Employed full time: Wage and salary workers: Software developers, applications and systems software occupations: 16 years and over: Men (LEU0254584000A)

Employed full time: Wage and salary workers: Software developers, applications and systems software occupations: 16 years and over: Women (LEU0254690800A)

index <-fredr_series_observations(series_id = "LEU0254584000A",
        observation_start = as.Date("2000-01-01")) 
index2 <-fredr_series_observations(series_id = "LEU0254690800A",
        observation_start = as.Date("2000-01-01")) 


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

colnames(indicator) <- "date"
indicator$male <- index$value
indicator$female <- index2$value



indicator %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) %>%
  column_spec(3, T, color = "blue")
date male female
2000-01-01 528 179
2001-01-01 551 160
2002-01-01 523 151
2003-01-01 545 156
2004-01-01 572 184
2005-01-01 612 165
2006-01-01 623 176
2007-01-01 694 178
2008-01-01 759 196
2009-01-01 710 192
2010-01-01 773 200
2011-01-01 812 179
2012-01-01 808 197
2013-01-01 844 209
2014-01-01 917 224
2015-01-01 1054 232
2016-01-01 1084 266
2017-01-01 1174 265
2018-01-01 1283 327
2019-01-01 1379 335 
data_long <- melt(indicator, id.vars=c("date"))
colnames(data_long) <- c("date","Gender","Value")

# plotting data
data_long %>% ggplot() + 
  geom_line(mapping = aes(x=date,y=Value,color=Gender),size=1)  +
     labs(title = "Software developers by gender", 
       subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
       x="Yearly", y="Thousands of Persons",
       caption = "Data source: FRED Federal Reserve.   Illustration by @JoeLongSanDiego")+
    theme_economist()

highchart() %>% 
    hc_chart(type = "column") %>%
    hc_xAxis(categories = indicator$date) %>%
    hc_add_series(name="Male _ Software Developers",data = indicator$male) %>% 
    hc_add_series(name="Female _ Software Developers",data = indicator$female) %>%
  hc_subtitle(text=str_glue("From {min(indicator$date)} through {max(indicator$date)}"), align = "center") %>%
     hc_title(text = "Software developers by gender",
             style = list(fontWeight = "bold", fontSize = "20px"),
             align = "center")  %>%
  hc_credits(enabled = TRUE,text = "Data Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego") %>%
   hc_yAxis(title = list(text = "Thousands of Persons")) %>%
    hc_add_theme(hc_theme_economist())

Employed full time: Wage and salary workers: Registered nurses occupations: Women (LEU0254701500A)

Employed full time: Wage and salary workers: Registered nurses occupations: Men (LEU0254594700A)

index <-fredr_series_observations(series_id = "LEU0254594700A",
        observation_start = as.Date("2000-01-01")) 
index2 <-fredr_series_observations(series_id = "LEU0254701500A",
        observation_start = as.Date("2000-01-01")) 


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

colnames(indicator) <- "date"
indicator$male <- index$value
indicator$female <- index2$value



indicator %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) %>%
  column_spec(3, T, color = "blue")
date male female
2000-01-01 143 1449
2001-01-01 148 1511
2002-01-01 140 1595
2003-01-01 179 1650
2004-01-01 148 1651
2005-01-01 151 1654
2006-01-01 185 1713
2007-01-01 192 1773
2008-01-01 210 1904
2009-01-01 197 1931
2010-01-01 207 1970
2011-01-01 208 1937
2012-01-01 230 1946
2013-01-01 254 2023
2014-01-01 245 2064
2015-01-01 278 2104
2016-01-01 285 2213
2017-01-01 283 2253
2018-01-01 315 2270
2019-01-01 320 2321 
data_long <- melt(indicator, id.vars=c("date"))
colnames(data_long) <- c("date","Gender","Value")

