Code
library(tidyverse)
library(gt)
library(gtExtras)
jobs <- read_csv("https://jsuleiman.com/datasets/me_grad_employment.csv")Assignment 4
Occupation Table
Code
jobs <- jobs |>
mutate(
growth_rate = str_remove(growth_rate, "%") |> as.numeric() / 100,
median_wage = parse_number(median_wage),
entry_wage = parse_number(entry_wage)
) |>
arrange(desc(median_wage))
# Create the gt table
jobstable <- jobs |>
select(
occupation, base_employment, projected_employment, growth_rate,
annual_openings, median_wage, entry_wage
) |>
gt() |>
cols_label(
occupation = "Occupation",
base_employment = "Base Employment",
projected_employment = "Projected Employment",
growth_rate = "Growth Rate",
annual_openings = "Annual Openings",
median_wage = "Median Wage",
entry_wage = "Entry Wage"
) |>
tab_header(
title = "Maine Employment Projections",
subtitle = "Jobs That Require an Educational Degree"
) |>
fmt_number(
columns = c("base_employment", "projected_employment", "annual_openings"),
decimals = 0,
use_seps = TRUE
) |>
fmt_percent(
columns = c("growth_rate"),
decimals = 2
) |>
fmt_currency(
columns = c("median_wage", "entry_wage"),
currency = "USD",
decimals = 2
) |>
tab_style(
style = cell_text(align = "left", weight = "normal"),
locations = cells_body(columns = c("occupation", "growth_rate"))
) |>
tab_style(
style = cell_text(align = "right"),
locations = cells_body(columns = c("base_employment", "projected_employment", "annual_openings", "median_wage", "entry_wage"))
) |>
tab_style(
style = cell_text(align = "left", weight = "bold"),
locations = cells_column_labels(columns = everything())
) |>
tab_style(
style = cell_fill(color = "lightgrey"),
locations = cells_body(
columns = c("median_wage"),
rows = median_wage == max(jobs$median_wage) | median_wage == min(jobs$median_wage)
)
) |>
tab_style(
style = cell_borders(sides = "top", color = "grey", weight = px(1)),
locations = cells_column_labels()
) |>
tab_style(
style = cell_borders(sides = "bottom", color = "grey", weight = px(1)),
locations = cells_column_labels()
)
jobstable| Maine Employment Projections | ||||||
|---|---|---|---|---|---|---|
| Jobs That Require an Educational Degree | ||||||
| Occupation | Base Employment | Projected Employment | Growth Rate | Annual Openings | Median Wage | Entry Wage |
| Pharmacists | 1,366 | 1,388 | 2.00% | 53 | $135,430.00 | $102,210.00 |
| Physician Assistants | 795 | 971 | 22.00% | 61 | $131,540.00 | $109,100.00 |
| Veterinarians | 536 | 571 | 7.00% | 22 | $128,240.00 | $90,250.00 |
| Nurse Practitioners | 1,332 | 1,804 | 35.00% | 117 | $123,140.00 | $99,830.00 |
| Education Administrators, Kindergarten through Secondary | 1,673 | 1,683 | 1.00% | 111 | $98,800.00 | $74,900.00 |
| Lawyers | 2,725 | 2,862 | 5.00% | 121 | $98,760.00 | $66,090.00 |
| Physical Therapists | 1,580 | 1,667 | 6.00% | 71 | $90,590.00 | $75,330.00 |
| Psychologists, All Other | 893 | 920 | 3.00% | 60 | $85,520.00 | $62,970.00 |
| Health Specialties Teachers, Postsecondary | 706 | 797 | 13.00% | 67 | $84,500.00 | $59,510.00 |
| Biochemists and Biophysicists | 414 | 388 | −6.00% | 25 | $84,440.00 | $67,990.00 |
| Education Administrators, Postsecondary | 752 | 738 | −2.00% | 48 | $82,460.00 | $60,440.00 |
| English Language and Literature Teachers, Postsecondary | 428 | 411 | −4.00% | 30 | $81,960.00 | $61,310.00 |
| Business Teachers, Postsecondary | 309 | 313 | 1.00% | 24 | $81,340.00 | $56,730.00 |
| Speech-Language Pathologists | 764 | 852 | 12.00% | 52 | $80,210.00 | $60,110.00 |
