Unemployment Rate

no_hs <-fredr_series_observations(series_id = "LNU04027659",
        observation_start = as.Date("2019-01-01")) 
hs <-fredr_series_observations(series_id = "LNU04027660",
        observation_start = as.Date("2019-01-01")) 
as <-fredr_series_observations(series_id = "LNU04027689",
        observation_start = as.Date("2019-01-01")) 
ba <-fredr_series_observations(series_id = "CGBD25O",
        observation_start = as.Date("2019-01-01")) 


indicator <-as.data.frame(no_hs$date)

colnames(indicator) <- "date"
indicator$no_highschool <- no_hs$value
indicator$highschool <- hs$value
indicator$some_college <- as$value
indicator$bachelor <- ba$value



indicator %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "black" ) %>%
  column_spec(3, T, color = "blue") %>%
  column_spec(4, T, color = "green") %>%
   column_spec(5, T, color = "#CC79A7") %>%
  row_spec(16,T,color="red",background = "lightyellow")
date no_highschool highschool some_college bachelor
2019-01-01 7.4 4.3 3.7 2.8
2019-02-01 6.7 4.2 3.4 2.4
2019-03-01 6.9 4.0 3.5 2.2
2019-04-01 5.2 3.3 2.9 2.1
2019-05-01 4.4 3.3 2.5 2.1
2019-06-01 4.6 3.7 3.0 2.3
2019-07-01 4.6 3.6 3.3 2.5
2019-08-01 4.7 3.6 3.2 2.5
2019-09-01 4.0 3.3 2.8 2.2
2019-10-01 4.7 3.5 2.7 2.1
2019-11-01 5.1 3.5 2.8 1.9
2019-12-01 5.9 3.7 2.6 1.9
2020-01-01 7.4 4.4 3.0 2.2
2020-02-01 7.2 4.1 3.3 2.2
2020-03-01 8.1 4.8 3.9 2.5
2020-04-01 20.9 17.0 14.8 9.4
2020-05-01 18.5 15.0 12.9 8.4
2020-06-01 15.4 11.9 10.8 8.1
2020-07-01 14.9 10.8 10.2 8.1
#---------------------------
library(reshape2)

# Specify id.vars: the variables to keep but not split apart on
data_long <- melt(indicator, id.vars=c("date"))
colnames(data_long) <- c("date","Education","Percent")

# plotting data
data_long %>% ggplot() + 
  geom_line(mapping = aes(x=date,y=Percent,color=Education),size=1)  +
        
   labs(title = "Unemployment rates by education attainment", 
       subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
       x="Monthly", y="Percent",
       caption = "Data source: FRED Federal Reserve.   Illustration by @JoeLongSanDiego")+
    theme_economist()

Unemployment Rate - High School Graduates, No College, 18 to 19 years

_ Men (HSGS1819M) - Women (HSGS1819W)

men <-fredr_series_observations(series_id = "HSGS1819M",
        observation_start = as.Date("2019-01-01")) 
women <-fredr_series_observations(series_id = "HSGS1819W",
        observation_start = as.Date("2019-01-01")) 



indicator <-as.data.frame(men$date)

colnames(indicator) <- "date"
indicator$men <- men$value
indicator$women <- women$value




indicator %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "green" ) %>%
  column_spec(3, T, color = "blue")  %>%
  row_spec(16,T,color="red",background = "lightyellow")
date men women
2019-01-01 14.5 14.0
2019-02-01 18.4 13.6
2019-03-01 18.4 9.2
2019-04-01 20.4 7.1
2019-05-01 13.7 11.7
2019-06-01 17.6 11.3
2019-07-01 13.7 11.4
2019-08-01 13.7 13.3
2019-09-01 16.6 15.9
2019-10-01 16.2 11.5
2019-11-01 15.6 12.7
2019-12-01 14.5 12.9
2020-01-01 20.9 18.3
2020-02-01 16.3 20.7
2020-03-01 18.8 10.6
2020-04-01 30.3 36.4
2020-05-01 30.3 26.8
2020-06-01 26.6 15.9
2020-07-01 23.8 14.2
#---------------------------

# Specify id.vars: the variables to keep but not split apart on
data_long <- melt(indicator, id.vars=c("date"))
colnames(data_long) <- c("date","Gender","Percent")

