Package {fred}

fred: Access 'Federal Reserve Economic Data'

Description

Provides clean, tidy access to economic data from the 'Federal Reserve Economic Data' ('FRED') API https://fred.stlouisfed.org/docs/api/fred/. 'FRED' is maintained by the 'Federal Reserve Bank of St. Louis' and contains over 800,000 time series from 118 sources covering GDP, employment, inflation, interest rates, trade, and more. Dedicated functions fetch series observations, search for series, browse categories, releases, and tags, and retrieve series metadata. Multiple series can be fetched in a single call, in long or wide format. Server-side unit transformations (percent change, log, etc.) and frequency aggregation are supported, with readable transform aliases such as 'yoy_pct' and 'log_diff'. Real-time and vintage helpers (built on 'ALFRED') return a series as it appeared on a given date, the first-release version, every revision, or a panel of selected vintages. An offline curated catalogue of around fifty popular series, NBER recession reference dates, and FOMC meeting dates support discoverability and event-study workflows. Default 'plot' method shades NBER recession periods. Reproducibility helpers produce BibTeX or plain-text citations and YAML manifests with per-object hashes. Data is cached locally for subsequent calls. This product uses the 'FRED' API but is not endorsed or certified by the 'Federal Reserve Bank of St. Louis'.

Author(s)

Maintainer: Charles Coverdale charlesfcoverdale@gmail.com

See Also

Useful links:

• https://charlescoverdale.github.io/fred/

• https://github.com/charlescoverdale/fred

• Report bugs at https://github.com/charlescoverdale/fred/issues

Subset method for fred_tbl

Description

Preserves the fred_tbl class and fred_query attribute when subsetting rows or columns. Falls back to a plain vector when drop = TRUE reduces the result to a single column (matching data.frame behaviour).

Usage

## S3 method for class 'fred_tbl'
x[i, j, ..., drop = TRUE]

Arguments

Value

A fred_tbl (or a vector if drop collapses the result).

Clear the fred cache

Description

Deletes all locally cached FRED data files. The next call to any data function will re-download from the FRED API.

Usage

clear_cache()

Value

Invisible NULL.

See Also

Other configuration: fred_cache_info(), fred_get_key(), fred_request(), fred_set_key()

Examples

op <- options(fred.cache_dir = tempdir())
clear_cache()
options(op)

Aggregate FRED observations to a coarser frequency

Description

Aggregates a long-format fred_tbl (with date, series_id, value) or a wide-format fred_tbl (date plus one column per series) to a coarser calendar frequency. For long format, aggregation is performed per series_id; for wide format, per numeric column.

Usage

fred_aggregate(data, fun = "mean", by = "month")

Arguments

Details

Use this when you have, say, daily Treasury yields and need a monthly average, or weekly initial claims aggregated to monthly totals. For server-side aggregation that mirrors FRED's own interpolation conventions, pass frequency = "m" to fred_series() instead.

Value

A fred_tbl with the same columns as the input, with date collapsed to period start.

See Also

Other utilities: fred_event_window(), fred_interpolate()

Examples

# Synthetic example: aggregate daily synthetic data to monthly means
d <- seq(as.Date("2024-01-01"), as.Date("2024-06-30"), by = "day")
daily <- data.frame(date = d, series_id = "X", value = rnorm(length(d)))
fred_aggregate(daily, fun = "mean", by = "month")

# Wide-format input also works
wide <- data.frame(date = d, A = rnorm(length(d)), B = rnorm(length(d)))
fred_aggregate(wide, fun = "sum", by = "quarter")


op <- options(fred.cache_dir = tempdir())
try({
  ## Not run: 
  daily_yields <- fred_series("DGS10", from = "2023-01-01")
  monthly_yields <- fred_aggregate(daily_yields, fun = "mean", by = "month")
  
## End(Not run)
})
options(op)

Fetch every vintage of a series

Description

Returns the full revision history: one row per (observation date, realtime range) combination. This is the FRED API's output_type = 2 mode. The result can be reshaped into a vintage matrix or used to compute revision statistics.

