Assignment 4

Author

Azam Jiwa

1. Occupation Table

Code 
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
Code 
library(tidyverse)
library(gt)
library(gtExtras)

b_jobs <- read_csv("https://jsuleiman.com/datasets/me_grad_employment.csv") |> 
  mutate(
    growth_rate = parse_number(growth_rate) / 100,  
)

b_jobs_table <- b_jobs |> 
  select(occupation, base_employment, projected_employment, growth_rate, annual_openings, median_wage, entry_wage) |> 
  gt() |> 
  tab_header(
    title = "Occupational Employment in Maine",
    subtitle = "Jobs Requiring a Master's, Doctoral, or Professional Degree"
  ) |> 
  fmt_number(columns = c(base_employment, projected_employment, annual_openings), sep_mark = ",", decimals = 0) |> 
  fmt_percent(columns = growth_rate, decimals = 2) |> 
  fmt_currency(columns = c(median_wage, entry_wage), currency = "USD") |> 
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_column_labels()
  ) |> 
  tab_style(
    style = cell_borders(sides = "bottom", color = "gray", weight = px(1)),
    locations = cells_column_labels()
  ) |> 
  cols_align(align = "right", columns = c(base_employment, projected_employment, annual_openings, median_wage, entry_wage)) |> 
  cols_align(align = "left", columns = occupation) |> 
  fmt_number(columns = growth_rate, decimals = 2) |> 
  tab_style(
    style = cell_text(weight = "bold", color = "blue"),
    locations = cells_body(columns = growth_rate, rows = growth_rate > 0.05)
  ) |> 
  tab_source_note("Data Source: Maine Department of Labor") 

b_jobs_table
Occupational Employment in Maine
Jobs Requiring a Master's, Doctoral, or Professional Degree
occupation base_employment projected_employment growth_rate annual_openings median_wage entry_wage
Lawyers 2,725 2,862 0.05 121 $98760 $66090
Educational, Guidance, and Career Counselors and Advisors 1,679 1,712 0.02 122 $56780 $42910
Education Administrators, Kindergarten through Secondary 1,673 1,683 0.01 111 $98800 $74900
Physical Therapists 1,580 1,667 0.06 71 $90590 $75330
Mental Health and Substance Abuse Social Workers 1,459 1,460 0.00 100 $66380 $49370
Pharmacists 1,366 1,388 0.02 53 $135430 $102210
Nurse Practitioners 1,332 1,804 0.35 117 $123140 $99830
Occupational Therapists 1,161 1,200 0.03 68 $79700 $63760
Librarians and Media Collections Specialists 914 904 −0.01 83 $59010 $41020
Instructional Coordinators 895 895 0.00 76 $73760 $53990
Psychologists, All Other 893 920 0.03 60 $85520 $62970
Physician Assistants 795 971 0.22 61 $131540 $109100
Speech-Language Pathologists 764 852 0.12 52 $80210 $60110
Education Administrators, Postsecondary 752 738 −0.02 48 $82460 $60440
Health Specialties Teachers, Postsecondary 706 797 0.13 67 $84500 $59510
Art, Drama, and Music Teachers, Postsecondary 587 575 −0.02 45 $78380 $51380
Veterinarians 536 571 0.07 22 $128240 $90250
Healthcare Social Workers 484 495 0.02 42 $64040 $54770
Acupuncturists 432 432 0.00 26 $65040 $44520
English Language and Literature Teachers, Postsecondary 428 411 −0.04 30 $81960 $61310
Biochemists and Biophysicists 414 388 −0.06 25 $84440 $67990
Education Teachers, Postsecondary 410 404 −0.01 31 $76480 $51410
Nursing Instructors and Teachers, Postsecondary 404 452 0.12 38 $77370 $55710
Postsecondary Teachers, All Other 378 371 −0.02 28 $72170 $43980
Business Teachers, Postsecondary 309 313 0.01 24 $81340 $56730
Biological Science Teachers, Postsecondary 303 311 0.03 25 $79220 $59190
Data Source: Maine Department of Labor

Table Narrative

In this section, I will describe how I implemented each of the six required rules from Chapter 11 and the formatting decisions I made for the occupational employment table for jobs requiring a master’s, doctoral, or professional degree in Maine. The goal was to create a clear, legible table that adheres to Schwabish’s guidelines for better tables, ensuring that important trends are easy to spot and the table overall is easy to interpret.

