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

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 = "Most emmployees are satified (satifaction > .5)",
         xaxis = list(title = "Satisfaction Level"),
         yaxis = list(title = "Frequency"))

- Most employees are satisfied (satisfaction > .5)

- About 6% of employees are extremely dissatisfied ( satisfaction level <= .1)

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 = ~satisfaction_level, type = "box") %>%
  layout(title = "Distribution of Satisfaction Level",
         yaxis = list(title = "Level of satisfaction (satifaction > .5)"))

- median satisfaction level is .64

- Middle 50% of employees range in satisfaction level

of .44 to .82

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", color = ~Department) %>%
  layout(title = "Miles Per Gallon by Number of Cylinders",
         xaxis = list(title = "Average Monthly Hours"),
         yaxis = list(title = "Satisfaction level"))

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

- Not much difference in medians across departments, all fall between 197 to 204.

- There seems to be no large outliers for any of the department.

- Based on these graphs satifaction levels seem to be fairly similar across departments with no

one department largely more or less satisfied than the rest.

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.

plot_ly(hr, labels = ~salary, values = ~left, type = 'pie') %>%
  layout(title = 'Low salary has the highest percentage attrition')

- Very small group of high salary employees with high attrition, only 2.3 %

- Seems to be an association with salary and attrition, the higher salaries have lower 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

plot_ly(hr, x = ~Department, y = ~satisfaction_level, type = 'bar') %>%
  layout(title = 'Sales Department is most satified',
         xaxis = list(title = 'Department'),
         yaxis = list(title = 'Satisfaction Level'))

Assignment #6

Nick Cerimeli

2024-10-25

1)

Histogram: Distribution of Employee Satisfaction Create a histogram

of the satisfaction_level variable. The title should reflect a

key takeaway from the distribution.

- Most employees are satisfied (satisfaction > .5)

- About 6% of employees are extremely dissatisfied ( satisfaction level <= .1)

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.

- median satisfaction level is .64

- Middle 50% of employees range in satisfaction level

of .44 to .82

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.

- Not much difference in medians across departments, all fall between 197 to 204.

- There seems to be no large outliers for any of the department.

- Based on these graphs satifaction levels seem to be fairly similar across departments with no

one department largely more or less satisfied than the rest.

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.

- Very small group of high salary employees with high attrition, only 2.3 %

- Seems to be an association with salary and attrition, the higher salaries have lower 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

- Most departments have similar levels of satisfaction except for sales, support, and technical

- Sales have by far the most satisfaction out of all the departments