tidyTuesday meets the Economics of Majors

Last updated on Apr 17, 2021 3 min read

This week’s tidyTuesday focuses on degrees and majors and their deployment in the labor market. The original data came from 538. A description of sources and measures. The tidyTesday writeup is here.

library(tidyverse)
options(scipen=6)
library(extrafont)
font_import()

## Importing fonts may take a few minutes, depending on the number of fonts and the speed of the system.
## Continue? [y/n]

Major.Employment <- read.csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2018/2018-10-16/recent-grads.csv")
library(skimr)
skim(Major.Employment)

Table 1: Data summary
Name	Major.Employment
Number of rows	173
Number of columns	21
_______________________
Column type frequency:
character	2
numeric	19
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
Major	0	1	5	65	0	173	0
Major_category	0	1	4	35	0	16	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Rank	0	1.00	87.00	50.08	1	44.00	87.00	130.00	173.00	▇▇▇▇▇
Major_code	0	1.00	3879.82	1687.75	1100	2403.00	3608.00	5503.00	6403.00	▃▇▅▃▇
Total	1	0.99	39370.08	63483.49	124	4549.75	15104.00	38909.75	393735.00	▇▁▁▁▁
Men	1	0.99	16723.41	28122.43	119	2177.50	5434.00	14631.00	173809.00	▇▁▁▁▁
Women	1	0.99	22646.67	41057.33	0	1778.25	8386.50	22553.75	307087.00	▇▁▁▁▁
ShareWomen	1	0.99	0.52	0.23	0	0.34	0.53	0.70	0.97	▂▆▆▇▃
Sample_size	0	1.00	356.08	618.36	2	39.00	130.00	338.00	4212.00	▇▁▁▁▁
Employed	0	1.00	31192.76	50675.00	0	3608.00	11797.00	31433.00	307933.00	▇▁▁▁▁
Full_time	0	1.00	26029.31	42869.66	111	3154.00	10048.00	25147.00	251540.00	▇▁▁▁▁
Part_time	0	1.00	8832.40	14648.18	0	1030.00	3299.00	9948.00	115172.00	▇▁▁▁▁
Full_time_year_round	0	1.00	19694.43	33160.94	111	2453.00	7413.00	16891.00	199897.00	▇▁▁▁▁
Unemployed	0	1.00	2416.33	4112.80	0	304.00	893.00	2393.00	28169.00	▇▁▁▁▁
Unemployment_rate	0	1.00	0.07	0.03	0	0.05	0.07	0.09	0.18	▂▇▆▁▁
Median	0	1.00	40151.45	11470.18	22000	33000.00	36000.00	45000.00	110000.00	▇▅▁▁▁
P25th	0	1.00	29501.45	9166.01	18500	24000.00	27000.00	33000.00	95000.00	▇▂▁▁▁
P75th	0	1.00	51494.22	14906.28	22000	42000.00	47000.00	60000.00	125000.00	▅▇▂▁▁
College_jobs	0	1.00	12322.64	21299.87	0	1675.00	4390.00	14444.00	151643.00	▇▁▁▁▁
Non_college_jobs	0	1.00	13284.50	23789.66	0	1591.00	4595.00	11783.00	148395.00	▇▁▁▁▁
Low_wage_jobs	0	1.00	3859.02	6945.00	0	340.00	1231.00	3466.00	48207.00	▇▁▁▁▁

A scatterplot of the unemployment rate by majors is the first goal with a color scheme that reflects the proportion of females in the industry.

my.plot <- Major.Employment %>% ggplot(aes(Unemployment_rate,Median, label=str_to_title(Major), color=ShareWomen)) +
  geom_point() +
  geom_text(check_overlap = T, vjust=-0.5, nudge_y=0.1, size=2.5) +
  theme_minimal() +
  scale_color_gradient(name="Share of Women", low="#de2d26", high = "#e9a3c9") + 
  scale_y_continuous(labels = scales::comma) +
  scale_x_continuous(labels = scales::percent) + 
  xlab("Unemployment Rate") +
  ylab("Median Income") +
  ggtitle("Median Income and Unemployment") +
  theme(text=element_text(size=8), title = element_text(size=12)) 
my.plot

Major.Employment <- Major.Employment %>% mutate(ShareCol= College_jobs / Total)
my.plot <- Major.Employment %>% ggplot(aes(Unemployment_rate,ShareCol, label=str_to_title(Major), color=ShareWomen)) +
  geom_point(alpha=0.1) +
  geom_text(check_overlap = T, size=1.5) +
  theme_minimal() +
  scale_color_gradient(name="Share of Women", low="#de2d26", high = "#e9a3c9") + 
#  scale_y_continuous(labels = scales::comma) +
  scale_x_continuous(labels = scales::percent) + 
  xlab("Unemployment Rate") +
  ylab("College Pct.") +
  ggtitle("College Pct. Jobs and Unemployment")
my.plot

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_text).

An Esquisse starter. Unemployment rate is x. Median wage is y. Major categories are colors and size is a function of Total

ggplot(data = Major.Employment) +
aes(x = Unemployment_rate, y = Median, color = Major_category, size = Total) +
geom_point() +
theme_minimal()

## Warning: Removed 1 rows containing missing values (geom_point).

Major.Employment %>% drop_na() %>% ggplot() +
  aes(x = Unemployment_rate, y = Median, color = ShareWomen, label=str_to_title(Major)) +
#  geom_point() +
  geom_text(check_overlap = T, size=2) +
  theme_minimal() +
  scale_color_gradient(name="Share of Women", low="#cda7ca", high = "#3d323c") + 
  scale_x_continuous(labels = scales::percent) + 
  scale_y_continuous(labels = scales::comma) +
  xlab("Unemployment Rate") +
  ylab("Median Wage") +
  ggtitle("Wages and Unemployment with Women in the Profession")