Rule 1: Offset the Headers from the Body

Make column headers distinct from the data using boldface text or lines to clearly separate them.

To implement Rule 1, I made the column headers bold to offset them from the body of the table. This visual distinction makes it clear that the headers are categories (such as “Occupation” and “Growth Rate”) and not data values. The bolded headers help the reader quickly navigate the table by separating the metadata (headers) from the numerical data.

Rule 2: Use Subtle Dividers Instead of Heavy Gridlines

Lighten or remove heavy gridlines, using subtle dividers or shading to separate important data without creating visual clutter.

For Rule 2, I replaced heavy gridlines with subtle dividers between rows and columns. Specifically, I used thin gray borders to separate the headers and data. This reduces visual clutter and enhances readability by allowing the reader to focus on the data without being distracted by excessive lines. I also ensured that the table’s readability was maintained by only applying borders to the necessary elements.

Rule 3: Right-align Numbers

Align numbers to the right to make it easier to compare values. Ensure that the column headers are also right-aligned with the numbers.

To implement Rule 3, I right-aligned all numeric columns in the table, including base employment, projected employment, annual openings, median wage, and entry wage. Right alignment helps the reader easily compare numbers, especially for large values, as it ensures that digits are properly aligned by their decimal point or comma. This improves the legibility of the numeric data and allows for easier visual comparisons across rows.

Rule 4: Left-align Text

Left-align text in the table for readability, making it easier for the reader to scan labels or text entries.

For Rule 4, I left-aligned the occupation column. This is the natural alignment for textual data in tables, as it makes it easier to scan and read the occupation names. Left alignment also ensures that the text is aligned consistently, creating a clean visual structure for the reader.

Rule 5: Select the Appropriate Level of Precision

Avoid over-precision in numbers, and balance between adequate detail and a clean, readable table.

For Rule 5, I limited the growth rate to two decimal places. The growth rate was initially in percentage form (e.g., 5% for .05) rather than decimal format. Since the data was in decimal format, I decided that two decimal places provided an appropriate level of precision for the growth rate. This ensures that the values are clear and concise without unnecessary detail, which would otherwise clutter the table.

Rule 8: Highlight Outliers

Highlight outliers or important data points using bold text or shading to make them stand out and capture the reader’s attention.

To implement Rule 8, I identified and highlighted outlier occupations based on their growth rate. Specifically, I focused on occupations where the growth rate was greater than 5%, or 0.05. These occupations were highlighted in blue text for quick identification. Of the 26 occupations in the dataset, 7 had growth rates above 5%. The following occupations had the highest growth rates:

  • Physical Therapists (6% growth rate)

  • Nurse Practitioners (35% growth rate)

  • Physician Assistants (22% growth rate)

  • Speech-Language Pathologists (12% growth rate)

  • Health Specialties Teachers, Postsecondary (13% growth rate)

  • Veterinarians (7% growth rate)

  • Nursing Instructors and Teachers, Postsecondary (12% growth rate)

Among these, Nurse Practitioners had the highest growth rate of 35%, with base employment of 1,332 in 2020 and projected employment of 1804 in 2030. Highlighting these outliers helps the reader quickly identify the fastest-growing occupations, which could be of particular interest to anyone exploring job trends in Maine.

Generative AI

I used ChatGPT to draft the report structure, brainstorm visualization choices, and refine analysis language. Here is the transcript.

make me a table using r with 7 columns and 26 rows

ChatGPT said:

Here’s an R code snippet to create a table with 7 columns and 26 rows. You can run this in your R environment:

R

CopyEdit

# Create an empty data frame with 26 rows and 7 columns table_data <- data.frame(matrix(ncol = 7, nrow = 26)) # Assign column names colnames(table_data) <- c("Column1", "Column2", "Column3", "Column4", "Column5", "Column6", "Column7") # View the table print(table_data)

This code creates a table with 7 columns and 26 rows, where the columns are named Column1, Column2, etc. You can modify the column names and fill in the table with actual data as needed.