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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
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##     layout
library(dplyr)
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## Attaching package: 'dplyr'
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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...
## 
## 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:Create a histogram of the satisfaction_level variable.

plot_ly(hr,
        x = ~satisfaction_level,
        type = "histogram") %>%
  layout(title = "About 50% of employees are satisfied (unsatisfaction > .7)",
         xaxis = list(title = "Satisfaction Level"),
         yaxis = list(title = "Count of employees"))

2 Box Plot: Last Evaluation Scores

plot_ly(hr, x = ~last_evaluation, type = "box") %>%
  layout(title = "Majority of scores fall between .56 and .87  ",
         xaxis = list(title = "Last Evaluation"),
         yaxis = list(title = "Count of employees"))

3 Comparative Box Plot: Monthly Hours by Department

plot_ly(hr, x = ~Department, y = ~average_montly_hours, type = "box") %>%
  layout(title = "Management has highest hours per month",
         xaxis = list(title = "Department"),
         yaxis = list(title = "average monthly hours"))

4 Pie Chart of Frequencies: Attrition by Salary Level

cyl_counts <- hr %>% count(salary)
plot_ly(cyl_counts, labels = ~salary, values = ~n, type = 'pie') %>%
  layout(title = 'The Majority of salary level is low')

5 Bar Plot of Averages: Average Satisfaction by Department

dep_mean <- hr %>% 
  group_by(Department) %>% 
  summarise(satisfaction_level = mean(satisfaction_level))

plot_ly(dep_mean, 
        x = ~factor(Department), 
        y = ~satisfaction_level, 
        type = 'bar') %>%
  layout(title = 'Average Satisfaction Level by Department: IT Leads in Employee Satisfaction',
         xaxis = list(title = 'Department'),
         yaxis = list(title = 'Average Satisfaction Level'))