SAS Data Science

Overview

This repository contains SAS code snippets for data science tasks and analyses. The code provided here serves as examples and templates for various data manipulation, analysis, and visualization tasks using SAS.

Contents

SAS Data Processing Example: "characters" Dataset

This SAS code snippet demonstrates the creation of a dataset named "characters" containing information about fictional characters. The dataset has four variables:

1. name: Represents the name of the character.
2. gender: Indicates the gender of the character (M for male, F for female).
3. height: Represents the height of the character.
4. weight: Indicates the weight of the character.

The data is manually entered using the cards section, where each line represents information about a character, including their name, gender, height, and weight.

Explanation:

The characters dataset is structured to store information about fictional characters, including their name, gender, height, and weight. Each observation in the dataset corresponds to a single character and includes their respective information. The data is manually entered into the code using the input statement followed by the cards section. This example serves as a template for creating datasets to manage information about characters in a fictional universe, which can be valuable for various analytical and storytelling purposes in SAS.

SAS Data Processing Example: "employees" Dataset

This SAS code snippet demonstrates the creation of a dataset named "employees" containing information about employees. The dataset has four variables:

1. name: Represents the name of the employee.
2. gender: Indicates the gender of the employee (M for male, F for female).
3. age: Represents the age of the employee.
4. weight: Indicates the weight of the employee.

The data is read from the "cards" section using the INFILE statement with a specified delimiter (,). Each line in the "cards" section represents information about an employee, including their name, gender, age, and weight.

The resulting dataset provides a structured representation of employees' demographic information, which can be further analyzed or used for statistical modeling and visualization tasks in SAS.

Explanation:

The employees dataset is structured to store information about employees, including their first name, last name, gender, age, and weight. The data is read from the "cards" section using the INFILE statement with a specified delimiter (,). The dsd option is used to handle consecutive delimiters as separate, empty fields. Each line in the "cards" section represents information about an employee, including their first name, last name, gender, age, and weight. The resulting dataset provides a structured representation of employees' demographic information, which can be further analyzed or used for statistical modeling and visualization tasks in SAS.

SAS Data Processing Example: "guardians" Dataset

This SAS code snippet demonstrates the creation of a dataset named "guardians" containing information about guardians. The dataset has three variables:

1. first_name: Represents the first name of the guardian.
2. last_name: Indicates the last name of the guardian.
3. height: Represents the height of the guardian.

The data is read from the "cards" section using the INFILE statement with a specified delimiter (,). The dsd option is also used, which stands for "Delimiter-Sensitive Data". This option treats consecutive delimiters as separate, empty fields. In this case, it allows the code to properly handle the data where the delimiter is a comma, and there are no missing values for some variables.

Explanation:

The guardians dataset is structured to store information about guardians, including their first and last names, and height. Each observation in the dataset corresponds to a single guardian and includes their respective information. The dataset can be further analyzed or used for statistical modeling and visualization tasks in SAS.

SAS Data Processing Example: "custom_dataset" Dataset

This SAS code snippet demonstrates the creation of a dataset named "custom_dataset" containing information about individuals. The dataset has six variables:

1. region: Represents the region where the individual resides.
2. first: Indicates the first name of the individual.
3. last: Represents the last name of the individual.
4. sex: Indicates the sex of the individual (M for male, F for female).
5. years: Represents the age of the individual.
6. mass: Indicates the mass of the individual.

The data is entered using the cards section, where each line represents information about an individual, including their region, first name, last name, sex, age, and mass. Additionally, there is a conditional statement that deletes observations where the region is 'Midwest'.

Explanation:

• The custom_dataset dataset is structured to store information about individuals, including their region of residence, name, sex, age, and mass.
• The @ symbol in the input statement indicates that SAS should hold the input pointer at the current line after reading the value. This allows for reading multiple variables from the same line of data without advancing to the next line.
• The data is manually entered using the input statement followed by the cards section, where each line represents information about an individual.
• A conditional statement is used to delete observations where the region is 'Midwest', demonstrating data manipulation capabilities in SAS. This example serves as a template for creating datasets to manage information about individuals, which can be valuable for various analytical and reporting purposes in SAS.

