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Calculate new variables

Source: R/calc_addVariable.R
calc_addVariable.Rd

Calculate new variables from existing ones, using generic formulas.

Usage

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
)

Arguments

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 ... or of length one (in which case all new variables receive the same unit).

na.rm

If TRUE (the default), remove items calculated as NA. This is generally the case for all calculations involving NA values, and all calculations involving missing variables. See completeMissing parameter.

completeMissing

If TRUE, implicitly missing data, i.e. missing combinations of input data, are filled up with 0 before the calculation, and they are therefore not computed as NA (and potentially removed from the output). Make sure 0 is a sensible value for your calculations, else complete missing values manually. Defaults to FALSE.

only.new

If FALSE (the default), add new variables to existing ones. If TRUE, return only new variables.

variable

Column name of variables. Defaults to "variable".

unit

Column name of units. Defaults to "unit". Ignored if no column with the same name is in data (e.g. data frames without unit column).

value

Column name of values. Defaults to "value".

overwrite

If TRUE (the default), values are overwritten if they already exist. If FALSE values are discarded and not overwritten if they already exist

skip.missing.rhs

If FALSE (the default), fail if any right-hand-side variable is missing. If TRUE, warn, and skip that calculation. If "silent", skip without warning.

.dots

Used to work around non-standard evaluation. See details.

If ... is a list of name-value pairs, it has the general format

"lhs" = "rhs + calculations - formula", "`lhs 2`" = "lhs / `rhs 2`"

where lhs are the names of new variables to be calculated and rhs are the variables to calculate from. If lhs and rhs are no proper identifiers, they need to be quoted (see Quotes for details). When in doubt, just quote.

If the new variables should have units, set units appropriately.

.dots is a named list of strings denoting formulas and optionally units. The general format is

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 \n line breaks)

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`')

... and .dots are processed in order, and variables already calculated in the same call can be used for further calculations. Other existing columns, including period, can be referenced, but this is not supported and the results are considered undefined.

Value

A data frame.

Author

Michaja Pehl

Examples

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))
#> # A tibble: 13 × 7
#>    model  scenario region variable   unit               period    value
#>    <chr>  <chr>    <chr>  <chr>      <chr>               <int>    <dbl>
#>  1 REMIND Baseline USA    GDP|MER    billion US$2005/yr   2010 12990   
#>  2 REMIND Baseline USA    Population million              2010   310.  
#>  3 REMIND Baseline USA    PE         EJ/yr                2010    91.6 
#>  4 REMIND Baseline CHN    GDP|MER    billion US$2005/yr   2020  8882   
#>  5 REMIND Baseline CHN    GDP|MER    billion US$2005/yr   2010  4119   
#>  6 REMIND Baseline CHN    Population million              2020  1387   
#>  7 REMIND Baseline CHN    Population million              2010  1349   
#>  8 REMIND Baseline USA    GDPpC      US$2005/cap          2010 41849.  
#>  9 REMIND Baseline CHN    GDPpC      US$2005/cap          2020  6404.  
#> 10 REMIND Baseline CHN    GDPpC      US$2005/cap          2010  3053.  
#> 11 REMIND Baseline USA    ln GDPpC   NA                   2010    10.6 
#> 12 REMIND Baseline CHN    ln GDPpC   NA                   2020     8.76
#> 13 REMIND Baseline CHN    ln GDPpC   NA                   2010     8.02
calc_addVariable_(
    data,
    list("`GDPpC`"    = c("`GDP|MER` / `Population` * 1e3", "US$/cap"),
         "`ln GDPpC`" = "log(`GDPpC`)")
)
#> # A tibble: 13 × 7
#>    model  scenario region variable   unit               period    value
#>    <chr>  <chr>    <chr>  <chr>      <chr>               <int>    <dbl>
#>  1 REMIND Baseline USA    GDP|MER    billion US$2005/yr   2010 12990   
#>  2 REMIND Baseline USA    Population million              2010   310.  
#>  3 REMIND Baseline USA    PE         EJ/yr                2010    91.6 
#>  4 REMIND Baseline CHN    GDP|MER    billion US$2005/yr   2020  8882   
#>  5 REMIND Baseline CHN    GDP|MER    billion US$2005/yr   2010  4119   
#>  6 REMIND Baseline CHN    Population million              2020  1387   
#>  7 REMIND Baseline CHN    Population million              2010  1349   
#>  8 REMIND Baseline USA    GDPpC      US$/cap              2010 41849.  
#>  9 REMIND Baseline CHN    GDPpC      US$/cap              2020  6404.  
#> 10 REMIND Baseline CHN    GDPpC      US$/cap              2010  3053.  
#> 11 REMIND Baseline USA    ln GDPpC   NA                   2010    10.6 
#> 12 REMIND Baseline CHN    ln GDPpC   NA                   2020     8.76
#> 13 REMIND Baseline CHN    ln GDPpC   NA                   2010     8.02