Package 'iotables'

Description

Retrieves Air Emissions Accounts (env_ac_ainah_r2) by NACE Rev. 2 activity for environmental impact assessments. Automatically manages Eurostat caching via tempdir() or a user-specified data_directory.

Usage

airpol_get(
  airpol = "GHG",
  geo = "BE",
  year = 2020,
  unit = "THS_T",
  data_directory = NULL,
  force_download = FALSE
)

Arguments

Details

The Eurostat dataset Air emissions accounts by NACE Rev. 2 activity (env_ac_ainah_r2) contains emissions of major pollutants, including: CO2, biomass CO2, N2O, CH4, PFCs, HFCs, SF6, NOx, NMVOC, CO, PM10, PM2.5, SO2, and NH3. See Eurostat metadata for definitions of aggregated indicators (GHG, ACG, O3PR, etc.).

Value

A tidy data frame with air pollutant emissions aligned to IO classifications.

Note

The function adjusts the invalid industry codes, like C10-C12 to C10-12.

Source

Eurostat dataset: env_ac_ainah_r2

Description

align_to_io_codes() harmonises an external dataset (such as air pollution data, energy use, employment, or satellite accounts) to the industry structure of an input–output (IO) table.

The function standardises industry identifiers, drops irrelevant aggregate categories, pads missing industries with zeros, and reorders columns so that identifiers appear first followed by IO industry codes in the correct order.

This enables consistent integration of satellite accounts with IO tables for downstream operations such as indicator creation, coefficient matrix construction, or Leontief multiplier analysis.

Usage

align_to_io_codes(ext_data, io_data)

Arguments

Details

The function performs six harmonisation steps:

1. Detect identifier columns using a combination of known names ("indicator", "multiplier", "prod_na", etc.) and regular expressions matching "_id$", "_code$", or "indicator".

2. Remove high-level aggregates (e.g., "TOTAL", "HH", "TOTAL_HH") that do not correspond to IO industries.

3. Harmonise naming conventions including common underscore/ hyphen inconsistencies (e.g., "J62-J63" → "J62_63").

4. Add missing industry columns that exist in io_data but not in ext_data. These are initialised to zero.

5. Reorder the resulting dataset so that identifier columns appear first, followed by industry columns in IO order.

6. Return the cleaned tibble ready for combination with the IO table (e.g., via supplementary_add()).

Value

A tibble with:

• the detected identifier columns first

• followed by all industry codes from io_data in the same order

Industry columns missing from ext_data are added and filled with zeros.

Typical use cases

• Align NAMEA air emission accounts to IO industries

• Align energy-use accounts, employment, material flows

• Prepare satellite accounts for Leontief multiplier analysis

• Harmonise any NACE-like external dataset to IO structure

See Also

iotable_get, supplementary_add, input_indicator_create, leontief_inverse_create

Examples

# Minimal fictitious IO table with three industries
io_example <- data.frame(
  prod_na = c("A", "B", "C31_32"),
  A = c(10, 2, 1),
  B = c(1, 15, 3),
  C31_32 = c(0, 1, 20),
  check.names = FALSE
)

# External satellite account:
# * includes an identifier column
# * matches industries A and B
# * uses a hyphenated code C31-32 that needs harmonisation
# * includes TOTAL which should be dropped
ext_example <- data.frame(
  indicator = "GHG_emission",
  A = 0.5,
  B = 0.2,
  `C31-32` = 0.3,
  TOTAL = 0.7,
  check.names = FALSE
)

# Align external data to the IO industry codes
aligned <- align_to_io_codes(ext_example, io_example)

# The aligned table contains:
# * a single identifier column named 'prod_na'
# * only IO industries (A, B, C31_32)
# * renamed and reordered industry columns
aligned

Description

Compute the backward linkages of each industry or product sector from a Leontief inverse matrix. Backward linkages indicate how strongly a sector is interconnected on the demand side: when a sector increases its output, it will increase intermediate demand on all other sectors.

Usage

backward_linkages(Im)

Arguments

Details

Backward linkages are defined as the column sums of the Leontief inverse, in line with the Eurostat Manual of Supply, Use and Input–Output Tables (pp. 506–507) and the United Nations Handbook on Supply and Use Tables and Input–Output Tables with Extensions and Applications (p. 636).

Value

A one-row data.frame containing the backward linkage values for each column (industry or product) of the Leontief inverse. The first column is the sector key column, and the remaining columns correspond to the linkage values.

See Also

Other linkage functions: forward_linkages()

Examples

de_coeff <- input_coefficient_matrix_create(iotable_get(), digits = 4)
I <- leontief_inverse_create(de_coeff)
backward_linkages(I)

# Trivial example: identity matrix gives linkages = 1
I <- diag(3)
colnames(I) <- rownames(I) <- c("A", "B", "C")
I_df <- data.frame(sector = rownames(I), I, check.names = FALSE)
backward_linkages(I_df)

Description

Creates an input (technical) coefficient matrix from a symmetric input–output table (SIOT) or similar product × product table.

The function divides every element of the inter-industry use block by an output (or other) total, producing the classical technology matrix \(A\) used for Leontief models.

In addition, when households = TRUE, the function constructs the Type-II extension:

• a household consumption column (typically P3_S14) rescaled as coefficients, and

• a household earnings row (typically D1) identifying labour income.

This produces an augmented coefficient matrix that can be used directly for Type-II multipliers (closed household sector).

Usage

coefficient_matrix_create(
  data_table,
  total = "output",
  digits = NULL,
  remove_empty = TRUE,
  households = FALSE,
  return_part = NULL
)

Arguments

Details

The coefficient matrix is computed as:

$A_{ij} = Z_{ij} / x_j$

where \(Z\) is the intermediate use matrix and \(x\) is the chosen denominator row (usually output P1).

The denominator row is detected case-insensitively using the vocabulary: "output", "p1", "output_bp", "total output", or "total" / "cpa_total" depending on the total argument.

The function supports returning either:

• the full matrix (return_part = NULL),

• only the technology block (return_part = "products" or "industries"),

• only the primary inputs (return_part = "primary_inputs").

When households = TRUE:

• the function identifies the household final-consumption column (e.g. P3_S14 or final_consumption_households),

• divides it by the corresponding output totals,

• constructs the appropriate coefficient column,

• includes the household earnings row (e.g. D1) for Type-II multiplier calculations.

This behaviour matches traditional Type-II IO modelling practice.

Value

A data.frame containing:

• the key row identifier (first column),

• technology coefficients (columns 2…n),

• and, when households = TRUE, the household coefficient column.

If return_part is used, only the requested block is returned.

References

Beutel, J. (2008). Eurostat Manual of Supply, Use and Input–Output Tables, ch. 15. Luxembourg: Publications Office of the European Union.

See Also

Other indicator functions: direct_effects_create()

Examples

# Technology matrix (Germany 1995 example)
cm <- coefficient_matrix_create(
  data_table = iotable_get(source = "germany_1995"),
  total = "output",
  digits = 4
)

# Type-II coefficient matrix with household column
cm_hh <- coefficient_matrix_create(
  data_table = iotable_get(source = "germany_1995"),
  households = TRUE
)

Description

Create a named vector (in wide format) that conforms to the structure of a given analytical object, such as a use table, coefficient matrix, or Leontief matrix. This helps avoid mistakes when manually defining large vectors (e.g., for 60 × 60 matrices).

Usage

conforming_vector_create(data_table)

Arguments

Details

The empty conforming vector can also be exported to .csv format and used as a template for importing scenarios from a spreadsheet application.

Value

A one-row data.frame with the same column names as data_table, but with all values set to zero.

See Also

Other iotables processing functions: empty_remove(), household_column_find(), household_column_get(), iotable_year_get(), key_column_create(), matrix_round(), output_get(), primary_input_get(), rows_add(), supplementary_add(), total_tax_add(), vector_transpose_longer(), vector_transpose_wider()

Examples

de_input_flow <- input_flow_get(data_table = iotable_get())
conforming_vector_create(de_input_flow)

Description

A utility function to adjust column names for supplementary data, for example, air pollution data, where the industry codes are denoted with ind_ava or ind_use (or similar codes).

Usage

convert_industry_to_product(data_table)

Arguments

Details

For example, A01 (agriculture) is converted to CPA_A01 (product of agriculture).

This corresponds to the bridge matrix (B) defined in Eurostat Manual §15.4.2, used to transform industry-based extensions to product-based form.

Value

A converted, named vector or table where the columns are denoted with prd_ava codes, i.e., A01 is converted to CPA_A01.

See Also

supplementary_add()

Examples

data_table <- data.frame(
  ind_use = "A01",
  A01 = 1, A02 = 3, TOTAL = 4,
  HH = 2
)
convert_industry_to_product(data_table)

Description

A reference vocabulary derived from Eurostat’s CPA 2.1 — Statistical Classification of Products by Activity, restricted to high-level (summary) items, extensions, and 0-, 1-, and 2-digit product aggregates.

Usage

cpa2_1

Format

A tibble (data frame) with 10 variables:

id

Canonical Eurostat concept identifier (SKOS URI fragment).

label

Official English label from the Eurostat CPA 2.1 vocabulary.

status

Concept validity flag ("valid" for all entries).

status_modified

Date of the most recent status update.

notation

Official CPA 2.1 notation (e.g. "CPA_A01", "CPA_C10").

quadrant

Integer flag used internally in iotables to identify the analytical block (10 = intermediate, 20 = value added, 30 = control totals).

numeric_order

Ordinal position key ensuring correct row sequencing in reshaped input–output matrices.

iotables_label

Simplified legacy label used internally by the iotables package.

block

Semantic block category inferred from quadrant (intermediate, primary_inputs, control_total, extension).

uri

Stable URI for the SKOS concept at https://dd.eionet.europa.eu/vocabulary/eurostat/cpa2_1/.

