Manufacturing

GDP-Based Recession Indicator Index (JHGDPBRINDX)

This index measures the probability that the U.S. economy was in a recession during the indicated quarter. It is based on a mathematical description of the way that recessions differ from expansions. The index corresponds to the probability (measured in percent) that the underlying true economic regime is one of recession based on the available data. Whereas the NBER business cycle dates are based on a subjective assessment of a variety of indicators that may not be released until several years after the event, this index is entirely mechanical, is based solely on currently available GDP data and is reported every quarter. Due to the possibility of data revisions and the challenges in accurately identifying the business cycle phase, the index is calculated for the quarter just preceding the most recently available GDP numbers. Once the index is calculated for that quarter, it is never subsequently revised. The value at every date was inferred using only data that were available one quarter after that date and as those data were reported at the time.

If the value of the index rises above 67% that is a historically reliable indicator that the economy has entered a recession. Once this threshold has been passed, if it falls below 33% that is a reliable indicator that the recession is over.

indicator <-fredr_series_observations(series_id = "JHGDPBRINDX",
        observation_start = as.Date("1960-01-01")) 

# plotting data
indicator %>% ggplot() + geom_line(mapping = aes(x=date,y=value), 
                              color = "red4") +
  labs(title = "Based Recession Indicator Index _ GDP Based", 
       subtitle = str_glue("Monthly from {min(indicator$date)} through {max(indicator$date)}"),
       x="Quaterly", y="Percent",
       caption = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego")+
   
    theme_economist()

Velocity of M2 Money Stock (M2V)

M2 Money Stock (M2)

indicator <-fredr_series_observations(series_id = "M2V",
        observation_start = as.Date("1960-01-01")) 
indicator1 <-fredr_series_observations(series_id = "M2SL",
        observation_start = as.Date("1960-01-01")) 

both <- right_join(indicator,indicator1,by="date")

both <- both[,c(1,3,5)]
both <- na.omit(both)
colnames(both) <- c("date","M2V","M2SL")
both_long <- melt(both, id="date")  # convert to long format
# plotting data
indicator1 %>% ggplot() + geom_line(mapping = aes(x=date,y=value), 
                              color = "red4") +
  labs(title = "M2 Money Stock (M2SL)", 
       subtitle = str_glue("Quarterly from {min(indicator$date)} through {max(indicator$date)},
                  M2 includes a broader set of financial assets held principally by households. M2 consists of M1 plus: 
                  (1) savings deposits (which include money market deposit accounts, or MMDAs); 
                  (2) small-denomination time deposits (time deposits in amounts of less than $100,000); and 
                  (3) balances in retail money market mutual funds (MMMFs). 
                  Seasonally adjusted M2 is computed by summing savings deposits, small-denomination time deposits, and retail MMMFs,
                  each seasonally adjusted separately, and adding this result to seasonally adjusted M1."),
       x="Quarterly", y="Billions of Dollars",
       caption = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego")+
   
    theme_economist()

indicator %>% ggplot() + geom_line(mapping = aes(x=date,y=value), 
                              color = "maroon4") +
  labs(title = "Velocity of M2 Money Stock (M2V)", 
       subtitle = str_glue("Quarterly from {min(indicator$date)} through {max(indicator$date)},
                  The velocity of money is the frequency at which one unit of currency is used to purchase domestically- produced 
                  goods and services within a given time period. In other words, it is the number of times one dollar is spent to 
                  buy goods and services per unit of time. If the velocity of money is increasing, then more transactions are
                  occurring between individuals in an economy.
                  The broader M2 component includes M1 in addition to saving deposits, certificates of deposit (less than $100,000),
                  and money market deposits for individuals. Comparing the velocities of M1 and M2 provides some insight into how
                  quickly the economy is spending and how quickly it is saving."),
       x="Quaterly", y="Ratio",
       caption = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego")+
   
    theme_economist()

Initial Unemployment Claims (ICSA)

indicator <-fredr_series_observations(series_id = "ICSA",
        observation_start = as.Date("2020-03-01")) 

