Human Resource Management plays an important role in any organization and business to evaluate the company's growth and performance based on the employees. This helps in analyzing the human assets working in a company and how they can be motivated to do better by different means. Human Resource Management helps in deriving useful information from employee data to make the working environment better and more efficient.
Project Overview
There are various needs for having HRM in a company:
- Talent Acquisition and Retention: HR Management helps in keeping good talent in the company.
- Employee Development: Focuses on continuous employee training and development, enhancing skills and career growth.
- Performance Management: Implements performance appraisal systems to evaluate and improve employee productivity.
- Workplace Culture: Fosters a positive work environment, improving employee satisfaction and engagement which helps them grow and increases productivity.
- Compensation and Benefits: Designs competitive compensation packages to attract and retain top talent.
Now we will discuss step by step to create Human Resource Management Analytics Dashboard in R Programming Language.
1. Load libraries and Dataset
Replace the path of the dataset from the original one. We are using multiple libraries to for our dashboard.
Dataset Link: Human Resource Management Analytics
This dataset contains 1480 observations and 38 variables, representing various employee attributes and metrics.
library(dplyr)
library(ggplot2)
library(lubridate)
data <- read.csv("/content/HR_Analytics.csv")
head(data)
Output:
2. Data Preprocessing
Data preprocessing is a crucial step to ensure the data is clean and ready for analysis. Here, we will handle missing values, convert data types and create new variables as needed.
sum(is.na(data))
if (sum(is.na(data)) > 0) {
data <- na.omit(data)
}
Output:
[1] 57
3. Exploratory Data Analysis (EDA)
This step is important to identify and explore the dataset so that we can analyze it and make informed decisions. Here, we will create multiple such visualization to understand better.
gender_distribution <- data %>%
group_by(Gender) %>%
summarise(Count = n()) %>%
mutate(Percentage = round(Count / sum(Count) * 100, 1))
ggplot(gender_distribution, aes(x = "", y = Percentage, fill = Gender)) +
geom_bar(width = 1, stat = "identity") +
coord_polar("y", start = 0) +
theme_void() +
geom_text(aes(label = paste0(Percentage, "%")),
position = position_stack(vjust = 0.5)) +
labs(title = "Gender Distribution of Employees") +
scale_fill_brewer(palette = "Set3")
Output:
This pie chart indicates that the total employees of the given company has 60% male employees and 40% female.
3.1. Education Level of Employees
Now we will visualize the Education Level of Employees.
ggplot(data, aes(x = factor(Education, levels = c(1, 2, 3, 4, 5)),
fill = factor(Education))) +
geom_bar() +
labs(title = "Distribution of Education Levels",
x = "Education Level",
y = "Number of Employees",
fill = "Education Level") +
scale_x_discrete(labels = c("1" = "Below College", "2" = "College", "3" = "Bachelor",
"4" = "Master", "5" = "Doctor"))
Output:
3.2. Employee Age distribution
This will give us insights about the age group of the employees.
ggplot(data, aes(x = Age)) +
geom_histogram(binwidth = 5, fill = "green", color = "black") +
labs(title = "Age Distribution of Employees",
x = "Age",
y = "Count")
Output:
As we can see that the age group from 30-40 have the highest number of employees.
3.3. Job Role Distribution
This explains us the kind of jobs provided and the number of employees in the certain sector.
ggplot(data, aes(x = JobRole, fill = JobRole)) +
geom_bar() +
labs(title = "Distribution of Job Roles",
x = "Job Role",
y = "Count") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Output:
The highest number of job roles in the company is sales executive.
3.4. Monthly Income Distribution
This helps us analyze the income of the employees.
ggplot(data, aes(x = MonthlyIncome)) +
geom_histogram(binwidth = 1000, fill = "purple", color = "black") +
labs(title = "Distribution of Monthly Income",
x = "Monthly Income",
y = "Count")
Output:
3.5. Department-wise Gender Distribution
This plot will help us estimate the gender ratio in the company for better hiring to maintain equality and diversity.
ggplot(data, aes(x = Department, fill = Gender)) +
geom_bar(position = "fill") +
scale_y_continuous(labels = scales::percent) +
labs(title = "Gender Distribution by Department",
x = "Department",
y = "Percentage",
fill = "Gender") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Output:
As we can see the least number of female employee is in Human resource department.
