Instructions

Welcome to the midterm project exercise! In this exercise, you will work on a project that involves analyzing employee attrition and performance using the HR Analytics Employee Attrition & Performance dataset. The primary goal is to develop insights into the factors that contribute to employee attrition and provide recommendations for HR interventions that could help reduce attrition and improve overall employee satisfaction and performance.

Download the project files using this link: Midterm project files. Be sure to download the entire folder midterm-econ148-project-exercise. The project files include the dataset and a template Quarto markdown file that you will use to complete the exercise.

First, rename the lastname-firstname.qmd file to your actual last name and first name. For example, if your name is John Doe, the file should be named doe-john.qmd. Afterward, open the R project 20241007-econ148-project-exercise-1.Rproj by double-clicking the file and render the Quarto markdown file to generate the HTML file.

Project overiew

In this project, we will explore employee attrition and performance using the HR Analytics Employee Attrition & Performance dataset. The primary goal is to develop insights into the factors that contribute to employee attrition.

The dataset used for this project provides information about employee demographics, performance metrics, and various satisfaction ratings. By analyzing a range of factors, including demographic data, job satisfaction, work-life balance, and job role, we aim to help businesses identify key areas where they can improve employee retention.

## datatable function from DT package create an HTML widget display of the dataset
## install DT package if the package is not yet available in your R environment
readxl::read_excel("dataset/dataset-variable-description.xlsx") |> 
  DT::datatable()

Data wrangling and management

Libraries

Task: Load the necessary libraries

Before we start working on the dataset, we need to load the necessary libraries that will be used for data wrangling, analysis and visualization. Make sure to load the following libraries here. For packages to be installed, you can use the install.packages function. There are packages to be installed later on this project, so make sure to install them as needed and load them here.

# load all your libraries here

Data importation

Task 2.1. Merging dataset

  • Import the two dataset Employee.csv and PerformanceRating.csv. Save the Employee.csv as employee_dta and PerformanceRating.csv as perf_rating_dta.

  • Merge the two dataset using the left_join function from dplyr. Use the EmployeeID variable as the varible to join by. You may read more information about the left_join function here.

  • Save the merged dataset as hr_perf_dta and display the dataset using the datatable function from DT package.

## import the two data here


## merge employee_dta and perf_rating_dta using left_join function.
## save the merged dataset as hr_perf_dta



## Use the datatable from DT package to display the merged dataset

Data management

Task 2.2. Standardizing variable names

  • Using the clean_names function from janitor package, standardize the variable names by using the recommended naming of variables.

  • Save the renamed variables as hr_perf_dta to update the dataset.

## clean names using the janitor packages and save as hr_perf_dta


## display the renamed hr_perf_dta using datatable function
Task 2.3. Recode data entries

  • Create a new variable cat_education wherein education is 1 = No formal education; 2 = High school; 3 = Bachelor; 4 = Masters; 5 = Doctorate. Use the case_when function to accomplish this task.

  • Similarly, create new variables cat_envi_sat, cat_job_sat, and cat_relation_sat for environment_satisfaction, job_satisfaction, and relationship_satisfaction, respectively. Re-code the values accordingly as 1 = Very dissatisfied; 2 = Dissatisfied; 3 = Neutral; 4 = Satisfied; and 5 = Very satisfied.

  • Create new variables cat_work_life_balance, cat_self_rating, cat_manager_rating for work_life_balance, self_rating, and manager_rating, respectively. Re-code accordingly as 1 = Unacceptable; 2 = Needs improvement; 3 = Meets expectation; 4 = Exceeds expectation; and 5 = Above and beyond.

  • Create a new variable bi_attrition by transforming attrition variable as a numeric variabe. Re-code accordingly as No = 0, and Yes = 1.

  • Save all the changes in the hr_perf_dta. Note that saving the changes with the same name will update the dataset with the new variables created.

## create cat_education



## create cat_envi_sat,  cat_job_sat, and cat_relation_sat




## create cat_work_life_balance, cat_self_rating, and cat_manager_rating





## create bi_attrition



## print the updated hr_perf_dta using datatable function

Exploratory data analysis

Descriptive statistics of employee attrition

Task 3.1. Breakdown of attrition by key variables

  • Select the variables attrition, job_role, department, age, salary, job_satisfaction, and work_life_balance. Save as attrition_key_var_dta.

