knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )

library(magrittr) library(RCLabels)

Working with matrices
often requires manipulating row and column labels
to achieve desired outcomes for matrix mathematics.
The `RCLabels`

package (Row and Column Labels)
provides convenient tools for manipulating those labels.

Two applications of matrix mathematics are input-output analysis in economics and physical supply-use table (PSUT) matrices for energy conversion chain (ECC) analysis. In those contexts, row and column labels describe processing stages or flows of goods or services between processing stages. Row and column labels can benefit those applications, ensuring that like quantities are added, subtracted, multiplied, or divided, etc., provided that row and column labels are respected during matrix operations.

One package that respects row and column labels is `matsbyname`

,
thereby making economic and ECC input-output analyses easier.
Easy manipulation of row and column labels
is, therefore, an enabling capability for using the `matsbyname`

package.
This package (`RCLabels`

) provides easy manipulation of row and column labels.
In fact, the `matsbyname`

package uses `RCLabels`

functions internally.

Row and column labels are always character strings, often with a prefix--suffix structure, where the prefix and suffix are denoted by a separator or delimited in other ways. Example row and column labels include

- "pref -> suff" (separator "->")
- "pref [suff]" (suffix delimited by “ [” and “]”)
- "(pref) (suff)" (prefix and suffix both surrounded by “(” and “)”)
- "pref.suff" (separator ".")

Prefixes are usually the "thing" of interest, e.g. an energy carrier ("Coal") or a processing stage in an energy conversion chain ("Main activity producer electricity plants"). Suffixes are usually modifiers or metadata about the thing (the prefix). Suffixes can describe the destination of an energy carrier ("Light [-> Industry in USA]"). Suffixes can describe the output of a processing stage ("Production [of Coal in ZAR]").

The `RCLabels`

package streamlines working with row and column labels.

`RCLabels`

enables creation of notation objects that describe the structure
of a row or column label
via the `notation_vec()`

function.

# Create a notation object. my_notation <- notation_vec(pref_start = "(", pref_end = ") ", suff_start = "[", suff_end = "]") # Notation objects are character vectors. my_notation

Several notation objects are provided for convenience within RCLabels.

arrow_notation paren_notation bracket_notation first_dot_notation from_notation of_notation to_notation bracket_arrow_notation

Note that identical `pref_end`

and `suff_start`

values
(as shown in all notations above)
are interpreted as a single delimiter throughout the `RCLables`

package.
Empty strings (`""`

) mean that no indication is given
for the start or end of a prefix or suffix.

Row and column labels can be created with the `paste_pref_suff()`

function

my_label <- paste_pref_suff(pref = "Coal", suff = "from Coal mines in USA", notation = my_notation) my_label

Row and column labels can be manipulated using several helpful functions.

# Split the prefix from the suffix to obtain a named list of strings. split_pref_suff(my_label, notation = my_notation) # Flip the prefix and suffix, maintaining the same notation. flip_pref_suff(my_label, notation = my_notation) # Change the notation. switch_notation(my_label, from = my_notation, to = paren_notation) # Change the notation and flip the prefix and suffix. switch_notation(my_label, from = my_notation, to = paren_notation, flip = TRUE)

The prefix or suffix can be extracted from a row or column label.

get_pref_suff(my_label, which = "pref", notation = my_notation) get_pref_suff(my_label, which = "suff", notation = my_notation)

The functions in `RCLabels`

work with vectors and lists of row and column labels.

labels <- c("a [of b in c]", "d [of e in f]", "g [of h in i]") labels split_pref_suff(labels, notation = bracket_notation)

This feature means that the functions in `RCLabels`

can be used on data frames.
Note that `transpose = TRUE`

ensures that a single list
column is created.

labels df <- tibble::tibble(labels = labels) result <- df %>% dplyr::mutate( split = split_pref_suff(labels, notation = bracket_notation, transpose = TRUE) ) result$split[[1]] result$split[[2]] result$split[[3]]

