Title: Data Sets for Craig Starbuck's Book, "The Fundamentals of People Analytics: With Applications in R"
Version: 0.1.0
Description: Data sets associated with modeling examples in Craig Starbuck's book, "The Fundamentals of People Analytics: With Applications in R".
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.2.1
Depends: R (≥ 3.5.0)
LazyData: true
NeedsCompilation: no
Packaged: 2022-10-21 15:03:52 UTC; craig.starbuck
Author: Craig Starbuck ORCID iD [aut, cre]
Maintainer: Craig Starbuck <cstarbuck@orgacuity.com>
Repository: CRAN
Date/Publication: 2022-10-25 17:22:35 UTC

benefits

Description

Fictitious benefits data for employees in a mid-size company

Usage

data("benefits")

Format

A data frame with 1471 observations on the following 3 variables.

employee_id

Unique identifier for each employee

stock_opt_lvl

Job level, where 1 = 'Junior' and 5 = 'Senior'

trainings

Number of trainings completed within the past year

Examples

data(benefits)

demographics

Description

Fictitious demographics data for employees in a mid-size company

Usage

data("demographics")

Format

A data frame with 1470 observations on the following 7 variables.

employee_id

Unique identifier for each employee

age

Employee age in years

commute_dist

Commute distance in miles

ed_lvl

Education level, where 1 = 'High School', 2 = 'Associate Degree', 3 = 'Bachelor's Degree', 4 = 'Master's Degree', and 5 = 'Doctoral Degree'

ed_field

Education field associated with most recent degree

gender

Gender self-identification

marital_sts

Marital status

Examples

data(demographics)

employees

Description

Fictitious data on employees in a mid-size company

Usage

data("employees")

Format

A data frame with 1470 observations on the following 36 variables.

employee_id

Unique identifier for each employee

active

Flag set to 'Yes' for active employees and 'No' for inactive employees

stock_opt_lvl

Stock option level

trainings

Number of trainings completed within the past year

age

Employee age in years

commute_dist

Commute distance in miles

ed_lvl

Education level, where 1 = 'High School', 2 = 'Associate Degree', 3 = 'Bachelor's Degree', 4 = 'Master's Degree', and 5 = 'Doctoral Degree'

ed_field

Education field associated with most recent degree

gender

Gender self-identification

marital_sts

Marital status

dept

Department of which an employee is a member

engagement

Employee engagement score measured on a 4-point Likert scale, where 1 = 'Highly Disengaged' and 4 = 'Highly Engaged'

job_lvl

Job level, where 1 = 'Junior' and 5 = 'Senior'

job_title

Job title

overtime

Flag set to 'Yes' if the employee is nonexempt and works overtime and 'No' if the employee does not work overtime

business_travel

Business travel frequency

hourly_rate

Hourly rate calculated irrespective of hourly/salaried employees

daily_comp

Hourly rate * 8

monthly_comp

Hourly rate * 2080 / 12

annual_comp

Hourly rate * 2080

ytd_leads

Year-to-date (YTD) number of leads generated for employees in Sales Executive and Sales Representative positions

ytd_sales

Year-to-date (YTD) sales measured in USD for employees in Sales Executive and Sales Representative positions

standard_hrs

Expected working hours over a two-week payroll cycle

salary_hike_pct

The percent increase in salary for the employee's most recent compensation adjustment (whether due to a standard merit increase, off-cycle adjustment, or promotion)

perf_rating

Most recent performance rating, where 1 = 'Needs Improvement', 2 = 'Core Contributor', 3 = 'Noteworthy', and 4 = 'Exceptional'

prior_emplr_cnt

Number of prior employers

env_sat

Environment satisfaction score measured on a 4-point Likert scale, where 1 = 'Highly Dissatisfied' and 4 = 'Highly Satisfied'

job_sat

Job satisfaction score measured on a 4-point Likert scale, where 1 = 'Highly Dissatisfied' and 4 = 'Highly Satisfied'

rel_sat

Collegue relationship satisfaction score measured on a 4-point Likert scale, where 1 = 'Highly Dissatisfied' and 4 = 'Highly Satisfied'

wl_balance

Work-life balance score measured on a 4-point Likert scale, where 1 = 'Poor Balance' and 4 = 'Excellent Balance'

work_exp

Total years of work experience

org_tenure

Years at current company

job_tenure

Years in current job

last_promo

Years since last promotion

mgr_tenure

Years under current manager

interview_rating

Average rating across the interview loop for the onsite stage of the employee's recruiting process, where 1 = 'Definitely Not' and 5 = 'Definitely Yes'

Examples

data(employees)

job

Description

Fictitious job data for employees in a mid-size company

Usage

data("job")

Format

A data frame with 1470 observations on the following 6 variables.

employee_id

Unique identifier for each employee

dept

Department of which an employee is a member

job_lvl

Job level, where 1 = 'Junior' and 5 = 'Senior'

job_title

Job title

overtime

Flag set to 'Yes' if the employee is nonexempt and works overtime and 'No' if the employee does not work overtime

business_travel

Business travel frequency

Examples

data(job)

payroll

Description

Fictitious payroll data for employees in a mid-size company

Usage

data("payroll")

Format

A data frame with 1470 observations on the following 6 variables.