SAS Data Processing Example: "individuals" Dataset

This SAS code snippet demonstrates the creation of a dataset named "individuals" containing information about individuals. The dataset has six variables:

1. first: Indicates the first name of the individual.
2. last: Represents the last name of the individual.
3. sex: Indicates the sex of the individual (M for male, F for female).
4. years: Represents the age of the individual.
5. mass: Indicates the mass of the individual.

The data is entered using the cards section, where each line represents information about an individual, including their first name, last name, gender, age, and mass.

Explanation:

The individuals dataset is structured to store information about individuals, including their first name, last name, gender, age, and mass. The @@ symbols in the input statement indicate that SAS should hold the input pointer at the current line after reading the values. This allows for reading multiple observations from the same line of data without advancing to the next line. The data is manually entered using the input statement followed by the cards section, where each line represents information about an individual. This example serves as a template for creating datasets to manage information about individuals, which can be valuable for various analytical and reporting purposes in SAS.

SAS Data Processing Example: "personnel" Dataset

This SAS code snippet demonstrates the creation of a dataset named "personnel" containing information about individuals. The dataset has four variables:

1. identifier: Represents the unique identifier of the individual.
2. years: Indicates the age of the individual.
3. sex: Indicates the gender of the individual (M for male, F for female).
4. mass: Indicates the weight of the individual.

The data is read from the "cards" section using the INPUT statement with specified column positions. Each line in the "cards" section represents information about an individual, including their unique identifier, age, gender, and weight.

Explanation:

• The personnel dataset is structured to store information about individuals, including their unique identifier, age, gender, and weight.
• The data is read from the "cards" section using the INPUT statement with specified column positions. Each line in the "cards" section represents information about an individual, including their unique identifier, age, gender, and weight.
• The resulting dataset provides a structured representation of individuals' demographic information, which can be further analyzed or used for statistical modeling and visualization tasks in SAS.

SAS Data Processing Example: "individual_info" Dataset

This SAS code snippet demonstrates the creation of a dataset named "individual_info" containing information about individuals. The dataset has four variables:

1. forename: Represents the forename of the individual.
2. surname: Represents the surname of the individual.
3. sex: Indicates the gender of the individual (M for male, F for female).
4. years: Represents the age of the individual.
5. mass: Indicates the weight of the individual.

The data is read from the "cards" section using the INPUT statement with specified column positions. The # symbol is used to skip a specified number of columns before reading the next variable. Each line in the "cards" section represents information about an individual, including their forename, surname, gender, age, and weight.

Explanation:

• The individual_info dataset is structured to store information about individuals, including their forename, surname, gender, age, and weight.
• The data is read from the "cards" section using the INPUT statement with specified column positions. The # symbol is used to skip a specified number of columns before reading the next variable. This allows for reading variables that are not in contiguous columns.
• Each line in the "cards" section represents information about an individual, including their forename, surname, gender, age, and weight.
• The resulting dataset provides a structured representation of individuals' demographic information, which can be further analyzed or used for statistical modeling and visualization tasks in SAS.

SAS Data Processing Example: "personal_data" Dataset

This SAS code snippet demonstrates the creation of a dataset named "personal_data" containing information about individuals. The dataset has four variables:

1. first_name: Represents the first name of the individual.
2. last_name: Represents the last name of the individual.
3. sex: Indicates the gender of the individual (M for male, F for female).
4. years: Represents the age of the individual.
5. mass: Indicates the weight of the individual.

The data is read from the "cards" section using the INPUT statement with specified column positions. The / symbol is used to indicate a continuation of input data from the previous line. Each line in the "cards" section represents information about an individual, including their first name, last name, gender, age, and weight.

Explanation:

• The personal_data dataset is structured to store information about individuals, including their first name, last name, gender, age, and weight.
• The data is read from the "cards" section using the INPUT statement with specified column positions. The / symbol is used to indicate a continuation of input data from the previous line. This allows for splitting the input data over multiple lines for better readability.
• Each line in the "cards" section represents information about an individual, including their first name, last name, gender, age, and weight.
• The resulting dataset provides a structured representation of individuals' demographic information, which can be further analyzed or used for statistical modeling and visualization tasks in SAS.