Details

This vocabulary provides the product-side codes used in Eurostat’s supply–use and input–output frameworks, consistent with NACE Rev. 2 activity groupings. It is used by the iotables package to order and label the product × product blocks of symmetric input–output tables (SIOTs) and their supply–use counterparts.

The cpa2_1 vocabulary was obtained from the official Eurostat SKOS registry (https://dd.eionet.europa.eu/vocabulary/eurostat/cpa2_1/, filter: conceptStatusInt = 128, i.e. active concepts).

From the full CPA 2.1 hierarchy, this dataset retains:

• 0-digit sections (e.g. "CPA_L", "CPA_O"),

• 1-digit aggregates,

• 2-digit divisions, and

• published summary and extension items.

These levels correspond to the structure of Eurostat’s aggregated supply–use tables and are suitable for analytical applications such as cultural, tourism, or environmental satellite accounts.

All entries are marked with status "valid", reflecting their current publication state in the Eurostat metadata repository.

Source

Eurostat metadata registry (DD EIONET vocabulary: https://dd.eionet.europa.eu/vocabulary/eurostat/cpa2_1/)

See Also

Other Eurostat vocabularies included in the package: ind_use, ind_ava, prd_use, prd_ava.

Examples

data(cpa2_1)

Description

Symmetric input-output table at basic prices (product × product). Original code: 1700. Values are expressed in thousand kunas (T_NAC).

Usage

croatia_2010_1700

Format

A data frame with 13 variables:

t_rows2

Technology codes in row names, following Eurostat conventions.

t_rows2_lab

Longer labels for t_rows2.

t_cols2

Technology codes in column names, following Eurostat conventions.

t_cols2_lab

Longer labels for t_cols2.

iotables_col

Standardized column labels for easier reading.

col_order

Ordering index to keep the matrix legible.

row_order

Ordering index to keep the matrix legible.

iotables_row

Standardized row labels for easier reading.

unit

Unit of measure. Here: thousand national currency units (kunas).

geo

ISO/Eurostat country code for Croatia.

geo_lab

ISO/Eurostat country name, "Croatia".

time

Date of the SIOT.

values

Observed values in thousand kunas.

Source

Državni zavod za statistiku

See Also

Other Croatia 2010 datasets: croatia_2010_1800, croatia_2010_1900, croatia_employment_2013, croatia_employment_aggregation, primary_inputs

Description

Symmetric input-output table (SIOT) for domestic production (product × product), code 1800.

Values are expressed in thousand kunas (T_NAC).

Usage

croatia_2010_1800

Format

A data frame with 13 variables:

t_rows2

Technology codes in row names, following Eurostat conventions.

t_rows2_lab

Longer labels for t_rows2.

values

Actual values of the table in thousand kunas.

t_cols2

Column labels, following Eurostat conventions. A CPA_ suffix was added to original DZS column names.

t_cols2_lab

Longer labels for t_cols2.

iotables_col

Standardized iotables column labels for easier reading.

col_order

Column ordering to keep the matrix legible.

iotables_row

Standardized iotables row labels for easier reading.

row_order

Row ordering to keep the matrix legible.

unit

Different from Eurostat tables, in thousand national currency units.

geo

ISO/Eurostat country code for Croatia.

geo_lab

ISO/Eurostat country name, Croatia.

time

Date of the SIOT.

Source

Državni zavod za statistiku

See Also

Other Croatia 2010 datasets: croatia_2010_1700, croatia_2010_1900, croatia_employment_2013, croatia_employment_aggregation, primary_inputs

Description

Symmetric input-output table (SIOT) for imports (product × product), code 1900.

Values are expressed in thousand kunas (T_NAC).

Usage

croatia_2010_1900

Format

A data frame with 13 variables:

t_rows2

Technology codes in row names, following Eurostat conventions.

t_rows2_lab

Longer labels for t_rows2.

values

Actual values of the table in thousand kunas.

t_cols2

Column labels, following Eurostat conventions. A CPA_ suffix was added to original DZS column names.

t_cols2_lab

Longer labels for t_cols2.

iotables_col

Standardized iotables column labels for easier reading.

col_order

Column ordering to keep the matrix legible.

iotables_row

Standardized iotables row labels for easier reading.

row_order

Row ordering to keep the matrix legible.

unit

Different from Eurostat tables, in thousand national currency units.

geo

ISO/Eurostat country code for Croatia.

geo_lab

ISO/Eurostat country name, Croatia.

time

Date of the SIOT.

Source

Državni zavod za statistiku

See Also

Other Croatia 2010 datasets: croatia_2010_1700, croatia_2010_1800, croatia_employment_2013, croatia_employment_aggregation, primary_inputs

Description

Aggregated employment statistics for Croatia in 2013, formatted to match the Eurostat standard symmetric input-output table (SIOT) structure.

Usage

data(croatia_employment_2013)

Format

A data frame with 107 observations and 3 variables:

code

Short labels for industries or sectors.

iotables_row

iotables-style row labels.

employment

Employment in the sector (absolute values, not in thousands).

See Also

Other Croatia 2010 datasets: croatia_2010_1700, croatia_2010_1800, croatia_2010_1900, croatia_employment_aggregation, primary_inputs

Description

A mapping table to aggregate detailed Croatian employment statistics into the Croatian (EU-standard) symmetric input–output table (SIOT) format.

Usage

croatia_employment_aggregation

Format

A data frame with 105 rows (including empty rows) and 2 variables:

employment_label

Labels from the DZS (Croatian Bureau of Statistics) English-language export.

t_cols2

Labels used in EU/DZS symmetric input–output tables (SIOTs).

Details

This dataset provides a concordance between Croatian employment classifications and the EU/DZS SIOT framework, enabling consistent integration of employment data into input–output analysis.

See Also

Other Croatia 2010 datasets: croatia_2010_1700, croatia_2010_1800, croatia_2010_1900, croatia_employment_2013, primary_inputs

Description

The function creates the effects.

Usage

direct_effects_create(input_requirements, inverse, digits = NULL)

Arguments

Value

A data.frame containing the direct effects and the necessary metadata to sort them or join them with other matrixes.

See Also

Other indicator functions: coefficient_matrix_create()

Examples

nl <- netherlands_2000

input_coeff_nl <- input_coefficient_matrix_create(
  data_table = netherlands_2000,
  households = FALSE
)

compensation_indicator <- input_indicator_create(netherlands_2000, "compensation_employees")

I_nl <- leontief_inverse_create(input_coeff_nl)

direct_effects_create(
  input_requirements = compensation_indicator,
  inverse = I_nl
)

Description

Download employment data from Eurostat (dataset lfsq_egan22d) and arrange it to match 64 × 64 symmetric input–output tables (SIOTs).

Usage

employment_get(
  geo,
  year = "2010",
  sex = "Total",
  age = "Y_GE15",
  labelling = "iotables",
  data_directory = NULL,
  force_download = FALSE
)

Arguments

Details

• Currently implemented only for product × product tables.

• Country codes are harmonized: "GB" → "UK", "GR" → "EL".

• Sex is normalized internally to Eurostat codes "T", "F", "M".

• Results are cached as .rds files in data_directory if supplied.

• An imputed rent column (L68A/CPA_L68A) with zero is always added.

Value

A one-row data.frame containing employment input values aligned with the chosen SIOT labelling, including an imputed rent column set to 0.

Source

Eurostat dataset lfsq_egan22d

See Also

Other import functions: iotables_download(), iotables_download_old(), iotables_read_tempdir()

Examples

## Not run: 
employment <- employment_get(
  geo = "CZ",
  year = "2010",
  sex = "Total",
  age = "Y_GE15",
  data_directory = NULL,
  force_download = TRUE
)

## End(Not run)

Description

A reference dataset linking Eurostat national accounts vocabulary with employment statistics data.

Usage

employment_metadata

Format

A data frame with 6 variables:

emp_code

Codes used in the employment statistics.

code

Eurostat labels for SIOTs corresponding to emp_code.

label

Eurostat label descriptions for SIOTs corresponding to emp_code.

variable

Eurostat vocabulary source (e.g., t_rows, t_cols, prod_na, induse).

group

Grouping of accounts (different from Eurostat tables), in thousands of national currency units.

iotables_label

Custom machine-readable snake_case variable names.

Details

This dataset provides a mapping between employment statistics codes and the vocabulary used in Eurostat input–output tables, ensuring compatibility when joining employment and national accounts data.

See Also

Other metadata datasets: metadata, metadata_uk_2010

Description

Remove columns and their corresponding rows if they contain only zeros or missing values. This ensures that the resulting table remains symmetric (same dimensions in rows and columns).

Usage

empty_remove(data_table)

Arguments

Details

The function first identifies columns that contain only zeros or missing values, then removes both those columns and the rows with matching labels in the first (key) column. A message is printed listing the removed columns.

Value

A data.frame (or tibble) with a key column and symmetric matrix, after removing all-zero (or all-missing) columns and their corresponding rows.

See Also

Other iotables processing functions: conforming_vector_create(), household_column_find(), household_column_get(), iotable_year_get(), key_column_create(), matrix_round(), output_get(), primary_input_get(), rows_add(), supplementary_add(), total_tax_add(), vector_transpose_longer(), vector_transpose_wider()

Examples

# Example using the built-in demo table
test_table <- input_coefficient_matrix_create(
  iotable_get(source = "germany_1995")
)

# Set one column to zero, then remove it
test_table[, 2] <- 0
empty_remove(test_table)

Description

Match a left-hand side (LHS) vector to a Leontief inverse by column names and compute the matrix product $\text{LHS} \times \text{Im}$.

Usage

equation_solve(LHS = NULL, Im = NULL)

Arguments

Details

This helper is used by higher-level wrappers such as multiplier_create(). It assumes both inputs have a first key column, followed by numeric columns whose names define the alignment. The function multiplies the numeric row of LHS by the numeric block of Im after a basic conformity check.

