library(readr)
library(plotly)
## Loading required package: ggplot2
## 
## 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)
## 
## 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.

##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 = "Distribution of Employee Satisfaction",
         xaxis = list(title = "Satisfaction Level"),
         yaxis = list(title = "Count"))

##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.

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

##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 = "Average Monthly Hours by Department",
         xaxis = list(title = "Department"),
         yaxis = list(title = "Average Monthly Hours"))

##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.

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

##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.

avg_satisfaction_level <- hr %>% group_by(Department) %>% summarise(avg_satisfaction_level = mean(satisfaction_level))
plot_ly(avg_satisfaction_level, x = ~factor(Department), y = ~avg_satisfaction_level, type = 'bar') %>%
  layout(title = 'Average Satisfaction Level by Department',
         xaxis = list(title = 'Department'),
         yaxis = list(title = 'Average Satisfaction Level'))