library(readr)
library(plotly)
## Loading required package: ggplot2
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
hr <- read_csv('https://raw.githubusercontent.com/aiplanethub/Datasets/refs/heads/master/HR_comma_sep.csv')
## Rows: 14999 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Department, salary
## dbl (8): satisfaction_level, last_evaluation, number_project, average_montly...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
chisq.test(hr$left , hr$Work_accident)
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  hr$left and hr$Work_accident
## X-squared = 357.56, df = 1, p-value < 2.2e-16
p-value: The p-value is very small, therefore the probability of these results being random is very small.
chi-square test interpretation: There is a dependency between the work accident and leaving the company.
Non-technical: Employees that did not have a work accident are more likely to leave

Calculate proportions

prop_data <- hr %>%
  group_by(Work_accident) %>%
  summarise(
    Stayed = sum(left == 0) / n(),
    Left = sum(left == 1) / n()
  )

Create a stacked bar chart

plot_ly(prop_data) %>%
  add_bars(x = ~Work_accident, y = ~Stayed, name = "Stayed",
           marker = list(color = "#1f77b4")) %>%
  add_bars(x = ~Work_accident, y = ~Left, name = "Left",
           marker = list(color = "ff7f0e")) %>%
  layout(
    barmode = "stack",
    xaxis = list(title = "Work Accident"),
    yaxis = list(title = "Proportion", tickformat = ",.0%"),
    title = "Employees that did not have a work accident are more than 3 times likely to leave"
  )
chisq.test(hr$left , hr$promotion_last_5years)
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  hr$left and hr$promotion_last_5years
## X-squared = 56.262, df = 1, p-value = 6.344e-14
p-value: The p-value is very small, therefore the probability of these results being random is very small.
chi-square test interpretation: The result is statistically significant, suggesting that promotion status plays a role in whether employees leave.
Non-technical: Employees that had a promotion in the last 5 years are more likely to stay
prop_data <- hr %>%
  group_by(promotion_last_5years) %>%
  summarise(
    Stayed = sum(left == 0) / n(),
    Left = sum(left == 1) / n()
  )
plot_ly(prop_data) %>%
  add_bars(x = ~promotion_last_5years, y = ~Stayed, name = "Stayed",
           marker = list(color = "#1f77b4")) %>%
  add_bars(x = ~promotion_last_5years, y = ~Left, name = "Left",
           marker = list(color = "ff7f0e")) %>%
  layout(
    barmode = "stack",
    xaxis = list(title = "Promotion_last_5years"),
    yaxis = list(title = "Proportion", tickformat = ",.0%"),
    title = "Employees that had a promotion in the last 5 years more than likely stayed"
  )
chisq.test(hr$left , hr$salary)
## 
##  Pearson's Chi-squared test
## 
## data:  hr$left and hr$salary
## X-squared = 381.23, df = 2, p-value < 2.2e-16
p-value:There is strong evidence of an association between salary levels and whether an employee left the company.
chi-square test interpretation: Salary might be a factor that affects retention, and improving salary levels could help reduce employee turnover.”
Non-technical: Employees with lower salaries are more likely to leave the company compared to those with medium or high salaries.
prop_data <- hr %>%
  group_by(salary) %>%
  summarise(
    Stayed = sum(left == 0) / n(),
    Left = sum(left == 1) / n()
  )
plot_ly(prop_data) %>%
  add_bars(x = ~salary, y = ~Stayed, name = "Stayed",
           marker = list(color = "#1f77b4")) %>%
  add_bars(x = ~salary, y = ~Left, name = "Left",
           marker = list(color = "ff7f0e")) %>%
  layout(
    barmode = "stack",
    xaxis = list(title = "salary"),
    yaxis = list(title = "Proportion", tickformat = ",.0%"),
    title = "Employees that had different salaries either stayed or left"
  )
chisq.test(hr$left , hr$Department)
## 
##  Pearson's Chi-squared test
## 
## data:  hr$left and hr$Department
## X-squared = 86.825, df = 9, p-value = 7.042e-15
p-value:The p-value is very small, therefore the probability of these results being random is very small
chi-square interpretation: This indicates that department and attrition are not independent, suggesting a statistically significant relationship between an employee’s department and their likelihood of leaving the company.
Non-technical: Employees in some departments are more likely to leave the company compared to others.
prop_data <- hr %>%
  group_by(Department) %>%
  summarise(
    Stayed = sum(left == 0) / n(),
    Left = sum(left == 1) / n()
  )
plot_ly(prop_data) %>%
  add_bars(x = ~Department, y = ~Stayed, name = "Stayed",
           marker = list(color = "#1f77b4")) %>%
  add_bars(x = ~Department, y = ~Left, name = "Left",
           marker = list(color = "ff7f0e")) %>%
  layout(
    barmode = "stack",
    xaxis = list(title = "Department"),
    yaxis = list(title = "Proportion", tickformat = ",.0%"),
    title = "Employees of departments mostly stayed"
  )