Dictionary for AssetCodeRCB2 variable of RCB2 file

Description

Dictionary for AssetCodeRCB2 variable of RCB2 file

Usage

RCB2__AssetCodeRCB2

Format

RCB2__AssetCodeRCB2

A data frame with 17 rows and 2 columns:

code

Integer code

value

Character description

Source

D_RCB2.TXT

Dictionary for DebtMaturityCode variable of RCB3 file

Description

Dictionary for DebtMaturityCode variable of RCB3 file

Usage

RCB3__DebtMaturityCode

Format

RCB3__DebtMaturityCode

A data frame with 10 rows and 2 columns:

code

Integer code

value

Character description

Source

D_RCB3.TXT

Dictionary for INV_CODE variable of RCB file

Description

Dictionary for INV_CODE variable of RCB file

Usage

RCB__INV_CODE

Format

RCB__INV_CODE

A data frame with 35 rows and 2 columns:

code

Integer code

value

Character description

Source

D_RCB.TXT

Dictionary for LOANSTATUS variable of RCF1 file

Description

Dictionary for LOANSTATUS variable of RCF1 file

Usage

RCF1__LOANSTATUS

Format

RCF1__LOANSTATUS

A data frame with 13 rows and 2 columns:

code

Integer code

value

Character description

Source

D_RCF1.TXT

Dictionary for LOANSTATUS variable of RCF file

Description

Dictionary for LOANSTATUS variable of RCF file

Usage

RCF__LOANSTATUS

Format

RCF__LOANSTATUS

A data frame with 6 rows and 2 columns:

code

Integer code

value

Character description

Source

D_RCF.TXT

Dictionary for DerivCode variable of RCI2B file

Description

Dictionary for DerivCode variable of RCI2B file

Usage

RCI2B__DerivCode

Format

RCI2B__DerivCode

A data frame with 23 rows and 2 columns:

code

Integer code

value

Character description

Source

D_RCI2B_2018.TXT

Dictionary for ExposureCode variable of RCI2C file

Description

Dictionary for ExposureCode variable of RCI2C file

Usage

RCI2C__ExposureCode

Format

RCI2C__ExposureCode

A data frame with 12 rows and 2 columns:

code

Integer code

value

Character description

Source

D_RCI2C_2018.TXT

Dictionary for DerivRMCode variable of RCI2D file

Description

Dictionary for DerivRMCode variable of RCI2D file

Usage

RCI2D__DerivRMCode

Format

RCI2D__DerivRMCode

A data frame with 11 rows and 2 columns:

code

Integer code

value

Character description

Source

D_RCI2D_2018.TXT

Dictionary for ASSET_CODE variable of RCO file

Description

Dictionary for ASSET_CODE variable of RCO file

Usage

RCO__ASSET_CODE

Format

RCO__ASSET_CODE

A data frame with 12 rows and 2 columns:

code

Integer code

value

Character description

Source

D_RCO.TXT

Dictionary for RegCapCode variable of RCR3 file

Description

Dictionary for RegCapCode variable of RCR3 file

Usage

RCR3__RegCapCode

Format

RCR3__RegCapCode

A data frame with 15 rows and 2 columns:

code

Integer code

value

Character description

Source

D_RCR3.TXT

Dictionary for RegCapCode variable of RCR7 file

Description

Dictionary for RegCapCode variable of RCR7 file

Usage

RCR7__RegCapCode

Format

RCR7__RegCapCode

A data frame with 29 rows and 2 columns:

code

Integer code

value

Character description

Source

D_RCR7.TXT

Dictionary for RegCapCode variable of RID file

Description

Dictionary for RegCapCode variable of RID file

Usage

RID__CAP_CODE

Format

RID__CAP_CODE

A data frame with 12 rows and 2 columns:

code

Integer code

value

Character description

Source

D_RID.TXT

Dictionary for ACLCode variable of RIE1 file

Description

Dictionary for ACLCode variable of RIE1 file

Usage

RIE1__ACLCode

Format

RIE1__ACLCode

A data frame with 7 rows and 2 columns:

code

Integer code

value

Character description

Source

D_RIE1.TXT

Compare content of a specific file between two folders

Description

compare_files_content() reads the content of a specified file from two folders and compares the content using the waldo::compare function. It identifies any differences in the content and returns the comparison results.

Usage

compare_files_content(filename, dir1, dir2)

Arguments

Details

compare_files_content() reads the content of the specified file from both folders using readLines(), and compares the content using the waldo::compare() function.

Value

A list containing information about differences in the content of the specified file.

Compare FCA Call Report metadata files between two folders

Description

compare_metadata() compares the content of the metadata files (files that start with "D_") between two specified folders containing FCA Call Report data (from two different quarters).

Usage

compare_metadata(dir1, dir2)

Arguments

Details

compare_metadata() lists metadata files in each folder, identifies shared metadata files, and then compares (a) the number of files, (b) file names, (c) file order, and (d) file content (using the waldo::compare() function).

