Fred’s API

Data - Fred

Author

Info

source	dataset	Title	.html	.rData
fred	api	NA	NA	NA

LAST_COMPILE

LAST_COMPILE
2025-12-25

Info

API Documentation: https://data.oecd.org/api/sdmx-ml-documentation/
Example 1: https://stats.oecd.org/restsdmx/sdmx.ashx/GetData/GOV_DEBT/FIN+BEL.AMT.A.USD.3.
Example 2: https://stats.oecd.org/restsdmx/sdmx.ashx/GetData/QNA/AUS+AUT.GDP+B1_GE.CUR+VOBARSA.Q/all?startTime=2009-Q2&endTime=2011-Q4

Example 1

Code

"https://api.stlouisfed.org/fred/series?series_id=GNPCA&api_key=abcdefghijklmnopqrstuvwxyz123456"

# [1] "https://api.stlouisfed.org/fred/series?series_id=GNPCA&api_key=abcdefghijklmnopqrstuvwxyz123456"

Find a Data series, or multiple

Code

fredr_series_search_text(search_text = "cpi cbsa rent",
                         order_by = "popularity",
                         sort_order = "desc") %>%
  select(id, title) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

API

(ref:oil) Oil Prices

Code

map_dfr(c("CPIAUCSL", "WTISPLC"), fredr) %>% 
  spread(series_id, value) %>%
  na.omit %>%
  mutate(WTISPLC_real = CPIAUCSL[860]* WTISPLC / CPIAUCSL) %>% 
  select(date, WTISPLC, WTISPLC.real = WTISPLC_real) %>% 
  gather(variable, value, -date) %>%
  mutate(variable_desc = case_when(variable == "WTISPLC" ~ "Oil Prices (WTI)",
                                   variable == "WTISPLC.real" ~ "Real Oil Prices (WTI)")) %>%
  select(variable, variable_desc, everything()) %>%
  ggplot(data = .) + 
  geom_line(aes(x = date, y = value, linetype = variable_desc, color = variable_desc)) +
  labs(x = "Observation Date", y = "Rate") + theme_minimal() +
  scale_color_manual(values = viridis(3)[1:2]) +
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1928-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_y_continuous(breaks = seq(0, 150, 25),
                labels = scales::dollar_format(accuracy = 1)) + 
  scale_x_date(breaks = as.Date(paste0(seq(1945, 2100, 5), "-01-01")),
               labels = date_format("%y"),
               limits = c(as.Date("1945-01-01"), as.Date("2020-01-01"))) +
  xlab("") + ylab("Oil Prices")

Modern Data

Code

fredr(series_id = "UNRATE") %>%
  tail(10) %>%
  {if (is_html_output()) print_table(.) else .}

date	series_id	value	realtime_start	realtime_end
2025-02-01	UNRATE	4.1	2025-12-16	2025-12-16
2025-03-01	UNRATE	4.2	2025-12-16	2025-12-16
2025-04-01	UNRATE	4.2	2025-12-16	2025-12-16
2025-05-01	UNRATE	4.2	2025-12-16	2025-12-16
2025-06-01	UNRATE	4.1	2025-12-16	2025-12-16
2025-07-01	UNRATE	4.2	2025-12-16	2025-12-16
2025-08-01	UNRATE	4.3	2025-12-16	2025-12-16
2025-09-01	UNRATE	4.4	2025-12-16	2025-12-16
2025-10-01	UNRATE	NA	2025-12-16	2025-12-16
2025-11-01	UNRATE	4.6	2025-12-16	2025-12-16

Code

fredr(series_id = "UNRATE",
      observation_start = as.Date("1990-01-01")) %>%
  head(10) %>%
  {if (is_html_output()) print_table(.) else .}

date	series_id	value	realtime_start	realtime_end
1990-01-01	UNRATE	5.4	2025-12-20	2025-12-20
1990-02-01	UNRATE	5.3	2025-12-20	2025-12-20
1990-03-01	UNRATE	5.2	2025-12-20	2025-12-20
1990-04-01	UNRATE	5.4	2025-12-20	2025-12-20
1990-05-01	UNRATE	5.4	2025-12-20	2025-12-20
1990-06-01	UNRATE	5.2	2025-12-20	2025-12-20
1990-07-01	UNRATE	5.5	2025-12-20	2025-12-20
1990-08-01	UNRATE	5.7	2025-12-20	2025-12-20
1990-09-01	UNRATE	5.9	2025-12-20	2025-12-20
1990-10-01	UNRATE	5.9	2025-12-20	2025-12-20

What are other data series for unemployment?

