This vignette is intended to introduce the user to fredr functions for the Tags endpoint of the FRED API.
FRED series are assigned tags as an attribute for classification. Each FRED tag is identified by a string ID. For example:
The following examples illustrate usage of the Tags endpoint functions in fredr.
The function fredr_tags() returns a list of tags matching the request. The data returned is a tibble in which each row represents a FRED tag. For example, running fredr_tags() without any parameters returns the top 1000 FRED tags ordered by number of series who are assigned the tag (but here we limit to just 10):
fredr_tags(limit = 10)
#> # A tibble: 10 × 6
#> name group_id notes created popularity series_count
#> <chr> <chr> <chr> <chr> <int> <int>
#> 1 nsa seas "Not… 2012-0… 99 726692
#> 2 usa geo "Uni… 2012-0… 100 655988
#> 3 public domain: citation reque… cc NA 2018-1… 99 609670
#> 4 annual freq "" 2012-0… 88 490336
#> 5 county geot "Cou… 2012-0… 76 330784
#> 6 nation geot "" 2012-0… 99 262252
#> 7 census src "Cen… 2012-0… 79 237162
#> 8 monthly freq "" 2012-0… 94 214574
#> 9 copyrighted: citation required cc NA 2018-1… 87 206936
#> 10 bls src "Bur… 2012-0… 88 175632To return specific tags by tag name, specify multiple tags in a single string by delimiting with a semicolon:
fredr_tags(tag_names = "gdp;oecd", limit = 10)
#> # A tibble: 2 × 6
#> name group_id notes created popularity series_count
#> <chr> <chr> <chr> <chr> <int> <int>
#> 1 gdp gen Gross Domestic Product 2012-0… 81 74166
#> 2 oecd src Org. for Economic Co-operation… 2012-0… 75 63930Return tags for a given group ID:
fredr_tags(
tag_group_id = "geo",
limit = 50L
)
#> # A tibble: 50 × 6
#> name group_id notes created popularity series_count
#> <chr> <chr> <chr> <chr> <int> <int>
#> 1 usa geo United States of America 2012-02-27 1… 100 655592
#> 2 tx geo Texas 2012-02-27 1… 59 35424
#> 3 ga geo Georgia (U.S. state) 2012-02-27 1… 51 20796
#> 4 ca geo California 2012-02-27 1… 62 18054
#> 5 nc geo North Carolina 2012-02-27 1… 52 17440
#> 6 va geo Virginia 2012-02-27 1… 50 17420
#> 7 il geo Illinois 2012-02-27 1… 50 16426
#> 8 oh geo Ohio 2012-02-27 1… 50 16236
#> 9 ky geo Kentucky 2012-02-27 1… 45 15680
#> 10 in geo Indiana 2012-02-27 1… 49 15334
#> # ℹ 40 more rowsSearch for tags by text:
fredr_tags(search_text = "unemployment")
#> # A tibble: 2 × 6
#> name group_id notes created popularity series_count
#> <chr> <chr> <chr> <chr> <int> <int>
#> 1 unemployment gen "" 2012-0… 72 32582
#> 2 nairu gen "Non-Accelerating Infla… 2012-0… 18 2Note that the example above searches for tags matching "unemployment". To search for the set of series with tags matching "unemployment", use fredr_series_search_tags():
fredr_series_search_tags(
series_search_text = "unemployment",
limit = 100L
)
#> # A tibble: 100 × 6
#> name group_id notes created popularity series_count
#> <chr> <chr> <chr> <chr> <int> <int>
#> 1 nsa seas "Not… 2012-0… 99 44396
#> 2 usa geo "Uni… 2012-0… 100 41728
#> 3 public domain: citation reque… cc NA 2018-1… 99 41412
#> 4 bls src "Bur… 2012-0… 88 37482
#> 5 unemployment gen "" 2012-0… 72 32748
#> 6 county geot "Cou… 2012-0… 76 28198
#> 7 annual freq "" 2012-0… 88 24022
#> 8 household survey gen "Cur… 2012-0… 62 19848
#> 9 monthly freq "" 2012-0… 94 18814
#> 10 rate gen "" 2012-0… 85 14938
#> # ℹ 90 more rowsThe function fredr_related_tags() returns a list of tags related to the set of tags matching the request. The data returned is a tibble in which each row represents a tag related to the tags specified in tag_names. For example, to get the set of tags related to the tags "monetary aggregates" and "weekly":
fredr_related_tags(tag_names = "monetary aggregates;weekly")
#> # A tibble: 16 × 6
#> name group_id notes created popularity series_count
#> <chr> <chr> <chr> <chr> <int> <int>
#> 1 nation geot "" 2012-0… 99 14
#> 2 usa geo "Uni… 2012-0… 100 14
#> 3 frb src "Boa… 2012-0… 81 12
#> 4 h6 rls "H.6… 2012-0… 50 12
#> 5 nsa seas "Not… 2012-0… 99 12
#> 6 public domain: citation reque… cc NA 2018-1… 99 12