Usage

fred_all_vintages(
  series_id,
  from = NULL,
  to = NULL,
  units = "lin",
  frequency = NULL,
  aggregation = "avg",
  cache = TRUE
)

Arguments

Details

Be aware that some series have hundreds of thousands of vintage rows, so consider narrowing the date range with from/to for long-running indicators like GDP.

Value

A fred_tbl with columns date, series_id, value, realtime_start, realtime_end. The realtime_start and realtime_end columns identify the vintage window for each row.

See Also

Other vintages: fred_as_of(), fred_first_release(), fred_real_time_panel()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  # All vintages of recent GDP releases
  gdp_vint <- fred_all_vintages("GDP", from = "2020-01-01")
})
options(op)

Fetch a series as it appeared on a given vintage date

Description

Returns the values that were available in FRED on date, before any subsequent revisions. This is the standard real-time data access pattern: set realtime_start = realtime_end = date. Useful for backtesting forecasting models against the data that was actually available at the time, not the revised series we see today.

Usage

fred_as_of(
  series_id,
  date,
  from = NULL,
  to = NULL,
  units = "lin",
  frequency = NULL,
  aggregation = "avg",
  cache = TRUE
)

Arguments

Details

Underneath, this calls the series/observations endpoint with the realtime parameters set. Results are cached separately from the default (latest-vintage) cache, so calling fred_series("GDP") and fred_as_of("GDP", "2020-01-15") keep distinct cache entries.

Value

A fred_tbl with columns date, series_id, value, realtime_start, realtime_end.

See Also

Other vintages: fred_all_vintages(), fred_first_release(), fred_real_time_panel()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  # GDP as it looked on 1 March 2020
  gdp_2020 <- fred_as_of("GDP", "2020-03-01")
})
options(op)

Browse the FRED category tree

Description

Pretty-prints the FRED category tree. With no arguments, shows the eight top-level FRED categories from a built-in static reference (no API call). Pass a category_id to drill into its children, which fetches from the API (and is cached). Use this to discover where series live before searching inside a category with fred_category_series().

Usage

fred_browse(category_id = 0L, depth = 1L)

Arguments

Value

A fred_tbl of categories at the requested level (invisibly).

See Also

Other catalogue: fred_catalogue()

Examples

# Top-level categories (no API call)
fred_browse()

op <- options(fred.cache_dir = tempdir())
try({
  # Drill into "National Accounts" (id 32992)
  fred_browse(32992)
})
options(op)

Inspect the local fred cache

Description

Returns information about the local cache: where it lives, how many files it contains, and how much disk space they take. Useful when debugging stale results or deciding whether to call clear_cache().

Usage

fred_cache_info()

Value

A list with elements dir, n_files, size_bytes, size_human, and files (a data frame with name, size_bytes, and modified columns). Returns the same shape with zero counts if the cache directory does not yet exist.

See Also

Other configuration: clear_cache(), fred_get_key(), fred_request(), fred_set_key()

Examples

op <- options(fred.cache_dir = tempdir())
fred_cache_info()
options(op)

Browse a curated catalogue of popular FRED series

Description

Returns a data frame of around 50 of the most widely used FRED series organised by economic category. The catalogue is curated and embedded in the package: no API call is made. Use it as a starting point for common workflows or as a quick reference when you cannot remember a series ID.

Usage

fred_catalogue(category = NULL, query = NULL)

Arguments

Details

Filter by category, by free-text query, or both. Free-text matches against the series ID, title, and description (case-insensitive substring match).

Value

A fred_tbl (a data.frame subclass) with columns id, title, frequency, units, category, description.

See Also

Other catalogue: fred_browse()

Examples

# Full catalogue
fred_catalogue()

# Inflation series only
fred_catalogue(category = "Inflation")

# Free-text search
fred_catalogue(query = "mortgage")

# Combined
fred_catalogue(category = "Interest Rates", query = "treasury")

Get a FRED category

Description

Returns information about a single category. The FRED category tree starts at category 0 (the root) and branches into 8 top-level categories: Money, Banking & Finance; Population, Employment & Labor Markets; National Accounts; Production & Business Activity; Prices; International Data; U.S. Regional Data; and Academic Data.