Code

unemp.1929.1942 <- fredr(series_id = "M0892AUSM156SNBR")
unemp.1947.1966 <- fredr(series_id = "M0892CUSM156NNBR")
unemp.1948.now <- fredr(series_id = "UNRATE")

unemp.1929.1942 %>%
  head %>%
  {if (is_html_output()) print_table(.) else .}

date	series_id	value	realtime_start	realtime_end
1929-04-01	M0892AUSM156SNBR	0.69	2025-11-20	2025-11-20
1929-05-01	M0892AUSM156SNBR	1.65	2025-11-20	2025-11-20
1929-06-01	M0892AUSM156SNBR	2.06	2025-11-20	2025-11-20
1929-07-01	M0892AUSM156SNBR	0.79	2025-11-20	2025-11-20
1929-08-01	M0892AUSM156SNBR	0.04	2025-11-20	2025-11-20
1929-09-01	M0892AUSM156SNBR	0.91	2025-11-20	2025-11-20

Code

fredr_series_search_text(search_text = "unemployment",
                         order_by = "popularity",
                         sort_order = "desc") %>%
  select(id, observation_start, title) %>%
  as.tibble %>%
  head(20) %>%
  {if (is_html_output()) print_table(.) else .}

id	observation_start	title
UNRATE	1948-01-01	Unemployment Rate
CPIAUCSL	1947-01-01	Consumer Price Index for All Urban Consumers: All Items in U.S. City Average
SAHMREALTIME	1959-12-01	Real-time Sahm Rule Recession Indicator
PAYEMS	1939-01-01	All Employees, Total Nonfarm
ICSA	1967-01-07	Initial Claims
CES0500000003	2006-03-01	Average Hourly Earnings of All Employees, Total Private
CCSA	1967-01-07	Continued Claims (Insured Unemployment)
U6RATE	1994-01-01	Total Unemployed, Plus All Persons Marginally Attached to the Labor Force, Plus Total Employed Part Time for Economic Reasons, as a Percent of the Civilian Labor Force Plus All Persons Marginally Attached to the Labor Force (U-6)
UNEMPLOY	1948-01-01	Unemployment Level
LNS14000006	1972-01-01	Unemployment Rate - Black or African American
SAHMCURRENT	1949-03-01	Sahm Rule Recession Indicator
IC4WSA	1967-01-28	4-Week Moving Average of Initial Claims
LNS14024887	1948-01-01	Unemployment Rate - 16-24 Yrs.
NROU	1949-01-01	Noncyclical Rate of Unemployment
CAUR	1976-01-01	Unemployment Rate in California
USSLIND	1982-01-01	Leading Index for the United States
WEI	2008-01-05	Weekly Economic Index (Lewis-Mertens-Stock)
LNS14000024	1948-01-01	Unemployment Rate - 20 Yrs. & over
ICNSA	1967-01-07	Initial Claims
UEMP27OV	1948-01-01	Number Unemployed for 27 Weeks & over

Code

fredr_series_search_text(search_text = "price index",
                         order_by = "popularity",
                         sort_order = "desc") %>%
  select(id, title) %>%
  as.tibble %>%
  head(20) %>%
  {if (is_html_output()) print_table(.) else .}