SAS Data Import Example: "employee_data" Dataset

This SAS code snippet demonstrates the import of data from an Excel file named "employee_data.xlsx" into a dataset named "employee_data".

The PROC IMPORT procedure is used to import data from the specified Excel file. The FILENAME statement assigns a fileref "inputfile" to the Excel file path. The PROC IMPORT statement specifies the input data file as "inputfile", the DBMS (Database Management System) as "xlsx" to indicate that the input file is an Excel file, and the OUT option specifies the name of the output SAS dataset as "employee_data". The GETNAMES option is set to "yes" to indicate that the first row of the Excel file contains variable names.

Explanation:

• The FILENAME statement assigns a fileref "inputfile" to the Excel file located at '/home/x62422869/The Simplest Guide to SAS/employee_data.xlsx'.
• The PROC IMPORT procedure reads the data from the Excel file specified by the FILENAME statement.
• The DBMS option is set to "xlsx" to indicate that the input file format is Excel.
• The OUT option specifies the name of the output SAS dataset as "employee_data".
• The GETNAMES option is set to "yes" to indicate that variable names should be taken from the first row of the Excel file.
• The resulting dataset "employee_data" contains the imported data from the Excel file, which can be further analyzed or used for various tasks in SAS.
• This code snippet demonstrates the process of importing data from an Excel file into a SAS dataset using PROC IMPORT, providing a way to access and analyze external data within the SAS environment.

SAS Data Processing Example: "selected_females" Dataset

This SAS code snippet demonstrates the creation of a dataset named "selected_females" containing information about selected females from the "sashelp.class" dataset.

The DATA step is used to create the "selected_females" dataset. Data is copied from the existing dataset "sashelp.class", and the WHERE statement filters observations to include only females.

Explanation:

• The DATA statement begins the DATA step and specifies the name of the output dataset as "selected_females".
• The SET statement reads data from the existing dataset "sashelp.class".
• The WHERE statement filters observations based on the condition specified (sex = "F"), selecting only females.
• The RUN statement marks the end of the DATA step.
• This code snippet creates a new dataset named "selected_females" by copying data from the existing "sashelp.class" dataset and including only observations where the sex variable is "F" (indicating females). The resulting dataset can be further analyzed or used for various tasks in SAS.

SAS Data Printing Example: "selected_females_age_12" Dataset

This SAS code snippet demonstrates the printing of data from the "sashelp.class" dataset for selected females aged 12.

The PROC PRINT procedure is used to print data from the specified dataset. The DATA statement is not used in this code snippet as PROC PRINT operates differently from a DATA step. PROC PRINT is a procedure used for data reporting and does not involve the creation or modification of datasets. Instead, it directly operates on existing datasets to display or summarize their contents.

Explanation:

• The PROC PRINT statement specifies the procedure used for printing data.
• The DATA option indicates the dataset from which data will be printed, in this case, "sashelp.class".
• The WHERE statement filters observations based on the conditions specified (sex = "F" and age = 12), selecting only females aged 12.
• The RUN statement marks the end of the PROC PRINT procedure.
• This code snippet prints data from the "sashelp.class" dataset for selected females aged 12, demonstrating the use of PROC PRINT for data reporting and filtering. Unlike a DATA step, PROC PRINT does not involve the creation or modification of datasets; instead, it operates on existing datasets for reporting purposes.

SAS Data Processing Example: "modified_student_info" Dataset

This SAS code snippet demonstrates the processing of data from the "student_info" dataset to create a new dataset named "modified_student_info" with modified variables.

The DATA step is used to process data and create the "modified_student_info" dataset. Several transformations are applied to the variables first_name and last_name.

Explanation:

• The DATA statement begins the DATA step and specifies the name of the output dataset as "modified_student_info".
• The SET statement reads data from the existing dataset "student_info".
• Various DATA step statements are used to manipulate the data:
  • The UPCASE function is used to convert the last_name variable to uppercase and store the result in the ufname variable.
  • The PROPCASE function is used to convert the first_name variable to proper case (capitalize the first letter of each word) and store the result in the plname variable.
  • The LENGTH function is used to calculate the length of the first_name variable and store the result in the lenlname variable.
  • The CAT function is used to concatenate the first_name and last_name variables and store the result in the fullname variable.
  • The LOWCASE function is used to convert the last_name variable to lowercase and store the result in the lfname variable.
• The RUN statement marks the end of the DATA step.
• This code snippet creates a new dataset named "modified_student_info" by processing data from the "student_info" dataset. It applies various transformations to the first_name and last_name variables, creating new variables with modified values. The resulting dataset can be further analyzed or used for various tasks in SAS.