Usage

fred_category(category_id = 0L)

Arguments

Value

A data frame with category metadata.

See Also

Other categories: fred_category_children(), fred_category_series()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  # Root category
  fred_category()

  # National Accounts (category 32992)
  fred_category(32992)
})
options(op)

List child categories

Description

Returns the child categories for a given parent category.

Usage

fred_category_children(category_id = 0L)

Arguments

Value

A data frame of child categories.

See Also

Other categories: fred_category(), fred_category_series()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  # Top-level categories
  fred_category_children()
})
options(op)

List series in a category

Description

Returns all series belonging to a given category. Automatically paginates through all results.

Usage

fred_category_series(category_id, limit = 1000L)

Arguments

Value

A data frame of series metadata.

See Also

Other categories: fred_category(), fred_category_children()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  fred_category_series(32992)
})
options(op)

Generate a citation for a FRED series

Description

Produces a citation string for a single FRED series in BibTeX, plain text, or bibentry form. Works offline by default: the citation falls back to the series ID as the title. Pass fetch_metadata = TRUE to call fred_info() and use the official series title (requires an API key).

Usage

fred_cite_series(
  series_id,
  vintage_date = NULL,
  format = c("bibtex", "text", "bibentry"),
  fetch_metadata = FALSE
)

Arguments

Details

Cite a specific vintage by passing vintage_date. This is essential for reproducible research: a 2023 GDP figure published in October 2023 may differ materially from the same observation as it appears today.

Value

A character string ("bibtex", "text") or a bibentry object.

See Also

Other reproducibility: fred_manifest(), fred_vintage_revisions()

Examples

# BibTeX without an API call
fred_cite_series("GDPC1")

# Plain-text citation pinned to a vintage date
fred_cite_series("UNRATE", vintage_date = "2024-06-01", format = "text")

## Not run: 
  # Use the official title (requires an API key)
  fred_cite_series("GDPC1", fetch_metadata = TRUE)

## End(Not run)

Extract data windows around event dates

Description

Given a fred_tbl (or any data frame with a date column) and a vector of event dates, returns one row per (event, observation) pair where the observation falls inside the requested window. Useful for event studies around FOMC decisions, recession peaks, or release dates.

Usage

fred_event_window(data, events, window = c(-30L, 90L))

Arguments

Details

Long-format input with series_id/value is supported, as is wide-format input from fred_series(..., format = "wide"). The window is in calendar days; for monthly or quarterly data, choose a window large enough to capture at least one observation.

Value

A fred_tbl with the original columns plus event_date and days_from_event.

See Also

Other utilities: fred_aggregate(), fred_interpolate()

Examples

# Synthetic example — works offline
d <- seq(as.Date("2024-01-01"), as.Date("2024-12-31"), by = "month")
df <- data.frame(date = d, value = seq_along(d))
events <- as.Date(c("2024-03-15", "2024-09-15"))
fred_event_window(df, events = events, window = c(-30L, 60L))


op <- options(fred.cache_dir = tempdir())
try({
  # With live FRED data: UNRATE around 2024 SEP meetings (needs API key)
  ## Not run: 
  ur <- fred_series("UNRATE", from = "2023-01-01")
  sep <- fred_fomc_dates(year = 2024, sep_only = TRUE)
  fred_event_window(ur, events = sep$date, window = c(-60L, 60L))
  
## End(Not run)
})
options(op)

Fetch the first-release ("real-time") version of a series

Description

Returns only the value that was published when each observation first appeared in FRED, with no subsequent revisions. Internally this fetches the full revision history and keeps the earliest realtime_start row for each observation date. Useful when you want a clean comparison between what policymakers saw at the time versus what the data look like after revisions.

Usage

fred_first_release(
  series_id,
  from = NULL,
  to = NULL,
  units = "lin",
  frequency = NULL,
  aggregation = "avg",
  cache = TRUE
)

Arguments

Value

A fred_tbl with columns date, series_id, value, realtime_start, realtime_end.

