Most companies would love to keep their valuable employees for a very long period of time, unfortunately this is not the case. Many employees walk off the door with very short or no notice, surpervisors are also not able to quickly anticipate these actions to revert them.

If employers can anticipate employees desire to leave, they would be better prepared to curb any negative outcome it would have on the company.

In this brief post we shall illustrate how to help a company reduce employee turn-over by providing valuable insight through visualization.

Problem Statement: The CEO of ADD consulting wants us to use their employee data to find out why and how people are leaving his company and also to make predictions on who would leave next.

Project Goal.

  1. I seek to briefly demonstrate how questions are asked from data
  2. To illustrate how to present insights through visualizations

Information Contained In The Data:

  1. Satisfaction Level: The employee was asked to give a score on how happy he/she is in their current role

  2. Last Evaluation: Recent work assessment from supervisors

  3. Number of Projects A way of quantifying the job assigned to employees

  4. Work accident: Indicates whether or not an employee had had an accident during work

  5. promotion in 5 years: Indicates whether or not employee had a promotion in the last five years.

  6. Department: Lists the various departmens in the company

  7. Salary: shows the Income level of all employees

  8. Time spent in the company: Number of years employee had been in the company

  9. Average monthly hours Total hours worked per month

  10. left: A label that tells who left or remained

So now we have the raw material to proceed with our analysis. The full reproducible code for this project is available here. In this post i’ll briefly outline some data cleaning that was carried out on the data.

  • I changed some dummy variables into more meaningful values.
  • I splitted some numeric variables into three equal intervals
satisfaction_level last_evaluation number_project average_montly_hours time_spend_company Work_accident left promotion_last_5years sales salary
0.38 0.53 2 157 3 0 1 0 sales low
0.80 0.86 5 262 6 0 1 0 sales medium
0.11 0.88 7 272 4 0 1 0 sales medium
0.72 0.87 5 223 5 0 1 0 sales low
0.37 0.52 2 159 3 0 1 0 sales low
0.41 0.50 2 153 3 0 1 0 sales low
0.10 0.77 6 247 4 0 1 0 sales low
0.92 0.85 5 259 5 0 1 0 sales low
0.89 1.00 5 224 5 0 1 0 sales low
0.42 0.53 2 142 3 0 1 0 sales low 
summary(hr_data)
##        satisfaction_level last_evaluation  number_project
##  disatisfied    :1941     Min.   :0.3600   2:2388        
##  partly_satified:6760     1st Qu.:0.5600   3:4055        
##  very_satisfied :6298     Median :0.7200   4:4365        
##                           Mean   :0.7161   5:2761        
##                           3rd Qu.:0.8700   6:1174        
##                           Max.   :1.0000   7: 256        
##                                                          
##  average_montly_hours       time_spend_company Work_accident
##  Min.   : 96.0        less_than_3    :9687     0:12830      
##  1st Qu.:156.0        between 4 and 7:4936     1: 2169      
##  Median :200.0        greater than 7 : 376                  
##  Mean   :201.1                                              
##  3rd Qu.:245.0                                              
##  Max.   :310.0                                              
##                                                             
##      left        promotion_last_5years         sales         salary    
##  stayed:12830   promoted    :14680     sales      :4140   low   :7316  
##  left  : 2169   not_promoted:  319     technical  :2720   medium:6446  
##                                        support    :2229   high  :1237  
##                                        IT         :1227                
##                                        product_mng: 902                
##                                        marketing  : 858                
##                                        (Other)    :2923

Summary:

  1. About 14.5% of all staff in the data left

  2. About 2.13% had not been promoted for the past five years

  3. The average monthly hours of employee is 201.1

Why are people not happy with their job?

Remember we have a variable called “Satisfaction Level” - employees rank how happy they are in the company. I created three levels from that variable namely:

  • dissatisfaction
  • partly_satisfied
  • very_satisfied

Our focus in the next plot would be targeted towards the dissatisfied group

Take a carefull look at the plot. What do you find? Majority of staff who claimed to be dissatisfied actually worked longer, some working as high as 300 hours a month.

Questions you could still ask:

  1. Why are they woking longer?

Long hours at work may not mean much if it doesn’t translate into more projects being completed

The next questions we seek to answer is: which group of employees completed more projects

For those employees who claim to be dissatisfied - not only are they working longer but they seem to be completing more projects.

What is the reward for their extra effort?, We have to find out how well they are paid and also check if they have beeen promoted You could plot satisfaction level against promotion in last 5 years

Let’s move on and find out how many long serving employees we still have left

Very few employees stay longer than seven years and most of those employees are rewarded with higher positions

Our final plot verifies this claim:

Conclusion

Data visualization helps with presenting data in a clear and intuitive manner. The systematic approach to asking questions from data and answering those questions with beautiful plots is very crucial to any data analytic project. The full reproducible code to this project can be downloaded here.

Thanks for taking time to read - See you soon with more cool data stuff!