Combining correspondence tables

fullPath("names1.csv", "names.csv")

Execute the following code to apply function newCorrespondenceTable() and generate the correspondence table linking ISIC Rev. 4 (classification A) to CPA 2.1 (classification B) through the intermediate classification CPC 2.1. When no trimming is executed (Redundancy_trim = FALSE), redundant records are shown, together with the redundancy flag.

NCT <- newCorrespondenceTable(
        Tables = file.path(tmp_dir, "names.csv"),
        Reference = "A",
        MismatchTolerance = 0.5,
        Redundancy_trim = FALSE,
        Progress = FALSE
)

knitr::kable(
  (NCT[[1]][3748:3753, 1:9]),
  caption = "ISIC Rev. 4 to CPA Ver. 2.1 (via CPC Ver. 2.1): Subsample of the new Correspondence Table",
  align = "c"
)

The table above represents a subset of the correspondence table generated in this example. Each row represents a candidate correspondence between an ISIC code and a CPA code, possibly mediated by one or more intermediate classifications.

Here, the ISIC code 1030 is linked to several CPA codes:

• The rows linking 1030 to 10.39.23 and 10.39.24 are unique and unambiguous.
  These rows have Redundancy = 0, Unmatched = 0, and no review or mismatch flags set.

• The CPA code 10.39.25 appears multiple times in combination with the same ISIC code 1030, via different CPC codes.
  These rows are therefore flagged with Redundancy = 1.

When Redundancy_trim = FALSE, all redundant rows are retained and an additional column, Redundancy_keep, is included:

• Redundancy_keep = 1 identifies the record that would be kept if redundancy trimming were applied.
• Rows with Redundancy_keep = 0 represent redundant alternatives.

All rows in this example have Unmatched = 0, indicating that each ISIC code is matched to at least one CPA code and vice versa.
Similarly, NoMatchFromA = 0 and NoMatchFromB = 0 show that no codes from the original classification tables are missing from the correspondence tables involved in the construction.

Finally, the Review flag is equal to 0 for all rows, indicating that given the selected reference classification, no hierarchical inconsistencies are detected.

knitr::kable(
  head(NCT[[2]]),
  caption = "ISIC Rev. 4 to CPA Ver. 2.1 (via CPC Ver. 2.1): Names of the classifications involved",
  align = "c"
)

The table above is the second element generated with newCorrespondenceTable, which simply is a data frame containing the names of all classifications involved.

fullPath("names4.csv", "names.csv")

Execute the following code to apply function newCorrespondenceTable() and generate the correspondence table linking NACE Rev. 2 (classification A) to SITC 4 (classification B) through the intermediate classifications CPA Ver. 2.1 and CN 2022. Given the option Redundancy_trim = TRUE, when there are redundant records, these are removed and kept exactly one record for each unique combination.

NCT <- newCorrespondenceTable(
        Tables = file.path(tmp_dir, "names.csv"),
        Reference = "none",
        MismatchTolerance = 0.96,
        Redundancy_trim = TRUE,
        Progress = FALSE
      )



knitr::kable(
  head(NCT[[1]][5442:5450, 1:8]),
  caption = "NACE Rev. 2 : SITC 4 (via CPA Ver. 2.1 and CN 2022): Subsample of the new Correspondence Table",
  align = "c"
)

Also in this case, the table above represents a subset of the correspondence table generated in this example. Each row corresponds to a correspondence between a NACE code and a SITC code, possibly mediated by multiple intermediate classifications.

In this example, the NACE code 28.41 is mapped to several SITC codes:

• The first four rows represent unique and unambiguous correspondences, where specific CPA and CN codes are associated with specific SITC codes.
  These rows have Redundancy = 0 and Unmatched = 0, indicating clear one-to-one mappings across all classifications involved.

• The last two rows are flagged with Redundancy = 1.
  In these cases, multiple intermediate codes (in CPA and/or CN) contribute to the same NACE–SITC mapping. As a result, the corresponding intermediate classification values are reported as "Multiple".

All rows have Unmatched = 0, indicating that each correspondence links a valid NACE code to a valid SITC code.
Additionally, NoMatchFromA = 0 and NoMatchFromB = 0 for all rows confirm that no classification codes are missing from the correspondence tables used to construct the result.

knitr::kable(
  head(NCT[[2]]),
  caption = "NACE Rev. 2 : SITC 4 (via CPA Ver. 2.1 and CN 2022): Names of the classifications involved",
  align = "c"
)

The table above corresponds to the second element returned by newCorrespondenceTable and is a data frame containing the names of all the classifications involved in the process.

Execute the following code in order to get the path of the required input files.

A <- read.csv(
  system.file("extdata/test", "CN2021.csv", package = "correspondenceTables"),
  colClasses = "character"
)

AStar <- read.csv(
  system.file("extdata/test", "CN2022.csv", package = "correspondenceTables"),
  colClasses = "character"
)

B <- read.csv(
  system.file("extdata/test", "CPA21.csv", package = "correspondenceTables"),
  colClasses = "character"
)

AB <- read.csv(
  system.file("extdata/test", "CN2021_CPA21.csv", package = "correspondenceTables"),
  colClasses = "character"
)

AAStar <- read.csv(
  system.file("extdata/test", "CN2021_CN2022.csv", package = "correspondenceTables"),
  colClasses = "character"
)

Execute the following code line to apply function updateCorrespondenceTable() and generate the updated correspondence table. In this case the classification CN 2021 (A) has been updated to CN 2022 (A*), and the correspondence to CPA 2.1 (B) is revised accordingly. Given the option Redundancy_trim = TRUE, when there are redundant records, these are removed and kept exactly one record for each unique combination.