See Also

Other vintages: fred_all_vintages(), fred_as_of(), fred_real_time_panel()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  # Initial-release GDP, never revised
  gdp_first <- fred_first_release("GDP", from = "2018-01-01")
})
options(op)

FOMC scheduled meeting dates and decisions

Description

Returns scheduled regular FOMC meeting decision dates from 2017 to 2026, plus selected unscheduled meetings during stress periods (e.g. March 2020). Each row carries the decision date, meeting type, and a flag for whether a Summary of Economic Projections (SEP) was released. Useful as a left table for event-study windows around monetary-policy decisions.

Usage

fred_fomc_dates(from = NULL, to = NULL, year = NULL, sep_only = FALSE)

Arguments

Details

Source: Federal Reserve Board press release calendars (https://www.federalreserve.gov/monetarypolicy/fomccalendars.htm). Curated and embedded; not auto-synced.

Value

A fred_tbl with columns date, type, sep.

See Also

Other dates: fred_recession_dates()

Examples

# All meetings since 2017
fred_fomc_dates()

# 2022 hiking cycle
fred_fomc_dates(year = 2022)

# SEP meetings only (Mar/Jun/Sep/Dec)
fred_fomc_dates(year = 2024, sep_only = TRUE)

Get the current FRED API key

Description

Returns the API key set via fred_set_key() or the FRED_API_KEY environment variable. Raises an error if no key is found.

Usage

fred_get_key()

Value

Character. The API key.

See Also

Other configuration: clear_cache(), fred_cache_info(), fred_request(), fred_set_key()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  fred_get_key()
})
options(op)

Get metadata for a FRED series

Description

Returns descriptive information about a series, including its title, units, frequency, seasonal adjustment, and notes.

Usage

fred_info(series_id)

Arguments

Value

A data frame with one row containing series metadata.

See Also

Other series: fred_search(), fred_series(), fred_updates(), fred_vintages()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  fred_info("GDP")
})
options(op)

Fill missing values in a FRED series

Description

Fills NA values in the value column (long format) or in numeric columns (wide format). Two methods are supported: last-observation-carry-forward ("locf") and linear interpolation between adjacent observed values ("linear"). Use this for mixed-frequency analysis where a low-frequency series needs to be interpolated to a higher frequency.

Usage

fred_interpolate(data, method = c("locf", "linear"))

Arguments

Details

Boundary behaviour: with method = "locf", leading NAs remain NA because there is no prior observation to carry forward. With method = "linear", neither leading nor trailing NAs are filled because stats::approx() is called with rule = 1 (no extrapolation). If you need extrapolation, post-process the result.

Value

A fred_tbl with interior NAs filled (see boundary note above).

See Also

Other utilities: fred_aggregate(), fred_event_window()

Examples

# Synthetic example: fill interior NAs
d <- seq(as.Date("2024-01-01"), by = "month", length.out = 6L)
df <- data.frame(date = d, series_id = "X",
                 value = c(NA, 2, NA, NA, 5, NA))
fred_interpolate(df, method = "locf")
fred_interpolate(df, method = "linear")


op <- options(fred.cache_dir = tempdir())
try({
  ## Not run: 
  gdp <- fred_series("GDPC1", from = "2020-01-01")
  gdp_monthly <- fred_interpolate(gdp, method = "linear")
  
## End(Not run)
})
options(op)

Snapshot a session's FRED downloads as a YAML manifest

Description

Produces a YAML manifest describing one or more fred_tbl objects, with query metadata, dimensions, date ranges, and an MD5 hash of each object. Intended to be saved alongside paper code for reproducibility checks: if the manifest still hashes to the same values, the data underlying the analysis has not changed.

Usage

fred_manifest(..., file = NULL)

Arguments

Details

Pass fred_tbl objects positionally, named, or as a list. If passed a bare list, names from the list are used; otherwise objects are labelled obj_1, obj_2, ...