# plotting data
indicator %>% ggplot() + geom_line(mapping = aes(x=date,y=value), 
                              color = "red4") +
  labs(title = "New Unemployment Claims", 
       subtitle = str_glue("Weekly from {min(indicator$date)} through {max(indicator$date)}"),
       x="Week", y="Count",
       caption = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego")+
   
    theme_economist()

indicator[,c(1,3)] %>%
  kable() %>%
   kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

date	value
2020-03-07	211000
2020-03-14	282000
2020-03-21	3307000
2020-03-28	6867000
2020-04-04	6615000
2020-04-11	5237000
2020-04-18	4442000
2020-04-25	3867000
2020-05-02	3176000
2020-05-09	2687000
2020-05-16	2446000
2020-05-23	2123000
2020-05-30	1897000
2020-06-06	1566000
2020-06-13	1540000
2020-06-20	1482000
2020-06-27	1408000
2020-07-04	1310000
2020-07-11	1308000
2020-07-18	1422000
2020-07-25	1435000
2020-08-01	1186000

Consumer Revolving Credit Owned by Finance Companies, Outstanding (DTCOLRHFNM)

indicator <-fredr_series_observations(series_id = "DTCOLRHFNM",
        observation_start = as.Date("1990-01-01")) 

# plotting data
indicator %>% ggplot() + geom_line(mapping = aes(x=date,y=value), 
                              color = "000066") +
  labs(title = "Consumer Revolving Credit Owned by Finance Companies", 
       subtitle = str_glue("Monthly from {min(indicator$date)} through {max(indicator$date)}"),
       x="Monthly", y="Millions of Dollars",
       caption = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego")+
   
    theme_economist()

indicator%>%
    hchart( type = "column",hcaes(x = date, y = value)) %>% hc_colors("steelblue") %>%
    hc_subtitle(text=str_glue("from {min(indicator$date)} through {max(indicator$date)}"), align = "center") %>%
     hc_title(text = "Consumer Revolving Credit Owned by Finance Companies",
             style = list(fontWeight = "bold", fontSize = "20px"),
             align = "center")  %>%
  hc_credits(enabled = TRUE,text = "Source: FRED Federal Reserve (DTCOLRHFNM) _ Illustration by @JoeLongSanDiego") %>%
   hc_yAxis(title = list(text = "Millions of Dollars")) %>%
   hc_add_theme(hc_theme_economist())

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All Employees, Manufacturing (MANEMP)

indicator <-fredr_series_observations(series_id = "CALEIH",
        observation_start = as.Date("2019-01-01")) 

# plotting data
indicator %>% ggplot() + geom_line(mapping = aes(x=date,y=value), 
                              color = "red4") +
  labs(title = "National Employment, Manufacturing", 
       subtitle = str_glue("Monthly from {min(indicator$date)} through {max(indicator$date)}"),
       x="Monthly", y="Thousands of Persons",
      caption = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego")+
    theme_economist()

indicator%>%
    hchart( type = "column",hcaes(x = date, y = value)) %>% hc_colors("steelblue") %>%
    hc_subtitle(text=str_glue("from {min(indicator$date)} through {max(indicator$date)}"), align = "center") %>%
     hc_title(text = "Manufacturing Employment",
             style = list(fontWeight = "bold", fontSize = "20px"),
             align = "center")  %>%
  hc_credits(enabled = TRUE,text = "Source: FRED Federal Reserve (CALEIH) _ Illustration by @JoeLongSanDiego") %>%
   hc_yAxis(title = list(text = "Thousands of Persons")) %>%
   hc_add_theme(hc_theme_economist())

Manufacturers’ New Orders: Durable Goods (DGORDER)

indicator <-fredr_series_observations(series_id = "DGORDER",
        observation_start = as.Date("2019-01-01")) 

