Unemployment

Initial Claims (ICSA)

The unemployment rate represents the number of unemployed as a percentage of the labor force.

Labor force data are restricted to people 16 years of age and older, who currently reside in 1 of the 50 states or the District of Columbia, who do not reside in institutions (e.g., penal and mental facilities, homes for the aged), and who are not on active duty in the Armed Forces.

This rate is also defined as the U-3 measure of labor underutilization.

The series comes from the ‘Current Population Survey (Household Survey)’

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

indicator %>% ggplot() +  
   geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-11-2020'), ymin = -Inf, ymax = Inf),
                   fill = "lightgrey", alpha = 0.025)+
  geom_line(mapping = aes(x=date,y=value),color="red",size=1)  + 
      labs(title = "Initial Unemployment Claims (ICSA)", 
          subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
       x="Weekly", y="Numbers",
       caption = "Data source: FRED St. Louis Federal Reserve\nIllustration by @JoeLongSanDiego")+
  theme_economist()

Delinquency Rate on Credit Card Loans, All Commercial Banks (DRCCLACBS)

• Charge-Off and Delinquency Rates on Loans and Leases at Commercial Banks
• Note : no 2020 data available yet

credit <- 
  "DRCCLACBS" %>% 
  tq_get(get = "economic.data", 
         from = "1980-01-01") %>% 
  rename(rate = price) 

  credit %>% 
    ggplot(aes(x = date, y = rate)) +
    geom_line(color = "blue2",size=1) + 
    labs(
      x = "Date",
      y = "Percentage",
      title = "Delinquency Rate on Credit Card ",
      subtitle = str_glue("Quarterly from {min(credit$date)} through {max(credit$date)}")
    ) +
   theme_economist()+
    scale_y_continuous(labels = scales::comma)

Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, All Commercial Banks (DRSFRMACBS)

• Note : no 2020 data available yet

reo <- 
  "DRSFRMACBS" %>% 
  tq_get(get = "economic.data", 
         from = "2000-01-01") %>% 
  rename(rate = price) 





  reo %>% 
    ggplot(aes(x = date, y = rate)) +
    geom_line(color = "blue",size=1) + 
    labs(
      x = "Quarterly",
      y = "Rate",
      title = "Delinquency Rate on SFR Mortgages",
      subtitle = str_glue("Quarterly from {min(reo$date)} through {max(reo$date)}")
    ) +
   theme_economist() +
    scale_y_continuous(labels = scales::comma)

California Unemployment

Getting economic data from tidyquant package

ca_claims <- 
  "CAICLAIMS" %>% 
  tq_get(get = "economic.data", 
         from = "2019-01-01") %>% 
  rename(claims = price) 


  ca_claims %>% 
    ggplot(aes(x = date, y = claims)) +
    geom_line(color = "blue",size=1) + 
    labs(
      x = "",
      y = "",
      title = "Unemployment Claims _ California",
      subtitle = str_glue("Weekly from {min(ca_claims$date)} through {max(ca_claims$date)}")
    ) +
  theme_economist() +
    scale_y_continuous(labels = scales::comma)

Unemployment Rate in Riverside County, CA (CARIVE5URN)

indicator <- 
  "CARIVE5URN" %>% 
  tq_get(get = "economic.data", 
         from = "2019-01-01") %>% 
  rename(claims = price) 


indicator %>% 
    ggplot(aes(x = date, y = claims)) +
   geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
                   fill = "white", alpha = 0.03)+
    geom_line(color = "blue",size=1) + 
    labs(
      x = "",
      y = "Rate",
      title = "Unemployment Rate _ Riverside County",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}")
    ) +
  theme_economist() 

Unemployment Rate in San Bernardino County, CA (CASANB1URN)

indicator <- 
  "CASANB1URN" %>% 
  tq_get(get = "economic.data", 
         from = "2019-01-01") %>% 
  rename(claims = price) 


indicator %>% 
    ggplot(aes(x = date, y = claims)) +
   geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
                   fill = "white", alpha = 0.03)+
    geom_line(color = "blue",size=1) + 
    labs(
      x = "",
      y = "Rate",
      title = "Unemployment Rate _ San Bernardino County",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}")
    ) +
  theme_economist() 

Unemployment Rate in San Diego County, CA (CASAND5URN)

indicator <- 
  "CASAND5URN" %>% 
  tq_get(get = "economic.data", 
         from = "2019-01-01") %>% 
  rename(claims = price) 


indicator %>% 
    ggplot(aes(x = date, y = claims)) +
   geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
                   fill = "white", alpha = 0.03)+
    geom_line(color = "blue",size=1) + 
    labs(
      x = "",
      y = "Rate",
      title = "Unemployment Rate _ San Diego County",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}")
    ) +
  theme_economist() 