Value

A fred_manifest object (a character string with an attached print method). If file is supplied, written to disk and returned invisibly.

See Also

Other reproducibility: fred_cite_series(), fred_vintage_revisions()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  ## Not run: 
  gdp <- fred_series("GDPC1", from = "2020-01-01")
  un  <- fred_series("UNRATE", from = "2020-01-01")
  m <- fred_manifest(gdp = gdp, unrate = un)
  print(m)
  fred_manifest(gdp = gdp, file = file.path(tempdir(), "manifest.yml"))
  
## End(Not run)
})
options(op)

Fetch a real-time panel of a series across selected vintages

Description

Returns the values that were available on each of a chosen set of vintage dates. This is the FRED API's vintage_dates parameter: instead of asking for every revision (potentially huge), you ask for only the snapshots you care about, e.g. quarterly vintages aligned to GDP release dates.

Usage

fred_real_time_panel(
  series_id,
  vintages,
  from = NULL,
  to = NULL,
  units = "lin",
  frequency = NULL,
  aggregation = "avg",
  cache = TRUE
)

Arguments

Value

A fred_tbl with columns date, series_id, value, realtime_start, realtime_end.

See Also

Other vintages: fred_all_vintages(), fred_as_of(), fred_first_release()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  # GDP as published at three quarterly snapshots
  gdp_panel <- fred_real_time_panel(
  "GDP",
  vintages = c("2023-04-30", "2023-07-31", "2023-10-31")
  )
})
options(op)

NBER US business-cycle reference dates

Description

Returns the NBER Business Cycle Dating Committee's reference dates for US business-cycle peaks and troughs since 1857. With flag = NULL (default), returns one row per recession (peak, trough, duration). Pass a vector of observation dates as flag to get back a data frame indicating whether each observation falls within a recession.

Usage

fred_recession_dates(from = NULL, to = NULL, flag = NULL)

Arguments

Details

Source: NBER Business Cycle Dating Committee (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions). Dates are ISO month-start (peak = first month of recession, trough = last month of recession).

Value

A fred_tbl. Default columns: peak, trough, duration_months. With flag: date, in_recession.

See Also

Other dates: fred_fomc_dates()

Examples

# All recessions since 1857
fred_recession_dates()

# Modern era only
fred_recession_dates(from = "1948-01-01")

# Flag a vector of dates
fred_recession_dates(flag = seq(as.Date("2007-01-01"), as.Date("2010-12-01"),
                                by = "month"))

Find tags related to a given tag

Description

Returns tags that are frequently used together with the specified tag.

Usage

fred_related_tags(tag_names)

Arguments

Value

A data frame of related tags.

See Also

Other tags: fred_tags()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  fred_related_tags("gdp")
})
options(op)

Get release dates

Description

Returns the dates on which data for a release were published. Useful for understanding the data calendar and when revisions occurred.

Usage

fred_release_dates(release_id)

Arguments

Value

A data frame with columns release_id and date.

See Also

Other releases: fred_release_series(), fred_releases()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  fred_release_dates(53)
})
options(op)

List series in a release

Description

Returns all series belonging to a given release.

Usage

fred_release_series(release_id)

Arguments

Value

A data frame of series metadata.

See Also

Other releases: fred_release_dates(), fred_releases()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  # G.19 Consumer Credit release
  fred_release_series(14)
})
options(op)

List all FRED releases

Description

Returns all data releases available on FRED. A release is a collection of related series published together (e.g. "Employment Situation", "GDP").

Usage

fred_releases()

Value

A data frame of releases with columns including id, name, press_release, and link.

See Also

Other releases: fred_release_dates(), fred_release_series()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  fred_releases()
})
options(op)

Make a raw request to the FRED API

Description

Low-level function that sends a request to any FRED API endpoint and returns the parsed JSON as a list. Most users should use the higher-level functions such as fred_series() or fred_search().

Usage

fred_request(endpoint, ...)

Arguments

Value

A list parsed from the JSON response.

