Assignment 6

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

library(ggplot2)
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.

Histogram: Distribution of Employee Satisfaction Create a histogram of the satisfaction_level variable. The title should reflect a key takeaway from the distribution.

plot_ly(hr,
        x = ~satisfaction_level,
        type = "histogram") %>%
  layout(title = "About 50% of employees are satisified (satisfaction > 0.7)",
         xaxis = list(title = "Satisfaction Level"),
         yaxis = list(title = "Count of Employees"))

About 50% of employees are satisfied with the company (> 0.7)

About 5% of employees are very unsatisfied with the company (< .1)

Box Plot: Last Evaluation Scores Create a box plot of the last_evaluation variable. The title should highlight an important insight about the evaluation scores.

plot_ly(hr
        , y = ~last_evaluation,
        type = "box") %>%
  layout(title = "The Average Employee is Satisfied with the Company (> .7)",
         yaxis = list(title = "Satisfaction Level of Employees"))

The distribution is slightly skewed towards lower satisfaction levels

50% of employees satisfaction is between 0.56 and 0.87

Comparative Box Plot: Monthly Hours by Department Create a comparative box plot of average_montly_hours grouped by department.The title should emphasize a significant difference or pattern among departments.

plot_ly(hr, 
        x = ~as.factor(Department),
        y = ~average_montly_hours,
         type = "box") %>%
  layout(title = "Each Department Works Roughly Same Amount of Hours",
         xaxis = list(title = "Department"),
         yaxis = list(title = "Average Monthly Hours"))

Accounting and randd departments slightly work more then everyone else

Marketing and HR slightly work less then everyone else

Pie Chart of Frequencies: Attrition by Salary Level Create a pie chart showing the frequency of employee attrition (left) for each salary category. The title should point out the relationship between salary and attrition.

attrition <- hr %>%
  group_by(left , salary) %>%
  summarise(count = n(), .groups = 'drop')

plot_ly(attrition, labels = ~salary, values = ~count, type = 'pie') %>%
layout(title = '91.75% of employees do not have a high salary')

43% of employees have a medium salary

About 49% of employees have a low salary

Bar Plot of Averages: Average Satisfaction by Department Create a bar plot displaying the average satisfaction_level for each department. The title should highlight a key observation about departmental satisfaction.

q5 <- hr %>% 
  group_by(Department) %>%
  summarise(avg_satisfaction = mean(satisfaction_level, na.rm = TRUE), .groups = 'drop')

plot_ly(q5, x = ~factor(Department), y = ~avg_satisfaction, type = 'bar') %>%
  layout(title = 'Most departments have a similar satisfaction level, averaging around 0.6',
         xaxis = list(title = 'Department'),
         yaxis = list(title = 'Average Satisfaction Level'))

Management is the most satisfied department

Accounting is the least satisfied department