = "l", colNames = NULL, factors = deprecated(), squeeze = TRUE )read.gdx( gdxName, requestList.name, fields = "l", colNames = NULL, factors = deprecated(), squeeze = TRUE )
gdxName |
Path to a |
requestList.name |
Name of the item to read. |
fields |
Fields to read from variables and equations. When using
gdxrrw, any of |
colNames |
String vector of column names to override dimension and field names. |
factors |
Deprecated. Do not use any more. |
squeeze |
If |
read.gdx() will use gdxrrw::rgdx() if gdxrrw is
installed and the option quitte_force_gamstransfer is not TRUE, otherwise
it will use gamstransfer::readGDX().
A quitte data frame.
Michaja Pehl
Reads IAMC-style .csv or .xlsx files or object from rds file into a quitte data frame.
read.quitte( file, sep = NULL, quote = "", na.strings = c("UNDF", "NA", "N/A", "n_a"), convert.periods = FALSE, check.duplicates = TRUE, factors = TRUE, drop.na = FALSE, comment = "#", filter.function = identity, chunk_size = 200000L )read.quitte( file, sep = NULL, quote = "", na.strings = c("UNDF", "NA", "N/A", "n_a"), convert.periods = FALSE, check.duplicates = TRUE, factors = TRUE, drop.na = FALSE, comment = "#", filter.function = identity, chunk_size = 200000L )
file |
Path of IAMC-style .csv, .xlsx, or rds file or vector of paths to read. |
sep |
Column separator, defaults to ";" in read_mif_header(). |
quote |
Quote characters, empty by default. |
na.strings |
Entries to interpret as NA; defaults to
|
convert.periods |
If |
check.duplicates |
If |
factors |
Return columns as factors ( |
drop.na |
Should |
comment |
A character which at line start signifies the optional comment
header with metadata at the head of |
filter.function |
A function used to filter data during read. See Details. |
chunk_size |
Number of lines to read at a time. Defaults to 200000. (REMIND .mif files have between 55000 and 105000 lines for H12 and EU21 regional settings, respectively.) |
In order to process large data sets, like IIASA data base snapshots,
read.quitte() reads provided files in chunks of chunk_size lines
(not for Excel files), and applies filter.function() to the chunks. This
allows for filtering data piece-by-piece, without exceeding available memory.
filter.function is a function taking one argument, a quitte data frame of
the read chunk, and is expected to return a data frame. Usually it should
simply contain all the filters usually applied after all the data is read in.
Suppose there is a file big_IIASA_snapshot.csv, from which only data for
the REMIND and MESSAGE models between the years 2020 to 2050 is of interest.
Normally, this data would be processed as
read.quitte(file = 'big_IIASA_snapshot.csv') %>%
filter(grepl('^(REMIND|MESSAGE)', .data$model),
between(.data$period, 2020, 2060))
If however big_IIASA_snapshot.csv is too large to be read in completely,
it can be read using
read.quitte(file = 'big_IIASA_snapshot.csv',
filter.function = function(x) {
x %>%
filter(grepl('^(REMIND|MESSAGE)', .data$model),
between(.data$period, 2020, 2060))
})
A quitte data frame.
Michaja Pehl
## Not run: read.quitte(c("some/data/file.mif", "some/other/data/file.mif")) read.quitte("some/data/file.csv", sep = ",", quote = '"') ## End(Not run)## Not run: read.quitte(c("some/data/file.mif", "some/other/data/file.mif")) read.quitte("some/data/file.csv", sep = ",", quote = '"') ## End(Not run)
Reads IAMC-style .csv or .xlsx files obtained as a IIASA snapshot into a quitte data frame, or data from rds file, allowing to filter the loaded data. If head, tail and grep are on your system, a pre-filtering improves performance for csv files.
read.snapshot(file, keep = list(), filter.function = identity)read.snapshot(file, keep = list(), filter.function = identity)
file |
Path of single IAMC-style .csv/.mif/.xlsx/.rds file |
keep |
list with quitte columns as names and data points that should be kept. If head, tail and grep are available and a csv/mif file is read, this list is used to extract the data before reading it into R. The more you restrict the data here, the faster the data is read. |
filter.function |
A function used to filter data during read, see read.quitte description. This allows for more complex filtering, but no performance-enhancing pre-filtering using grep is used. The 'keep' list and the 'filter.function' can be combined. |
A quitte data frame.