Value

A numeric 1×N matrix containing the solution $\text{LHS} \times \text{Im}$.

Examples

Im <- data.frame(
  a = c("row1", "row2"),
  b = c(1, 1),
  c = c(2, 0)
)
LHS <- data.frame(
  a = "lhs",
  b = 1,
  c = 0.5
)
equation_solve(Im = Im, LHS = LHS)

Description

Forward linkages capture how the increased output of a sector provides additional inputs to other sectors, enabling them to expand production.

Usage

forward_linkages(output_coefficient_matrix, digits = NULL)

Arguments

Details

Defined as the row sums of the Ghosh inverse, in line with the Eurostat Manual of Supply, Use and Input-Output Tables (pp. 506–507) and the United Nations Handbook on Supply and Use Tables and Input-Output Tables with Extensions and Applications (p. 637).

Value

A data.frame with two columns:

• The metadata column from the input matrix (sector/product names)

• forward_linkages: the forward linkage indicator values

See Also

Other linkage functions: backward_linkages()

Examples

data_table <- iotable_get()

de_out <- output_coefficient_matrix_create(
  data_table, "tfu",
  digits = 4
)

forward_linkages(
  output_coefficient_matrix = de_out,
  digits = 4
)

Description

Reproduction of Table 15.4 in the Eurostat Manual (Input–output table of domestic output at basic prices, Version A). This is a small, well-documented benchmark dataset that accompanies the iotables package. It is reformatted into the same long structure as Eurostat warehouse SIOTs, so that functions and tests can work identically on this example and on real Eurostat downloads.

Usage

germany_1995

Format

A data frame with 247 rows and 11 columns:

prod_na

Row code (ESA 2010 / CPA aggregate).

prod_na_lab

Row label, long description.

iotables_row

Row identifier used internally.

iotables_col

Column identifier (factor with 13 levels).

values

Cell value, in millions of euros (integer).

induse

Column code (ESA 2010 / CPA aggregate or national accounts item).

geo

Country code, always "DE".

geo_lab

Country name, "Germany".

time

Reference year, as a Date ("1995-01-01").

unit

Unit code, "MIO_EUR".

unit_lab

Unit label, "Million euro".

Details

The values come from Beutel (2008), Eurostat Manual of Supply, Use and Input–Output Tables, Table 15.4. Labels and codes follow ESA 2010 conventions (e.g. CPA_A, CPA_B-E, P3_S14), allowing direct comparison with modern Eurostat releases.

This dataset underpins many unit tests in iotables: multipliers, coefficients, and linkage indices are validated against the published benchmark. Because it is small (247 rows), it is also used in vignettes and examples to demonstrate workflows.

Source

Beutel, J. (2008). Eurostat Manual of Supply, Use and Input–Output Tables, Table 15.4. Luxembourg: Office for Official Publications of the European Communities.

See Also

iotable_get() for extracting comparable tables from Eurostat.

Examples

data(germany_1995)
head(germany_1995)
# Verify against the Eurostat manual:
subset(germany_1995, prod_na == "CPA_A" & iotables_col == "agriculture_group")

Description

Air pollution values for validation and cross-checking with the Eurostat Manual.

Usage

germany_airpol

Format

A data frame with 72 observations and 4 variables:

airpol

Abbreviation of the air pollutant.

induse

Column labels, following Eurostat conventions with minor differences.

iotables_col

Column labels using iotables abbreviations.

value

Values in thousand tons.

Details

This dataset is provided for testing purposes. Labels were slightly adjusted to reflect the transition from ESA95 to ESA2010 vocabulary since the publication of the Eurostat Manual.

Source

Eurostat (2008). Eurostat Manual of Supply, Use and Input–Output Tables, p. 482. Available at https://ec.europa.eu/eurostat/documents/3859598/5902113/KS-RA-07-013-EN.PDF

See Also

Other validation datasets: netherlands_2000, uk_2010_data, uk_test_results

Description

Compute the Ghosh inverse from an output-coefficient matrix.

Usage

ghosh_inverse_create(output_coefficients_matrix, digits = NULL)

Arguments

Details

The Ghosh inverse is defined as $G = (I - B)^{-1}$, where $B$ is the output-coefficient matrix created by output_coefficient_matrix_create().

See the United Nations Handbook on Supply and Use Tables and Input–Output Tables with Extensions and Applications (2018, Rev. 1), pp. 622–639 (PDF).

For the analogous inverse based on input coefficients, see leontief_inverse_create().

Value

A data.frame with the original key column and the Ghosh inverse in the remaining columns. If digits is provided, values are rounded.

See Also

Other analytic object functions: input_flow_get(), leontief_inverse_create(), leontief_matrix_create(), output_coefficient_matrix_create()

Examples

# Minimal example
om <- output_coefficient_matrix_create(iotable_get())
ghosh_inverse_create(om)

# Using the Germany 1995 benchmark table (Eurostat manual)
# data(germany_1995)
# om_de <- output_coefficient_matrix_create(germany_1995)
# ghosh_inverse_create(om_de)

Description

Identify the column position corresponding to final household expenditure in a symmetric input–output table or related table.

Usage

household_column_find(data_table)

Arguments

Details

The function searches column names case-insensitively. It first looks for exact matches among the following alternatives:

• "households"

• "p3_s14"

• "final_consumption_households"

• "final_consumption_household"

• "consumption_expenditure_household"

• "consumption_expenditure_households"

If none of these are found, it falls back to any column name that contains "households".

Value

An integer vector giving the position(s) of household expenditure columns. Returns NULL if none are found.

See Also

Other iotables processing functions: conforming_vector_create(), empty_remove(), household_column_get(), iotable_year_get(), key_column_create(), matrix_round(), output_get(), primary_input_get(), rows_add(), supplementary_add(), total_tax_add(), vector_transpose_longer(), vector_transpose_wider()

Examples

# German SIOT includes a household final consumption column
household_column_find(iotable_get(source = "germany_1995"))

# Custom example
df <- data.frame(
  sector = c("A", "B"),
  households = c(100, 200)
)
household_column_find(df)

Description

Extracts the column of final household expenditure from a symmetric input-output table, a use table, or a supply table. If no household expenditure column is detected, returns NULL.

Usage

household_column_get(data_table)

Arguments

Value

A tibble/data frame with the key column and the household expenditure column. Returns NULL if no household column is found.

See Also

Other iotables processing functions: conforming_vector_create(), empty_remove(), household_column_find(), iotable_year_get(), key_column_create(), matrix_round(), output_get(), primary_input_get(), rows_add(), supplementary_add(), total_tax_add(), vector_transpose_longer(), vector_transpose_wider()

Examples

household_column_get(iotable_get(source = "germany_1995"))

Description

A reference codelist used in Eurostat’s national accounts framework for the industry × industry (NACE-based) symmetric input–output tables (SIOTs). This vocabulary enumerates the rows of the table, covering both the intermediate (Quadrant 1) and value-added (Quadrant 3) blocks, and includes the control total “Total supply at basic prices” published by Eurostat.

Usage

ind_ava

Format

A tibble (data frame) with 10 variables:

id

Canonical Eurostat concept identifier.

label

Official Eurostat label (preferred English term).

status

"Valid" or "Adopted from CPA2_1".

status_modified

Date of the most recent status update.

notation

Alternate human-readable code or short form (e.g. CPA_A01).

group

Optional grouping of related industries or aggregates.

quadrant

Integer designating the analytical block: 10 = Intermediate (technology block, Quadrant 1); 20 = Value added (primary inputs, Quadrant 3); 30 = Control total(s).

numeric_order

Ordinal position key for sorting within quadrant.

iotables_label

Simplified legacy label used in the iotables package.

block

Semantic block category inferred from quadrant (intermediate, primary_inputs, control_total, extension).

uri

Stable URI linking to the Eurostat SKOS concept at https://dd.eionet.europa.eu/.

Details

The ind_ava vocabulary aligns with the official Eurostat SDMX codelist “Industries, adjustments and value added” and is used by the iotables package to order the rows of industry-by-industry tables (datasets such as naio_10_cp1750).

Codes correspond to NACE Rev. 2 divisions and ESA 2010 accounting aggregates. Each entry includes its quadrant classification and an ordinal numeric order to preserve correct row sequencing when reshaping long-form data.

The dataset retains the published control total (“Total supply at basic prices”) for consistency with Eurostat’s metadata, but this row is normally excluded from analytical matrix operations.

Because the Eurostat SIOT datasets sometimes contain more detailed industry breakup than their vocabulary, we adopted industry codeing from cpa2_1. These codes are marked the "Adopted from CPA2_1" status.

Source

Eurostat metadata registry (DD EIONET vocabulary: https://dd.eionet.europa.eu/vocabulary/eurostat/ind_ava/)

See Also

Other Eurostat vocabularies included in the package: ind_use, prd_use, prd_ava, cpa2_1.

Examples

data(ind_ava)

Description

A reference codelist used in Eurostat’s national accounts framework for industry × industry (NACE-based) symmetric input–output tables (SIOTs). This vocabulary enumerates the columns of the table, covering the intermediate-use block (Quadrant 1), the value-added block (Quadrant 3), and the final-use and balancing items (Quadrant 2/50).

Usage

ind_use

Format

A tibble (data frame) with 10 variables:

id

Canonical Eurostat concept identifier.

label

Official Eurostat label (preferred English term).

status

"Valid" or "Adopted from CPA2_1".

status_modified

Date of last status modification.

notation

Alternate human-readable code or short form (e.g. CPA_A01).

quadrant

Integer designating the analytical block: 10 = Intermediate (technology block, Q1); 20 = Primary inputs / GVA (Q3); 30 = Final use / control totals (Q2); 50 = Extensions / diagnostics.

numeric_order

Ordinal key for ordering columns within quadrants.

iotables_label

Simplified legacy label used in the iotables package.

block

Semantic block category inferred from quadrant (intermediate, primary_inputs, final_use, extension).

uri

Stable URI linking to the Eurostat SKOS concept at https://dd.eionet.europa.eu/.