# plotting data
data_long %>% ggplot() + 
  geom_line(mapping = aes(x=date,y=Percent,color=Gender),size=1)  +
        
   labs(title = "Unemployment rates _ High School Graduates by Gender", 
       subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
       x="Monthly", y="Percent",
       caption = "Data source: FRED Federal Reserve.   Illustration by @JoeLongSanDiego")+
    theme_economist()

Unemployment Rate - Less Than a High School Diploma, 25 Yrs. & Over (LNS14027659)

women <-fredr_series_observations(series_id = "LHSD2564W",
        observation_start = as.Date("2019-01-01")) 
men <-fredr_series_observations(series_id = "LHSD2564M",
        observation_start = as.Date("2019-01-01")) 



indicator <-as.data.frame(women$date)

colnames(indicator) <- "date"
indicator$women <- women$value
indicator$men <- men$value


indicator %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "green" ) %>%
  column_spec(3, T, color = "blue") %>%
  row_spec(16,T,color="red",background = "lightyellow")
date women men
2019-01-01 7.6 7.5
2019-02-01 6.6 7.0
2019-03-01 7.4 6.8
2019-04-01 5.9 4.9
2019-05-01 6.8 3.2
2019-06-01 5.9 3.9
2019-07-01 6.5 3.5
2019-08-01 6.6 3.7
2019-09-01 5.2 3.5
2019-10-01 6.1 4.2
2019-11-01 6.9 4.4
2019-12-01 5.6 6.3
2020-01-01 7.2 7.7
2020-02-01 7.0 7.3
2020-03-01 8.5 7.7
2020-04-01 25.5 18.2
2020-05-01 22.8 16.0
2020-06-01 19.3 13.2
2020-07-01 17.8 13.2
#---------------------------

# Specify id.vars: the variables to keep but not split apart on
data_long <- melt(indicator, id.vars=c("date"))
colnames(data_long) <- c("date","Gender","Thousands")

# plotting data
data_long %>% ggplot() + 
  geom_line(mapping = aes(x=date,y=Thousands,color=Gender),size=1)  +
        
   labs(title = "Unemployment Rates _ Less than a High School Diploma, 25 to 64 years", 
       subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
       x="Monthly", y="Rate",
       caption = "Data source: FRED Federal Reserve.   Illustration by @JoeLongSanDiego")+
    theme_economist()

### Unemployment Rate - 18-19 Yrs. - Men (LNU04000154) - Women (LNU04000319)

women <-fredr_series_observations(series_id = "LNU04000319",
        observation_start = as.Date("2019-01-01")) 
men <-fredr_series_observations(series_id = "LNU04000154",
        observation_start = as.Date("2019-01-01")) 



indicator <-as.data.frame(women$date)

colnames(indicator) <- "date"
indicator$women <- women$value
indicator$men <- men$value


indicator %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "green" ) %>%
  column_spec(3, T, color = "blue") %>%
  row_spec(16,T,color="red",background = "lightyellow")
date women men
2019-01-01 12.0 14.6
2019-02-01 10.7 14.5
2019-03-01 9.1 14.6
2019-04-01 6.8 14.7
2019-05-01 11.9 12.2
2019-06-01 11.2 14.9
2019-07-01 10.1 12.1
2019-08-01 11.5 13.1
2019-09-01 11.2 14.7
2019-10-01 9.6 12.9
2019-11-01 10.0 12.6
2019-12-01 9.7 11.1
2020-01-01 13.7 14.4
2020-02-01 12.4 12.2
2020-03-01 10.5 13.7
2020-04-01 37.3 30.1
2020-05-01 31.4 28.9
2020-06-01 19.8 25.8
2020-07-01 17.6 20.0
#---------------------------

# Specify id.vars: the variables to keep but not split apart on
data_long <- melt(indicator, id.vars=c("date"))
colnames(data_long) <- c("date","Gender","Thousands")

# plotting data
data_long %>% ggplot() + 
  geom_line(mapping = aes(x=date,y=Thousands,color=Gender),size=1)  +
        
   labs(title = "Unemployment Rates _  18-19 Years.", 
       subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
       x="Monthly", y="Rate",
       caption = "Data source: FRED Federal Reserve.   Illustration by @JoeLongSanDiego")+
    theme_economist()