Title: | Data Sets for Craig Starbuck's Book, "The Fundamentals of People Analytics: With Applications in R" |
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Description: | Data sets associated with modeling examples in Craig Starbuck's book, "The Fundamentals of People Analytics: With Applications in R". |
Authors: | Craig Starbuck [aut, cre] |
Maintainer: | Craig Starbuck <[email protected]> |
License: | MIT + file LICENSE |
Version: | 0.1.0 |
Built: | 2024-10-31 05:04:52 UTC |
Source: | https://github.com/cran/peopleanalytics |
Fictitious benefits data for employees in a mid-size company
data("benefits")
data("benefits")
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
data(benefits)
data(benefits)
Fictitious demographics data for employees in a mid-size company
data("demographics")
data("demographics")
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
data(demographics)
data(demographics)
Fictitious data on employees in a mid-size company
data("employees")
data("employees")
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'
data(employees)
data(employees)
Fictitious job data for employees in a mid-size company
data("job")
data("job")
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
data(job)
data(job)
Fictitious payroll data for employees in a mid-size company
data("payroll")
data("payroll")
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
data(payroll)
data(payroll)
Fictitious performance data for employees in a mid-size company
data("performance")
data("performance")
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'
data(performance)
data(performance)
Fictitious prior employment data for employees in a mid-size company
data("prior_employment")
data("prior_employment")
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
data(prior_employment)
data(prior_employment)
Fictitious sentiment data for employees in a mid-size company
data("sentiment")
data("sentiment")
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'
data(sentiment)
data(sentiment)
Fictitious data on the active status of employees in a mid-size company
data("status")
data("status")
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
data(status)
data(status)
Fictitious survey responses for anonymized employees in a mid-size company
data("survey_responses")
data("survey_responses")
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'
data(survey_responses)
data(survey_responses)
Fictitious tenure data for employees in a mid-size company
data("tenure")
data("tenure")
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
data(tenure)
data(tenure)
Fictitious monthly employee turnover rates by several dimensions
data("turnover_trends")
data("turnover_trends")
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
data(turnover_trends)
data(turnover_trends)