As discussed above, the prefix is often the "thing" of interest, and
the remainder of the label (the suffix) modifies the prefix.
This use case is so common that we introduce additional terms
that enable additional functionality.
The prefix is usually a *noun* (one or more words), and
the suffix usually consists of *prepositional phrases*
(each consisting of a preposition and an object).
`RCLabels`

includes a list of common prepositions.

prepositions

`RCLabels`

supports the "nouns and prepositions" view
of row and column labels
with several functions.
`get_nouns()`

extracts the nouns from a row or column label.

labels # Extract the nouns. get_nouns(labels, notation = bracket_notation) # Extract the prepositional phrases. get_pps(labels, notation = bracket_notation) # Extract the prepositions themselves. get_prepositions(labels, notation = bracket_notation) # Extract the objects of the prepositions. # Objects are named by the preposition of their phrase. get_objects(labels, notation = bracket_notation) # The get_piece() function is a convenience function # that extracts just what you want. get_piece(labels, piece = "noun", notation = bracket_notation) get_piece(labels, piece = "pref") get_piece(labels, piece = "suff") get_piece(labels, piece = "of") get_piece(labels, piece = "in") # An empty string is returned when the preposition is missing. get_piece(labels, piece = "bogus")

Labels can be split into their component pieces.

labels # Split the labels into pieces, named by "noun" and prepositions. split_labels <- split_labels(labels, prepositions = prepositions, notation = bracket_notation) split_labels # Recombine split labels. paste_pieces(split_labels, notation = bracket_notation) # Recombine with a new notation. paste_pieces(split_labels, notation = paren_notation)

To modify row and column labels, use one of the `modify_*`

functions.

labels # Set new values for nouns. modify_nouns(labels, new_nouns = c("Coal", "Oil", "Natural gas"), notation = bracket_notation)

To modify other pieces of labels, use the `modify_label_pieces()`

function.
`modify_label_pieces()`

enables assigning new values using a "one-to-many" approach
that enables aggregation.

labels # Change nouns in several labels to "Production" and "Manufacture", # as indicated by the modification map. modify_label_pieces(labels, piece = "noun", mod_map = list(Production = c("a", "b", "c", "d"), Manufacture = c("g", "h", "i", "j")), notation = bracket_notation) # Change the objects of the "in" preposition, # according to the modification map. modify_label_pieces(labels, piece = "in", mod_map = list(GHA = "c", ZAF = c("f", "i")), notation = bracket_notation) # Change the objects of "of" prepositions, # according to the modification map. modify_label_pieces(labels, piece = "of", mod_map = list(Coal = "b", `Crude oil` = c("e", "h")), notation = bracket_notation)

To eliminate a piece of a label altogether, use the `remove_label_pieces()`

function.

labels # Eliminate all of the prepositional phrases that begin with "in". remove_label_pieces(labels, piece = "in", notation = bracket_notation) # Eliminate all of the prepositional phrases that begin with "of" and "in". # Note that some spaces remain. remove_label_pieces(labels, piece = c("of", "in"), notation = bracket_notation)

With much power comes much responsibility!

There are times when it is helpful to know if a string is in a label.
`match_by_pattern()`

searches for matches in row and column labels
by regular expression.
Internally, `match_by_pattern()`

uses `grepl()`

for regular expression matching.

