The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. Indexes are available for the U.S. and various geographic areas. Average price data for select utility, automotive fuel, and food items are also available.

CPI for All Urban Consumers (CPI-U)

Series Title : All items in U.S. city average, all urban consumers, not seasonally adjusted Series ID : CUUR0000SA0 Seasonality : Not Seasonally Adjusted Survey Name : CPI for All Urban Consumers (CPI-U) Measure Data Type : All items Area : U.S. city average Item : All items

Consumer Price Index for All Urban Consumers: All Items in U.S. City Average (CPIAUCNS)

indicator <-fredr_series_observations(series_id = "CPIAUCNS", 
      observation_start = as.Date("2010-01-01"))
      
indicator2 <- indicator%>% mutate(Difference=value-lag(value,default=value[[1]]),          
                                  Percent=100*(Difference/lag(value,default=value[[1]])))

tail(indicator2[,c(1,7)],n=12) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(1:2, T, color = "blue" )
date Percent
2020-06-01 0.54720
2020-07-01 0.50582
2020-08-01 0.31532
2020-09-01 0.13927
2020-10-01 0.04149
2020-11-01 -0.06106
2020-12-01 0.09415
2021-01-01 0.42538
2021-02-01 0.54744
2021-03-01 0.70833
2021-04-01 0.82189
2021-05-01 0.80171 
indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "",
      y = "Index Dec 1985 = 100",
      title = "Consumer Price Index for All Urban Consumers: All Items",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

Consumer Price Index for All Urban Consumers: Food and Beverages in U.S. City Average (CPIFABNS)

symbol <-fredr_series_observations(series_id = "CPIFABNS", 
      observation_start = as.Date("2010-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2:3, T, color = "blue" )
date value
132 2020-12-01 269.38
133 2021-01-01 270.26
134 2021-02-01 270.67
135 2021-03-01 271.13
136 2021-04-01 272.37
137 2021-05-01 273.44 
indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Monthly",
      y = "Index 1982-1984=100",
      title = "CPI: Food and Beverages in U.S. City Average",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Monthly",
      y = "Index 1982-1984=100",
      title = "CPI: Food and Beverages in U.S. City Average",
      subtitle = str_glue("From {min(indicator2$date)} through {max(indicator2$date)}"), 
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

Total Discharges for General Medical and Surgical Hospitals, All Establishments (DISC6221ALLEST176QNSA)

symbol <-fredr_series_observations(series_id = "DISC6221ALLEST176QNSA", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" )
date value
30 2019-10-01 9169
31 2020-01-01 8958
32 2020-04-01 7526
33 2020-07-01 8326
34 2020-10-01 8305
35 2021-01-01 8196 
indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Quarterly",
      y = "Thousands of Patents",
      title = " Total Discharges for General Medical and Surgical Hospitals",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Quarterly",
      y = "Thousands of Patents",
      title = "Total Discharges for General Medical and Surgical Hospitals _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"), 
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

Total Expense for Ambulatory Health Care Services, All Establishments (EXP621ALLEST144QNSA)

symbol <-fredr_series_observations(series_id = "EXP621ALLEST144QNSA", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" )
date value
44 2019-10-01 258316
45 2020-01-01 244116
46 2020-04-01 214401
47 2020-07-01 244732
48 2020-10-01 262083
49 2021-01-01 247008 
indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Quarterly",
      y = "Millions of Dollars",
      title = " Total Expense for Ambulatory Health Care Services",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Quarterly",
      y = "Millions of Dollars",
      title = "Total Expense for Ambulatory Health Care Services _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"), 
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

Total Expense for Child Day Care Services, All Establishments (EXP6244ALLEST144QNSA)

symbol <-fredr_series_observations(series_id = "EXP6244ALLEST144QNSA", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" )
date value
44 2019-10-01 11082
45 2020-01-01 11067
46 2020-04-01 7634
47 2020-07-01 9130
48 2020-10-01 10204
49 2021-01-01 9989 
indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Quarterly",
      y = "Millions of Dollars",
      title = " Total Expense for Child Day Care Services",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Quarterly",
      y = "Millions of Dollars",
      title = "Total Expense for Child Day Care Services _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"), 
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

Consumer Price Index for All Urban Consumers: Airline Fares in U.S. City Average (CUUR0000SETG01)

symbol <-fredr_series_observations(series_id = "CUUR0000SETG01", 
      observation_start = as.Date("2010-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" )
date value
132 2020-12-01 205.98
133 2021-01-01 200.82
134 2021-02-01 197.20
135 2021-03-01 197.13
136 2021-04-01 222.95
137 2021-05-01 250.21 
indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Monthly",
      y = "Index 1982-1984=100",
      title = "Airline Fares in U.S. City Average (CUUR0000SETG01)",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Monthly",
      y = "Index 1982-1984=100",
      title = "Airline Fares in U.S. City Average (CUUR0000SETG01) _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"), 
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

Consumer Price Index for All Urban Consumers: Gasoline (All Types) in U.S. City Average (CUUR0000SETB01)

symbol <-fredr_series_observations(series_id = "CUUR0000SETB01", 
      observation_start = as.Date("2010-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" )
date value
132 2020-12-01 193.99
133 2021-01-01 207.41
134 2021-02-01 221.69
135 2021-03-01 247.65
136 2021-04-01 252.60
137 2021-05-01 263.17 
indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Monthly",
      y = "Index 1982-1984=100",
      title = "Gasoline (All Types) in U.S. City Average (CUUR0000SETB01)",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Monthly",
      y = "Index 1982-1984=100",
      title = "Gasoline (All Types) in U.S. City Average (CUUR0000SETB01) _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"), 
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()