R Markdown

Load the packages

library(readr)
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
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
## The following object is masked from 'package:graphics':
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##     layout

Load the hr dataset

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 = "About 50% of Employees are Satisfied (satisfaction_level > .7)",
         xaxis = list(title = "Satisfaction Level"),
         yaxis = list(title = "Count of Employees"))
  1. about 50% of employees are satisfied (satisfied > 0.7)
  2. about 10% of people are extremely dissatisfied (dissatisfied < 0.2)

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, x = ~last_evaluation, type = "box") %>%
  layout(title = "About 50% of Employees Scored Well on Last Evaluation (last_evalutation > 0.7)",
         xaxis = list(title = "Last Evaluation Score"),
         yaxis = list(title = "Number of Evaulations"))
  1. The distribution is slightly scored toward lower evaluation scores
  2. About 50% of employees scored between 0.56 and 0.87

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 = ~Department, y = ~average_montly_hours, type = "box") %>%
  layout(title = "Work Hours are Consistent Across Departments",
         xaxis = list(title = "Department"),
         yaxis = list(title = "Average Monthly Hours "))
  1. The average monthly hours between departments seem consistent

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.

attrition_counts <- hr %>% count(salary, left)
plot_ly(attrition_counts %>% filter(left == 1), labels = ~salary, values = ~n, type = 'pie') %>%
  layout(title = 'The Majority of Employees With Low Salaries Have High Attrition')
  1. There is nearly no attrition with employees who have a high salary
  2. The majority of employees with low salaries have high attrition

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

plot_ly(avg_satisfaction, x = ~Department, y = ~avg_satisfaction, type = 'bar') %>%
  layout(title = 'The Average Satisfaction Level is Consistent Across Departments',
         xaxis = list(title = 'Department'),
         yaxis = list(title = 'Average Satisfaction Level'))
  1. The accounting department has the lowest average satisfaction level across all departments
  2. The satisfaction level across departments is relatively consistent