id	title
CPIAUCSL	Consumer Price Index for All Urban Consumers: All Items in U.S. City Average
SP500	S&P 500
DFII10	Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis, Inflation-Indexed
FPCPITOTLZGUSA	Inflation, consumer prices for the United States
APU0000708111	Average Price: Eggs, Grade A, Large (Cost per Dozen) in U.S. City Average
CSUSHPINSA	S&P Cotality Case-Shiller U.S. National Home Price Index
VIXCLS	CBOE Volatility Index: VIX
CPILFESL	Consumer Price Index for All Urban Consumers: All Items Less Food and Energy in U.S. City Average
APU0000703112	Average Price: Ground Beef, 100% Beef (Cost per Pound/453.6 Grams) in U.S. City Average
PCEPI	Personal Consumption Expenditures: Chain-type Price Index
CORESTICKM159SFRBATL	Sticky Price Consumer Price Index less Food and Energy
CPIAUCNS	Consumer Price Index for All Urban Consumers: All Items in U.S. City Average
PPIACO	Producer Price Index by Commodity: All Commodities
APU000072610	Average Price: Electricity per Kilowatt-Hour in U.S. City Average
PCEPILFE	Personal Consumption Expenditures Excluding Food and Energy (Chain-Type Price Index)
APU0000709112	Average Price: Milk, Fresh, Whole, Fortified (Cost per Gallon/3.8 Liters) in U.S. City Average
USSTHPI	All-Transactions House Price Index for the United States
GASREGW	US Regular All Formulations Gas Price
CUUR0000SA0R	Consumer Price Index for All Urban Consumers: Purchasing Power of the Consumer Dollar in U.S. City Average
CUSR0000SETA02	Consumer Price Index for All Urban Consumers: Used Cars and Trucks in U.S. City Average

Integrate with tidyverse package

Code

fredr_series_search_text(search_text = "federal funds",
                         order_by = "popularity",
                         sort_order = "desc",
                         limit = 1) %>%
  pull(id) %>%
  map_dfr(., fredr) %>%
  filter(date >= as.Date("1990-01-01")) %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = value/100)) +
  labs(x = "Observation Date", y = "Rate") + theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.8)) +
  scale_x_date(breaks = as.Date(paste0(seq(1990, 2100, 2), "-01-01")),
               labels = date_format("%y")) +
  scale_y_continuous(breaks = seq(-0.01, 0.12, 0.01),
                     labels = scales::percent_format(accuracy = 1)) + 
  xlab("") + ylab("10-Year Treasury Rate")

Look for series: debt and gross domestic product

Code

fredr_series_search_text(search_text = "debt",
                         order_by = "popularity",
                         sort_order = "desc",
                         limit = 5) %>%
  as.tibble %>%
  arrange(observation_start) %>%
  select(id, title) %>%
  {if (is_html_output()) print_table(.) else .}

id	title
GFDEGDQ188S	Federal Debt: Total Public Debt as Percent of Gross Domestic Product
GFDEBTN	Federal Debt: Total Public Debt
BAMLH0A0HYM2	ICE BofA US High Yield Index Option-Adjusted Spread
BAMLH0A0HYM2EY	ICE BofA US High Yield Index Effective Yield
RRPONTSYD	Overnight Reverse Repurchase Agreements: Treasury Securities Sold by the Federal Reserve in the Temporary Open Market Operations

Code

fredr_series_search_text(search_text = "gross domestic product",
                         order_by = "popularity",
                         sort_order = "desc",
                         limit = 5) %>%
  as.tibble %>%
  arrange(observation_start) %>%
  select(id, title) %>%
  {if (is_html_output()) print_table(.) else .}

id	title
PAYEMS	All Employees, Total Nonfarm
GDP	Gross Domestic Product
GDPC1	Real Gross Domestic Product
A939RX0Q048SBEA	Real gross domestic product per capita
GFDEGDQ188S	Federal Debt: Total Public Debt as Percent of Gross Domestic Product

Code

fredr_series_observations(series_id = "UNRATE",
                          observation_start = as.Date("2018-01-01"),
                          frequency = "q",
                          units = "chg") %>%
  {if (is_html_output()) print_table(.) else .}

