R Markdown
library(readr)
library(plotly)
## Loading required package: ggplot2
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## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
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## last_plot
## The following object is masked from 'package:stats':
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## filter
## The following object is masked from 'package:graphics':
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## layout
library(dplyr)
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## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
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## filter, lag
## The following objects are masked from 'package:base':
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## 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.
First test
2. Interpret the results in technical terms.
The p-value is 6.344e-14. This is very small, meaning there is a
very small chance that this is random. There is a dependence between the
promotion in the last 5 years and the employee leaving.
3. Interpret the results in non-technical terms.
People promoted in the last 5 years are less likely to leave. 18%
more people stayed after getting promoted.
4. Create a plot that helps visualize the chi-square test.
Calculate proportions
prop_data <- hr %>%
mutate(promotion_last_5years = as.factor(promotion_last_5years)) %>%
group_by(promotion_last_5years) %>%
summarise(
Stayed = sum(left == 0) / n(),
Left = sum(left == 1) / n()
)
Create stacked bar chart
plot_ly(prop_data) %>%
add_bars(x = ~promotion_last_5years, y = ~Stayed, name = "Stayed",
marker = list(color = "#f5428a")) %>%
add_bars(x = ~promotion_last_5years, y = ~Left, name = "Left",
marker = list(color = "#42e6f5")) %>%
layout(
barmode = "stack",
xaxis = list(title = "Promotion Last 5 Years"),
yaxis = list(title = "Proportion", tickformat = ",.0%"),
title = "18% More People Stayed After Getting Promoted in The Last 5 Years"
)
Second test
2. Interpret the results in technical terms.
The p-value is 7.042e-15. This is very small, meaning there is a
very small chance that this is random. There is a dependence between the
Department and the employee leaving.
3. Interpret the results in non-technical terms.
The department is going to have an affect on people leaving. HR
(29%) is 2x more likely to leave than Management (14%).
4. Create a plot that helps visualize the chi-square test.
prop_data1 <- hr %>%
mutate(Department = as.factor(Department)) %>%
group_by(Department) %>%
summarise(
Stayed = sum(left == 0) / n(),
Left = sum(left == 1) / n()
)
plot_ly(prop_data1) %>%
add_bars(x = ~Department, y = ~Stayed, name = "Stayed",
marker = list(color = "#03fcd7")) %>%
add_bars(x = ~Department, y = ~Left, name = "Left",
marker = list(color = "#8403fc")) %>%
layout(
barmode = "stack",
xaxis = list(title = "Department"),
yaxis = list(title = "Proportion", tickformat = ",.0%"),
title = "HR is 2x More Likely to Leave Than Management"
)
Third test
2. Interpret the results in technical terms.
p-value is extremely small, it is very unlikely these results were
random. There is a dependence between the work accidents and working at
the company.
3. Interpret the results in non-technical terms.
Employees who have work accidents are less likely to no longer be
working for the company.
4. Create a plot that helps visualize the chi-square test.
prop_data <- hr %>%
group_by(Work_accident) %>%
summarise(
Stayed = sum(left == 0) / n(),
Left = sum(left == 1) / n()
)
Create stacked bar chart
plot_ly(prop_data) %>%
add_bars(x = ~Work_accident, y = ~Stayed, name = "Stayed",
marker = list(color = "#f7e20e")) %>%
add_bars(x = ~Work_accident, y = ~Left, name = "Left",
marker = list(color = "#503203")) %>%
layout(
barmode = "stack",
xaxis = list(title = "Work Accidents"),
yaxis = list(title = "Proportion", tickformat = ",.0%"),
title = "Workers who get into accidents are 19% less likely to leave"
)
Fourth test
2. Interpret the results in technical terms.
p-value is extremely small, it is very unlikely these results were
random. There is a dependence between salary and working for the
company.
3. Interpret the results in non-technical terms.
Workers who have lower salaries are more likely to leave.
4. Create a plot that helps visualize the chi-square test.
prop_data <- hr %>%
group_by(salary) %>%
summarise(
Stayed = sum(left == 0) / n(),
Left = sum(left == 1) / n()
)
Create stacked bar chart
plot_ly(prop_data) %>%
add_bars(x = ~salary, y = ~Stayed, name = "Stayed",
marker = list(color = "#f7e")) %>%
add_bars(x = ~salary, y = ~Left, name = "Left",
marker = list(color = "#32e1f9")) %>%
layout(
barmode = "stack",
xaxis = list(title = "Salary Level"),
yaxis = list(title = "Proportion", tickformat = ",.0%"),
title = "Workers who have lower salary are more likely to leave"
)