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Transpose a vector to wider format

Source: R/vector_transpose.R
vector_transpose_wider.Rd

Many vectors (indicators, multipliers) are create in the wide form to conform matrixes in analytical functions. For binding it is more useful to have them in wide format.

Usage

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

Arguments

data_table

A matrix or vector that normally has a key column. If the key column must be created or replaced, used key_column_name and key_column_values.

names_from, values_from

A pair of arguments describing which column (or columns) to get the name of the output column (`names_from`), and which column (or columns) to get the cell values from (`values_from`).

key_column_name

The name of the key column.

key_column_values

You can explicitly supply key column values. Defaults to NULL when the key column values will be created from the long data.

Details

This is a wrapper around pivot_wider so you do not necessarily need to import or load the entire tidyr package.

See also

Other iotables processing functions: conforming_vector_create(), 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')
#> # A tibble: 9 × 9
#>   airpol CPA_A `CPA_B-E` CPA_F `CPA_G-I` `CPA_J-N` `CPA_O-T` P3_S14     P1
#>   <chr>  <int>     <int> <int>     <int>     <int>     <int>  <int>  <int>
#> 1 CO2    10448    558327 11194     71269      8792     26990 217137 904158
#> 2 CH4     1534      1160     1         4         1      1058    136   3894
#> 3 N2O       77       100     0         3         0        11     17    209
#> 4 SO2       12      1705    18        50         4        24    180   1994
#> 5 NOx       62       722    64       452        23        58    585   1967
#> 6 CO        43      1616    86       434       103       188   4198   6667
#> 7 NMVOC     20      1209    17       101        15       143    520   2024
#> 8 Dust      57       165     7        34         1         7     58    329
#> 9 Total  12252    565005 11388     72347      8939     28479 222831 921241
                        
vector_transpose_wider (data_table =  germany_airpol[1:8, 3:4],
                        names_from = 'induse',
                        values_from = 'value', 
                        key_column_values = "CO2_emission" )
#> # A tibble: 1 × 9
#>   induse       CPA_A `CPA_B-E` CPA_F `CPA_G-I` `CPA_J-N` `CPA_O-T` P3_S14     P1
#>   <chr>        <int>     <int> <int>     <int>     <int>     <int>  <int>  <int>
#> 1 CO2_emission 10448    558327 11194     71269      8792     26990 217137 904158