  • Compute and plot the attrition rate across job_role, department, and age, salary, job_satisfaction, and work_life_balance. To compute for the attrition rate, group the dataset by job role. Afterward, you can use the count function to get the frequency of attrition for each job role and then divide it by the total number of observations. Save the computation as pct_attrition. Do not forget to ungroup before storing the output. Store the output as attrition_rate_job_role.

  • Plot for the attrition rate across job_role has been done for you! Study each line of code. You have the freedom to customize your plot accordingly. Show your creativity!

## selecting attrition key variables and save as `attrition_key_var_dta`




## compute the attrition rate across job_role and save as attrition_rate_job_role



## print attrition_rate_job_role
## Plot the attrition rate

Analysis of compensation and turnover

Task 3.2. Analyzing compensation and turnover

  • Compare the average monthly income of employees who left the company (bi_attrition = 1) and those who stayed (bi_attrition = 0). Use the t.test function to conduct a t-test and determine if there is a significant difference in average monthly income between the two groups. Save the results in a variable called attrition_ttest_results.

  • Install the report package and use the report function to generate a report of the t-test results.

  • Install the ggstatsplot package and use the ggbetweenstats function to visualize the distribution of monthly income for employees who left and those who stayed. Make sure to map the bi_attrition variable to the x argument and the salary variable to the y argument.

  • Visualize the salary variable for employees who left and those who stayed using geom_histogram with geom_freqpoly. Make sure to facet the plot by the bi_attrition variable and apply alpha on the histogram plot.

  • Provide recommendations on whether revising compensation policies could be an effective retention strategy.

## compare the average monthly income of employees who left and those who stayed




## print the results of the t-test
## install the report package and use the report function to generate a report of the t-test results
# install ggstatsplot package and use ggbetweenstats function to visualize the distribution of monthly income for employees who left and those who stayed
# create histogram and frequency polygon of salary for employees who left and those who stayed
Discussion:

Provide your discussion here.

Employee satisfaction and performance analysis

Task 3.3. Analyzing employee satisfaction and performance

  • Analyze the average performance ratings (both ManagerRating and SelfRating) of employees who left vs. those who stayed. Use the group_by and count functions to calculate the average performance ratings for each group.

  • Visualize the distribution of SelfRating for employees who left and those who stayed using a bar plot. Use the ggplot function to create the plot and map the SelfRating variable to the x argument and the bi_attrition variable to the fill argument.

  • Similarly, visualize the distribution of ManagerRating for employees who left and those who stayed using a bar plot. Make sure to map the ManagerRating variable to the x argument and the bi_attrition variable to the fill argument.

  • Create a boxplot of salary by job_satisfaction and bi_attrition to analyze the relationship between salary, job satisfaction, and attrition. Use the geom_boxplot function to create the plot and map the salary variable to the x argument, the job_satisfaction variable to the y argument, and the bi_attrition variable to the fill argument. You need to transform the job_satisfaction and bi_attrition variables into factors before creating the plot or within the ggplot function.

  • Discuss the results of the analysis and provide recommendations for HR interventions based on the findings.

# Analyze the average performance ratings (both ManagerRating and SelfRating) of employees who left vs. those who stayed.
# Visualize the distribution of SelfRating for employees who left and those who stayed using a bar plot.
# Visualize the distribution of ManagerRating for employees who left and those who stayed using a bar plot.
# create a boxplot of salary by job_satisfaction and bi_attrition to analyze the relationship between salary, job satisfaction, and attrition.
Discussion:

Provide your discussion here.

Work-life balance and retention strategies

Task 3.4. Analyzing work-life balance and retention strategies

At this point, you are already well aware of the dataset and the possible factors that contribute to employee attrition. Using your R skills, accomplish the following tasks:

  • Analyze the distribution of WorkLifeBalance ratings for employees who left versus those who stayed.

  • Use visualizations to show the differences.

  • Assess whether employees with poor work-life balance are more likely to leave.

You have the freedom how you will accomplish this task. Be creative and provide insights that will help HR develop effective retention strategies.