Justin Kaplan

Assignment 6

library(plotly)
library(readr)
library(dplyr)
HR <- read_csv('https://raw.githubusercontent.com/aiplanethub/Datasets/refs/heads/master/HR_comma_sep.csv')

Tasks

1. 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 = "Most employees are generally satisfied (Satisfaction > 0.5) ",
         xaxis = list(title = "Satisfaction Level"),
         yaxis = list(title = "Count"))

Takeaways

• The majority of employees are satisfied which was categorized by having a satisfaction of 0.5 or greater
• There are a large sum of employees who are very unsatisfied which was categorized by a satisfaction of 0.2 or less
• The most common answer was a satisfaction of 0.1 which had a count of 693 ### 2. 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.

library(plotly)
plot_ly(HR, x = ~last_evaluation, type = "box") %>%
  layout(title = "Most employees recieved passing evaluation grades (Evaluation Grade > 0.7) ",
         xaxis = list(title = "Evaluation Grade"),
         yaxis = list(title = "Count"))

Takeaways

• The median evaluation grade is 0.72
• 25% of the workers scored over a 0.87
• The minimum grade is a 0.36
• The majority of workers got a passing level grade (Evaluation grade > 0.70)

3. 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 = "Departments have similar monthly hours",
         xaxis = list(title = "Departments"),
         yaxis = list(title = "Miles Per Gallon"))

Takeaways

• Departments generally have the same amount of hours with similar ceilings and floors

4. 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.

Question4 <- HR %>%
  filter(left == 1) %>% 
  count(salary)
plot_ly(Question4, labels = ~salary, values = ~n, type = 'pie') %>%
  layout(title = 'Most employees that left the company had a low salary level')

Takeaways

• Most of the employees that leave the company have a low salary
• Very few of the employees that left the company had high salaries

5. 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.

plot_ly(HR, x = ~as.factor(Department), y = ~satisfaction_level, type = "bar") %>%
  layout(title = "Sales employees have the highest level of satisfaction",
         xaxis = list(title = "Departments"),
         yaxis = list(title = "Satisfaction"))

Takeaways

• Sales has the highest level of satisfaction
• Management and Research and Development have thew lowest level of satisfaction