employee_id

Unique identifier for each employee

hourly_rate

Hourly rate calculated irrespective of hourly/salaried employees

daily_comp

Hourly rate * 8

monthly_comp

Hourly rate * 2080 / 12

annual_comp

Hourly rate * 2080

standard_hrs

Expected working hours over a two-week payroll cycle

Examples

data(payroll)

performance

Description

Fictitious performance data for employees in a mid-size company

Usage

data("performance")

Format

A data frame with 1470 observations on the following 3 variables.

employee_id

Unique identifier for each employee

salary_hike_pct

The percent increase in salary for the employee's most recent compensation adjustment (whether due to a standard merit increase, off-cycle adjustment, or promotion)

perf_rating

Most recent performance rating, where 1 = 'Needs Improvement', 2 = 'Core Contributor', 3 = 'Noteworthy', and 4 = 'Exceptional'

Examples

data(performance)

prior_employment

Description

Fictitious prior employment data for employees in a mid-size company

Usage

data("prior_employment")

Format

A data frame with 1470 observations on the following 2 variables.

employee_id

Unique identifier for each employee

prior_emplr_cnt

Number of prior employers

Examples

data(prior_employment)

sentiment

Description

Fictitious sentiment data for employees in a mid-size company

Usage

data("sentiment")

Format

A data frame with 1470 observations on the following 6 variables.

employee_id

Unique identifier for each employee

env_sat

Environment satisfaction score measured on a 4-point Likert scale, where 1 = 'Highly Dissatisfied' and 4 = 'Highly Satisfied'

engagement

Employee engagement score measured on a 4-point Likert scale, where 1 = 'Highly Disengaged' and 4 = 'Highly Engaged'

job_sat

Job satisfaction score measured on a 4-point Likert scale, where 1 = 'Highly Dissatisfied' and 4 = 'Highly Satisfied'

rel_sat

Colleague relationship satisfaction score measured on a 4-point Likert scale, where 1 = 'Highly Dissatisfied' and 4 = 'Highly Satisfied'

wl_balance

Work-life balance score measured on a 4-point Likert scale, where 1 = 'Poor Balance' and 4 = 'Excellent Balance'

Examples

data(sentiment)

status

Description

Fictitious data on the active status of employees in a mid-size company

Usage

data("status")

Format

A data frame with 1470 observations on the following 2 variables.

employee_id

Unique identifier for each employee

active

Flag set to 'Yes' for active employees and 'No' for inactive employees

Examples

data(status)

survey_responses

Description

Fictitious survey responses for anonymized employees in a mid-size company

Usage

data("survey_responses")

Format

A data frame with 400 observations on the following 12 variables.

belong

Belonging score measured on a 5-point Likert scale, where 1 = 'Highly Unfavorable' and 5 = 'Highly Favorable'

effort

Discretionary Effort score measured on a 5-point Likert scale, where 1 = 'Highly Unfavorable' and 5 = 'Highly Favorable'

incl

Inclusion score measured on a 5-point Likert scale, where 1 = 'Highly Unfavorable' and 5 = 'Highly Favorable'

eng_1

Engagement score on item 1 of 3 measured on a 5-point Likert scale, where 1 = 'Highly Disengaged' and 5 = 'Highly Engaged'

eng_2

Engagement score on item 2 of 3 measured on a 5-point Likert scale, where 1 = 'Highly Disengaged' and 5 = 'Highly Engaged'

eng_3

Engagement score on item 3 of 3 measured on a 5-point Likert scale, where 1 = 'Highly Disengaged' and 5 = 'Highly Engaged'

happ

Happiness score measured on a 5-point Likert scale, where 1 = 'Highly Unfavorable' and 5 = 'Highly Favorable'

psafety

Psychological Safety score measured on a 7-point Likert scale, where 1 = 'Highly Unfavorable' and 7 = 'Highly Favorable'

ret_1

Retention score on item 1 of 3 measured on a 5-point Likert scale, where 1 = 'Highly Unfavorable' and 5 = 'Highly Favorable'

ret_2

Retention score on item 2 of 3 measured on a 5-point Likert scale, where 1 = 'Highly Unfavorable' and 5 = 'Highly Favorable'

ret_3

Retention score on item 3 of 3 measured on a 5-point Likert scale, where 1 = 'Highly Unfavorable' and 5 = 'Highly Favorable'

ldrshp

Senior Leadership score measured on a 5-point Likert scale, where 1 = 'Highly Unfavorable' and 5 = 'Highly Favorable'

Examples

data(survey_responses)

tenure

Description

Fictitious tenure data for employees in a mid-size company

Usage

data("tenure")

Format

A data frame with 1470 observations on the following 6 variables.

employee_id

Unique identifier for each employee

work_exp

Flag set to 'Yes' for active employees and 'No' for inactive employees

org_tenure

Years at current company

job_tenure

Years in current job

last_promo

Years since last promotion

mgr_tenure

Years under current manager

Examples

data(tenure)

Description

Fictitious monthly employee turnover rates by several dimensions

Usage

data("turnover_trends")

Format

A data frame with 3000 observations on the following 6 variables.

year

Integer representing the year, which ranges from 1 (earliest) to 5 (most recent)

month

Integer representing the month, which ranges from 1 (January) to 12 (December)

job

Job title

level

Job level, where 1 = 'Junior' and 5 = 'Senior'

remote

Flag set to 'Yes' for a remote worker and 'No' for a non-remote worker

turnover_rate

Monthly turnover rate, calculated by dividing the termination count into the average headcount (beginning headcount + ending headcount / 2) for the respective month

Examples

data(turnover_trends)