58 lines
1.5 KiB
R
58 lines
1.5 KiB
R
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# Creating a data frame
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employee_data <- data.frame(
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EmployeeID = c(1, 2, 3, 4),
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Name = c("Alice", "Bob", "Charlie", "David"),
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Age = c(28, 34, 29, 40),
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Department = c("HR", "IT", "Marketing", "Finance"),
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stringsAsFactors = FALSE
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)
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print("Original Data Frame:")
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print(employee_data)
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# Accessing data frame columns
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print("Names Column:")
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print(employee_data$Name)
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# Accessing rows and columns using indices
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print("Second Row, Third Column:")
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print(employee_data[2, 3])
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# Adding a new column
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employee_data$Salary <- c(50000, 55000, 49000, 53000)
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print("Data Frame with Salary Column:")
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print(employee_data)
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# Removing a column
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employee_data$Age <- NULL
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print("Data Frame after Removing Age Column:")
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print(employee_data)
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# Filtering rows
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it_department <- subset(employee_data, Department == "IT")
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print("Employees in IT Department:")
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print(it_department)
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# Summarizing data
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average_salary <- mean(employee_data$Salary)
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print(paste("Average Salary:", average_salary))
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# Using dplyr for more advanced data frame manipulation
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# Uncomment the next lines if dplyr is not installed
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# install.packages("dplyr")
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library(dplyr)
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# Selecting specific columns with dplyr
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selected_columns <- select(employee_data, Name, Salary)
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print("Selected Columns:")
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print(selected_columns)
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# Filtering with dplyr
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high_earners <- filter(employee_data, Salary > 50000)
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print("High Earners:")
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print(high_earners)
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# Arranging rows by a column
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sorted_employees <- arrange(employee_data, desc(Salary))
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print("Employees Sorted by Salary:")
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print(sorted_employees)
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