Unemployment_Demography during COVID19

National Unemployment Rate (UNRATE)

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


  usa_claims %>% 
    ggplot(aes(x = date, y = claims)) +
    geom_line(color = "blue",size=1) + 
    labs(
      x = "Rate",y = "Date", caption = "Illusration by @JoeLongSanDiego",
      title = "New Unemployment Claims  National",
      subtitle = str_glue("Weekly from {min(usa_claims$date)} through {max(usa_claims$date)}")
    ) +
    theme_economist() +
    scale_y_continuous(labels = scales::comma)

• Employment Level - Men (LNS12000001) Women (Women=LNS12000002)

gender <- 
  c("LNS12000001","LNS12000002") %>% 
  tq_get(get = "economic.data", from = "2019-01-01") %>%
  spread(symbol,price) %>%
  rename(Men=LNS12000001,Women=LNS12000002)


ggplot(data = gender, aes(x = date)) + 
    geom_line(aes(y = Men, color = "Men"),size=.8) +
    geom_line(aes(y = Women, color = "Women"),size=.8) +
    labs(x = "Time",y = "Thousands of persons",title = "Employment by gender ",
         caption = "Men=LNS12000001   Women=LNS12000002",
      subtitle = str_glue("Monthly from {min(gender$date)} through {max(gender$date)}") ) +
    theme_economist() +
    scale_y_continuous(labels = scales::comma)

• Mostly entry level jobs

• Employment Level - 16-19 Yrs., Women (LNS12000014)

• Employment Level - 16-19 Yrs., Men (LNS12000013)

gender <- 
  c("LNS12000014","LNS12000013") %>% 
  tq_get(get = "economic.data", from = "2019-01-01") %>%
  spread(symbol,price) %>%
  rename(Women=LNS12000014,Men=LNS12000013)


ggplot(data = gender, aes(x = date)) + 
    geom_line(aes(y = Men, color = "Men"),size=.8) +
    geom_line(aes(y = Women, color = "Women"),size=.8) +
    labs(x = "Time",y = "Thousands of persons",title = "Youth Employment 16-19 Yrs",
         caption = "Women=LNS12000014  Men=LNS12000013",
      subtitle = str_glue("Monthly from {min(gender$date)} through {max(gender$date)}") ) +
    theme_economist() +
    scale_y_continuous(labels = scales::comma)

• Black or African American (LNS12000006)
• Hispanic or Latino (LNS12000009)

race <- 
  c("LNS12000006","LNS12000009") %>% 
  tq_get(get = "economic.data", from = "2019-01-01") %>%
  spread(symbol,price) %>%
  rename(Black=LNS12000006,Hispanic=LNS12000009)


ggplot(data = race, aes(x = date)) + 
    geom_line(aes(y = Black, color = "Black"),size=.8) +
    geom_line(aes(y = Hispanic, color = "Hispanic"),size=.8) +
    labs(x = "Time",y = "Thousands of persons",
         caption = "Black=LNS12000006 Hispanic=LNS12000009",
         title = "Employment Black/Afican American and Hispanic/Latino ",
      subtitle = str_glue("Monthly from {min(race$date)} through {max(race$date)}") ) +
    theme_economist() +
    scale_y_continuous(labels = scales::comma)

• Employment Level - Less Than a High School Diploma, 25 Yrs. & Over (LNS12027659)
• Employment Level - High School Graduates, No College, 25 Yrs. & Over (LNS12027660)
• Employment Level - Some College or Associate Degree, 25 Yrs. & Over (LNS12027689)

ed <- 
  c("LNS12027659","LNS12027660","LNS12027689") %>% 
  tq_get(get = "economic.data", from = "2019-01-01") %>%
  spread(symbol,price) %>%
  rename(No_highschool=LNS12027659,Highschool=LNS12027660,Associate=LNS12027689)


ggplot(data = ed, aes(x = date)) + 
    geom_line(aes(y = No_highschool, color = "No_highschool"),size=.8) +
    geom_line(aes(y = Highschool, color = "Highschool"),size=.8) +
   geom_line(aes(y = Associate, color = "Associate"),size=.8) +
    labs(x = "Time",y = "Thousands of persons",
         title = "Education level  ",
          caption = "No_highschool=LNS12027659 Highschool=LNS12027660 Associate=LNS12027689",
      subtitle = str_glue("Monthly from {min(ed$date)} through {max(ed$date)}") ) +
    theme_economist_white()+
    scale_y_continuous(labels = scales::comma)

• Employment Level - 16-19 Yrs., Black or African American (LNS12000018)
• Employment Level - 16-19 Yrs., Hispanic or Latino (LNU02000021)

youth <- 
  c("LNS12000018","LNU02000021") %>% 
  tq_get(get = "economic.data", from = "2019-01-01") %>%
  spread(symbol,price) %>%
  rename(Black=LNS12000018,Hispanic=LNU02000021)


ggplot(data = youth, aes(x = date)) + 
    geom_line(aes(y = Black, color = "Black"),size=.8) +
    geom_line(aes(y = Hispanic, color = "Hispanic"),size=.8) +
  
    labs(x = "Time",y = "Thousands of persons",
         title = "Minority Youth Employment ",
         caption = "Black=LNS12000018   Hispanic=LNU02000021",
      subtitle = str_glue("Monthly from {min(youth$date)} through {max(youth$date)}") ) +
   theme_economist_white()+
    scale_y_continuous(labels = scales::comma)