Oliver Richters
## Not run: read.filter.snapshot("snapshot.csv", list(scenario = c("CurPol", "NDC"), region = "World")) ## End(Not run)## Not run: read.filter.snapshot("snapshot.csv", list(scenario = c("CurPol", "NDC"), region = "World")) ## End(Not run)
A data frame containing the weights with which years contribute to specific periods.
remind_timestepsremind_timesteps
Michaja Pehl
require(tidyverse) remind_timesteps %>% filter(period %in% c(2055, 2060, 2070)) %>% spread(period, weight, fill = 0) %>% print(n = Inf)require(tidyverse) remind_timesteps %>% filter(period %in% c(2055, 2060, 2070)) %>% spread(period, weight, fill = 0) %>% print(n = Inf)
removeColNa() Removes all columns of a data frame for which all entries are
NA, or the default of fct_explict_na
removeColNa(df)removeColNa(df)
df |
a data frame |
a data frame
Antoine Levesque
df <- data.frame( character = letters[1:5], factor = as.factor(LETTERS[1:5]), value = 1:5, unit = NA, unit2 = forcats::fct_na_value_to_level(factor(NA), level = '(Missing)'), stringsAsFactors = FALSE) str(df) str(removeColNa(df))df <- data.frame( character = letters[1:5], factor = as.factor(LETTERS[1:5]), value = 1:5, unit = NA, unit2 = forcats::fct_na_value_to_level(factor(NA), level = '(Missing)'), stringsAsFactors = FALSE) str(df) str(removeColNa(df))
Replaces the column of a data frame with that from a mask data frame.
replace_column( data, mask, ..., drop.extra = FALSE, ignore.ambiguous.match = FALSE ) replace_column_( data, mask, old_column, match_column, new_column, drop.extra = FALSE, ignore.ambiguous.match = FALSE )replace_column( data, mask, ..., drop.extra = FALSE, ignore.ambiguous.match = FALSE ) replace_column_( data, mask, old_column, match_column, new_column, drop.extra = FALSE, ignore.ambiguous.match = FALSE )
data |
A data frame or a quitte object. |
mask |
A data frame containing the |
... |
Definition of old, match, and new columns, see details. |
drop.extra |
Drop rows not present in match column? |
ignore.ambiguous.match |
|
old_column |
old column name, see details. |
match_column |
match column name, see details. |
new_column |
new column name, see details. |
Replaces the old column in data frame data by the new column from data
frame mask based on the matching between old (data) and match
(mask) columns.
This can be used to replace columns based on a mapping to, e.g., rename scenarios, regions, etc. in model data.
A data frame or a quitte object, same as data.
Michaja Pehl
# simple example with matching old and match column names (model_data <- data.frame( model = c('Model1', '2ndModel', 'Model Three'), region = c('Region 1', 'Region 2', 'Region 1'), value = 1:3)) (mask <- data.frame( model = c('Model1', '2ndModel', 'Model Three', 'fourth Model'), clear_name = paste('Model', 1:4))) replace_column(model_data, mask, model, clear_name) # mismatched column names (model_data <- data.frame( model = c('Model1', '2ndModel', 'Model Three', 'fourth Model'), region = c('Region 1', 'Region 2', 'Region 1', 'Region 2'), value = 1:4)) (mask <- data.frame( ugly_name = c('Model1', '2ndModel', 'Model Three'), clear_name = paste('Model', 1:3))) replace_column(model_data, mask, model = ugly_name, clear_name) # SE example replace_column_(model_data, mask, 'model', 'ugly_name', 'clear_name') # dropping the extra entries in model replace_column(model_data, mask, model = ugly_name, clear_name, drop.extra = TRUE) # also works on quitte objects require(dplyr) (quitte <- tibble( model = c('Model1', '2ndModel'), scenario = 'Scenario', region = 'Region', variable = 'Variable', unit = 'Unit', period = 2010, value = 1:2) %>% as.quitte()) replace_column(quitte, mask, model = ugly_name, clear_name) str(.Last.value)# simple example with matching old and match column names (model_data <- data.frame( model = c('Model1', '2ndModel', 'Model Three'), region = c('Region 1', 'Region 2', 'Region 1'), value = 1:3)) (mask <- data.frame( model = c('Model1', '2ndModel', 'Model Three', 'fourth Model'), clear_name = paste('Model', 1:4))) replace_column(model_data, mask, model, clear_name) # mismatched column names (model_data <- data.frame( model = c('Model1', '2ndModel', 'Model Three', 'fourth Model'), region = c('Region 1', 'Region 2', 'Region 1', 'Region 2'), value = 1:4)) (mask <- data.frame( ugly_name = c('Model1', '2ndModel', 'Model Three'), clear_name = paste('Model', 1:3))) replace_column(model_data, mask, model = ugly_name, clear_name) # SE example replace_column_(model_data, mask, 'model', 'ugly_name', 'clear_name') # dropping the extra entries in model replace_column(model_data, mask, model = ugly_name, clear_name, drop.extra = TRUE) # also works on quitte objects require(dplyr) (quitte <- tibble( model = c('Model1', '2ndModel'), scenario = 'Scenario', region = 'Region', variable = 'Variable', unit = 'Unit', period = 2010, value = 1:2) %>% as.quitte()) replace_column(quitte, mask, model = ugly_name, clear_name) str(.Last.value)
Finds duplicates in a quitte object and warns about them per model and scenario. Also warn if variables are identical but value or units differ.