# plotting data
indicator %>% ggplot() + geom_line(mapping = aes(x=date,y=value), 
                              color = "red4") +
  labs(title = "Manufacturers' New Orders: Durable Goods", 
       subtitle = str_glue("Monthly from {min(indicator$date)} through {max(indicator$date)}"),
       x="Monthly", y="Millions of Dollars",
      caption = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego")+
    theme_economist()

indicator%>%
    hchart( type = "column",hcaes(x = date, y = value)) %>% hc_colors("steelblue") %>%
    hc_subtitle(text=str_glue("from {min(indicator$date)} through {max(indicator$date)}"), align = "center") %>%
     hc_title(text = "Manufacturers' New Orders: Durable Goods",
             style = list(fontWeight = "bold", fontSize = "20px"),
             align = "center")  %>%
  hc_credits(enabled = TRUE,text = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego") %>%
   hc_yAxis(title = list(text = "Millions of Dollars")) %>%
   hc_add_theme(hc_theme_economist())

Capacity Utilization: Manufacturing (NAICS) (MCUMFN)

indicator <-fredr_series_observations(series_id = "MCUMFN",
        observation_start = as.Date("2019-01-01")) 

# plotting data
indicator %>% ggplot() + geom_line(mapping = aes(x=date,y=value), 
                              color = "red4") +
  labs(title = "Capacity Utilization: Manufacturing", 
       subtitle = str_glue("Monthly from {min(indicator$date)} through {max(indicator$date)}"),
       x="Monthly", y="Percent of Capacity",
       caption = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego")+
   
    theme_economist()

indicator%>%
    hchart( type = "column",hcaes(x = date, y = value)) %>% hc_colors("steelblue") %>%
    hc_subtitle(text=str_glue("from {min(indicator$date)} through {max(indicator$date)}"), align = "center") %>%
     hc_title(text = "Capacity Utilization: Manufacturing",
             style = list(fontWeight = "bold", fontSize = "20px"),
             align = "center")  %>%
  hc_credits(enabled = TRUE,text = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego") %>%
   hc_yAxis(title = list(text = "Percent of Capacity")) %>%
   hc_add_theme(hc_theme_economist())

Manufacturers: Inventories to Sales Ratio (MNFCTRIRSA)

indicator <-fredr_series_observations(series_id = "MNFCTRIRSA",
        observation_start = as.Date("2019-01-01")) 

# plotting data
indicator %>% ggplot() + geom_line(mapping = aes(x=date,y=value), 
                              color = "red4") +
  labs(title = "Manufacturers: Inventories to Sales Ratio", 
       subtitle = str_glue("Monthly from {min(indicator$date)} through {max(indicator$date)}"),
       x="Monthly", y="Ratio",
      caption = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego")+
    theme_economist()

indicator%>%
    hchart( type = "column",hcaes(x = date, y = value)) %>% hc_colors("steelblue") %>%
    hc_subtitle(text=str_glue("from {min(indicator$date)} through {max(indicator$date)}"), align = "center") %>%
     hc_title(text = "Manufacturers: Inventories to Sales Ratio",
             style = list(fontWeight = "bold", fontSize = "20px"),
             align = "center")  %>%
  hc_credits(enabled = TRUE,text = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego") %>%
   hc_yAxis(title = list(text = "Ratio")) %>%
   hc_add_theme(hc_theme_economist())

Industrial Production: Manufacturing (NAICS) (IPMAN)

indicator <-fredr_series_observations(series_id = "IPMAN",
        observation_start = as.Date("2019-01-01")) 

# plotting data
indicator %>% ggplot() + geom_line(mapping = aes(x=date,y=value), 
                              color = "green4") +
  labs(title = "Industrial Production: Manufacturing", 
       subtitle = str_glue("Monthly from {min(indicator$date)} through {max(indicator$date)}"),
       x="Monthly", y="Index 2012=100",
      caption = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego")+
    theme_economist()

indicator%>%
    hchart( type = "column",hcaes(x = date, y = value)) %>% hc_colors("steelblue") %>%
    hc_subtitle(text=str_glue("from {min(indicator$date)} through {max(indicator$date)}"), align = "center") %>%
     hc_title(text = "Industrial Production: Manufacturing",
             style = list(fontWeight = "bold", fontSize = "20px"),
             align = "center")  %>%
  hc_credits(enabled = TRUE,text = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego") %>%
   hc_yAxis(title = list(text = "Index 2012=100")) %>%
   hc_add_theme(hc_theme_economist())

