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

I used cell_text(weight = “bold”) together with tab_style() to make the column headings stand out from the rest of the table. In order to distinguish them from the numbers below, this made the headers bold. It makes the table seem more ordered, and the reader is aware of the precise boundaries between the data and the labels. I used opt_table_lines(extent = “none”) to eliminate the thick lines that were generated between cells in order to simplify the table and make it less cluttered. I used opt_row_striping() to add decorated rows in place of borders, which helps direct the reader’s attention across the table without making it seem cluttered. I used cols_align() to right-align all of the numeric columns, including employment, growth, and earnings, since it makes comparing figures simpler. When you want to rapidly identify the greatest or lowest numbers in a column, this is helpful. I also used cols_align() to align the text on the job titles to the left. This is the most natural method to read words in English, and it facilitates the process of looking through job titles. Additionally, the numbers’ alignment to the right creates an attractive balance. Additionally, I improved the way the numbers are shown. Since large values, such as job counts and vacant positions, don’t need decimal places, I rounded them and added commas using fmt_number(). I displayed salaries as entire dollar amounts using fmt_currency() and growth rates as one decimal place using fmt_percent(). This keeps the table tidy and prevents unnecessary information from being shown. I used data_color() to add a color scale to the growth_rate column in order to draw attention to the most significant numbers. The blue becomes darker as the growth rate increases. This eliminates the need for more explanation and makes it very simple to identify the occupations with the greatest rates of growth.

Generative AI Help

https://chatgpt.com/share/67eef19d-3874-800f-956c-68c384b5f163