Details

The ind_use vocabulary corresponds to the official Eurostat SDMX codelist “Industry uses”. It is used by the iotables package to order the column dimension of industry-by-industry tables (datasets such as naio_10_cp1750).

Codes follow NACE Rev. 2 for industries and ESA 2010 for accounting aggregates. Each entry includes its quadrant classification and an ordinal numeric sequence for reproducible column ordering when reshaping long-form data.

The vocabulary also includes recent Eurostat additions introduced in 2023 (ITTM – International trade and transport margins, MCH – Merchanting, and OP – Purchases by residents abroad). These appear in the final-use block (quadrant = 30) and are retained for completeness and balancing, though they are not part of the analytical technology matrix.

Because the Eurostat SIOT datasets sometimes contain more detailed industry breakup than their vocabulary, we adopted industry codeing from cpa2_1. These codes are marked the "Adopted from CPA2_1" status.

Source

Eurostat metadata registry (https://dd.eionet.europa.eu/vocabulary/eurostat/ind_use/)

See Also

Related Eurostat vocabularies: ind_ava, prd_ava, prd_use, cpa2_1.

Examples

data(ind_use)
dplyr::count(ind_use, quadrant)

Description

Compute an indirect-effects vector from an input requirement indicator and a Leontief inverse.

Usage

indirect_effects_create(input_requirements, inverse, digits = NULL)

Arguments

Value

A data.frame containing the indirect effects and the first (key) column to allow sorting or joins with other tables.

Examples

data(netherlands_2000)

input_coeff_nl <- input_coefficient_matrix_create(
  data_table = netherlands_2000,
  households = FALSE
)

compensation_indicator <- input_indicator_create(
  netherlands_2000, "compensation_employees"
)

I_nl <- leontief_inverse_create(input_coeff_nl)

indirect_effects_create(
  input_requirements = compensation_indicator,
  inverse = I_nl
)

Description

Computes the input (technical) coefficient matrix from a symmetric input–output table or use table. Each element expresses the share of a product’s output used as input in another production activity.

Usage

input_coefficient_matrix_create(data_table, households = FALSE, digits = NULL)

Arguments

Details

This is a wrapper around coefficient_matrix_create() with total = "output" and return_part = "products". The resulting matrix \(A\) satisfies \(A_ij = z_ij / x_j\), where \(z_ij\) is intermediate use of product i by industry j and \(x_j\) is total output. Terminology follows the Eurostat Manual of Supply, Use and Input–Output Tables, where this is also called the technological coefficients matrix.

Value

A data.frame containing the input coefficient matrix (products × products), with the key (row label) as the first column. Rows and columns labelled "cpa_total" or "total" are removed.

References

Beutel, J. (2008). Eurostat Manual of Supply, Use and Input–Output Tables. Luxembourg: Office for Official Publications of the European Communities, Table 15.6 (page 485). Validation against this reference confirms that input_coefficient_matrix_create() reproduces the standard Eurostat input–output coefficients for Germany 1995.

United Nations et al. (2009). System of National Accounts 2008, §§14.90–14.91.

Examples

cm <- input_coefficient_matrix_create(
  iotable_get(source = "germany_1995"),
  digits = 4
)
head(cm)

Description

Return the Quadrant I block (intermediate consumption) of a symmetric input–output table. Optionally append the final household consumption column for Type-II modelling.

Usage

input_flow_get(data_table, empty_remove = FALSE, households = TRUE)

Arguments

Details

In the Eurostat framework, the Quadrant I block shows intermediate consumption by industry (columns) and product (rows), valued at purchasers’ prices. Final household consumption belongs to the final uses block (not Quadrant I); when households = TRUE, that column is appended for convenience in Type-II analyses that endogenise private consumption. See the Eurostat Manual of Supply, Use and Input-Output Tables for the quadrant layout and definitions.

Value

A data frame with the key column and the Quadrant I block; if households = TRUE, the household final consumption column is appended.

See Also

input_coefficient_matrix_create(), leontief_inverse_create()

Other analytic object functions: ghosh_inverse_create(), leontief_inverse_create(), leontief_matrix_create(), output_coefficient_matrix_create()

Examples

# Basic extraction (Quadrant I + households column)
x <- input_flow_get(
  data_table   = iotable_get(),
  empty_remove = FALSE,
  households   = TRUE
)

# Quadrant I only (no households column)
y <- input_flow_get(
  data_table   = iotable_get(),
  empty_remove = FALSE,
  households   = FALSE
)

Description

Compute input indicators (e.g., GVA, compensation of employees) by selecting specific input rows from the input-coefficient matrix.

Usage

input_indicator_create(
  data_table,
  input_row = c("gva_bp", "net_tax_production"),
  digits = NULL,
  households = FALSE,
  indicator_names = NULL
)

Arguments

Details

Let $A$ be the input-coefficient matrix (rows are inputs, columns are products/industries). An input indicator for a given input row $r$ is simply the row $A_{r\cdot}$. These indicators are used in Beutel (2012) and the Eurostat Manual of Supply, Use and Input-Output Tables (e.g., pp. 495–498) to derive effects and multipliers.

Internally, the function builds $A$ via coefficient_matrix_create(), then keeps only the requested input rows and renames the key column to ⁠*_indicator⁠. Optional rounding is applied to numeric columns.

Value

A data.frame whose first column is a key, followed by the selected input-indicator rows as numeric columns.

Examples

input_indicator_create(
  data_table = iotable_get(),
  input_row = c("gva", "compensation_employees"),
  digits = 4,
  indicator_names = c("GVA indicator", "Income indicator")
)

# Beutel/Eurostat example: GVA indicator (cf. Manual, ~p. 498)
ii <- input_indicator_create(
  data_table = iotable_get(),
  input_row = "gva",
  digits = 4
)
head(ii)

Description

The function creates the multipliers (direct + indirect effects).

Usage

input_multipliers_create(
  input_requirements,
  Im,
  multiplier_name = NULL,
  digits = NULL
)

Arguments

Value

A data frame with the vector of multipliers and the an auxiliary metadata column, containing an automatically given row identifier (for joining with other matrixes) which can be overruled with setting multiplier_name.

See Also

Other multiplier functions: output_multiplier_create()

Examples

nl <- netherlands_2000

input_coeff_nl <- input_coefficient_matrix_create(
  data_table = netherlands_2000,
  households = FALSE
)

compensation_indicator <- input_indicator_create(netherlands_2000, "compensation_employees")

I_nl <- leontief_inverse_create(input_coeff_nl)

input_multipliers_create(
  input_requirements = compensation_indicator,
  Im = I_nl
)

Description

Filter and reshape one IO/SUT table from a bulk dataset (typically a Eurostat download). In most workflows you will call this function rather than iotables_download(), which it invokes as needed.

Usage

iotable_get(
  labelled_io_data = NULL,
  source = "germany_1995",
  geo = "DE",
  year = 1990,
  unit = "MIO_EUR",
  stk_flow = "DOM",
  labelling = "iotables",
  data_directory = NULL,
  force_download = TRUE
)

Arguments

Details

The Eurostat bulk tables arrive in long form and are not ordered for matrix algebra. This function selects the requested country (geo), year, unit and stock/flow (stk_flow), joins iotables metadata for consistent row/column labelling, and returns a wide table ready for analysis.

Supported sources include:

• naio_10_cp1700 — symmetric IO, basic prices (prod × prod)

• naio_10_pyp1700 — previous years' prices

• naio_10_cp1750 — symmetric IO, basic prices (ind × ind)

• naio_10_pyp1750 — previous years' prices

• naio_10_cp15 — supply at basic prices incl. margins/taxes

• naio_10_cp16 — use at purchasers' prices

• naio_10_cp1610 — use at basic prices

• naio_10_cp1620 — trade & transport margins (basic prices)

• naio_10_cp1630 — taxes less subsidies on products (basic prices)

• ⁠naio_10_pyp*⁠ — corresponding previous years' prices variants

• germany_1995 — packaged Beutel example

• croatia_2010_1700/1800/1900 — packaged examples

• ⁠uk_2010_*⁠ — packaged UK 2010 variants

Value

A wide data.frame representing the selected IO table, with a key column followed by ordered numeric columns.

Note

This is a wrapper around the legacy getter function now only used for built-in datasets.

See Also

Other iotables import functions: iotable_get_builtin()

Examples

germany_table <- iotable_get(
  source = "germany_1995",
  geo = "DE", year = 1990, unit = "MIO_EUR",
  labelling = "iotables"
)

Description

Filter and reshape one IO/SUT table from a bulk dataset.

Usage

iotable_get_builtin(
  labelled_io_data = NULL,
  source = "germany_1995",
  geo = "DE",
  year = 1990,
  unit = "MIO_EUR",
  stk_flow = "DOM",
  labelling = "iotables",
  data_directory = NULL,
  force_download = TRUE
)

Arguments

Details

The Eurostat bulk tables arrive in long form and are not ordered for matrix algebra. This function selects the requested country (geo), year, unit and stock/flow (stk_flow), joins iotables metadata for consistent row/column labelling, and returns a wide table ready for analysis.