# plotting data
data_long %>% ggplot() + 
  geom_line(mapping = aes(x=date,y=Value,color=Gender),size=1)  +
     labs(title = "Registered nurses occupations by gender", 
       subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
       x="Yearly", y="Thousands of Persons",
       caption = "Data source: FRED Federal Reserve.   Illustration by @JoeLongSanDiego")+
    theme_economist()

highchart() %>% 
    hc_chart(type = "column") %>%
    hc_xAxis(categories = indicator$date) %>%
    hc_add_series(name="Male _ Registered nurses",data = indicator$male) %>% 
    hc_add_series(name="Female _ Registered nurses",data = indicator$female) %>%
  hc_subtitle(text=str_glue("From {min(indicator$date)} through {max(indicator$date)}"), align = "center") %>%
     hc_title(text = "Registered nurses occupations by gender",
             style = list(fontWeight = "bold", fontSize = "20px"),
             align = "center")  %>%
  hc_credits(enabled = TRUE,text = "Data Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego") %>%
   hc_yAxis(title = list(text = "Thousands of Persons")) %>%
    hc_add_theme(hc_theme_economist())

#—————————————————————

Employed full time: Wage and salary workers: Physicians and surgeons occupations: 16 years and over: Women (LEU0254701200A)

Employed full time: Wage and salary workers: Physicians and surgeons occupations: 16 years and over: Men (LEU0254594400A)

index <-fredr_series_observations(series_id = "LEU0254594400A",
        observation_start = as.Date("2000-01-01")) 
index2 <-fredr_series_observations(series_id = "LEU0254701200A",
        observation_start = as.Date("2000-01-01")) 


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

colnames(indicator) <- "date"
indicator$male <- index$value
indicator$female <- index2$value



indicator %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) %>%
  column_spec(3, T, color = "blue")
date male female
2000-01-01 327 144
2001-01-01 345 167
2002-01-01 369 162
2003-01-01 364 167
2004-01-01 382 173
2005-01-01 375 187
2006-01-01 370 188
2007-01-01 413 197
2008-01-01 405 189
2009-01-01 404 211
2010-01-01 416 189
2011-01-01 366 206
2012-01-01 429 226
2013-01-01 426 241
2014-01-01 475 284
2015-01-01 457 283
2016-01-01 497 308
2017-01-01 463 352
2018-01-01 475 352
2019-01-01 507 349 
data_long <- melt(indicator, id.vars=c("date"))
colnames(data_long) <- c("date","Gender","Value")

# plotting data
data_long %>% ggplot() + 
  geom_line(mapping = aes(x=date,y=Value,color=Gender),size=1)  +
     labs(title = "Physicians and surgeons  by gender", 
       subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
       x="Yearly", y="Thousands of Persons",
       caption = "Data source: FRED Federal Reserve.   Illustration by @JoeLongSanDiego")+
    theme_economist()

highchart() %>% 
    hc_chart(type = "column") %>%
    hc_xAxis(categories = indicator$date) %>%
    hc_add_series(name="Male _ Physicians and surgeons",data = indicator$male) %>% 
    hc_add_series(name="Female _ Physicians and surgeons",data = indicator$female) %>%
  hc_subtitle(text=str_glue("From {min(indicator$date)} through {max(indicator$date)}"), align = "center") %>%
     hc_title(text = "Physicians and surgeons by gender",
             style = list(fontWeight = "bold", fontSize = "20px"),
             align = "center")  %>%
  hc_credits(enabled = TRUE,text = "Data Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego") %>%
   hc_yAxis(title = list(text = "Thousands of Persons")) %>%
    hc_add_theme(hc_theme_economist())

### Employed full time: Wage and salary workers: Secondary school teachers occupations: Men (LEU0254591000A) ### Employed full time: Wage and salary workers: Secondary school teachers occupations: Women (LEU0254697800A)

index <-fredr_series_observations(series_id = "LEU0254591000A",
        observation_start = as.Date("2000-01-01")) 
index2 <-fredr_series_observations(series_id = "LEU0254697800A",
        observation_start = as.Date("2000-01-01")) 


