Assignment 6

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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## The following object is masked from 'package:stats':
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## The following object is masked from 'package:graphics':
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library(dplyr)

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## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
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## The following objects are masked from 'package:base':
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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...
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## 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.

Task 1

plot_ly(hr, x = ~satisfaction_level, type = "histogram") %>% 
  layout(title = "Histogram of Satisfaction Level",
    xaxis = list(title = "Satisfaction Level"),
    yaxis = list(title = "Frequency"))

• A majority of employyes are satisifed and have a satifaction level about >0.5
• A group of 693 employees had a satifaction level below .11

Task 2

plot_ly(hr,
        y = ~last_evaluation, type = "box") %>%
   layout(title = "Histogram of Last Evaluation",
          yaxis = list(title = "Last Evaluation"))

• Most of the employees evaluation scores were above .5 indicating overall the employees did well.
• Overall the employees did well, nearly all within the same performance level.

Task 3

plot_ly(hr, x = ~as.factor(Department), y = ~average_montly_hours, type = "box") %>%
  layout(
    title = "Average Monthly Hours by Department",
    xaxis = list(title = "Department"),
    yaxis = list(title = "Average Monthly Hours")
  )

• All the departments have average monthly hours within 150 to 250 hours
• The hours for each department are relativley equal across and consistent for all departments.

Task 4

attrition_data <- hr %>% filter (left == 1)
  salary_attrition_counts <- attrition_data %>% count(salary)
  
plot_ly(salary_attrition_counts, labels = ~salary, values = ~n, type = 'pie') %>%
  layout(title = 'Attrition by Salary Level')

• Most of employee attrition comes from low salary employees nearly 60%.
• Very few employees with high salaries leave the company.

Task 5

plot_ly(hr, x = ~Department, y = ~satisfaction_level, type = 'bar') %>%
 layout(
    title = 'Average Satisfaction by Department',
    xaxis = list(title = 'Department'),
    yaxis = list(title = 'Satisfaction Level')
  )

• The sales department has the highest satisfaction for employees
• The other departments have low satisfaction with sales, support, and technical being higher outliers.