R Tutorial: Recruiting and quality of hire

Want to learn more? Take the full course at https://learn.datacamp.com/courses/human-resources-analytics-exploring-employee-data-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Now that you've learned about the general process for HR analytics, you can apply that process to a real-life question: where are your best hires coming from, and where could you get more of them? Success in business relies on how well your workforce performs, and your workforce is largely a product of who you hire. Using the first two steps of the general process described in the last video, the groups you'll be comparing are employees who were hired from different recruiting channels, and the statistics you'll calculate are how good the hires were, or the average quality of hire for each group. How do you measure quality of hire? There isn't one accepted way to compute it for every type of employee, but you can usually use metrics such as retention, hiring manager satisfaction, job performance, or "time-to-productivity", which is the amount of time it takes for the employee to become fully productive. How exactly you combine these measures together into a single metric is something to discuss with stakeholders, including those in your talent acquisition department. In this chapter, you'll be working with these four variables. One is a measure of attrition, and performance_rating and sales_quota_pct are measures of job performance. Attrition rates can be calculated in a few different ways. For this course, you'll only be looking at one snapshot in time, so you can use a simple formula - number of employees who left the company, or attrition, divided by total headcount. In the dataset for this chapter, attrition is coded as 1 if the employee left, and 0 otherwise. This means that an equivalent formula for attrition is to take the average of the attrition variable. This is the formula you'll use in the exercises. The