Supported sources include:

• naio_10_cp1700 — symmetric IO, basic prices (prod × prod)

• naio_10_pyp1700 — previous years' prices

• naio_10_cp1750 — symmetric IO, basic prices (ind × ind)

• naio_10_pyp1750 — previous years' prices

• naio_10_cp15 — supply at basic prices incl. margins/taxes

• naio_10_cp16 — use at purchasers' prices

• naio_10_cp1610 — use at basic prices

• naio_10_cp1620 — trade & transport margins (basic prices)

• naio_10_cp1630 — taxes less subsidies on products (basic prices)

• ⁠naio_10_pyp*⁠ — corresponding previous years' prices variants

• germany_1995 — packaged Beutel example

• croatia_2010_1700/1800/1900 — packaged examples

• ⁠uk_2010_*⁠ — packaged UK 2010 variants

Value

A wide data.frame representing the selected IO table, with a key column followed by ordered numeric columns.

Note

This is a legacy downloader that handles Eurostat datasets, too, but with inefficient cache management.

See Also

Other iotables import functions: iotable_get()

Examples

germany_table <- iotable_get(
  source = "germany_1995",
  geo = "DE", year = 1990, unit = "MIO_EUR",
  labelling = "iotables"
)

Description

Filters and reshapes one IO/SUT table from a bulk Eurostat dataset. This version excludes all built-in datasets (e.g. Germany 1995, Croatia 2010, UK 2010) which are retrieved with the iotable_get() function.

Usage

iotable_get_eurostat(
  source = "naio_10_cp1700",
  geo = "IT",
  year = 2022,
  unit = "MIO_EUR",
  stk_flow = "TOTAL",
  labelling = "iotables",
  data_directory = NULL,
  force_download = TRUE,
  labelled_io_data = NULL
)

Arguments

Details

The Eurostat bulk tables arrive in long form and are not ordered for matrix algebra. This function selects the requested country (geo), year, unit and stock/flow (stk_flow), joins iotables metadata for consistent row/column labelling, and returns a wide table ready for analysis.

Value

A wide data.frame representing the selected IO table.

Description

Query which years are available for a given Eurostat IO product, country (geo), and currency unit in a bulk download.

Usage

iotable_year_get(
  labelled_io_data = NULL,
  source = "germany_1995",
  geo = "DE",
  unit = "MIO_EUR",
  time_unit = "year",
  stk_flow = "TOTAL",
  data_directory = NULL,
  force_download = TRUE
)

Arguments

Details

This function is usually called indirectly via iotable_get(). You normally do not need to call iotables_download() yourself unless working with bulk Eurostat files.

Supported Eurostat products include (non-exhaustive):

• "naio_10_cp1700" — Symmetric IO table, basic prices (product × product)

• "naio_10_cp1750" — Symmetric IO table, basic prices (industry × industry)

• "naio_10_pyp1700" — Symmetric IO table (product × product), previous years’ prices

• "naio_10_pyp1750" — Symmetric IO table (industry × industry), previous years’ prices

• "naio_10_cp1620" / "naio_10_pyp1620" — Trade & transport margins

• "naio_10_cp1630" / "naio_10_pyp1630" — Taxes less subsidies on products

See the Eurostat Symmetric Input–Output Tables page.

Value

A numeric vector of years, or a date vector if time_unit = "time".

See Also

Other iotables processing functions: conforming_vector_create(), empty_remove(), household_column_find(), household_column_get(), key_column_create(), matrix_round(), output_get(), primary_input_get(), rows_add(), supplementary_add(), total_tax_add(), vector_transpose_longer(), vector_transpose_wider()

Examples

germany_years <- iotable_year_get(
  source = "germany_1995", geo = "DE", unit = "MIO_EUR"
)
# Return as dates
germany_dates <- iotable_year_get(
  source = "germany_1995", geo = "DE",
  unit = "MIO_EUR", time_unit = "time"
)

Description

Tools for downloading, structuring, and analysing input–output and supply–use tables, with metadata aligned to Eurostat’s ESA 2010 framework.

Author(s)

Maintainer: Daniel Antal [email protected] (ORCID)

Other contributors:

• Pyry Kantanen (ORCID) [contributor]

See Also

Useful links:

• https://iotables.dataobservatory.eu/

• https://github.com/rOpenGov/iotables

• Report bugs at https://github.com/rOpenGov/iotables/issues

Description

Downloads and prepares symmetric input–output (IO) or supply–use tables directly from Eurostat. This modern version excludes all built-in datasets (germany_1995, ⁠croatia_2010_*⁠, ⁠uk_2010_*⁠) which are handled internally by iotable_get().

Usage

iotables_download(
  source = "naio_10_cp1700",
  geo = NULL,
  year = NULL,
  unit = NULL,
  stk_flow = NULL,
  data_directory = NULL,
  force_download = FALSE
)

Arguments

Details

Files are cached under tempdir() as RDS (e.g. "naio_10_cp1750_processed.rds"). The temporary directory is cleared when the R session ends. To persist downloads across sessions (recommended for analytics), supply data_directory.

Value

A nested tibble with metadata columns (geo, year, unit, etc.) and a list-column data containing tidy IO tables.

See Also

Other import functions: employment_get(), iotables_download_old(), iotables_read_tempdir()

Description

Download standard input–output (IO) and related tables. At the moment, only Eurostat products are supported. You usually do not need to call this directly; iotable_get() will invoke it as needed and return a filtered, tidy table.

Usage

iotables_download_old(
  source = "naio_10_cp1700",
  data_directory = NULL,
  force_download = FALSE
)

Arguments

Details

Files are cached under tempdir() as RDS (e.g., "naio_10_cp1750.rds"). The temporary directory is cleared when the R session ends. To persist downloads across sessions (recommended for analytics), supply data_directory and the processed, nested output will also be written there as "<source>_processed.rds".

Supported Eurostat products include (non-exhaustive):

• naio_10_cp1700 — Symmetric IO table, basic prices (product × product)

• naio_10_pyp1700 — Same, previous years’ prices

• naio_10_cp1750 — Symmetric IO table, basic prices (industry × industry)

• naio_10_pyp1750 — Same, previous years’ prices

• naio_10_cp15 — Supply table at basic prices incl. margins/taxes

• naio_10_cp16 — Use table at purchasers’ prices

• naio_10_cp1610 — Use table at basic prices

• naio_10_pyp1610 — Use table at basic prices (previous years’ prices)

• naio_10_cp1620 — Trade and transport margins at basic prices

• naio_10_pyp1620 — Trade and transport margins at previous years’ prices

• naio_10_cp1630 — Taxes less subsidies on products at basic prices

• naio_10_pyp1630 — Taxes less subsidies on products, prev. years’ prices

• uk_2010 — United Kingdom IO Analytical Tables (handled internally)

Eurostat API/format changes (e.g., TIME_PERIOD vs time) are handled for backward compatibility.

Value

A nested data.frame (one row per IO table) with metadata columns (geo, unit, year, stk_flow, etc.) and a list-column data containing the tidy table for each combination.

See Also

Other import functions: employment_get(), iotables_download(), iotables_read_tempdir()

Examples

io_tables <- iotables_download(source = "naio_10_pyp1750")

Description

Returns only the metadata (e.g. country, year, unit, flow) from a nested input–output (IO) table created by iotables_download(). Removes the heavy data list-column, leaving just metadata rows.

Usage

iotables_metadata_get(dat = NULL, source = "naio_10_cp1700")

Arguments

Value

A tibble with metadata columns only (no data list-column).

Description

Validate the source input parameter and try to load the table from the current sessions' temporary directory.

Usage

iotables_read_tempdir(source = "naio_10_cp1700")

Arguments

Details

Possible source parameters:

naio_10_cp1700

Symmetric input-output table at basic prices (product by product)

naio_10_pyp1700

Symmetric input-output table at basic prices (product by product) (previous years prices)

naio_10_cp1750

Symmetric input-output table at basic prices (industry by industry)

naio_10_pyp1750

Symmetric input-output table at basic prices (industry by industry) (previous years prices)

naio_10_cp15

Supply table at basic prices incl. transformation into purchasers' prices

naio_10_cp16

Use table at purchasers' prices

naio_10_cp1610

Use table at basic prices

naio_10_pyp1610

Use table at basic prices (previous years prices) (naio_10_pyp1610)

naio_10_cp1620

Table of trade and transport margins at basic prices

naio_10_pyp1620

Table of trade and transport margins at previous years' prices

naio_10_cp1630

Table of taxes less subsidies on products at basic prices

naio_10_pyp1630

Table of taxes less subsidies on products at previous years' prices

uk_2010_siot

United Kingdom Input-Output Analytical Tables data

Value

A nested data frame. Each input-output table is in a separate row of the nested output, where all the metadata are in columns, and the actual, tidy, ordered input-output table is in the data data column.

See Also

Other import functions: employment_get(), iotables_download(), iotables_download_old()

Examples

# The table must be present in the sessions' temporary directory:
iotables_download(source = "naio_10_pyp1750")

iotables_read_tempdir(source = "naio_10_pyp1750")

Description

Check if HTML output is required

Description

Check if Latex output is required

Description

Create a key column for matching the dimensions of matrixes.

Usage

key_column_create(key_column_name, key_column_values = NULL)

Arguments

Details

This function will likely be used with the creation of coefficients that need to be matched with a matrix that has a key column.

Value

A tibble with one column, named key_column_name and with values key_column_values.

See Also

Other iotables processing functions: conforming_vector_create(), empty_remove(), household_column_find(), household_column_get(), iotable_year_get(), matrix_round(), output_get(), primary_input_get(), rows_add(), supplementary_add(), total_tax_add(), vector_transpose_longer(), vector_transpose_wider()

Examples

key_column_create("iotables_row", c("CO2_multiplier", "CH4_multiplier"))

Description

Computes the Leontief inverse, which measures the total (direct + indirect) requirements of all products or industries for one unit of final demand.

Usage

leontief_inverse_create(technology_coefficients_matrix, digits = NULL)

leontieff_inverse_create(technology_coefficients_matrix, digits = NULL)

Arguments

Details

The Leontief inverse is defined as

$L = (I - A)^{-1}$

where $A$ is the input-coefficient (or technology-coefficient) matrix created by input_coefficient_matrix_create().