reportDuplicates(mifdata, action = "warn")reportDuplicates(mifdata, action = "warn")
mifdata |
object that can be converted with as.quitte |
action |
if set to 'warn', a warning with duplicate variables is raised |
only the data that is duplicated. Has 0 rows if everything is fine
Revalue the names of a level or character column in a dataframe, according to a named vector given as an input
revalue.levels(df, ...) revalue.levels_(df, dots)revalue.levels(df, ...) revalue.levels_(df, dots)
df |
A data frame (or quitte object). |
... |
Name-value pairs assigning a named vector with new names to a column from the dataframe. |
dots |
A named list of columns containing the named vector with the old and new names for each column |
A data frame (or quitte object, same as data).
Antoine Levesque
data <- inline.data.frame(c( "model; scenario; region; variable; unit; period; value", "REMIND; Baseline; USA; GDP per Capita|MER; US$2005/yr; 2010; 40000", "REMIND; Baseline; USA; Population; million; 2010; 300", "REMIND; Baseline; CHN; GDP per Capita|MER; US$2005/yr; 2010; 7000")) reg_vec = c(USA = "United States") var_vec = c("GDP per Capita|MER" = "gdp", Population = "pop") revalue.levels(data,region = reg_vec) revalue.levels_(data,list(region = reg_vec, variable = var_vec))data <- inline.data.frame(c( "model; scenario; region; variable; unit; period; value", "REMIND; Baseline; USA; GDP per Capita|MER; US$2005/yr; 2010; 40000", "REMIND; Baseline; USA; Population; million; 2010; 300", "REMIND; Baseline; CHN; GDP per Capita|MER; US$2005/yr; 2010; 7000")) reg_vec = c(USA = "United States") var_vec = c("GDP per Capita|MER" = "gdp", Population = "pop") revalue.levels(data,region = reg_vec) revalue.levels_(data,list(region = reg_vec, variable = var_vec))
Generate regular sequence from a range. Wrapper function for
seq().
seq_range(range, by = NA, length.out = NULL)seq_range(range, by = NA, length.out = NULL)
range |
Vector with starting and end values of the sequence. Only first two elements are considered. |
by |
Number; increment of the sequence. |
length.out |
Desired length of the sequence. A non-negative number, which will be rounded up if fractional. |
Returns a vector of type "integer" or "double": programmers should not rely on which.
Michaja Pehl
seq_range(range(1:13), by = 3)seq_range(range(1:13), by = 3)
Performs on
parameters, returning all elements that are in either x or y, but not both.
setXor(x, y)setXor(x, y)
x, y
|
Objects to perform set function on. |
Michaja Pehl
x <- c('a', 'b', 'c') y <- c('b', 'c', 'd') setXor(x, y)x <- c('a', 'b', 'c') y <- c('b', 'c', 'd') setXor(x, y)
Returns the range of signs in a numerical vector as a character string.
signrange(x, na.rm = TRUE)signrange(x, na.rm = TRUE)
x |
A numerical vector. |
na.rm |
Should |
A character string of signs found in x.
Michaja Pehl
signrange(-1) signrange(0) signrange(1) signrange(c(-1, 0)) signrange(c(0, 1)) signrange(c(-1, 1))signrange(-1) signrange(0) signrange(1) signrange(c(-1, 0)) signrange(c(0, 1)) signrange(c(-1, 1))
Trim common portions from both sides of a vector of strings
strtrimcommon(x, split = "", USE.NAMES = FALSE, return.all = FALSE)strtrimcommon(x, split = "", USE.NAMES = FALSE, return.all = FALSE)
x |
A vector of strings |
split |
A |
USE.NAMES |
logical; if |
return.all |
logical; if |
A (named) vector of strings, or a list of string vectors (see
parameter return.all for details).
Michaja Pehl
x <- c('/tmp/remind2_test-convGDX2MIF_fulldata.gdx', '/tmp/remind2_test-Ariadne_fulldata.gdx', '/tmp/remind2_test-NAVIGATE_fulldata.gdx', '/tmp/remind2_test-NGFS_fulldata_oneRegi.gdx', '/tmp/remind2_test-SHAPE_fulldata.gdx') strtrimcommon(x, USE.NAMES = TRUE) x <- c('Some|name|with|common|text|elements', 'Some|name|without|extra|text|elements') strtrimcommon(x, split = '|', return.all = TRUE)x <- c('/tmp/remind2_test-convGDX2MIF_fulldata.gdx', '/tmp/remind2_test-Ariadne_fulldata.gdx', '/tmp/remind2_test-NAVIGATE_fulldata.gdx', '/tmp/remind2_test-NGFS_fulldata_oneRegi.gdx', '/tmp/remind2_test-SHAPE_fulldata.gdx') strtrimcommon(x, USE.NAMES = TRUE) x <- c('Some|name|with|common|text|elements', 'Some|name|without|extra|text|elements') strtrimcommon(x, split = '|', return.all = TRUE)
sum_total() is a short-hand function to calculate and insert the
(weighted) sum of a extensive (intensive) category in a data frame.