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

colnames(indicator) <- "date"
indicator$male <- index$value
indicator$female <- index2$value



indicator %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) %>%
  column_spec(3, T, color = "blue")
date male female
2000-01-01 417 595
2001-01-01 418 547
2002-01-01 403 512
2003-01-01 469 540
2004-01-01 458 555
2005-01-01 460 577
2006-01-01 453 535
2007-01-01 471 558
2008-01-01 490 596
2009-01-01 503 603
2010-01-01 504 612
2011-01-01 431 575
2012-01-01 465 578
2013-01-01 427 529
2014-01-01 432 549
2015-01-01 438 610
2016-01-01 403 562
2017-01-01 392 524
2018-01-01 392 556
2019-01-01 408 474 
data_long <- melt(indicator, id.vars=c("date"))
colnames(data_long) <- c("date","Gender","Value")

# plotting data
data_long %>% ggplot() + 
  geom_line(mapping = aes(x=date,y=Value,color=Gender),size=1)  +
     labs(title = " Secondary school teachers by gender", 
       subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
       x="Yearly", y="Thousands of Persons",
       caption = "Data source: FRED Federal Reserve.   Illustration by @JoeLongSanDiego")+
    theme_economist()

highchart() %>% 
    hc_chart(type = "column") %>%
    hc_xAxis(categories = indicator$date) %>%
    hc_add_series(name="Male _  Secondary school teachers",data = indicator$male) %>% 
    hc_add_series(name="Female _  Secondary school teachers",data = indicator$female) %>%
  hc_subtitle(text=str_glue("From {min(indicator$date)} through {max(indicator$date)}"), align = "center") %>%
     hc_title(text = " Secondary school teachers by gender",
             style = list(fontWeight = "bold", fontSize = "20px"),
             align = "center")  %>%
  hc_credits(enabled = TRUE,text = "Data Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego") %>%
   hc_yAxis(title = list(text = "Thousands of Persons")) %>%
    hc_add_theme(hc_theme_economist())

Employed full time: Wage and salary workers: Mathematicians occupations: 16 years and over (LEU0254477800A) Employed full time: Wage and salary workers: Statisticians occupations: 16 years and over (LEU0254478000A)

#——————————

index <-fredr_series_observations(series_id = "LEU0254477800A",
        observation_start = as.Date("2000-01-01")) 
index2 <-fredr_series_observations(series_id = "LEU0254478000A",
        observation_start = as.Date("2000-01-01")) 


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

colnames(indicator) <- "date"
indicator$mathematician <- index$value
indicator$statistician <- index2$value



indicator %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) %>%
  column_spec(3, T, color = "blue")
date mathematician statistician
2000-01-01 6 22
2001-01-01 7 20
2002-01-01 2 24
2003-01-01 3 15
2004-01-01 2 26
2005-01-01 2 30
2006-01-01 4 22
2007-01-01 3 31
2008-01-01 2 33
2009-01-01 3 33
2010-01-01 5 31
2011-01-01 2 30
2012-01-01 4 44
2013-01-01 2 65
2014-01-01 2 78
2015-01-01 6 76
2016-01-01 4 65
2017-01-01 2 77
2018-01-01 3 85
2019-01-01 2 90 
data_long <- melt(indicator, id.vars=c("date"))
colnames(data_long) <- c("date","Index","Value")

# plotting data
data_long %>% ggplot() + 
  geom_line(mapping = aes(x=date,y=Value,color=Index),size=1)  +
     labs(title = "Employment levels for mathematicians and statisticians", 
       subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
       x="Yearly", y="Thousands of Persons",
       caption = "Data source: FRED Federal Reserve.   Illustration by @JoeLongSanDiego")+
    theme_economist()

highchart() %>% 
    hc_chart(type = "column") %>%
    hc_xAxis(categories = indicator$date) %>%
    hc_add_series(name="Mathematicians",data = indicator$mathematician) %>% 
    hc_add_series(name="Statisticians",data = indicator$statistician) %>%
  hc_subtitle(text=str_glue("From {min(indicator$date)} through {max(indicator$date)}"), align = "center") %>%
     hc_title(text = "Employment levels for mathematicians and statisticians",
             style = list(fontWeight = "bold", fontSize = "20px"),
             align = "center")  %>%
  hc_credits(enabled = TRUE,text = "Data Source: FRED Federal Reserve _ Illustration by @JoeLongSanDiego") %>%
   hc_yAxis(title = list(text = "Thousands of Persons")) %>%
    hc_add_theme(hc_theme_economist())