date	series_id	value	realtime_start	realtime_end
2018-01-01	UNRATE	-0.1333333	2025-12-20	2025-12-20
2018-04-01	UNRATE	-0.1000000	2025-12-20	2025-12-20
2018-07-01	UNRATE	-0.1666667	2025-12-20	2025-12-20
2018-10-01	UNRATE	0.0666667	2025-12-20	2025-12-20
2019-01-01	UNRATE	0.0333333	2025-12-20	2025-12-20
2019-04-01	UNRATE	-0.2333333	2025-12-20	2025-12-20
2019-07-01	UNRATE	-0.0333333	2025-12-20	2025-12-20
2019-10-01	UNRATE	0.0000000	2025-12-20	2025-12-20
2020-01-01	UNRATE	0.2333333	2025-12-20	2025-12-20
2020-04-01	UNRATE	9.1666667	2025-12-20	2025-12-20
2020-07-01	UNRATE	-4.2000000	2025-12-20	2025-12-20
2020-10-01	UNRATE	-2.0333333	2025-12-20	2025-12-20
2021-01-01	UNRATE	-0.5333333	2025-12-20	2025-12-20
2021-04-01	UNRATE	-0.3000000	2025-12-20	2025-12-20
2021-07-01	UNRATE	-0.8666667	2025-12-20	2025-12-20
2021-10-01	UNRATE	-0.8666667	2025-12-20	2025-12-20
2022-01-01	UNRATE	-0.3666667	2025-12-20	2025-12-20
2022-04-01	UNRATE	-0.2000000	2025-12-20	2025-12-20
2022-07-01	UNRATE	-0.1000000	2025-12-20	2025-12-20
2022-10-01	UNRATE	0.0333333	2025-12-20	2025-12-20
2023-01-01	UNRATE	-0.0333333	2025-12-20	2025-12-20
2023-04-01	UNRATE	0.0000000	2025-12-20	2025-12-20
2023-07-01	UNRATE	0.1333333	2025-12-20	2025-12-20
2023-10-01	UNRATE	0.1333333	2025-12-20	2025-12-20
2024-01-01	UNRATE	0.0333333	2025-12-20	2025-12-20
2024-04-01	UNRATE	0.1666667	2025-12-20	2025-12-20
2024-07-01	UNRATE	0.1666667	2025-12-20	2025-12-20
2024-10-01	UNRATE	-0.0333333	2025-12-20	2025-12-20
2025-01-01	UNRATE	-0.0333333	2025-12-20	2025-12-20
2025-04-01	UNRATE	0.0666667	2025-12-20	2025-12-20
2025-07-01	UNRATE	0.1333333	2025-12-20	2025-12-20
2025-10-01	UNRATE	NA	2025-12-20	2025-12-20

Integrate the purrr package

This is how to create a wide database with various FRED Databases:

Code

map_dfr(c("FEDFUNDS", "UNRATE"), fredr) %>%
  spread(series_id, value) %>%
  top_n(10) %>%
  {if (is_html_output()) print_table(.) else .}

date	realtime_start	realtime_end	FEDFUNDS	UNRATE
1982-10-01	2025-12-16	2025-12-16	NA	10.4
1982-11-01	2025-12-16	2025-12-16	NA	10.8
1982-12-01	2025-12-16	2025-12-16	NA	10.8
1983-01-01	2025-12-16	2025-12-16	NA	10.4
1983-02-01	2025-12-16	2025-12-16	NA	10.4
1983-03-01	2025-12-16	2025-12-16	NA	10.3
1983-04-01	2025-12-16	2025-12-16	NA	10.2
2020-04-01	2025-12-16	2025-12-16	NA	14.8
2020-05-01	2025-12-16	2025-12-16	NA	13.2
2020-06-01	2025-12-16	2025-12-16	NA	11.0
2020-07-01	2025-12-16	2025-12-16	NA	10.2

This is how to map them:

Code

map_dfr(c("UNRATE", "FEDFUNDS"), fredr) %>%
  ggplot(.) + theme_minimal() + 
  geom_line(aes(x = date, y = value/100, color = series_id)) +
  labs(x = "Observation Date", y = "Rate", linetype = "Series") +
  scale_color_manual(values = viridis(3)[1:2]) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.8)) +
  scale_x_date(breaks = as.Date(paste0(seq(1920, 2100, 5), "-01-01")),
               labels = date_format("%y")) +
  scale_y_continuous(breaks = seq(-0.01, 0.3, 0.02),
                     labels = scales::percent_format(accuracy = 1)) + 
  xlab("") + ylab("") + theme(legend.title = element_blank())