Each element $l_{ij}$ of $L$ shows the total production of product i required—both directly and indirectly—to satisfy one unit of final demand for product j.

This formulation follows the Eurostat Manual of Supply, Use and Input–Output Tables (Beutel 2008, Chapter 15; see equations (19), (43)) and the UN Handbook on Supply and Use Tables and Input–Output Tables with Extensions and Applications (2018 Rev. 1, pp. 619–621). The Leontief inverse is also called the total requirements matrix in both manuals.

For the analogous inverse based on output coefficients, see ghosh_inverse_create().

Value

A data.frame with the original key column and the Leontief inverse in the remaining columns. If digits is supplied, values are rounded.

References

Beutel, J. (2008). Eurostat Manual of Supply, Use and Input–Output Tables. Luxembourg: Office for Official Publications of the European Communities.

Validation examples: – Table 15.6 (p. 485): Direct requirements (input coefficients) – Table 15.10 (p. 488): Total requirements (Leontief inverse)

Results reproduced by input_coefficient_matrix_create() and leontief_inverse_create() using the built-in dataset iotable_get(source = "germany_1995").

See Also

Other analytic object functions: ghosh_inverse_create(), input_flow_get(), leontief_matrix_create(), output_coefficient_matrix_create()

Examples

# --- Minimal hand-checkable 2×2 system ---
minimal_matrix <- data.frame(
  sector = c("A", "B"),
  A = c(0.2, 0.4),
  B = c(0.1, 0.2)
)
leontief_inverse_create(minimal_matrix, digits = 3)

# --- Full Eurostat example (Germany 1995) ---
cm_de <- input_coefficient_matrix_create(
  data_table = iotable_get(source = "germany_1995")
)
leontief_inverse_create(cm_de)

Description

Builds the Leontief matrix $(I - A)$ from a technology (input) coefficients matrix $A$. This is the intermediate step used before computing the Leontief inverse with leontief_inverse_create().

Usage

leontief_matrix_create(technology_coefficients_matrix)

leontieff_matrix_create(technology_coefficients_matrix)

Arguments

Details

In Eurostat terminology (Manual of Supply, Use and Input–Output Tables), the technology coefficients matrix $A$ is obtained by dividing each column of the inter-industry flows by the total output of that industry. The Leontief matrix is then $I - A$, also called the matrix of intermediate coefficients subtracted from identity.

This function removes any TOTAL rows/columns (e.g. "total", "cpa_total") before forming $I - A$. It returns a data.frame with the original key column followed by the numeric block of $I - A$.

Value

A data.frame whose first column is the key and whose remaining columns contain the Leontief matrix $(I - A)$.

References

Beutel, J. (2008). Eurostat Manual of Supply, Use and Input–Output Tables. Luxembourg: Office for Official Publications of the European Communities.

Validation examples: – Table 15.6 (p. 485): Direct requirements (input coefficients A) – Table 15.9 (p. 487): Leontief matrix (I − A) – Table 15.10 (p. 488): Leontief inverse (total requirements L = (I − A)⁻¹)

Results reproduced by input_coefficient_matrix_create(), leontief_matrix_create(), and leontief_inverse_create() using the built-in dataset iotable_get(source = "germany_1995").

See Also

Other analytic object functions: ghosh_inverse_create(), input_flow_get(), leontief_inverse_create(), output_coefficient_matrix_create()

Examples

# From input coefficients (usual case)
tm <- input_coefficient_matrix_create(iotable_get(), households = FALSE)
L <- leontief_matrix_create(tm)

Description

Round all numeric values in an input–output style table to a specified number of digits. The key column (first column) is preserved unchanged.

Usage

matrix_round(data_table, digits = 0)

Arguments

Details

This is useful for comparing results across software or publications that present rounded tables.

Value

A data.frame (or tibble) with the key column intact and all other numeric columns rounded to the given precision.

See Also

Other iotables processing functions: conforming_vector_create(), empty_remove(), household_column_find(), household_column_get(), iotable_year_get(), key_column_create(), output_get(), primary_input_get(), rows_add(), supplementary_add(), total_tax_add(), vector_transpose_longer(), vector_transpose_wider()

Examples

de_coeff <- input_coefficient_matrix_create(iotable_get())
head(matrix_round(de_coeff, digits = 2))

Description

A reference dataset derived from the United Kingdom Input–Output Analytical Tables, 2010. This version was imported from Excel and reformatted for internal testing.

Usage

metadata_uk_2010

Format

A data frame with 10 variables:

variable

A constant used by iotable_get().

uk_row

Row identifiers from the UK tables. Dots and & were converted to -.

uk_col

Column identifiers from the UK tables. Dots and & were converted to -.

uk_row_label

Original UK row labels.

uk_col_label

Original UK column labels.

eu_prod_na

Eurostat vocabulary equivalent of uk_row.

row_order

Ordering key for rows.

col_order

Ordering key for columns.

prod_na

Eurostat-like key values for rows.

induse

Eurostat-like key values for columns.

Details

This dataset provides a mapping between the UK 2010 analytical input–output tables and Eurostat-compatible codes, intended mainly for testing and validation.

See Also

Other metadata datasets: employment_metadata, metadata

Description

Wrapper around equation_solve() that computes total multipliers by post-multiplying an input indicator vector with a Leontief inverse and adds a key column carrying the multiplier name for consistent joins.

Usage

multiplier_create(
  input_vector,
  Im,
  multiplier_name = "multiplier",
  digits = NULL
)

Arguments

Details

In the Eurostat IO framework, multipliers measure total effects per unit of final demand, by product or industry (via the Leontief inverse $(I - A)^{-1}$). This contrasts with direct effects, which reflect only the immediate (first-round) impact.

The function delegates the numerical solve to equation_solve() and then formats the result for tidy joining with other IO tables. Ensure that the dimension ordering and names of input_vector and Im correspond; otherwise results will be misaligned.

Value

A data frame with:

• a first key column (character) named as the first column of input_vector and filled with multiplier_name, and

• one numeric column per product/industry containing the multipliers.

See Also

equation_solve(), input_indicator_create(), leontief_inverse_create()

Examples

# Minimal workflow -----------------------------------------------
data_table <- iotable_get()

coeff_de <- input_coefficient_matrix_create(data_table)

de_gva_indicator <- input_indicator_create(
  data_table = data_table,
  input = "gva"
)

I_de <- leontief_inverse_create(coeff_de)

de_gva_multipliers <- multiplier_create(
  input_vector = de_gva_indicator,
  Im = I_de,
  multiplier_name = "employment_multiplier",
  digits = 4
)

Description

Aggregated symmetric input–output table (SIOT) for the Netherlands, reference year 2000, reproduced from the Science Policy Integration for Coastal Systems Assessment (Spicosa) project’s multiplier specification sheet (D’Hernoncourt, Cordier & Hadley, 2011).

This dataset was originally created in the Spicosa project (circa 2006) as a simplified teaching table, based on OECD/Eurostat SIOT data. Column and row names were slightly adjusted to resemble Eurostat conventions and to align with the main example dataset germany_1995.

Usage

netherlands_2000

Format

A data frame with 14 observations and 13 variables:

prod_na

Simplified product/industry names.

agriculture_group

Aggregated agricultural products.

mining_group

Aggregated mining products.

manufacturing_group

Aggregated manufacturing products.

construction_group

Construction.

utilities_group

Aggregated utilities products/services.

services_group

Aggregated services products.

TOTAL

Row/column sums; a simple summary not present in the original source.

final_consumption_private

Aggregated final private consumption.

final_consumption_households

Aggregated final household consumption.

final_consumption_government

Aggregated final government consumption.

gross_fixed_capital_formation

Gross fixed capital formation (GFCF).

exports

Aggregated exports.

total_use

Aggregated total use.

Details

The Spicosa specification sheet demonstrates the derivation of type I and type II multipliers step by step from this table. This dataset corresponds to Table 1 of that report, the domestic transactions input–output table (million EUR, year 2000). It is not an official Statistics Netherlands SIOT, but a simplified, aggregated example for multiplier analysis.

Source

D’Hernoncourt, J., Cordier, M. & Hadley, D. (2011). Input–Output Multipliers: Specification sheet and supporting material. Spicosa Project Report. https://hal.science/hal-03233439

See Also

Other validation datasets: germany_airpol, uk_2010_data, uk_test_results

Description

Create an output-coefficient matrix from a symmetric input–output table or a use table. Output coefficients can be interpreted as the market shares of products in total output (row-wise normalization).

Usage

output_coefficient_matrix_create(data_table, total = "tfu", digits = NULL)

Arguments

Details

Let $Z$ be the inter-industry flow block and $x$ the vector of product output (or, for final-demand shares, total final use). The output-coefficient matrix $B$ is defined row-wise as $b_{ij} = z_{ij} / x_i$. In practice, zeros in the denominator can make equations unsolvable; this function replaces zeros with a small epsilon (1e-6) to avoid division by zero.

Eurostat, Manual of Supply, Use and Input-Output Tables (e.g., pp. 495, 507) describes output coefficients and the Ghosh framework you may use these with.

Value

A data.frame whose first column is the key (product labels) and the remaining columns form the output-coefficient matrix. Column order follows the input.

See Also

Other analytic object functions: ghosh_inverse_create(), input_flow_get(), leontief_inverse_create(), leontief_matrix_create()

Examples

data_table <- iotable_get()
output_coefficient_matrix_create(
  data_table = data_table,
  total = "tfu",
  digits = 4
)

Description

Convenience wrapper around primary_input_get() that returns the row labelled Output (P1) from a symmetric input–output table (SIOT) or from a use table retrieved by iotable_get().

