Assignment 4

Author

Justin Vachon

1. Occupation Table

Data Loading Code

Code 
library(tidyverse)
library(gt)
library(gtExtras)
jobs <- read_csv("https://jsuleiman.com/datasets/me_grad_employment.csv")

Adjusted text to remove “%”, fix decimals, and turn - into ()

Code 
jobs_table <- jobs |>
  mutate(
    growth_rate = as.numeric(str_replace(growth_rate, "%", ""))) |>
  select(-wage_year) |>
  gt() |>
  fmt_number(
    columns = c(base_employment, projected_employment, annual_openings, median_wage, entry_wage),
    decimals = 0,
    use_seps = TRUE
  ) |>
  fmt_number(
    columns = growth_rate, 
    accounting = TRUE,
    decimals = 1
  )

Added a title, adjusted heading, and added sources at the bottom

Code 
jobs_table <- jobs_table |>
  tab_header(
    title = "Maine Occupations 2023"
  ) |>
  tab_options(
    heading.align = "left"
  ) |>
  cols_align(
    align = "right"
  ) |>
  tab_source_note( 
    source_note = md("**Source:** https://jsuleiman.com/datasets/me_grad_employment.csv"))

Adjusted label titles

Code 
jobs_table <- jobs_table |>
  tab_spanner(label = "US $", columns = c(median_wage, entry_wage)) |>
  cols_label(
    occupation = md("**Occupation**"),
    base_employment = md("**Base Employment**"),
    projected_employment = md("**Projected Employment**"),
    growth_rate = md("**Growth Rate %**"),
    annual_openings = md("**Annual Openings**"),
    median_wage = md("**Median Wage**"),
    entry_wage = md("**Entry Wage**")
  )

Added a sum at the bottom of the table

Code 
jobs_table <- suppressWarnings(

  jobs_table |>
  grand_summary_rows(
    columns = c(base_employment, projected_employment, annual_openings),
    fns = list(Total = ~sum(.)),
    formatter = fmt_number,
    decimals = 0,
    use_seps = TRUE
  ) 
)

Added color to the negative numbers under the growth rate

Code 
jobs_table |> 
  tab_style(
    style = cell_text(color = "red", weight = "bold"), 
    locations = cells_body(
      columns = growth_rate, 
      rows = growth_rate < 0 
    )
  )
Maine Occupations 2023
Occupation Base Employment Projected Employment Growth Rate % Annual Openings
US $
Median Wage Entry Wage
Lawyers 2,725 2,862 5.0 121 $98760 $66090
Educational, Guidance, and Career Counselors and Advisors 1,679 1,712 2.0 122 $56780 $42910
Education Administrators, Kindergarten through Secondary 1,673 1,683 1.0 111 $98800 $74900
Physical Therapists 1,580 1,667 6.0 71 $90590 $75330
Mental Health and Substance Abuse Social Workers 1,459 1,460 0.0 100 $66380 $49370
Pharmacists 1,366 1,388 2.0 53 $135430 $102210
Nurse Practitioners 1,332 1,804 35.0 117 $123140 $99830
Occupational Therapists 1,161 1,200 3.0 68 $79700 $63760
Librarians and Media Collections Specialists 914 904 (1.0) 83 $59010 $41020
Instructional Coordinators 895 895 0.0 76 $73760 $53990
Psychologists, All Other 893 920 3.0 60 $85520 $62970
Physician Assistants 795 971 22.0 61 $131540 $109100
Speech-Language Pathologists 764 852 12.0 52 $80210 $60110
Education Administrators, Postsecondary 752 738 (2.0) 48 $82460 $60440
Health Specialties Teachers, Postsecondary 706 797 13.0 67 $84500 $59510
Art, Drama, and Music Teachers, Postsecondary 587 575 (2.0) 45 $78380 $51380
Veterinarians 536 571 7.0 22 $128240 $90250
Healthcare Social Workers 484 495 2.0 42 $64040 $54770
Acupuncturists 432 432 0.0 26 $65040 $44520
English Language and Literature Teachers, Postsecondary 428 411 (4.0) 30 $81960 $61310
Biochemists and Biophysicists 414 388 (6.0) 25 $84440 $67990
Education Teachers, Postsecondary 410 404 (1.0) 31 $76480 $51410
Nursing Instructors and Teachers, Postsecondary 404 452 12.0 38 $77370 $55710
Postsecondary Teachers, All Other 378 371 (2.0) 28 $72170 $43980
Business Teachers, Postsecondary 309 313 1.0 24 $81340 $56730
Biological Science Teachers, Postsecondary 303 311 3.0 25 $79220 $59190
Total — 23,379 24,576 — 1,546 — —
Source: https://jsuleiman.com/datasets/me_grad_employment.csv

Table Narrative

  1. To offset the head from the body, I made them bold using title = md(“new title”)
  2. Subtle borders are already added to the gt table separating each row and I don’t feel like they are too dark to distract the view
  3. To right align numbers, I used cols_align( align = “right”)
  4. To left align text and heads, I used tab_options( heading.align = “left” )
  5. Selecting the appropriate amount of precision, I stuck with 1 decimal point on the percentage growth rate and left everything else with no decimal
  6. To highlight the outliers (negatives), I used tab_style( style = cell_text(color = “red”, weight = “bold”)