sum_total(data, group, value = NA, name = "Total", na.rm = TRUE, weight = NA) sum_total_(data, group, value = NA, name = "Total", na.rm = TRUE, weight = NA)sum_total(data, group, value = NA, name = "Total", na.rm = TRUE, weight = NA) sum_total_(data, group, value = NA, name = "Total", na.rm = TRUE, weight = NA)
data |
a data frame |
group |
column for which the sum is to be calculated |
value |
column of the numbers to be summed |
name |
entry in column |
na.rm |
|
weight |
column of the weights to be applied, if any |
a data frame
Michaja Pehl
require(dplyr) (d <- expand.grid( UPPER = LETTERS[1:2], lower = letters[24:26], number = 1:2 ) %>% arrange(UPPER, lower, number) %>% mutate(value = c(1:6, NA, 8:12))) sum_total(d, UPPER) sum_total(d, lower, name = 'sum over lower', na.rm = FALSE) (e <- tibble( item = c('large', 'medium', 'small'), specific.value = c(1, 10, 100), size = c(1000, 100, 1))) sum_total(e, item, value = specific.value, name = 'Average', weight = size)require(dplyr) (d <- expand.grid( UPPER = LETTERS[1:2], lower = letters[24:26], number = 1:2 ) %>% arrange(UPPER, lower, number) %>% mutate(value = c(1:6, NA, 8:12))) sum_total(d, UPPER) sum_total(d, lower, name = 'sum over lower', na.rm = FALSE) (e <- tibble( item = c('large', 'medium', 'small'), specific.value = c(1, 10, 100), size = c(1000, 100, 1))) sum_total(e, item, value = specific.value, name = 'Average', weight = size)
prepare data for plots
toolExtractSortScaleQuitte( x, scen, vars, var.scaling = 1, regi = c("World"), prd = getPeriods(x) )toolExtractSortScaleQuitte( x, scen, vars, var.scaling = 1, regi = c("World"), prd = getPeriods(x) )
x |
dataframe to prepare |
scen |
scenario to select |
vars |
variables to select |
var.scaling |
scaling of the variables, default=1 |
regi |
region to select, default="World" |
prd |
period to select, default=getPeriods(x) |
Gunnar Luderer, Lavinia Baumstark
## Not run: p <- toolExtractSortScaleQuitte(x,scen=c("BAU"), vars=c("Emi|CO2","FE|Industry"), regi=c("EUR","LAM"),prd=c(2005,2030,2050)) ## End(Not run)## Not run: p <- toolExtractSortScaleQuitte(x,scen=c("BAU"), vars=c("Emi|CO2","FE|Industry"), regi=c("EUR","LAM"),prd=c(2005,2030,2050)) ## End(Not run)
Abstract the differences between character vectors and factors.
unique_or_levels(x)unique_or_levels(x)
x |
A character vector or a factor. |
A character vector with the unique elements of x if it is a
character vector, or the levels of x if it is a factor.
Michaja Pehl
Write a .xlsx file in line with IAMC standard.
write.IAMCxlsx(x, path, append = FALSE)write.IAMCxlsx(x, path, append = FALSE)
x |
A |
path |
Path or connection to write to. |
append |
Overwrite existing files ( |
Michaja Pehl, Oliver Richters
write.IAMCxlsx(quitte_example_data, tempfile())write.IAMCxlsx(quitte_example_data, tempfile())
A wrapper around readr::write_lines for writing files conforming to the
.mif standard.
write.mif( x, path, comment_header = NULL, comment = "#", append = FALSE, sep = ";", na = "NA" ) write.IAMCcsv( x, path, comment_header = NULL, comment = "#", append = FALSE, sep = ",", na = "" )write.mif( x, path, comment_header = NULL, comment = "#", append = FALSE, sep = ";", na = "NA" ) write.IAMCcsv( x, path, comment_header = NULL, comment = "#", append = FALSE, sep = ",", na = "" )
x |
A |
path |
Path or connection to write to. |
comment_header |
Comment header to be written to the |
comment |
A character to prefix comment header lines with. Must match
existing comment characters in file |
append |
Overwrite existing files ( |
sep |
Single character used to separate fields within a record.
Defaults to |
na |
String used for |
write.IAMCcsv() uses commas as filed separators instead of semi-colons.
Michaja Pehl
write.mif(quitte_example_data, tempfile())write.mif(quitte_example_data, tempfile())