## Producer Price Index by Industry: Food Manufacturing (PCU311311)

indicator <-fredr_series_observations(series_id = "PCU311311",
        observation_start = as.Date("2019-01-01")) 

# plotting data
indicator %>% ggplot() + geom_line(mapping = aes(x=date,y=value), 
                              color = "blue4") +
  labs(title = "Wholesale Price Index: Food Manufacturing", 
       subtitle = str_glue("Monthly from {min(indicator$date)} through {max(indicator$date)}"),
       x="Monthly", y="Index Dec 1984=100",
      caption = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego")+
    theme_economist()

indicator%>%
    hchart( type = "column",hcaes(x = date, y = value)) %>% hc_colors("steelblue") %>%
    hc_subtitle(text=str_glue("from {min(indicator$date)} through {max(indicator$date)}"), align = "center") %>%
     hc_title(text = "Wholesale Price Index: Food Manufacturing",
             style = list(fontWeight = "bold", fontSize = "20px"),
             align = "center")  %>%
  hc_credits(enabled = TRUE,text = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego") %>%
   hc_yAxis(title = list(text = "Index Dec 1984=100")) %>%
   hc_add_theme(hc_theme_economist())

Capacity Utilization: Durable Manufacturing: Iron and steel products (CAPUTLG3311A2S)

indicator <-fredr_series_observations(series_id = "CAPUTLG3311A2S",
        observation_start = as.Date("2019-01-01")) 

# plotting data
indicator %>% ggplot() + geom_line(mapping = aes(x=date,y=value), 
                              color = "blue4") +
  labs(title = "Capacity Utilization: Iron and steel products", 
       subtitle = str_glue("Monthly from {min(indicator$date)} through {max(indicator$date)}"),
       x="Monthly", y="Percent of Capacity",
      caption = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego")+
    theme_economist()

indicator%>%
    hchart( type = "column",hcaes(x = date, y = value)) %>% hc_colors("steelblue") %>%
    hc_subtitle(text=str_glue("from {min(indicator$date)} through {max(indicator$date)}"), align = "center") %>%
     hc_title(text = "Capacity Utilization: Iron and steel products",
             style = list(fontWeight = "bold", fontSize = "20px"),
             align = "center")  %>%
  hc_credits(enabled = TRUE,text = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego") %>%
   hc_yAxis(title = list(text = "Percent of Capacity")) %>%
   hc_add_theme(hc_theme_economist())

New Private Housing Units Authorized by Building Permits (PERMIT)

indicator <-fredr_series_observations(series_id = "PERMIT",
        observation_start = as.Date("1960-01-01")) 

# plotting data
indicator %>% ggplot() + geom_line(mapping = aes(x=date,y=value), 
                              color = "blue3") +
  labs(title = "New Private Housing Units Authorized by Building Permits", 
       subtitle = str_glue("Monthly from {min(indicator$date)} through {max(indicator$date)}.
                           60 Year Period"),
       x="Monthly", y="Thousands of Units",
      caption = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego")+
    theme_economist()

# plotting data
indicator <- filter(indicator, date >="2019-06-01")
indicator %>% filter(date >="2019-06-01") %>%
  ggplot() + geom_line(mapping = aes(x=date,y=value), 
                              color = "blue2",size=1) +
  labs(title = "New Private Housing Units Authorized by Building Permits", 
       subtitle = str_glue("Monthly from {min(indicator$date)} through {max(indicator$date)}.
                           One year"),
       x="Monthly", y="Thousands of Units",
      caption = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego")+
    theme_economist()

indicator%>%
    hchart( type = "column",hcaes(x = date, y = value)) %>% hc_colors("steelblue") %>%
    hc_subtitle(text=str_glue("from {min(indicator$date)} through {max(indicator$date)}"), align = "center") %>%
     hc_title(text = "New Private Housing Units Authorized by Building Permits",
             style = list(fontWeight = "bold", fontSize = "20px"),
             align = "center")  %>%
  hc_credits(enabled = TRUE,text = "Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego") %>%
   hc_yAxis(title = list(text = "Thousands of Units")) %>%
   hc_add_theme(hc_theme_economist())