See Also

Other configuration: clear_cache(), fred_cache_info(), fred_get_key(), fred_set_key()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  fred_request("series", series_id = "GDP")
})
options(op)

Search for FRED series

Description

Searches the FRED database by keywords or series ID substring. Returns matching series with their metadata, ordered by relevance.

Usage

fred_search(
  query,
  type = "full_text",
  limit = 100L,
  order_by = "search_rank",
  filter_variable = NULL,
  filter_value = NULL,
  tag_names = NULL
)

Arguments

Value

A data frame of matching series with columns including id, title, frequency, units, seasonal_adjustment, last_updated, popularity, and notes.

See Also

Other series: fred_info(), fred_series(), fred_updates(), fred_vintages()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  # Keyword search
  fred_search("unemployment rate")

  # Filter to monthly series only
  fred_search("consumer price index", filter_variable = "frequency",
  filter_value = "Monthly")

  # Search by series ID pattern
  fred_search("GDP*", type = "series_id")
})
options(op)

Fetch observations for one or more FRED series

Description

The main function in the package. Downloads time series observations from FRED and returns a tidy data frame. Multiple series can be fetched in a single call, in either long or wide format.

Usage

fred_series(
  series_id,
  from = NULL,
  to = NULL,
  units = "lin",
  transform = NULL,
  frequency = NULL,
  aggregation = "avg",
  format = c("long", "wide"),
  cache = TRUE
)

Arguments

Details

FRED supports server-side unit transformations via the units argument. This avoids the need to compute growth rates or log transforms locally. For readability you can pass transform instead of units:

• "level", "raw" -levels (the default)

• "diff", "change" -change from previous period

• "yoy_diff" -change from one year ago

• "qoq_pct", "mom_pct", "pop_pct" -percent change from previous period

• "yoy_pct" -percent change from one year ago

• "annualised", "qoq_annualised" -compounded annual rate of change

• "log" -natural log

• "log_diff" -continuously compounded rate of change

• "log_diff_annualised" -continuously compounded annual rate

Raw FRED units codes ("lin", "chg", "ch1", "pch", "pc1", "pca", "cch", "cca", "log") are also accepted.

Value

A fred_tbl (a data.frame subclass that prints with a one-line provenance header). In long format, columns are date, series_id, value. In wide format, columns are date plus one numeric column per series.

See Also

Other series: fred_info(), fred_search(), fred_updates(), fred_vintages()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  # Single series
  gdp <- fred_series("GDP")

  # Multiple series, long format
  macro <- fred_series(c("GDP", "UNRATE", "CPIAUCSL"))

  # Multiple series, wide format
  macro_w <- fred_series(c("GDP", "UNRATE"), format = "wide")

  # Readable transformation: year-on-year percent change
  gdp_growth <- fred_series("GDP", transform = "yoy_pct")

  # Aggregate daily to monthly
  rates <- fred_series("DGS10", frequency = "m")
})
options(op)

Set the FRED API key

Description

Sets the API key used to authenticate requests to the FRED API. The key persists for the current R session. Alternatively, set the FRED_API_KEY environment variable in your .Renviron file.

Usage

fred_set_key(key)

Arguments

Details

Register for a free API key at https://fredaccount.stlouisfed.org/apikeys.

Value

Invisible NULL.

See Also

Other configuration: clear_cache(), fred_cache_info(), fred_get_key(), fred_request()

Examples

## Not run: 
fred_set_key("your_api_key_here")

## End(Not run)

List releases from a source

Description

Returns all releases published by a given data source.

Usage

fred_source_releases(source_id)

Arguments

Value

A data frame of releases.

See Also

Other sources: fred_sources()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  # Bureau of Labor Statistics
  fred_source_releases(22)
})
options(op)

List all FRED data sources

Description

Returns all data sources that contribute series to FRED. Sources include the Bureau of Labor Statistics, Bureau of Economic Analysis, Federal Reserve Board, U.S. Census Bureau, and over 100 others.

Usage

fred_sources()

Value

A data frame of sources with columns including id, name, and link.