UPC <- updateCorrespondenceTable(
  A = A,
  B = B,
  AStar = AStar,
  AB = AB,
  AAStar = AAStar,
  Reference = "B",
  MismatchToleranceB = 0.4,
  MismatchToleranceAStar = 0.4,
  Redundancy_trim = TRUE
)


knitr::kable(
  (UPC[[1]][7950:7955, 1:11]),
  caption = "Updating CN 2021 : CPA Ver. 2.1  (triggered by CN update): Subsample of the new CorrespondenceTable",
  align = "c"
)

The table above represents a subset of the correspondence table generated in this example. Each row links a CN 2022 code to a CPA 2.1 code and reflects changes from the previous version.

In this example:

• The first three rows are flagged with CodeChange = 1, indicating that the original CN 2021 codes are associated with updated CN 2022 codes in a way that differs from the previous mapping.
  These rows also have LabelChange = 1, meaning that the labels of the corresponding CN codes have changed between versions.

• Rows where Review = 1 indicate potential hierarchical inconsistencies with respect to the selected reference classification, and therefore require manual inspection.

• The remaining rows have CodeChange = 0 and LabelChange = 0, showing that both the code and its label remain unchanged between CN 2021 and CN 2022 for the given correspondence to CPA 2.1.

All rows have Redundancy = 0, meaning that each CN 2022–CPA 2.1 combination appears only once in the updated correspondence table.
Similarly, NoMatchToAStar = 0 and NoMatchToB = 0 indicate that each row contains valid codes for both CN 2022 and CPA 2.1.

Finally, the flags NoMatchFromAStar = 0 and NoMatchFromB = 0 for all rows confirm that every code appearing in the updated correspondence is consistently represented in both the updated classification table and the underlying concordance tables.

knitr::kable(
  head(UPC[[2]]),
  caption = "Updating CN 2021 : CPA Ver. 2.1  (triggered by CN update): Names of the classifications involved",
  align = "c",
  col.names = "Classification: Name"
)

The table above is the second element generated with updateCorrespondenceTable, which simply is a data frame containing the names of all classifications involved.

Execute the following code in order to get the path of the required input files.

A <- read.csv(
  system.file("extdata/test", "NAICS2017.csv", package = "correspondenceTables"),
  colClasses = "character"
)

AStar <- read.csv(
  system.file("extdata/test", "NAICS2022.csv", package = "correspondenceTables"),
  colClasses = "character"
)

B <- read.csv(
  system.file("extdata/test", "NACE.csv", package = "correspondenceTables"),
  colClasses = "character"
)

AB <- read.csv(
  system.file("extdata/test", "NAICS2017_NACE.csv", package = "correspondenceTables"),
  colClasses = "character"
)

AAStar <- read.csv(
  system.file("extdata/test", "NAICS2017_NAICS2022.csv", package = "correspondenceTables"),
  colClasses = "character"
)

Execute the following code line to apply function updateCorrespondenceTable() and generate the updated correspondence table. In this case the classification NAICS 2017 (A) has been updated to NAICS 2022 (A*), and the correspondence to NACE Rev. 2 (B) is revised accordingly. Given the option Redundancy_trim = TRUE, when there are redundant records, these are removed and kept exactly one record for each unique combination.

UPC3 <- updateCorrespondenceTable(
  A = A,
  B = B,
  AStar = AStar,
  AB = AB,
  AAStar = AAStar,
  Reference = "none",
  MismatchToleranceB = 0.5,
  MismatchToleranceAStar = 0.8,
  Redundancy_trim = TRUE
)


knitr::kable(
  head(UPC3[[1]][1208:1218, 1:10]),
  caption = "Updating NAICS : NACE (triggered by NAICS update): Subsample of the new Correspondence Table",
  align = "c"
)

The table above represents a subset of the correspondence table generated in this example. Each row represents a candidate correspondence between a NAICS 2022 code and a NACE Rev. 2 code, derived from the previous version of the classification (NAICS 2017).

In this example:

• The NAICS code 332313 is unchanged between NAICS 2017 and NAICS 2022, as indicated by CodeChange = 0 for all rows. This shows that the classification update did not introduce any code-level changes for this activity.

• The same NAICS code 332313 is mapped to multiple NACE Rev. 2 codes (25.11, 25.29, 25.30, 28.22, 28.91, 30.11), reflecting a one-to-many correspondence that already existed and remains valid after the update.

• All rows have Redundancy = 0, meaning that each NAICS 2022–NACE Rev. 2 combination appears only once in the updated correspondence table.

• The flags NoMatchToAStar = 0 and NoMatchToB = 0 indicate that every row contains valid and consistent codes for both the updated classification (NAICS 2022) and the target classification (NACE Rev. 2).

• Similarly, NoMatchFromAStar = 0 and NoMatchFromB = 0 confirm that all codes appearing in the updated correspondence are present in the respective classification tables and supported by the underlying concordance tables.

• Finally, LabelChange = 0 for all rows shows that the labels associated with the NAICS codes are identical between the 2017 and 2022 versions.

  knitr::kable(
  head(UPC3[[2]]),
  caption = "Updating NAICS : NACE (triggered by NAICS update): Names of the classifications involved",
  align = "c",
  col.names = "Classification: Name"
)

The table above corresponds to the second element returned by updateCorrespondenceTable and is a data frame containing the names of all relevant classifications.