#> 7 discontinued gen "" 2012-0… 66 10
#> 8 m3 gen "M3 … 2012-0… 40 6
#> 9 m1 gen "M1 … 2012-0… 46 4
#> 10 copyrighted: citation required cc NA 2018-1… 87 2
#> 11 currency gen "" 2012-0… 61 2
#> 12 frb stl src "St.… 2012-0… 68 2
#> 13 funds gen NA 2020-0… 30 2
#> 14 m2 gen "M2 … 2012-0… 44 2
#> 15 mzm gen "MZM… 2012-0… 24 2
#> 16 sa seas "Sea… 2012-0… 89 2To filter these results to tags belonging to the “General” tag group:
fredr_related_tags(
tag_names = "monetary aggregates;weekly",
tag_group_id = "gen"
)
#> # A tibble: 6 × 6
#> name group_id notes created popularity series_count
#> <chr> <chr> <chr> <chr> <int> <int>
#> 1 discontinued gen "" 2012-02-27 10… 66 14
#> 2 m3 gen "M3 Money Stock" 2012-02-27 10… 40 6
#> 3 m1 gen "M1 Money Stock" 2012-02-27 10… 46 4
#> 4 m2 gen "M2 Money Stock" 2012-02-27 10… 44 4
#> 5 mzm gen "MZM Money Stock" 2012-02-27 10… 24 4
#> 6 funds gen NA 2020-05-11 13… 30 2To filter these results even further, keep only the tags matching the string "money stock":
fredr_related_tags(
tag_names = "monetary aggregates;weekly",
tag_group_id = "gen",
search_text = "money stock"
)
#> # A tibble: 3 × 6
#> name group_id notes created popularity series_count
#> <chr> <chr> <chr> <chr> <int> <int>
#> 1 m3 gen M3 Money Stock 2012-02-27 10:18:19-06 40 12
#> 2 m1 gen M1 Money Stock 2012-02-27 10:18:19-06 46 6
#> 3 m2 gen M2 Money Stock 2012-02-27 10:18:19-06 44 4The function fredr_tags_series() returns a list of series assigned tags matching the request. As with the functions for the Series endpoint, the data returned is a tibble in which each row represents a series. For example, to get all series tagged with "gdp":
fredr_tags_series(tag_names = "gdp")
#> # A tibble: 1,000 × 16
#> id realtime_start realtime_end title observation_start observation_end
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 A001RD3A… 2023-04-17 2023-04-17 Gros… 1929-01-01 2022-01-01
#> 2 A001RG3A… 2023-04-17 2023-04-17 Gros… 1929-01-01 2022-01-01
#> 3 A001RI1A… 2023-04-17 2023-04-17 Gros… 1930-01-01 2022-01-01
#> 4 A001RI1Q… 2023-04-17 2023-04-17 Gros… 1947-04-01 2022-10-01
#> 5 A001RL1A… 2023-04-17 2023-04-17 Real… 1930-01-01 2022-01-01
#> 6 A001RL1Q… 2023-04-17 2023-04-17 Real… 1947-04-01 2022-10-01
#> 7 A001RO1Q… 2023-04-17 2023-04-17 Real… 1948-01-01 2022-10-01
#> 8 A001RP1A… 2023-04-17 2023-04-17 Gros… 1930-01-01 2022-01-01
#> 9 A001RP1Q… 2023-04-17 2023-04-17 Gros… 1947-04-01 2022-10-01
#> 10 A001RV1A… 2023-04-17 2023-04-17 Gros… 1930-01-01 2022-01-01
#> # ℹ 990 more rows
#> # ℹ 10 more variables: frequency <chr>, frequency_short <chr>, units <chr>,
#> # units_short <chr>, seasonal_adjustment <chr>,
#> # seasonal_adjustment_short <chr>, last_updated <chr>, popularity <int>,
#> # group_popularity <int>, notes <chr>To get the top 100 most popular non-quarterly series tagged with "gdp":
fredr_tags_series(
tag_names = "gdp",
exclude_tag_names = "quarterly",
order_by = "popularity",
limit = 100L
)
#> # A tibble: 100 × 16
#> id realtime_start realtime_end title observation_start observation_end
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 RKMTACCF… 2023-04-17 2023-04-17 Chai… 1997-01-01 2022-01-01
#> 2 VAACCFDQ… 2023-04-17 2023-04-17 Chai… 1997-01-01 2022-01-01
#> 3 AZADMINW… 2023-04-17 2023-04-17 Chai… 1997-01-01 2022-01-01
#> 4 TNADMINW… 2023-04-17 2023-04-17 Chai… 1997-01-01 2022-01-01
#> 5 WVAGRQGSP 2023-04-17 2023-04-17 Chai… 1997-01-01 2022-01-01
#> 6 NVARTENT… 2023-04-17 2023-04-17 Chai… 1997-01-01 2022-01-01
#> 7 GLAKARTE… 2023-04-17 2023-04-17 Chai… 1997-01-01 2022-01-01
#> 8 IDCONSTQ… 2023-04-17 2023-04-17 Chai… 1997-01-01 2022-01-01
#> 9 IACONSTQ… 2023-04-17 2023-04-17 Chai… 1997-01-01 2022-01-01
#> 10 ORCONSTQ… 2023-04-17 2023-04-17 Chai… 1997-01-01 2022-01-01
#> # ℹ 90 more rows
#> # ℹ 10 more variables: frequency <chr>, frequency_short <chr>, units <chr>,
#> # units_short <chr>, seasonal_adjustment <chr>,
#> # seasonal_adjustment_short <chr>, last_updated <chr>, popularity <int>,
#> # group_popularity <int>, notes <chr>