library(readr)
sales_employee_data<-read.csv("C:/Users/salim/OneDrive/Documents/USIU/1.2/DSA/Datasets/Dataset.csv")
sales_employee_data## Transaction.ID Product Sales.Amount Region Employee.ID Employee.Name
## 1 1 X 250 North 10 John
## 2 2 Y 320 South 11 Jane
## 3 3 X 180 East 12 Mike
## 4 4 Z 410 West 13 Anna
## 5 5 Y 275 North 14 Paul
## 6 6 X 190 East 15 Sara
## 7 7 Z 390 South 11 Jane
## 8 8 Y 280 West 10 John
## 9 9 X 210 North 14 Paul
## 10 10 Z 370 East 12 Mike
## Department Experience..Years. Salary
## 1 Sales 4 48000
## 2 Sales 6 53000
## 3 HR 3 42000
## 4 IT 9 60000
## 5 HR 7 47000
## 6 IT 10 52000
## 7 Sales 6 53000
## 8 Sales 4 48000
## 9 HR 7 47000
## 10 HR 3 42000B
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniontotal_sales_by_region <- summarise(group_by(sales_employee_data, Region), Total_Sales = sum(`Sales.Amount`))
"Total Sales by Region:"## [1] "Total Sales by Region:"total_sales_by_region## # A tibble: 4 × 2
## Region Total_Sales
## <chr> <int>
## 1 East 740
## 2 North 735
## 3 South 710
## 4 West 690C
avg_salary_by_department <- summarise(group_by(sales_employee_data, Department), Average_Salary = mean(Salary))
avg_salary_by_department## # A tibble: 3 × 2
## Department Average_Salary
## <chr> <dbl>
## 1 HR 44500
## 2 IT 56000
## 3 Sales 50500D
experienced_employees <- filter(sales_employee_data, `Experience..Years.` > 5)
experienced_employees## Transaction.ID Product Sales.Amount Region Employee.ID Employee.Name
## 1 2 Y 320 South 11 Jane
## 2 4 Z 410 West 13 Anna
## 3 5 Y 275 North 14 Paul
## 4 6 X 190 East 15 Sara
## 5 7 Z 390 South 11 Jane
## 6 9 X 210 North 14 Paul
## Department Experience..Years. Salary
## 1 Sales 6 53000
## 2 IT 9 60000
## 3 HR 7 47000
## 4 IT 10 52000
## 5 Sales 6 53000
## 6 HR 7 47000E
experienced_employees %>%
arrange(desc('sales.Amount'))## Transaction.ID Product Sales.Amount Region Employee.ID Employee.Name
## 1 2 Y 320 South 11 Jane
## 2 4 Z 410 West 13 Anna
## 3 5 Y 275 North 14 Paul
## 4 6 X 190 East 15 Sara
## 5 7 Z 390 South 11 Jane
## 6 9 X 210 North 14 Paul
## Department Experience..Years. Salary
## 1 Sales 6 53000
## 2 IT 9 60000
## 3 HR 7 47000
## 4 IT 10 52000
## 5 Sales 6 53000
## 6 HR 7 47000F
sales_employee_data %>%
select('Employee.Name','Product','Sales.Amount','Salary')## Employee.Name Product Sales.Amount Salary
## 1 John X 250 48000
## 2 Jane Y 320 53000
## 3 Mike X 180 42000
## 4 Anna Z 410 60000
## 5 Paul Y 275 47000
## 6 Sara X 190 52000
## 7 Jane Z 390 53000
## 8 John Y 280 48000
## 9 Paul X 210 47000
## 10 Mike Z 370 42000G
sales_employee_data %>%
rename(Year_of_Experience='Experience..Years.')## Transaction.ID Product Sales.Amount Region Employee.ID Employee.Name
## 1 1 X 250 North 10 John
## 2 2 Y 320 South 11 Jane
## 3 3 X 180 East 12 Mike
## 4 4 Z 410 West 13 Anna
## 5 5 Y 275 North 14 Paul
## 6 6 X 190 East 15 Sara
## 7 7 Z 390 South 11 Jane
## 8 8 Y 280 West 10 John
## 9 9 X 210 North 14 Paul
## 10 10 Z 370 East 12 Mike
## Department Year_of_Experience Salary
## 1 Sales 4 48000
## 2 Sales 6 53000
## 3 HR 3 42000
## 4 IT 9 60000
## 5 HR 7 47000
## 6 IT 10 52000
## 7 Sales 6 53000
## 8 Sales 4 48000
## 9 HR 7 47000
## 10 HR 3 42000H
sales_employee_data <- mutate(sales_employee_data, New_Salary = Salary * 1.12)
(head(sales_employee_data))## Transaction.ID Product Sales.Amount Region Employee.ID Employee.Name
## 1 1 X 250 North 10 John
## 2 2 Y 320 South 11 Jane
## 3 3 X 180 East 12 Mike
## 4 4 Z 410 West 13 Anna
## 5 5 Y 275 North 14 Paul
## 6 6 X 190 East 15 Sara
## Department Experience..Years. Salary New_Salary
## 1 Sales 4 48000 53760
## 2 Sales 6 53000 59360
## 3 HR 3 42000 47040
## 4 IT 9 60000 67200
## 5 HR 7 47000 52640
## 6 IT 10 52000 58240I
total_sales_by_department <- summarise(group_by(sales_employee_data, Department), Total_Sales = sum(`Sales.Amount`))
print("Total Sales by Department:")## [1] "Total Sales by Department:"print(total_sales_by_department)## # A tibble: 3 × 2
## Department Total_Sales
## <chr> <int>
## 1 HR 1035
## 2 IT 600
## 3 Sales 1240J
sales_employee_data %>%
filter(Department == 'Sales',Region =='South') %>%
summarise(mean('Sales.Amount'))## Warning: There was 1 warning in `summarise()`.
## ℹ In argument: `mean("Sales.Amount")`.
## Caused by warning in `mean.default()`:
## ! argument is not numeric or logical: returning NA## mean("Sales.Amount")
## 1 NA