Unemployment Rate in Los Angeles County, CA (CALOSA7URN)

indicator <- 
  "CALOSA7URN" %>% 
  tq_get(get = "economic.data", 
         from = "2019-01-01") %>% 
  rename(claims = price) 


indicator %>% 
    ggplot(aes(x = date, y = claims)) +
   geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
                   fill = "white", alpha = 0.03)+
    geom_line(color = "blue",size=1) + 
    labs(
      x = "",
      y = "Rate",
      title = "Unemployment Rate _ Los Angeles County",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}")
    ) +
  theme_economist() 

library(knitr)
library(kableExtra)

tail(ca_claims) %>%
  kable() %>%
  kable_styling()

symbol	date	claims
CAICLAIMS	2020-09-12	226004
CAICLAIMS	2020-09-19	226179
CAICLAIMS	2020-09-26	171220
CAICLAIMS	2020-10-03	148213
CAICLAIMS	2020-10-10	176083
CAICLAIMS	2020-10-17	159876

ca_claims<- ca_claims %>%
  mutate(
    year = year(date),
    month =  month(date, label = T, abbr  = T),
    week = week(date))
    
ca_month <- ca_claims %>%
             group_by(year,month)%>%
             summarise(Ave_count =sum(claims))

## `summarise()` regrouping output by 'year' (override with `.groups` argument)

ca_claims 

## # A tibble: 94 x 6
##    symbol    date       claims  year month  week
##    <chr>     <date>      <int> <dbl> <ord> <dbl>
##  1 CAICLAIMS 2019-01-05  38536  2019 Jan       1
##  2 CAICLAIMS 2019-01-12  53519  2019 Jan       2
##  3 CAICLAIMS 2019-01-19  48844  2019 Jan       3
##  4 CAICLAIMS 2019-01-26  56886  2019 Jan       4
##  5 CAICLAIMS 2019-02-02  48904  2019 Feb       5
##  6 CAICLAIMS 2019-02-09  44850  2019 Feb       6
##  7 CAICLAIMS 2019-02-16  43516  2019 Feb       7
##  8 CAICLAIMS 2019-02-23  34449  2019 Feb       8
##  9 CAICLAIMS 2019-03-02  41085  2019 Mar       9
## 10 CAICLAIMS 2019-03-09  40896  2019 Mar      10
## # ... with 84 more rows

  ggplot(data=ca_claims,aes(x=date,y = claims,fill=month)) +
  geom_bar(stat = "identity")+
  labs(
    title = "Weekly Unemployment Claims in California",
    subtitle = str_glue("{min(ca_claims$date)} through {max(ca_claims$date)}"),
    caption = "Illustration by Joe Long",
    y = "",
    x = ""
  ) 

  ggplot(data=ca_month,aes(x=reorder(month,year),y = Ave_count,fill=month)) +
  geom_bar(stat = "identity")+
  labs(
    title = "Monthly Count of New Unemployment Claims in California",
    subtitle = "",
    caption = "Illustration by Joe Long",
    y = "",
    x = ""
  ) 

Unemployment Rate in San Diego County, CA (CASAND5URN)

par(mfrow=c(2,1))
sdurn <- 
  "CASAND5URN" %>% 
  tq_get(get = "economic.data", 
         from = "2019-01-01") %>% 
  rename(claims = price) 


  sdurn %>% 
    ggplot(aes(x = date, y = claims)) +
    geom_line(color = "blue",size=1) + 
    labs(
      x = "",
      y = "",
      title = "Unemployment Rate _ San Diego",
      subtitle = str_glue("From {min(sdurn$date)} through {max(sdurn$date)}")
    ) +
   theme_economist() +
    scale_y_continuous(labels = scales::comma)

sdun <- 
  "LAUCN060730000000004" %>% 
  tq_get(get = "economic.data", 
         from = "2019-01-01") %>% 
  rename(claims = price) 


  sdun %>% 
    ggplot(aes(x = date, y = claims)) +
    geom_line(color = "blue",size=1) + 
    labs(
      x = "",
      y = "",
      title = "Unemployment Population _ San Diego",
      subtitle = str_glue("From {min(sdun$date)} through {max(sdun$date)}")
    ) +
    theme_economist()+
    scale_y_continuous(labels = scales::comma)

Crude Oil Prices: West Texas Intermediate (WTI) - Cushing, Oklahoma (DCOILWTICO)

library(quantmod)
library(ggplot2)
wti <- tq_get("DCOILWTICO", get = "economic.data", from = "2020-01-01")

ggplot(wti, aes(x = date, y = price)) + geom_line(color = "darkblue",size=1) +
  labs(
      x = "Date",
      y = "Dollars",
      subtitle = str_glue("{min(wti$date)} through {max(wti$date)}"),
      title = "West Texas Intermediate (WTI) _ DCOILWTICO",
      caption = "Data source: FRED St. Louis Federal Reserve\nIllustration by Joe Long"
    ) +
    theme_economist() +
  theme(plot.title = element_text(color="blue")) +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  scale_x_date(date_breaks = "2 months")

## Warning: Removed 1 row(s) containing missing values (geom_path).