Dashboard Analysis for HR Management
A well-designed HR Management Analytics Dashboard provides important insights into various aspects of the workforce, enabling better management and strategic planning.
1. Department Based Dashboard for overview
Here we will build a dashboard which will help us navigate through different department and get all the necessary insights related to including an overview, employee engagement and attrition analysis.
library(shiny)
library(ggplot2)
library(shinydashboard)
ui <- fluidPage(
titlePanel("HR Management Analytics Dashboard"),
sidebarLayout(
sidebarPanel(
selectInput("department", "Select Department:",
choices = unique(data$Department))
),
mainPanel(
tabsetPanel(
tabPanel("Overview",
fluidRow(
valueBoxOutput("total_employees"),
valueBoxOutput("avg_salary"),
valueBoxOutput("avg_age")
),
plotOutput("gender_distribution"),
plotOutput("age_distribution"),
plotOutput("education_distribution"),
plotOutput("attrition_by_dept"),
plotOutput("job_satisfaction"),
plotOutput("performance_ratings"),
plotOutput("training_times")
),
tabPanel("Employee Engagement",
plotOutput("engagement_by_dept"),
plotOutput("years_with_manager"),
plotOutput("work_life_balance")
),
tabPanel("Attrition Analysis",
plotOutput("attrition_by_age"),
plotOutput("attrition_by_job_role"),
plotOutput("attrition_by_gender")
)
)
)
)
)
server <- function(input, output) {
filtered_data <- reactive({
data[data$Department == input$department, ]
})
output$total_employees <- renderValueBox({
valueBox(
nrow(filtered_data()), "Total Employees", icon = icon("users"),
color = "blue"
)
})
output$avg_salary <- renderValueBox({
avg_salary <- round(mean(filtered_data()$MonthlyIncome), 2)
valueBox(
avg_salary, "Average Salary", icon = icon("dollar-sign"),
color = "green"
)
})
output$avg_age <- renderValueBox({
avg_age <- round(mean(filtered_data()$Age), 2)
valueBox(
avg_age, "Average Age", icon = icon("calendar-alt"),
color = "purple"
)
})
output$gender_distribution <- renderPlot({
gender_dist <- table(filtered_data()$Gender)
pie(gender_dist, main="Gender Distribution",
col=c("pink", "lightblue"), labels=paste0(names(gender_dist), "\n",
round(prop.table(gender_dist)*100, 2), "%"))
})
output$age_distribution <- renderPlot({
ggplot(filtered_data(), aes(x=Age)) +
geom_histogram(binwidth=5, fill="blue", color="black") +
labs(title="Age Distribution", x="Age", y="Count")
})
output$education_distribution <- renderPlot({
ggplot(filtered_data(), aes(x=EducationField)) +
geom_bar(fill="orange", color="black") +
labs(title="Education Distribution", x="Education Field", y="Count")
})
output$attrition_by_dept <- renderPlot({
ggplot(filtered_data(), aes(x=Department, fill=Attrition)) +
geom_bar(position="fill") +
labs(title="Attrition by Department", x="Department", y="Proportion")
})
output$job_satisfaction <- renderPlot({
ggplot(filtered_data(), aes(x=JobSatisfaction)) +
geom_bar(fill="green", color="black") +
labs(title="Job Satisfaction Levels", x="Job Satisfaction", y="Count")
})
output$performance_ratings <- renderPlot({
ggplot(filtered_data(), aes(x=PerformanceRating)) +
geom_bar(fill="purple", color="black") +
labs(title="Performance Ratings Distribution", x="Performance Rating", y="Count")
})
output$training_times <- renderPlot({
ggplot(filtered_data(), aes(x=TrainingTimesLastYear)) +
geom_bar(fill="orange", color="black") +
labs(title="Training Times Last Year", x="Training Times", y="Count")
})
output$engagement_by_dept <- renderPlot({
ggplot(filtered_data(), aes(x=Department, y=JobInvolvement)) +
geom_boxplot(fill="lightblue", color="black") +
