In this tutorial, you’ll walk through a complete data analysis project using the HR Analytics dataset by Saad Haroon on Kaggle. You’ll start by loading and cleaning the data, then explore it visually using boxplots with ggplot2. Finally, you’ll learn about statistical modelling using linear regression and logistic regression in R.
By the end of this article, you should understand how to create boxplots in R, why they matter, and how they fit into a real-world analytics workflow.
Table of Contents
Prerequisites
Before you begin, you should be comfortable with the following:
Basic R syntax (variables, functions, data frames).
Installing and loading R packages.
Understanding what rows and columns represent in a dataset.
Very basic statistics (mean, median, distributions).
How to Set Up Your R Environment
Start by installing and loading the packages you will need.
install.packages(c("tidyverse", "ggplot2"))
library(tidyverse)
library(ggplot2)
tidyverse provides tools for data manipulation and visualization. ggplot2 is the visualization engine you will use for boxplots. Loading the libraries makes their functions available for use
How to Load and Inspect the Data
First, download the HR Analytics dataset by Saad Haroon from Kaggle.
Assuming the downloaded dataset is saved as "C:/Users/johndoe/Downloads/archive (2)/HR_Analytics.csv", load the path file into R.
You can view a sample of the the dataset by running the head function. To view the structure of the dataset, you can run the str function.
hr <- read.csv("C:/Users/johndoe/Downloads/archive (2)/HR_Analytics.csv")
head(hr)
str(hr)
The read.csv function imports the dataset into R. The head function shows the first six rows so you can preview the data. The str function reveals data types, helping you spot categorical versus numeric variables early.
Remember that understanding your data structure early prevents errors later when plotting or modeling. Once you run the head function, you should see the following in your console:
From the head function, you can see:
Structure
Each row represents one employee.
Each column represents a feature/variable about the employee.
Key Columns & Meaning
EmpID→ Employee identifierAge→ Age in yearsAgeGroup→ Age category (for example,18-25)Attrition→ Whether the employee left or not (Yes/No)BusinessTravel→ Travel frequency (Travel_Rarely,Travel_Frequently,Non-Travel)Department→ Employee departmentDistanceFromHome→ Distance from home to office (km)Education/EducationField→ Level and field of educationEmployeeCount→ Usually 1 per employee (redundant)Gender→ Male / FemaleJobRole/JobSatisfaction→ Job title and satisfaction levelMonthlyIncome/SalarySlab→ Salary amount and categoryYearsAtCompany/YearsInCurrentRole→ Experience metricsOverTime→ Works overtime (Yes/No)Other features:
PerformanceRating,TrainingTimesLastYear,WorkLifeBalance,StockOptionLevel, and so on.
Data Types
Numeric →
Age,DistanceFromHome,MonthlyIncome,YearsAtCompanyCategorical / Character →
Attrition,Gender,Department,JobRole
Observations
The dataset is tabular, like a spreadsheet.
There are multiple categorical columns
There are multiple numeric columns
Some columns seem redundant or constant; doesn’t provide useful information because of the same values (for example,
EmployeeCount)
From the str function, you can gather that:
The dataset contains 1,480 observations and 38 variables. Each row represents one employee, and each column represents a feature about that employee.
Each column has a name, data type, and example values. For instance, Age and DistanceFromHome are numeric (int), with values like 28 or 12. EmpID and Department are character strings (chr), with examples like Research & Development or Sales. Other features include JobRole (Analyst, Manager) and Attrition (Yes/No).
The dataset contains mixed data types. Some columns are numeric, such as MonthlyIncome or YearsAtCompany. Some are character or categorical, like Gender (Male/Female) and BusinessTravel (Travel_Rarely, Travel_Frequently). A few columns are redundant or constant. For example, EmployeeCount has the same value of 1 for all rows and does not provide useful information.
How to Clean and Prepare the Data
Before visualization, you must clean your data. In order to find out what you need to clean you can investigate the data.
Run the summary function to view the statistics of the dataset. You also need to run the is.na function to identify missing values to be removed.
summary(hr)
colSums(is.na(hr))
The summary function gives quick statistics and flags suspicious values. The is.na function checks for missing data. Boxplots are sensitive to extreme values, so knowing what you are working with is critical.
After running the summary function, the following will appear in your console:
This shows the basic statistics of each column. After running the is.na function, the following will also appear in your console:
From this output, you can see that only YearsWithCurrManager has 57, meaning that 57 employees don’t have a value for this column.
You can drop this whole column along with the other redundant columns we saw earlier on. You can do this with the code below.
hr <- hr %>% select(-c(EmployeeCount, Over18, StandardHours, YearsWithCurrManager))
To verify if the columns are gone, use this code:
colnames(hr)
Now we need to convert important categorical variables to factors. Doing this tells R that the column has two categories (‘Yes’ and ‘No’), not continuous text.
hr$Attrition <- as.factor(hr$Attrition)
hr$JobRole <- as.factor(hr$JobRole)
hr$Department <- as.factor(hr$Department)
This also ensures ggplot2 treats them correctly when grouping.
How to Use Boxplots
A boxplot displays key features of a dataset. The median is shown by the line in the middle of the box. The interquartile range is represented by the box itself while the whiskers show the spread of the data. Outliers appear as individual points.
Boxplots are mostly useful when you want to compare distributions across groups, such as income by job role or age by attrition status.
