Assignment 4

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

jobs <- read_csv("https://jsuleiman.com/datasets/me_grad_employment.csv")

Rows: 26 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): occupation, growth_rate, median_wage, entry_wage
dbl (4): base_employment, projected_employment, annual_openings, wage_year

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

jobs <- jobs |>
mutate(
growth_rate = round((projected_employment - base_employment) / base_employment * 100, 1)
) |>
select(
occupation,
base_employment,
projected_employment,
growth_rate,
annual_openings,
median_wage,
entry_wage
)

 jobs |>
  gt() |>
  tab_header(
    title = md("**Maine Job Outlook by Occupation (2020–2030)**"),
    subtitle = "Base vs. Projected Jobs, Growth, Openings, and Wages"
  ) |>

  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_column_labels(everything())
  ) |>

  opt_table_lines(extent = "none") |>
  opt_row_striping() |>

  cols_align(align = "right", columns = where(is.numeric)) |>

  cols_align(align = "left", columns = c(occupation)) |>

  fmt_number(columns = c(base_employment, projected_employment, annual_openings),
             decimals = 0, sep_mark = ",") |>
  fmt_percent(columns = growth_rate, scale_values = FALSE, decimals = 1) |>
  fmt_currency(columns = c(median_wage, entry_wage), currency = "USD", decimals = 0) |>

  data_color(
  columns = growth_rate,
  fn = scales::col_numeric(
    palette = c("white", "lightblue", "blue"),
    domain = NULL
  )
)|>

  cols_label(
    occupation = "Occupation",
    base_employment = "Base Employment (2020)",
    projected_employment = "Projected Employment (2030)",
    growth_rate = "Growth Rate",
    annual_openings = "Annual Openings",
    median_wage = "Median Wage",
    entry_wage = "Entry Wage"
  ) |>
  opt_table_font(font = google_font("Lato")) |>
  tab_options(
    table.width = pct(100),
    data_row.padding = px(4),
    heading.title.font.size = 16,
    heading.subtitle.font.size = 12
  )

Maine Job Outlook by Occupation (2020–2030)
Base vs. Projected Jobs, Growth, Openings, and Wages
Occupation	Base Employment (2020)	Projected Employment (2030)	Growth Rate	Annual Openings	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	0.6%	111	$98800	$74900
Physical Therapists	1,580	1,667	5.5%	71	$90590	$75330
Mental Health and Substance Abuse Social Workers	1,459	1,460	0.1%	100	$66380	$49370
Pharmacists	1,366	1,388	1.6%	53	$135430	$102210
Nurse Practitioners	1,332	1,804	35.4%	117	$123140	$99830
Occupational Therapists	1,161	1,200	3.4%	68	$79700	$63760
Librarians and Media Collections Specialists	914	904	−1.1%	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	22.1%	61	$131540	$109100
Speech-Language Pathologists	764	852	11.5%	52	$80210	$60110
Education Administrators, Postsecondary	752	738	−1.9%	48	$82460	$60440
Health Specialties Teachers, Postsecondary	706	797	12.9%	67	$84500	$59510
Art, Drama, and Music Teachers, Postsecondary	587	575	−2.0%	45	$78380	$51380
Veterinarians	536	571	6.5%	22	$128240	$90250
Healthcare Social Workers	484	495	2.3%	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	−6.3%	25	$84440	$67990
Education Teachers, Postsecondary	410	404	−1.5%	31	$76480	$51410
Nursing Instructors and Teachers, Postsecondary	404	452	11.9%	38	$77370	$55710
Postsecondary Teachers, All Other	378	371	−1.9%	28	$72170	$43980
Business Teachers, Postsecondary	309	313	1.3%	24	$81340	$56730
Biological Science Teachers, Postsecondary	303	311	2.6%	25	$79220	$59190

Analysis

To make the column headings bold, I used tab_style() with cell_text(weight = “bold”). This makes the headers more noticeable and makes it simpler to distinguish them from the numbers below. It improves the table’s organization and makes it easier for readers to understand where the data starts and the labels finish.

I used opt_table_lines(extent = “none”) to eliminate the thick lines that separated the cells in order to maintain the table’s cleanliness. I used opt_row_striping() to add striped rows in instead of borders. This keeps the table from appearing overly crowded while making it simpler to follow each row across it.

All of the numerical columns (like jobs, earnings, and growth rates) were aligned to the right using cols_align(). Because of matching the numbers in each column became easier. I aligned the text for the job titles to the left, which is how English words are typically read. The table appears more balanced and readable when the numbers are on the right and the sentences are on the left.

I rounded big numbers and added commas to make them easier to read using fmt_number() to improve the numbers’ appearance. Additionally, I displayed wages in full dollars using fmt_currency() and growth rates with one decimal place using fmt_percent(). This keeps the table tidy and keeps uneeded features from being displayed.

I used data_color() to add color to the growth_rate column even though it wasn’t necessary. This meets with Chapter 11 Rule 5, which says that important information should be highlighted. To make them less noticeable, I picked colors that range from grey to green. This keeps the table from seeming too vibrant or distracting while letting users easily determine which jobs are expanding the fastest.

Generative AI Help

https://chatgpt.com/share/67eef4af-797c-800f-8794-da7a37a17370