Usage

output_get(data_table)

Arguments

Details

In the Eurostat framework, Output is transaction P1, usually recorded at basic prices (often labelled "output" or "output_bp"). It is a balancing item of the use table / SIOT, not a “primary input” (primary inputs are value added components and imports, shown in the third quadrant). This helper merely selects the row labelled "output", "output_bp", "P1" or "p1" if present.

Value

A one-row data frame: the first column is the key column; remaining columns give output (P1) by product/industry.

See Also

primary_input_get(), iotable_get()

Other iotables processing functions: conforming_vector_create(), empty_remove(), household_column_find(), household_column_get(), iotable_year_get(), key_column_create(), matrix_round(), primary_input_get(), rows_add(), supplementary_add(), total_tax_add(), vector_transpose_longer(), vector_transpose_wider()

Examples

# Output (P1) from the package demo table
iot_germany <- iotable_get()
output_get(data_table = iot_germany)

Description

Compute output multipliers from a Leontief inverse matrix.

Usage

output_multiplier_create(input_coefficient_matrix)

Arguments

Details

The output multipliers are defined as the column sums of the Leontief inverse $(I - A)^{-1}$, where $A$ is the input coefficient matrix. They measure the total direct and indirect output generated in each industry per unit increase in final demand.

See Eurostat (2008), Manual of Supply, Use and Input–Output Tables, p. 500; UN (2018), Handbook on Supply and Use Tables and Input–Output Tables with Extensions and Applications, §15.35.

Value

A one-row data.frame (or tibble) with:

• The first column a label "output_multipliers".

• Remaining columns the multipliers for each industry.

See Also

Other multiplier functions: input_multipliers_create()

Examples

de_input_coeff <- input_coefficient_matrix_create(
  iotable_get(),
  digits = 4
)

output_multiplier_create(de_input_coeff)

Description

A reference codelist used in Eurostat’s national accounts framework for product × product symmetric input–output tables (SIOTs). This vocabulary enumerates the rows of product-based tables, covering intermediate products (Quadrant 1), value-added components (Quadrant 3), and selected control totals such as “Total supply at basic prices”.

Usage

prd_ava

Format

A tibble (data frame) with 10 variables:

id

Canonical Eurostat concept identifier.

label

Official Eurostat label (preferred English term).

status

"Valid" or "Valid in CPA2_1".

status_modified

Date of last status modification.

notation

Alternate human-readable code or short form (e.g. CPA_A01).

ind_ava_notation

Legacy comparison column retained for internal cross-checks.

quadrant

Integer designating the analytical block: 10 = Intermediate (technology block, Quadrant 1); 20 = Primary inputs / GVA (Quadrant 3); 30 = Control totals; 50 = Extensions / adjustments.

numeric_order

Ordinal position key for ordering within quadrants.

iotables_label

Simplified legacy label used in the iotables package.

block

Semantic block category inferred from quadrant (intermediate, primary_inputs, control_total, extension).

uri

Stable URI linking to the Eurostat SKOS concept at https://dd.eionet.europa.eu/.

Details

The prd_ava vocabulary aligns with the official Eurostat SDMX codelist “Products, adjustments and value added”. It is used by the iotables package to order the row dimension of product-by-product tables (datasets such as naio_10_cp1700).

Codes correspond to CPA 2.1 product classifications and ESA 2010 accounting aggregates. Each entry includes a quadrant identifier and an ordinal numeric order for consistent placement when reshaping long-form data.

The dataset retains published control totals and balancing items for completeness (e.g., Total supply at basic prices or CIF/FOB adjustments), but these rows are normally excluded from analytical matrices.

Because the Eurostat SIOT datasets sometimes contain more detailed industry breakup than their vocabulary, we adopted industry codeing from cpa2_1. These codes are marked the "Valid in CPA2_1" status.

Source

Eurostat metadata registry (https://dd.eionet.europa.eu/vocabulary/eurostat/prd_ava/)

See Also

Related Eurostat vocabularies: ind_ava, ind_use, prd_use, cpa2_1.

Examples

data(prd_ava)

Description

A reference codelist used in Eurostat’s national accounts framework for product × product symmetric input–output tables (SIOTs). This vocabulary enumerates the columns of product-based tables, covering intermediate product uses (Quadrant 1), value-added components (Quadrant 3), and the final-use block (Quadrant 2), as well as balancing or extension items.

Usage

prd_use

Format

A tibble (data frame) with 10 variables:

id

Canonical Eurostat concept identifier.

label

Official Eurostat label (preferred English term).

status

"Valid" or "Valid in CPA2_1".

status_modified

Date of last status modification.

notation

Alternate human-readable code or short form (e.g. CPA_A01).

quadrant

Integer designating the analytical block: 10 = Intermediate (technology block, Q1); 20 = Primary inputs / GVA (Q3); 30 = Final use (Q2); 50 = Extensions / diagnostics.

numeric_order

Ordinal key for ordering columns within quadrants.

iotables_label

Simplified legacy label used in the iotables package.

block

Semantic block category inferred from quadrant (intermediate, primary_inputs, final_use, extension).

uri

Stable URI linking to the Eurostat SKOS concept at https://dd.eionet.europa.eu/.

Details

The prd_use vocabulary corresponds to the official Eurostat SDMX codelist “Product uses”. It is used by the iotables package to order the column dimension of product-by-product tables (datasets such as naio_10_cp1700).

Codes follow CPA 2.1 for products and ESA 2010 for accounting aggregates. Each entry includes a quadrant identifier and a numeric order key that ensures consistent column ordering when reshaping Eurostat’s long-form data into analytical matrices.

The dataset also retains control totals and adjustment items (Quadrant 30/50) such as Total use at purchasers’ prices, CIF/FOB adjustments, or Discrepancy, which are part of Eurostat’s published codelist but are normally excluded from matrix algebra operations.

Because the Eurostat SIOT datasets sometimes contain more detailed industry breakup than their vocabulary, we adopted industry codeing from cpa2_1. These codes are marked the "Valid in CPA2_1" status.

Source

Eurostat metadata registry (https://dd.eionet.europa.eu/vocabulary/eurostat/prd_use/)

See Also

Related Eurostat vocabularies: prd_ava, ind_ava, ind_use.

Examples

data(prd_use)

Description

Retrieve a named primary-input row from a symmetric input–output table, a use table, or a supply table (as returned by iotable_get()).

Usage

primary_input_get(data_table, primary_input = "compensation_employees")

Arguments

Details

In I–O accounting, primary inputs (e.g., compensation of employees, consumption of fixed capital, taxes on production/subsidies, operating surplus/mixed income, and—when relevant—imports used for domestic production) are shown in the value-added block (third quadrant).

Value

A data frame containing the key column and the matching primary- input row.

References

Eurostat (2008). Eurostat Manual of Supply, Use and Input–Output Tables, ch. 13. United Nations (2018). Handbook on Supply and Use Tables and Input–Output Tables with Extensions and Applications (Rev. 1, “white cover”), ch. 10.

See Also

Other iotables processing functions: conforming_vector_create(), empty_remove(), household_column_find(), household_column_get(), iotable_year_get(), key_column_create(), matrix_round(), output_get(), rows_add(), supplementary_add(), total_tax_add(), vector_transpose_longer(), vector_transpose_wider()

Examples

# Get the Germany 1995 demo SIOT with default labelling
de_iot <- iotable_get(source = "germany_1995")

# Select compensation of employees (row code: "compensation_employees")
primary_input_get(de_iot, "compensation_employees")

# Get the same table with Eurostat short labelling
de_iot_short <- iotable_get(source = "germany_1995", labelling = "short")

# Consumption of fixed capital (row code: "K1")
primary_input_get(de_iot_short, "K1")

# Operating surplus and mixed income, net (row code: "B2A3N")
primary_input_get(de_iot_short, "B2A3N")

Description

Only currently used primary inputs. Abbreviations for filtering.

Usage

data("croatia_employment_aggregation")

Format

A data frame with 105 rows (including empty ones) and 2 variables.

t_rows2

Eurostat code of the input.

t_rows2_lab

Labelling of the input by Eurostat.

source

Eurostat / DZS

indicator

Human readable abbreviation

See Also

Other Croatia 2010 datasets: croatia_2010_1700, croatia_2010_1800, croatia_2010_1900, croatia_employment_2013, croatia_employment_aggregation

Description

Add a conforming row, or elements of a conforming row, to a named input–output style data frame.

Usage

rows_add(data_table, rows_to_add, row_names = NULL, empty_fill = 0)

Arguments

Details

You can add rows in several ways:

• A data frame with one or more rows, where the first column contains row identifiers.

• A named numeric vector, which will be turned into a single-row data frame.

If no row_names are supplied and the first column of rows_to_add is numeric, new rows will be automatically labelled as "new_row_1", "new_row_2", etc.

Missing column values are filled with empty_fill, which defaults to 0. If you want to avoid division by zero in later computations, you can set this to a very small value (e.g. 1e-6).

Value

A data.frame containing the original data_table extended with the new row(s).

See Also

Other iotables processing functions: conforming_vector_create(), empty_remove(), household_column_find(), household_column_get(), iotable_year_get(), key_column_create(), matrix_round(), output_get(), primary_input_get(), supplementary_add(), total_tax_add(), vector_transpose_longer(), vector_transpose_wider()

Examples

rows_to_add <- data.frame(
  iotables_row = "CO2_emission",
  agriculture_group = 10448,
  industry_group = 558327, # construction is omitted
  trade_group = 11194
)

rows_add(iotable_get(), rows_to_add = rows_to_add)

rows_add(iotable_get(),
  rows_to_add = c(
    industry_group = 1534,
    trade_group = 4
  ),
  row_names = "CH4_emission"
)

Description

Append supplementary indicators (e.g., emissions coefficients) as new rows to a symmetric input–output table (SIOT), use, supply, or margins table. This is a light wrapper around rows_add().