Value

A list containing information about differences in file names, file order, and content differences between the metadata files in dir1 and dir2

Examples

  # Download data from September 2025
  path_1 <- tempfile("fcadata1")
  dir.create(path_1)

  download_data(
    year = 2025,
    month = 9,
    dest = path_1
  )

  # Download data from September 2011
  path_2 <- tempfile("fcadata2")
  dir.create(path_2)

  download_data(
    year = 2011,
    month = 9,
    dest = path_2
  )

  compare_metadata(path_1, path_2)

Download data from FCA website

Description

Download data from FCA website

Usage

download_data(year, month, dest, files = NULL, quiet = FALSE)

Arguments

Details

FCA publishes Call Report data quarterly. These .zip files are typically named "YYYYMarch.zip", "YYYYJune.zip", "YYYYSeptember.zip" and "YYYYDecember.zip" (where YYYY represents the 4-digit year). Therefore, valid values to the month argument should be limited to c(3, 6, 9, 12), unless there is an anomaly in FCA's reporting/publishing. Check https://www.fca.gov/bank-oversight/call-report-data-for-download to ensure the data is available for the quarter you are interested in.

Value

Console message informing the user where the data was successfully downloaded (and unzipped) into

Examples

  path_1 <- tempfile("fcadata1")
  dir.create(path_1)

  download_data(
    year = 2025,
    month = "September",   # using the name of the month
    dest = path_1
  )

  list.files(path_1)

  path_2 <- tempfile("fcadata2")
  dir.create(path_2)

  download_data(
    year = 2025,
    month = 9,   # using the month number (to refer to September)
    dest = path_2,
    # only download the following files
    files = c(
      "D_INST.TXT",
      "INST_Q202509_G20251112.TXT"
    )
  )

  list.files(path_2)

Descriptions for data files

Description

Descriptions for data files

Usage

file_metadata

Format

file_metadata

A data frame with 36 rows and 2 columns:

file_prefix

Data file root name

description

Short description of data file

Source

Metadata files headers

Retrieve dictionary of lookup codes for a specified dataset name

Description

get_codes_dict() searches for an internal .rda file in the specified package and retrieves the codes dictionary based on the provided data name and naming convention. The naming convention is assumed to include the data name followed by a double underscore "__".

Usage

get_codes_dict(data_name)

Arguments

Details

get_codes_dict() uses the provided data name to construct the expected naming convention and searches for an internal .rda file in the specified package. If found, it attempts to retrieve the codes dictionary using get and returns it; otherwise, it returns NULL.

Value

A list with the codes dictionary (codes_dict) and the associated variable name (codes_varname) if found, otherwise each element will be NULL.

Examples

  rcb_dict <- get_codes_dict("RCB")

  # Access codes dictionary
  rcb_dict$codes_dict

  # Access the name of the variable that stores the codes
  rcb_dict$codes_varname

Process FCA Call Report data in a specified folder

Description

process_data() reads the downloaded (and unzipped) .TXT files into tidy data frames, applying the schema from the "D_" files to the corresponding raw comma-separated data files, as well as storing the metadata from the "D_" files

Usage

process_data(dir)

Arguments

Details

process_data() assumes that metadata and data files share a common root name (characters until the first underscore occurrence).

Value

A list containing processed data and metadata.

Examples

  path <- tempfile("fcadata")
  dir.create(path)

  download_data(
    year = 2025,
    month = "September",
    dest = path
  )

  processed_data <- process_data(path)

  # Access "RCB" data
  processed_data$data$RCB

  # Access "RCB" metadata
  processed_data$metadata$RCB

Process a data file using metadata and codes dictionary

Description

process_data_file() reads a data file, applies the provided metadata and codes dictionary, and organizes the data into a tidy format. The column names are determined based on the metadata scenario (e.g., "single", "single_multiple", "single_multiple_single").

Usage

process_data_file(file, metadata, dict = NULL)

Arguments

Details

process_data_file() processes the data file according to the metadata scenario. It handles cases where variables have multiple occurrences and organizes the data into a tidy format with appropriate column names. The function relies on the read_data_file function for the actual data reading.

Value

A tibble containing the processed data in a tidy format

Examples

  path <- tempfile("fcadata")
  dir.create(path)

  download_data(
    year = 2025,
    month = "September",
    dest = path
  )

  process_data_file(
    file = file.path(path, "RCB_Q202509_G20251112.TXT"),
    metadata = process_metadata_file(file.path(path, "D_RCB.TXT")),
    dict = RCB__INV_CODE
  )

Process metadata file to extract variable information

Description

process_metadata_file() reads a metadata file and extracts information about the column names, column types, decimal positions, and variable definitions.

Usage

process_metadata_file(file)

Arguments

Details

process_metadata_file() processes metadata files following specific rules to handle encoding, remove unnecessary information, and extract variable details. It detects the scenario based on the occurrence of double asterisks in variable names.

Value

A list containing the scenario (e.g., "single", "single_multiple", "single_multiple_single") and a tibble with variable information.

Examples

  path <- tempfile("fcadata")
  dir.create(path)

  download_data(
    year = 2025,
    month = "September",
    dest = path
  )

  process_metadata_file(file.path(path, "D_RC1.TXT"))

Read a data file based on metadata and codes dictionary

Description

read_data_file() reads a data file and processes it based on the provided metadata and codes dictionary. The processing depends on the metadata scenario, which includes cases like "single", "single_multiple", and "single_multiple_single". For certain scenarios, the function utilizes read.csv to infer column types without explicit specification.

Usage

read_data_file(file, metadata, dict)

Arguments

Details

read_data_file() reads the data file and applies necessary processing based on the metadata scenario. For scenarios like "single" and "single_multiple", it uses read.csv for convenient type inference. For "single_multiple_single", it reads the file line by line, collapses every (N_CODES + 2) lines, and then reads the collapsed lines using read.table.

Value

A tibble containing the processed data.