labs(title="Employee Engagement by Department", x="Department", y="Job Involvement")
})
output$years_with_manager <- renderPlot({
ggplot(filtered_data(), aes(x=YearsWithCurrManager)) +
geom_histogram(binwidth=1, fill="lightgreen", color="black") +
labs(title="Years with Current Manager", x="Years", y="Count")
})
output$work_life_balance <- renderPlot({
ggplot(filtered_data(), aes(x=WorkLifeBalance)) +
geom_bar(fill="lightcoral", color="black") +
labs(title="Work-Life Balance", x="Work-Life Balance", y="Count")
})
output$attrition_by_age <- renderPlot({
ggplot(filtered_data(), aes(x=Age, fill=Attrition)) +
geom_histogram(binwidth=5, position="dodge") +
labs(title="Attrition by Age", x="Age", y="Count")
})
output$attrition_by_job_role <- renderPlot({
ggplot(filtered_data(), aes(x=JobRole, fill=Attrition)) +
geom_bar(position="fill") +
labs(title="Attrition by Job Role", x="Job Role", y="Proportion")
})
output$attrition_by_gender <- renderPlot({
ggplot(filtered_data(), aes(x=Gender, fill=Attrition)) +
geom_bar(position="fill") +
labs(title="Attrition by Gender", x="Gender", y="Proportion")
})
}
shinyApp(ui, server)
Output:
Here we can navigate and find different insights on the particular department. With the help of the toggle bar we can select the department we want.
2. Employee analysis based on the department and time
The UI layout consists of a title, a sidebar for navigation and a main panel for displaying the plots and metrics. The sidebar allows navigation through different departments and time periods.
library(shiny)
library(ggplot2)
library(dplyr)
# Load your dataset
# data <- read.csv("path_to_your_dataset.csv")
ui <- fluidPage(
titlePanel("HR Management Analytics Dashboard"),
sidebarLayout(
sidebarPanel(
selectInput("department", "Select Department:",
choices = unique(data$Department)),
selectInput("time_period", "Select Time Period:",
choices = c("Last Month", "Last Quarter", "Last Year"))
),
mainPanel(
tabsetPanel(
tabPanel("Overview",
fluidRow(
valueBoxOutput("total_employees"),
valueBoxOutput("avg_salary"),
valueBoxOutput("avg_age")
),
fluidRow(
valueBoxOutput("gender_distribution"),
valueBoxOutput("marital_status_distribution"),
valueBoxOutput("job_level_distribution")
),
plotOutput("age_distribution"),
plotOutput("experience_distribution")
),
tabPanel("Performance",
plotOutput("performance_by_department"),
plotOutput("performance_by_age"),
plotOutput("performance_by_experience")
),
tabPanel("Engagement",
plotOutput("engagement_by_gender"),
plotOutput("engagement_by_marital_status"),
plotOutput("engagement_by_job_level")
),
tabPanel("Attrition",
plotOutput("attrition_by_age"),
plotOutput("attrition_by_department"),
plotOutput("attrition_by_experience")
)
)
)
)
)
server <- function(input, output) {
filtered_data <- reactive({
data %>% filter(Department == input$department)
})
output$total_employees <- renderValueBox({
valueBox(
nrow(filtered_data()), "Total Employees", icon = icon("users"),
color = "blue"
)
})
output$avg_salary <- renderValueBox({
avg_salary <- round(mean(filtered_data()$Salary), 2)
valueBox(
avg_salary, "Avg Salary", icon = icon("dollar-sign"),
color = "green"
)
})
output$avg_age <- renderValueBox({
avg_age <- round(mean(filtered_data()$Age), 2)
valueBox(
avg_age, "Avg Age", icon = icon("calendar"),
color = "purple"
)
})
output$gender_distribution <- renderPlot({
ggplot(filtered_data(), aes(x = "", fill = Gender)) +
geom_bar(width = 1) +
coord_polar(theta = "y") +
labs(title = "Gender Distribution", x = "", y = "") +