Let’s start with a simple boxplot of monthly income.
ggplot(hr, aes(y = MonthlyIncome)) +
geom_boxplot(fill = "blue") +
labs(
title = "Distribution of Monthly Income",
y = "Monthly Income")
The aes function tells ggplot what variable to plot. geom_boxplot draws the boxplot. The labs function labels parts of the plot drawn, that is the x axis, y axis, and the title.
How to Create Boxplots with ggplot2
Now lets compare income across job roles.
ggplot(hr, aes(x = JobRole, y = MonthlyIncome)) +
geom_boxplot(fill = "lightblue") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(
title = "Monthly Income by Job Role",
x = "Job Role",
y = "Monthly Income")
The x aesthetic lists all the job roles. The labels are rotated to improve readability. This visualization quickly reveals income differences across roles.
How to Perform Exploratory Data Analysis (EDA)
Exploratory data analysis involves using visual methods to ask questions and gain a deeper understanding of the data.
We can use the example of Years at company by department.
ggplot(hr, aes(x = Department, y = YearsAtCompany)) +
geom_boxplot(fill = "darkblue") +
labs(
title = "Years at Company by Department",
y = "Years at Company")
How to Build Linear Regression Models
To understand how to build linear regression models, you have to model MonthlyIncome using YearsAtCompany with the command below.
The first one creates the model while the second displays it.
hr_lm<- lm(MonthlyIncome ~ YearsAtCompany, data = hr)
summary(hr_lm)
Linear regression estimates how income changes with tenure. This works when the variables are numeric.
After running the code, your console should show you this output:
Call:
lm(formula = MonthlyIncome ~ YearsAtCompany, data = hr)
Residuals:
Min 1Q Median 3Q Max
-9506 -2488 -1186 1403 15483
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3734.47 159.41 23.43 <2e-16 ***
YearsAtCompany 395.25 17.14 23.07 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4032 on 1478 degrees of freedom
Multiple R-squared: 0.2647, Adjusted R-squared: 0.2642
F-statistic: 532 on 1 and 1478 DF, p-value: < 2.2e-16
Let’s interpret this model.
If an employee has 0 years at the company, their base monthly income is $3734.47. This comes from the intercept.
For each year an employee spends at the company, their monthly income is predicted to increase by $395.25.
Both coefficients have p-values < 2e-16. This means they are highly significant. It strongly shows that the years an employee spends at a company affects their income.
The model’s R-squared is 0.2647. This means about 26% of the variation in monthly income is explained by the years an employee spends at the company. This is low, so other factors like role, department, or education likely affect income too.
The model’s F-statistic is 532, with a p-value < 2.2e-16. This means the model is statistically significant overall.
In general, the longer an employee stays at a company, the more they earn, roughly $395 extra per year. But years at the company alone explain only about a quarter of their income. You need to consider other variables for better predictions.
How to Build Logistic Regression Models
You can now learn how to predict attrition. The first command generates the model while the second displays it.
hr_glm<- glm(
Attrition ~ MonthlyIncome + YearsAtCompany,
data = hr,
family = binomial)
summary(hr_glm)
Your console should show this as an output when you run both commands.
Call:
glm(formula = Attrition ~ MonthlyIncome + YearsAtCompany, family = binomial,
data = hr)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -8.094e-01 1.375e-01 -5.886 3.96e-09 ***
MonthlyIncome -9.449e-05 2.302e-05 -4.104 4.05e-05 ***
YearsAtCompany -5.047e-02 1.792e-02 -2.817 0.00485 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1305.4 on 1479 degrees of freedom
Residual deviance: 1252.5 on 1477 degrees of freedom
AIC: 1258.5
Number of Fisher Scoring iterations: 5
Logistic regression is used for binary outcomes, that is, yes or no. It estimates probability.
Let’s interpret this logistic regression model. The model predicts whether an employee is likely to leave the company (Attrition) based on their Monthly Income and Years at Company.
The intercept is -0.809. This is the baseline log-odds of leaving when their income and years at the company are zero.
The employees’ Monthly Income has a coefficient of -0.0000945. This means that as their income increases, their chance of leaving decreases slightly. An increase in income makes them less likely to quit.
The employees’ Years at Company have a coefficient of -0.0505. This shows that the longer they stay, the less likely they are to leave. Each additional year reduces their attrition probability.
All coefficients are statistically significant. Monthly Income and Years at Company both strongly affect their likelihood to stay.
The model’s residual deviance is 1252.5, lower than the null deviance of 1305.4. This means the model explains some of the variation in attrition.
The key takeaway is that if an employee earns more and stays longer at the company, they are less likely to leave. These factors matter, but other elements also influence attrition.
Why Visualization Comes Before Modeling
Boxplots help you to:
Detect outliers: Boxplots highlight extreme values that interfere with model results.
Compare groups: Boxplots allow quick comparison of distributions across different categories.
Form hypotheses: Visual patterns assist in identifying relationships worth testing in a model.
Validate modeling assumptions: Boxplots help check distribution shape and variance before modeling.
Modeling without visualization often leads to misinterpretation or false confidence.
Conclusion
In this tutorial, you learned how to load and clean data, understand boxplots and their importance. You also learned how to use ggplot2 to compare distributions, perform exploratory data analysis (EDA), build linear and logistic regression models, and link visualization insights to modeling results.