Usage

supplementary_add(data_table, supplementary_data, supplementary_names = NULL)

Arguments

Details

Column names in supplementary_data must match the numeric columns of data_table. If the key column is missing, it is created from supplementary_names or auto-generated as supplementary_row_#.

When a household final consumption column is present (e.g., final_consumption_households, P3_S14), new rows get 0 in that column if the supplied values are NA.

For terminology, see Eurostat’s Manual of Supply, Use and Input-Output Tables. (Eurostat, 2008; ISBN 978-92-79-04704-3)

Value

A data.frame with the rows of supplementary_data bound to data_table and aligned to its key and numeric columns.

References

Table 15.13 (p. 494) in Beutel (2008), Eurostat Manual of Supply, Use and Input–Output Tables, shows an environmental‐emission model built by adding CO₂ and CH₄ rows to the German 1995 input–output coefficients. The same construction is reproduced here by supplementary_add() and the results are checked against this example.

See Also

convert_industry_to_product()

Other iotables processing functions: conforming_vector_create(), empty_remove(), household_column_find(), household_column_get(), iotable_year_get(), key_column_create(), matrix_round(), output_get(), primary_input_get(), rows_add(), total_tax_add(), vector_transpose_longer(), vector_transpose_wider()

Examples

de_io <- iotable_get()
CO2_coefficients <- data.frame(
  agriculture_group = 0.2379,
  industry_group = 0.5172,
  construction = 0.0456,
  trade_group = 0.1320,
  business_services_group = 0.0127,
  other_services_group = 0.0530
)
CH4_coefficients <- data.frame(
  agriculture_group = 0.0349,
  industry_group = 0.0011,
  construction = 0,
  trade_group = 0,
  business_services_group = 0,
  other_services_group = 0.0021
)
CO2 <- cbind(
  data.frame(iotables_row = "CO2"),
  CO2_coefficients
)
CH4 <- cbind(
  data.frame(iotables_row = "CH4_coefficients"),
  CH4_coefficients
)
de_coeff <- input_coefficient_matrix_create ( iotable_get() )
emissions <- rbind (CO2, CH4)

# Check with the Eurostat Manual page 494:
supplementary_add(de_io, emissions)

Description

Create and append a total tax row by summing selected tax rows in the primary inputs block (Quadrant III) of a SIOT or use table.

Usage

total_tax_add(
  data_table,
  tax_names = c("d21x31", "d29x39"),
  total_tax_name = "TOTAL_TAX"
)

Arguments

Details

In Eurostat/ESA terminology, tax rows commonly include:

• Taxes less subsidies on products (codes D.2–D.3), and

• Other net taxes on production (codes D.29–D.39).

These appear in the value-added (primary inputs) section of the use/SIOT layout. The function sums the specified rows column-wise over all numeric columns and appends the result as total_tax_name. If a household final consumption column is present (e.g. final_consumption_households or p3_s14), any missing value in the new total row is replaced by zero.

Value

A data frame like data_table, with one additional row named total_tax_name that equals the element-wise sum of the rows in tax_names over numeric columns.

Terminology

Eurostat uses the lines “Taxes less subsidies on products” and “Other net taxes on production” in published tables; these correspond, respectively, to D.2–D.3 and D.29–D.39.

References

Eurostat (2008). Eurostat Manual of Supply, Use and Input–Output Tables, ch. 13. United Nations (2018). Handbook on Supply and Use Tables and Input–Output Tables with Extensions and Applications (Rev. 1, “white cover”), ch. 10.

See Also

Other iotables processing functions: conforming_vector_create(), empty_remove(), household_column_find(), household_column_get(), iotable_year_get(), key_column_create(), matrix_round(), output_get(), primary_input_get(), rows_add(), supplementary_add(), vector_transpose_longer(), vector_transpose_wider()

Examples

de_io <- iotable_get()
total_tax_add(
  data_table = de_io,
  tax_names = c("net_tax_products", "net_tax_production"),
  total_tax_name = "total_tax"
)

Description

Official Input–Output Analytical Tables for the United Kingdom (2010), published by the Office for National Statistics (ONS).

These replication data are harmonized to the iotables package format and can be accessed via iotable_get() by setting the source parameter to one of the available tables.

Usage

data(uk_2010_data)

Format

A tibble with 10 variables:

uk_row

Row identifier (dots and & converted to -).

uk_row_lab

Original ONS row label.

uk_col

Column identifier (dots and & converted to -).

uk_col_lab

Original ONS column label.

geo

Eurostat-style geocode (constant: "UK").

geo_lab

Human-readable country label (constant: "United Kingdom").

indicator

Indicator or table name, matching Excel sheet title.

unit

Eurostat-style unit code (e.g. "MIO_NAC").

unit_lab

Human-readable unit label (e.g. "Million national currency").

values

Numeric values for each indicator.

year

Constant value: 2010.

Details

The dataset includes five analytical table types from the 2010 UK Input–Output release:

• uk_2010_siot — Input–Output table (domestic use, basic prices, product × product)

• uk_2010_use — Domestic use table at basic prices (product × industry)

• uk_2010_imports — Imports use table at basic prices (product × product)

• uk_2010_coeff — Matrix of coefficients (product × product)

• uk_2010_inverse — Leontief inverse (product × product)

These correspond to the Excel sheets from the official ONS publication.

Source

Office for National Statistics (2010). United Kingdom Input–Output Analytical Tables, 2010. Retrieved from the UK National Archives (ONS Web Archive).

See Also

• iotable_get() for accessing and filtering IO tables.

• iotables_download() for downloading current Eurostat and UK datasets.

Other validation datasets: germany_airpol, netherlands_2000, uk_test_results

Description

This function will retrieve the published effects and multipliers from the United Kingdom Input-Output Analytical Tables, 2010 (consistent with UK National Accounts Blue Book 2013 & UK Balance of Payments Pink Book 2013) by Richard Wild.

Usage

uk_2010_results_get(path = NULL)

Arguments

Source

ukioanalyticaltablesio1062010detailedpubversion.xls

Examples

## Not run: 
uk_results <- iotables:::uk_2010_results_get()

## End(Not run)

Description

Published multipliers and effects from the United Kingdom Input–Output Analytical Tables, reference year 2010.

This dataset contains output, employment cost, and GVA multipliers and effects, together with their published rankings. It is imported from the official ONS Excel release and normalized for use in iotables. It is primarily used in the vignette("united_kingdom_2010", package = "iotables") to validate the package’s multiplier functions against official UK results.

Usage

uk_test_results

Format

A tibble with 127 rows and 12 variables:

uk_row_label

Product/industry label.

output_multiplier

Output multiplier (published).

output_multiplier_rank

Ranking of output multipliers.

employment_cost_multiplier

Employment cost multiplier.

employment_cost_multiplier_rank

Ranking of employment cost multipliers.

employment_cost_effects

Employment cost effects.

employment_cost_effects_rank

Ranking of employment cost effects.

gva_multiplier

GVA multiplier.

gva_multiplier_rank

Ranking of GVA multipliers.

gva_effects

GVA effects.

gva_effects_rank

Ranking of GVA effects.

indicator

Indicator label, usually "Multipliers and effects (product)".

Details

The Office for National Statistics (ONS) publishes Input–Output Analytical Tables (IOATs) for the UK. From these, Type I and Type II multipliers and effects are calculated. This dataset contains those published values at the product level for 2010, enabling direct cross-checks with iotables computations.

Source

Office for National Statistics (ONS), UK Input–Output Analytical Tables 2010 (Excel release).

See Also

vignette("united_kingdom_2010", package = "iotables")

Other validation datasets: germany_airpol, netherlands_2000, uk_2010_data

Description

Convert a wide-form vector (e.g., indicators or multipliers) into long form, which is often more useful for printing or joining. This is a thin wrapper around tidyr::pivot_longer(), provided so you do not need to load tidyr explicitly.

Usage

vector_transpose_longer(
  data_table,
  names_to = "nace_r2",
  values_to = "value",
  key_column_name = NULL,
  .keep = FALSE
)

vector_transpose(
  data_table,
  names_to = "nace_r2",
  values_to = "value",
  key_column_name = NULL,
  .keep = FALSE
)

Arguments

Value

A tibble in long format with a key column and, if requested, the indicator identifier column.

See Also

Other iotables processing functions: conforming_vector_create(), empty_remove(), household_column_find(), household_column_get(), iotable_year_get(), key_column_create(), matrix_round(), output_get(), primary_input_get(), rows_add(), supplementary_add(), total_tax_add(), vector_transpose_wider()

Examples

vector_transpose_longer(
  data.frame(
    indicator = "my_indicator",
    agriculture = 0.0123,
    manufacturing = 0.1436,
    trade = 0.0921
  )
)

# Keep the indicator column
vector_transpose_longer(
  data.frame(
    indicator = "my_indicator",
    agriculture = 0.0123,
    manufacturing = 0.1436
  ),
  .keep = TRUE
)

Description

Convert a long-form vector (e.g., indicators, multipliers) into wide form, which is often more useful for binding with input–output tables. This is a thin wrapper around tidyr::pivot_wider(), provided so you do not need to load tidyr explicitly.

Usage

vector_transpose_wider(
  data_table,
  names_from,
  values_from,
  key_column_name = NULL,
  key_column_values = NULL
)

Arguments

See Also

Other iotables processing functions: conforming_vector_create(), empty_remove(), household_column_find(), household_column_get(), iotable_year_get(), key_column_create(), matrix_round(), output_get(), primary_input_get(), rows_add(), supplementary_add(), total_tax_add(), vector_transpose_longer()

Examples

vector_transpose_wider(
  data_table = germany_airpol[, -2],
  names_from = "induse",
  values_from = "value"
)

vector_transpose_wider(
  data_table = germany_airpol[1:8, 3:4],
  names_from = "induse",
  values_from = "value",
  key_column_values = "CO2_emission"
)