See Also

Other sources: fred_source_releases()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  fred_sources()
})
options(op)

List or search FRED tags

Description

Returns FRED tags, optionally filtered by a search query. Tags are keywords used to categorise series (e.g. "gdp", "monthly", "usa", "seasonally adjusted").

Usage

fred_tags(query = NULL, limit = 1000L)

Arguments

Value

A data frame of tags with columns including name, group_id, notes, popularity, and series_count.

See Also

Other tags: fred_related_tags()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  fred_tags()
  fred_tags("inflation")
})
options(op)

List recently updated FRED series

Description

Returns series that have been recently updated or revised.

Usage

fred_updates(limit = 100L)

Arguments

Value

A data frame of recently updated series.

See Also

Other series: fred_info(), fred_search(), fred_series(), fred_vintages()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  fred_updates()
})
options(op)

Summarise revision behaviour for a FRED series

Description

For each observation date in a series' vintage history, computes summary statistics on how the value has been revised: number of vintages, first and final value, total revision (final minus first), mean and SD of inter-vintage changes, and elapsed days from first publication to final. Useful for choosing series for real-time analysis (low-revision series are more reliable for nowcasting).

Usage

fred_vintage_revisions(series_id, from = NULL, to = NULL, cache = TRUE)

Arguments

Details

Internally fetches fred_all_vintages(series_id, ...) and reduces. For long-running indicators, narrow the window with from/to to keep the API call manageable.

Value

A fred_tbl with columns series_id, date, n_vintages, first_value, final_value, revision_total, revision_total_pct, revision_mean, revision_sd, days_to_final.

See Also

Other reproducibility: fred_cite_series(), fred_manifest()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  ## Not run: 
  rev <- fred_vintage_revisions("GDPC1", from = "2018-01-01")
  summary(rev$revision_total_pct)
  
## End(Not run)
})
options(op)

Get vintage dates for a FRED series

Description

Returns the dates on which data for a series were revised. This is useful for real-time analysis and understanding data revisions.

Usage

fred_vintages(series_id)

Arguments

Value

A data frame with columns series_id and vintage_date.

See Also

Other series: fred_info(), fred_search(), fred_series(), fred_updates()

Examples

op <- options(fred.cache_dir = tempdir())
try({
  fred_vintages("GDP")
})
options(op)

Plot a fred_tbl

Description

Time-series plot for a fred_tbl. Detects long or wide format, draws one line per series, and optionally shades NBER recession periods. Uses base graphics so no extra dependencies are pulled in.

Usage

## S3 method for class 'fred_tbl'
plot(
  x,
  recessions = TRUE,
  legend = TRUE,
  col = NULL,
  type = "l",
  main = NULL,
  xlab = "",
  ylab = "value",
  ...
)

Arguments

Details

For long-format input (date, series_id, value), one line per series_id. For wide-format input (date plus one numeric column per series), one line per numeric column.

Value

x, invisibly.

Examples

op <- options(fred.cache_dir = tempdir())
try({
  ## Not run: 
  gdp <- fred_series("GDPC1", from = "2000-01-01")
  plot(gdp)

  panel <- fred_series(c("UNRATE", "CIVPART"), from = "2000-01-01",
  format = "wide")
  plot(panel)
  
## End(Not run)
})
options(op)

Print method for fred_manifest

Description

Print method for fred_manifest

Usage

## S3 method for class 'fred_manifest'
print(x, ...)

Arguments

Value

x, invisibly.

Print method for fred_tbl

Description

Adds a one-line provenance header above the data frame body. The header summarises the query: number of series, observation count, transformation in effect, vintage information, or for non-observation queries, the endpoint and result count.

Usage

## S3 method for class 'fred_tbl'
print(x, ...)

Arguments

Value

x, invisibly.

Summary method for fred_tbl

Description

Prints query metadata, dimensions, date range (when present), and value range (when present), then dispatches to the standard summary.data.frame.

Usage

## S3 method for class 'fred_tbl'
summary(object, ...)

Arguments

Value

Invisibly returns the standard data frame summary.