wakefield (version 0.3.3)

education: Generate Random Vector of Educational Attainment Level

Description

Generate a random vector of educational attainment level.

Usage

education(n, x = c("No Schooling Completed", "Nursery School to 8th Grade",
  "9th Grade to 12th Grade, No Diploma", "Regular High School Diploma",
  "GED or Alternative Credential", "Some College, Less than 1 Year",
  "Some College, 1 or More Years, No Degree", "Associate's Degree",
  "Bachelor's Degree", "Master's Degree", "Professional School Degree",
  "Doctorate Degree"), prob = c(0.013, 0.05, 0.085, 0.246, 0.039, 0.064, 0.15,
  0.075, 0.176, 0.072, 0.019, 0.012), name = "Education")

Arguments

n

The number elements to generate. This can be globally set within the environment of r_data_frame or r_list.

x

A vector of elements to chose from.

prob

A vector of probabilities to chose from.

name

The name to assign to the output vector's varname attribute. This is used to auto assign names to the column/vector name when used inside of r_data_frame or r_list.

Value

Returns a random vector of educational attainment level elements.

Details

The educational attainments and probabilities used match approximate U.S. educational attainment make-up (http://www.census.gov):

Highest Attainment	Percent
No Schooling Completed	1.3 %
Nursery School to 8th Grade	5 %
9th Grade to 12th Grade, No Diploma	8.5 %
Regular High School Diploma	24.6 %
GED or Alternative Credential	3.9 %
Some College, Less than 1 Year	6.4 %
Some College, 1 or More Years, No Degree	15 %
Associate's Degree	7.5 %
Bachelor's Degree	17.6 %
Master's Degree	7.2 %
Professional School Degree	1.9 %
Doctorate Degree	1.2 %

References

http://www.census.gov

See Also

Other variable functions: age, animal, answer, area, car, children, coin, color, date_stamp, death, dice, dna, dob, dummy, employment, eye, grade_level, grade, group, hair, height, income, internet_browser, iq, language, level, likert, lorem_ipsum, marital, military, month, name, normal, political, race, religion, sat, sentence, sex_inclusive, sex, smokes, speed, state, string, upper, valid, year, zip_code

Examples

# NOT RUN {
education(10)
pie(table(education(10000)))
# }