labels <- c("Production [of b in c]", "d [of Coal in f]", "g [of h in USA]") # With default `pieces` argument, matching is done for whole labels. match_by_pattern(labels, regex_pattern = "Production") match_by_pattern(labels, regex_pattern = "Coal") match_by_pattern(labels, regex_pattern = "USA") # Check beginnings of labels: match! match_by_pattern(labels, regex_pattern = "^Production") # Check at ends of labels: no match! match_by_pattern(labels, regex_pattern = "Production$") # Search by prefix or suffix. match_by_pattern(labels, regex_pattern = "Production", pieces = "pref") match_by_pattern(labels, regex_pattern = "Production", pieces = "suff") # When pieces is "pref" or "suff", only one can be specified. # The following function call gives an error. # match_by_pattern(labels, regex_pattern = "Production", pieces = c("pref", "to")) # Search by noun or preposition. match_by_pattern(labels, regex_pattern = "Production", pieces = "noun") match_by_pattern(labels, regex_pattern = "Production", pieces = "in") # Searching can be done with complicated regex patterns. match_by_pattern(labels, regex_pattern = make_or_pattern(c("c", "f")), pieces = "in") match_by_pattern(labels, regex_pattern = make_or_pattern(c("b", "Coal", "USA")), pieces = "in") match_by_pattern(labels, regex_pattern = make_or_pattern(c("b", "Coal", "USA")), pieces = c("of", "in")) # Works with custom lists of prepositions. match_by_pattern(labels, regex_pattern = make_or_pattern(c("b", "Coal", "GBR", "USA")), pieces = c("noun", "of", "in", "to"), prepositions = c("of", "to", "in"))

There are times when it is helpful to replace strings in labels.
The `replace_by_pattern()`

function will replace strings
in row and column labels by regular expression pattern.
Note that `replace_by_pattern()`

is similar to `match_by_pattern()`

,
except `replace_by_pattern()`

has an additional argument, `replacement`

.
Internally, `replace_by_pattern()`

uses `gsub()`

to perform regular expression matching.

labels <- c("Production [of b in c]", "d [of Coal in f]", "g [of h in USA]") labels # If `pieces = "all"` (the default), the entire label is available for replacements. replace_by_pattern(labels, regex_pattern = "Production", replacement = "Manufacture") replace_by_pattern(labels, regex_pattern = "Coal", replacement = "Oil") replace_by_pattern(labels, regex_pattern = "USA", replacement = "GHA") # Replace by prefix and suffix. replace_by_pattern(labels, regex_pattern = "Production", replacement = "Manufacture", pieces = "pref") replace_by_pattern(labels, regex_pattern = "Coa", replacement = "Bow", pieces = "suff") # Nothing should change, because USA is in the suffix. replace_by_pattern(labels, regex_pattern = "SA", replacement = "SSR", pieces = "pref") # Now USA --> USSR, because USA is in the suffix. replace_by_pattern(labels, regex_pattern = "SA", replacement = "SSR", pieces = "suff") # This will throw an error, because only "pref" or "suff" can be specified. # replace_by_pattern(labels, # regex_pattern = "SA", # replacement = "SSR", # pieces = c("pref", "suff") # Replace by noun or preposition. replace_by_pattern(labels, regex_pattern = "Production", replacement = "Manufacture", pieces = "noun") replace_by_pattern(labels, regex_pattern = "^Pro", replacement = "Con", pieces = "noun") # Won't match: wrong side of string. replace_by_pattern(labels, regex_pattern = "Pro$", replacement = "Con", pieces = "noun") # No change, because "Production" is a noun. replace_by_pattern(labels, regex_pattern = "Production", replacement = "Manufacture", pieces = "of") # Now try with "of". replace_by_pattern(labels, regex_pattern = "Coal", replacement = "Oil", pieces = "of") # No change, because "Coal" is not "in" anything. replace_by_pattern(labels, regex_pattern = "Coal", replacement = "Oil", pieces = "in") # Now try in "in". replace_by_pattern(labels, regex_pattern = "USA", replacement = "GBR", pieces = "in") replace_by_pattern(labels, regex_pattern = "A$", replacement = "upercalifragilisticexpialidocious", pieces = "in")

The `RCLabels`

package streamlines the manipulation of row and column labels
for matrices.
Applications include input-output analysis in economics and energy conversion chain analysis
or anywhere row and column labels are important for matrix mathematics.

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