| Occupational Therapists | 1,161 | 1,200 | 3.00% | 68 | $79,700.00 | $63,760.00 |
| Biological Science Teachers, Postsecondary | 303 | 311 | 3.00% | 25 | $79,220.00 | $59,190.00 |
| Art, Drama, and Music Teachers, Postsecondary | 587 | 575 | −2.00% | 45 | $78,380.00 | $51,380.00 |
| Nursing Instructors and Teachers, Postsecondary | 404 | 452 | 12.00% | 38 | $77,370.00 | $55,710.00 |
| Education Teachers, Postsecondary | 410 | 404 | −1.00% | 31 | $76,480.00 | $51,410.00 |
| Instructional Coordinators | 895 | 895 | 0.00% | 76 | $73,760.00 | $53,990.00 |
| Postsecondary Teachers, All Other | 378 | 371 | −2.00% | 28 | $72,170.00 | $43,980.00 |
| Mental Health and Substance Abuse Social Workers | 1,459 | 1,460 | 0.00% | 100 | $66,380.00 | $49,370.00 |
| Acupuncturists | 432 | 432 | 0.00% | 26 | $65,040.00 | $44,520.00 |
| Healthcare Social Workers | 484 | 495 | 2.00% | 42 | $64,040.00 | $54,770.00 |
| Librarians and Media Collections Specialists | 914 | 904 | −1.00% | 83 | $59,010.00 | $41,020.00 |
| Educational, Guidance, and Career Counselors and Advisors | 1,679 | 1,712 | 2.00% | 122 | $56,780.00 | $42,910.00 |
Table Narrative
Rule 1: Offset the Headers from the Body
Implementation: To clearly distinguish the table headers from the body, I applied styling to the headers. This is achieved by using tab_style() with the cell_text(align = “left”, weight = “bold”) function, which adjusts the text alignment to left and makes it bold. This approach ensures that the headers stand out from the data, making it easier for users to navigate through the table quickly.
Rule 2: Use Subtle Dividers Instead of Heavy Gridlines
Implementation: Instead of using heavy gridlines that can clutter the visual presentation of the table, I opted for subtle dividers to separate the headers from the data rows. This was done by employing tab_style() to add minimalistic borders at the top and bottom of the column headers using cell_borders(sides = “top” and “bottom”). The choice of subtle dividers enhances the table’s readability by providing necessary separation without overwhelming the data visually.
Rule 3: Right-Align Numbers and Headers
Implementation: Numerical data and their corresponding headers were right-aligned to facilitate easy comparison across rows. This was configured using tab_style() with cell_text(align = “right”) for cells containing numerical data such as base_employment and median_wage, enhancing the table’s readability and precision.
Rule 4: Left-Align Text and Headers
Implementation: For textual data and headers, I implemented left alignment. This is particularly useful for non-numerical information, where left alignment aids in readability. Adjustments were made via tab_style(), specifying cell_text(align = “left”) for columns like occupation and growth_rate.
Rule 5: Select The Appropriate Level of Precision
Implementation: Precision in numerical presentation was carefully adjusted to balance detail with clarity. For instance, employment figures were formatted without decimals, using fmt_number() with decimals = 0. Growth rates were formatted with two decimal points to reflect more precise variations, applied through fmt_percent().
Rule 8: Highlight Outliers
Implementation: Outliers in the median_wage data were highlighted with a light grey fill (cell_fill(color = “lightgrey”)) to draw attention to these key figures. This styling was applied conditionally to the maximum and minimum values within the median_wage column, using a logical condition within tab_style().