scale_fill_manual(values = c("Male" = "blue", "Female" = "pink")) +
theme_void() +
geom_text(aes(label = paste0(round((..count..)/sum(..count..)*100), "%")),
stat = "count", position = position_stack(vjust = 0.5))
})
output$marital_status_distribution <- renderPlot({
ggplot(filtered_data(), aes(x = MaritalStatus, fill = MaritalStatus)) +
geom_bar() +
labs(title = "Marital Status Distribution", x = "Marital Status", y = "Count") +
scale_fill_manual(values = c("Single" = "red", "Married" = "green",
"Divorced" = "orange"))
})
output$job_level_distribution <- renderPlot({
ggplot(filtered_data(), aes(x = JobLevel, fill = JobLevel)) +
geom_bar() +
labs(title = "Job Level Distribution", x = "Job Level", y = "Count")
})
output$age_distribution <- renderPlot({
ggplot(filtered_data(), aes(x = Age)) +
geom_histogram(binwidth = 1, fill = "blue", color = "black") +
labs(title = "Age Distribution", x = "Age", y = "Count")
})
output$experience_distribution <- renderPlot({
ggplot(filtered_data(), aes(x = TotalWorkingYears)) +
geom_histogram(binwidth = 1, fill = "green", color = "black") +
labs(title = "Total Working Years Distribution", x = "Total Working Years",
y = "Count")
})
output$performance_by_department <- renderPlot({
ggplot(filtered_data(), aes(x = Department, y = PerformanceRating, fill = Department)) +
geom_boxplot() +
labs(title = "Performance by Department", x = "Department",
y = "Performance Rating")
})
output$performance_by_age <- renderPlot({
ggplot(filtered_data(), aes(x = Age, y = PerformanceRating)) +
geom_point(color = "blue") +
labs(title = "Performance by Age", x = "Age", y = "Performance Rating")
})
output$performance_by_experience <- renderPlot({
ggplot(filtered_data(), aes(x = TotalWorkingYears, y = PerformanceRating)) +
geom_point(color = "green") +
labs(title = "Performance by Experience", x = "Total Working Years",
y = "Performance Rating")
})
output$engagement_by_gender <- renderPlot({
ggplot(filtered_data(), aes(x = Gender, y = JobInvolvement, fill = Gender)) +
geom_boxplot() +
labs(title = "Engagement by Gender", x = "Gender", y = "Engagement Score")
})
output$engagement_by_marital_status <- renderPlot({
ggplot(filtered_data(), aes(x = MaritalStatus, y = JobInvolvement,
fill = MaritalStatus)) +
geom_boxplot() +
labs(title = "Engagement by Marital Status", x = "Marital Status",
y = "Engagement Score")
})
output$engagement_by_job_level <- renderPlot({
ggplot(filtered_data(), aes(x = JobLevel, y = JobInvolvement, fill = JobLevel)) +
geom_boxplot() +
labs(title = "Engagement by Job Level", x = "Job Level", y = "Engagement Score")
})
output$attrition_by_age <- renderPlot({
ggplot(filtered_data(), aes(x = Age, y = Attrition, fill = Attrition)) +
geom_bar(stat = "identity") +
labs(title = "Attrition by Age", x = "Age", y = "Attrition")
})
output$attrition_by_department <- renderPlot({
ggplot(filtered_data(), aes(x = Department, y = Attrition, fill = Department)) +
geom_bar(stat = "identity") +
labs(title = "Attrition by Department", x = "Department", y = "Attrition")
})
output$attrition_by_experience <- renderPlot({
ggplot(filtered_data(), aes(x = TotalWorkingYears, y = Attrition,
fill = Attrition)) +
geom_bar(stat = "identity") +
labs(title = "Attrition by Total Working Years", x = "Total Working Years",
y = "Attrition")
})
}
shinyApp(ui, server)
Output:
Conclusion
In this article, we discussed the importance of HR management and the role of machine learning in it. We used R and its libraries to evaluate and analyze employee performance. We built multiple graphs to get insights as well as dashboards to understand it better.