Tidy Tuesday Week 4

Felipe Flores

Import Data

data <- read_csv(file = "week4_australian_salary.csv") %>% 
# The dataset for some reason included some \x96 and \x92 characters that I simply replaced
  mutate(occupation = str_replace(occupation, pattern = "\x96", replacement = "-")) %>% 
  mutate(occupation = str_replace(occupation, pattern = "\x92", replacement = "")) %>% 
# Then I arrange by gender and income
  arrange(gender, desc(average_taxable_income)) %>% 
# And let's only use these variables
  select(c(occupation, gender, average_taxable_income))

Tidy up

Here is my approach to getting the first 50

top_jobs <- data %>% 
  group_by(gender) %>% 
  top_n(50)

Female top 50 jobs

top_jobs %>% 
  filter(gender == "Female") %>% 
  select(-gender) %>% 
  knitr::kable()

gender	occupation	average_taxable_income
Female	Judge - law	355844
Female	Neurosurgeon	323682
Female	Plastic and reconstructive surgeon	281608
Female	Futures trader	281600
Female	Vascular surgeon	271529
Female	Gynaecologist; Obstetrician	264628
Female	Gastroenterologist	260925
Female	Magistrate	260161
Female	Anaesthetist	243582
Female	Ophthalmologist	217242
Female	Cardiologist	215920
Female	Urologist	213094
Female	Surgeon - general	210796
Female	Medical oncologist	208612
Female	Specialist physicians - other	207599
Female	Specialist physician - general medicine	207225
Female	Otorhinolaryngologist	200136
Female	Dermatologist	195030
Female	Diagnostic and interventional radiologist	180695
Female	Cardiothoracic surgeon	175500
Female	Paediatric surgeon	175314
Female	Endocrinologist	174542
Female	Member of parliament	173331
Female	Rheumatologist	169409
Female	Intensive care specialist	169369
Female	Emergency medicine specialist	165786
Female	Orthopaedic surgeon	159479
Female	Neurologist	155217
Female	Renal medicine specialist	155133
Female	Psychiatrist	152437
Female	Clinical haematologist	147970
Female	Paediatrician	147347
Female	Securities and finance dealer	145208
Female	Dental specialist	140505
Female	Actuary	136819
Female	Radiation oncologist	135678
Female	Financial investment manager	134481
Female	Petroleum engineer	133315
Female	Mining production manager	133061
Female	General medical practitioner	129834
Female	Thoracic medicine specialist	127645
Female	Stock exchange dealer; Stockbroker	124433
Female	Paving plant operator	123281
Female	Mining engineer	119564
Female	Tribunal member	119219
Female	Doctor - medical practitioner other; Occupational medicine specialist; Public health physician; Sports physician	118310
Female	Geophysicist	117575
Female	Chief executive officer; Executive director; Managing director; Public servant - secretary or deputy secretary	116855
Female	Engineering manager	116732
Female	Metallurgist	110359

Male top 50 jobs

top_jobs %>% 
  filter(gender == "Male") %>% 
  select(-gender) %>% 
  knitr::kable()

## Adding missing grouping variables: `gender`

gender	occupation	average_taxable_income
Male	Neurosurgeon	577674
Male	Ophthalmologist	552947
Male	Cardiologist	453253
Male	Plastic and reconstructive surgeon	448530
Male	Gynaecologist; Obstetrician	446507
Male	Otorhinolaryngologist	445939
Male	Orthopaedic surgeon	439629
Male	Urologist	433792
Male	Vascular surgeon	417524
Male	Gastroenterologist	415192
Male	Diagnostic and interventional radiologist	386003
Male	Dermatologist	383880
Male	Judge - law	381323
Male	Anaesthetist	370492
Male	Cardiothoracic surgeon	358043
Male	Surgeon - general	357996
Male	Specialist physicians - other	344860
Male	Radiation oncologist	336994
Male	Medical oncologist	322178
Male	Securities and finance dealer	320452
Male	Thoracic medicine specialist	315444
Male	Specialist physician - general medicine	315114
Male	Intensive care specialist	308033
Male	Renal medicine specialist	298681
Male	Neurologist	298543
Male	Financial investment manager	288790
Male	Investment broker	286530
Male	Paediatric surgeon	282508
Male	Clinical haematologist	271738
Male	Futures trader	264830
Male	Endocrinologist	258972
Male	Cricketer	257527
Male	Rheumatologist	256933
Male	Dental specialist	253442
Male	Magistrate	246737
Male	Equities analyst; Investment dealer	245826
Male	Paediatrician	239405
Male	Stock exchange dealer; Stockbroker	238192
Male	Psychiatrist	234557
Male	Emergency medicine specialist	232595
Male	Member of parliament	232093
Male	Pathologist	224378
Male	Company secretary - corporate governance	218432
Male	State governor	212652
Male	Actuary	196144
Male	Doctor - medical practitioner other; Occupational medicine specialist; Public health physician; Sports physician	187468
Male	Petroleum engineer	185808
Male	General medical practitioner	184639
Male	Chief executive officer; Executive director; Managing director; Public servant - secretary or deputy secretary	181849
Male	Mining production manager	179439

Graphing

data %>%
  # Scale by hundred thousand dollars
  mutate(income = average_taxable_income/100000) %>%
  # Select the top 10 per gender
  group_by(gender) %>% 
  top_n(10) %>% 
  # Create a rank for the plot
  mutate(rank = rank(income)) %>% 
  # This is so genders are split left and right
  mutate(income = ifelse(test = gender == "Female", yes = -income, no = income)) %>% 
  ggplot(aes(x = rank, y = income, fill = gender, ymin = -6, ymax = 8.5)) +
  geom_col() +
  geom_text(aes(x = rank,y = 0.5*income, label = occupation), size = 1.8) +
  geom_label(
    aes(
      x = rank,
      y = ifelse(test = gender == "Female", 1.5 * income, 1.3 * income),
      label = paste("$", format(100000 * abs(income), big.mark = ","))
    ), 
    fill = "white") +
  ylab("Average Taxable Income (Hundreds of Thousands of Dollars)") +
  labs(title = "Top 10 Highest-Paid Occupations", fill = "Gender") +
  scale_y_continuous(labels = abs) +
  theme_bw() +
  coord_flip() +
  theme(axis.title.y = element_blank(),
        axis.text.y = element_blank(), 
        axis.ticks.y = element_blank()) 

ggsave(filename = "plot1.png", device = "png", plot = last_plot())