Meer data toevoegen

Omdat overuren niet significant veranderden tussen de jaren, is het goed om te kijken welke andere variabelen je kunt onderzoeken. In overleg met je team stelt iemand voor om engagementscores te proberen, en specifiek het aantal niet-betrokken medewerkers op de locatie. Je hebt de surveydata nog niet klaarstaan, dus je moet die inladen en joinen met de data waarmee je al werkte. Meerdere joins uitvoeren is gebruikelijk in HR-analytics.

Pas na de join year aan naar factor(year). Omdat je alleen om het jaar als groeperingsvariabele geeft en niet om de numerieke waarde, maakt het omzetten naar een factor verdere analyses makkelijker.

Deze oefening maakt deel uit van de cursus

HR-analytics: werknemersgegevens verkennen in R

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Importeer "survey_data_2.csv" als survey_data met read_csv().
Gebruik left_join() om de engagementdata toe te voegen aan hr_joined. Join op jaar gevolgd door employee ID.
Gebruik na de join %>% en mutate() om disengaged toe te voegen: dit is 1 wanneer engagement 2 of lager is en anders 0; en vervang year door factor(year). Ken het resultaat van deze stappen toe aan safety.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import the survey data
survey_data <- ___

# Create the safety dataset
safety <- ___

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

HR-analytics: werknemersgegevens verkennen in R

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In this chapter, you will get an introduction to how data science is used in a human resources context. Then you will dive into a case study where you'll analyze and visualize recruiting data to determine which source of new candidates ultimately produces the best new hires. The dataset you'll use in this and the other chapters in this course is synthetic, to maintain the privacy of actual employees.

Exercise 1: Welcome to the course!Exercise 2: Applications of human resources (HR) analytics Exercise 3: Looking at the recruiting data Exercise 4: Recruiting and quality of hire Exercise 5: Identifying groups in data Exercise 6: Sales numbers by recruiting source Exercise 7: Attrition rates by recruiting source Exercise 8: Visualizing the recruiting data Exercise 9: Visualizing the sales performance differences Exercise 10: Visualizing the attrition differences Exercise 11: Drawing conclusions

Gallup defines engaged employees as those who are involved in, enthusiastic about and committed to their work and workplace. There is disagreement about the strength of the connection between employee engagement and business outcomes, but the idea is that employees that are more engaged will be more productive and stay with the organization longer. In this chapter, you'll look into potential reasons that one department's engagement scores are lower than the rest.

Exercise 1: Analyzing employee engagement Exercise 2: Importing the survey data Exercise 3: Which department has the lowest engagement?Exercise 4: Comparing other factors by department Exercise 5: Visualizing the engagement data Exercise 6: Visualizing several variables at once Exercise 7: Visualizing several variables at once with facets Exercise 8: Drawing conclusions from graphs Exercise 9: Is that difference meaningful?Exercise 10: Statistical significance - disengaged employees Exercise 11: Statistical significance - vacation days Exercise 12: Drawing conclusions from the tests

When employers make a new hire, they must determine what the new employee will be paid. If the employer is not careful, the new hires can come in with a higher salary than the employees that currently work at the same job, which can cause employee turnover and dissatisfaction. In this chapter, you will check whether new hires are really getting paid more than current employees, and how to double-check your initial observations.

Exercise 1: Paying new hires fairly Exercise 2: Importing the pay data Exercise 3: Is the difference significant?Exercise 4: Omitted variable bias Exercise 5: Omitted variable bias examples Exercise 6: What other differences exist?Exercise 7: What does the pay difference look like now?Exercise 8: Are hourly hires paid more?Exercise 9: Linear regression Exercise 10: New hire pay: a simple regression Exercise 11: New hire pay: accounting for job levels Exercise 12: Drawing conclusions from the tests

Performance management helps an organization keep track of which employees are providing extra value, or below-average value, and compensating them accordingly. Whether performance is a rating or the result of a questionnaire, whether employees are rated each year or more often than that, the process is somewhat subjective. An organization should check that ratings are being given with regard to performance, and not individual managers' preferences, or even biases (conscious or subconscious).

Exercise 1: Joining HR data Exercise 2: Importing the two datasets Exercise 3: Joining performance data Exercise 4: Performance ratings and fairness Exercise 5: Focus on high performers Exercise 6: Comparing distributions of high performers Exercise 7: Checking for omitted variable bias Exercise 8: Logistic regression Exercise 9: Linear and logistic regression Exercise 10: Performance ratings: a simple logistic regression Exercise 11: Performance ratings: accounting for job levels Exercise 12: Conclusions and recommendations

In many industries, workplace safety is a critical consideration. Maintaining a safe workplace provides employees with confidence and reduces costs for workers' compensation and legal liabilities. In this chapter, you'll look for explanations for an increase in workplace accidents.

Exercise 1: Veiligheid van werknemers Exercise 2: Ongevallengegevens importeren en samenvoegen Exercise 3: Waar is het ongevalspercentage het hoogst?Exercise 4: Waar nam het ongevalspercentage het meest toe?Exercise 5: Focussen op de locatie van interesse Exercise 6: Focus op de locatie van het probleem Exercise 7: Meer data toevoegen

Huidige oefening

Exercise 8: Controleren op weggelaten variabelen Exercise 9: Is die verandering beperkt tot de probleemlocatie?Exercise 10: De toename in ongevallen verklaren Exercise 11: Veranderingsoorzaken opsporen met regressie Exercise 12: Wat kun je concluderen?Exercise 13: Conclusie