Code

params <- list(series_id = c("UNRATE", "OILPRICE"),
               frequency = c("m", "q"))

pmap_dfr(.l = params,
         .f = ~ fredr(series_id = .x, frequency = .y)) %>%
  head(10) %>%
  {if (is_html_output()) print_table(.) else .}

date	series_id	value	realtime_start	realtime_end
1948-01-01	UNRATE	3.4	2025-12-16	2025-12-16
1948-02-01	UNRATE	3.8	2025-12-16	2025-12-16
1948-03-01	UNRATE	4.0	2025-12-16	2025-12-16
1948-04-01	UNRATE	3.9	2025-12-16	2025-12-16
1948-05-01	UNRATE	3.5	2025-12-16	2025-12-16
1948-06-01	UNRATE	3.6	2025-12-16	2025-12-16
1948-07-01	UNRATE	3.6	2025-12-16	2025-12-16
1948-08-01	UNRATE	3.9	2025-12-16	2025-12-16
1948-09-01	UNRATE	3.8	2025-12-16	2025-12-16
1948-10-01	UNRATE	3.7	2025-12-16	2025-12-16

Household Debt Service Payments as a Percent of Disposable Income

Code

map_dfr(c("TDSP"), fredr) %>%
  ggplot(data = ., mapping = aes(x = date, y = value/100)) +
  geom_line() + theme_minimal() +
  labs(x = "Observation Date", y = "Rate", color = "Series") +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.8)) +
  scale_x_date(breaks = as.Date(paste0(seq(1970, 2100, 5), "-01-01")),
               labels = date_format("%y")) +
  scale_y_continuous(breaks = seq(-0.01, 0.30, 0.01),
                     labels = scales::percent_format(accuracy = 1)) + 
  xlab("") + ylab("") + theme(legend.title = element_blank())

Nominal and Real Oil Prices

Oil Prices and Price Index

Data from FRED - Federal Reserve Bank of St. Louis:

CPIAUCSL: Consumer Price Index for All Urban Consumers: All Items. Available at: https://fred.stlouisfed.org/series/CPIAUCSL
WTISPLC: Spot Crude Oil Price: West Texas Intermediate (WTI). Available at: https://fred.stlouisfed.org/series/WTISPLC

Code

map_dfr(c("CPIAUCSL", "WTISPLC"), fredr) %>%
  ggplot(data = ., mapping = aes(x = date, y = value, linetype = series_id)) +
  geom_line() +
  labs(x = "Observation Date", y = "Rate", color = "Series") +
  theme_minimal() + xlab("") + ylab("") + theme(legend.title = element_blank())

Real Oil Prices

Code

map_dfr(c("CPIAUCSL", "WTISPLC"), fredr) %>%
  spread(series_id, value) %>%
  # Current House Prices from August 2018
  na.omit %>%
  mutate(WTISPLC_real = CPIAUCSL[860]* WTISPLC / CPIAUCSL) %>%
  select(-CPIAUCSL) %>%
  gather(series_id, value, -date) %>%
  ggplot(data = ., mapping = aes(x = date, y = value, linetype = series_id)) +
  geom_line() + labs(x = "Observation Date", y = "Rate", color = "Series") +
  theme_minimal() + xlab("") + ylab("") + theme(legend.title = element_blank())

Passenger car registration

Code

fredr_series_search_text(search_text = "Passenger Car Registrations",
                         order_by = "popularity",
                         sort_order = "desc",
                         limit = 5) %>%
  as.tibble %>%
  select(observation_start, id, title, everything()) %>%
  arrange(observation_start) %>%
  select(2, 3, frequency) %>%
  {if (is_html_output()) print_table(.) else .}

id	title	frequency
A01108USA258NNBR	Automobile Registrations, Passenger Cars, Total for United States	Annual, End of Year
M01109USM543NNBR	New Passenger Car Registrations for United States	Monthly
USASLRTCR03MLSAM	Sales: Retail Trade: Car Registration: Passenger Cars for United States	Monthly
SLRTCR03USA180S	Sales: Retail Trade: Car Registration: Passenger Cars for United States	Annual
AUSSLRTCR03GPSAM	Sales: Retail Trade: Car Registration: Passenger Cars for Australia	Monthly

Code

map_dfr(c("USASACRQISMEI"), fredr) %>%
  spread(series_id, value) %>%
  na.omit %>%
  # Current House Prices from August 2018
  gather(series_id, value, -date) %>%
  ggplot(data = ., mapping = aes(x = date, y = value)) +
  geom_line() + 
  scale_y_continuous(breaks = seq(80, 220, 20)) + xlab("") + ylab("") +
  theme_minimal() + theme(legend.title = element_blank())