SAS Data Processing Example: "modified_student_records" Dataset

This SAS code snippet demonstrates the processing of data from the "student_records" dataset to create a new dataset named "modified_student_records" with modified variables.

The DATA step is used to process data and create the "modified_student_records" dataset. Various transformations are applied to the variables first_name, last_name, and course.

Explanation:

• The DATA statement begins the DATA step and specifies the name of the output dataset as "modified_student_records".
• The SET statement reads data from the existing dataset "student_records".
• Various DATA step statements are used to manipulate the data:
  • The SUBSTR function is used to extract substrings from the course variable to create course_name and course_number variables.
  • The CAT function is used to concatenate the first_name and last_name variables to create the full_name variable.
  • The TRIM and LEFT functions are used to remove leading spaces from the first_name variable before concatenating it with last_name to create the full_name variable.
  • The STRIP function is used to remove leading and trailing spaces from the first_name variable before concatenating it with last_name to create the full_name1 variable.
• The RUN statement marks the end of the DATA step.
• This code snippet creates a new dataset named "modified_student_records" by processing data from the "student_records" dataset. It applies various transformations to the first_name, last_name, and course variables, creating new variables with modified values. The resulting dataset can be further analyzed or used for various tasks in SAS.

SAS Text Processing Example: "text_processing" Dataset

This SAS code snippet demonstrates text processing functions using the COMPRESS and COMPBL functions.

The DATA step is used to create the "text_processing" dataset and perform text processing operations on character strings.

Explanation:

• The DATA statement begins the DATA step and specifies the name of the output dataset as "text_processing".
• Character strings are assigned to variables a.
• The COMPRESS function removes all leading, trailing, and repeated spaces from the character string assigned to variable a and stores the result in variable b.
• The COMPBL function removes all multiple spaces from the character string assigned to variable a and stores the result in variable c.
• The RUN statement marks the end of the DATA step.
• This code snippet demonstrates the usage of text processing functions COMPRESS and COMPBL to manipulate character strings in SAS, providing flexibility in data cleansing and formatting tasks.

SAS Data Processing Example: "student_data" Dataset

This SAS code snippet demonstrates the processing of data from the "sashelp.class" dataset to calculate Body Mass Index (BMI) and determine the weight status of students.

The DATA step is used to process data and create the "student_data" dataset. BMI and weight status are calculated based on the Weight and Height variables.

Explanation:

• The DATA statement begins the DATA step and specifies the name of the output dataset as "student_data".
• The SET statement reads data from the existing dataset "sashelp.class".
• Various DATA step statements are used to calculate BMI and determine weight status:
  • Weight_kg variable is calculated by converting Weight from pounds to kilograms.
  • Height_m variable is calculated by converting Height from inches to meters.
  • BMI is calculated using the BMI formula: weight (kg) / (height (m) ** 2).
  • IF-THEN statements are used to assign weight status based on BMI values:
    • If BMI is less than or equal to 18.5, the student is classified as "Underweight".
    • If BMI is between 18.5 and 24.9, the student is classified as "Healthy Weight".
    • If BMI is between 24.9 and 29.9, the student is classified as "Overweight".
    • If BMI is greater than 29.9, the student is classified as "Obese".
• The RUN statement marks the end of the DATA step.
• This code snippet creates a new dataset named "student_data" by processing data from the "sashelp.class" dataset. It calculates BMI and determines weight status for each student based on BMI values. The resulting dataset can be further analyzed or used for various tasks in SAS.

SAS Date Comparison Example: "date_comparison" Dataset

This SAS code snippet demonstrates the comparison of dates in SAS.

The DATA step is used to create the "date_comparison" dataset and perform date calculations.

Explanation:

• The DATA statement begins the DATA step and specifies the name of the output dataset as "date_comparison".
• Date literals are assigned to variables a, b, c, and d, representing specific dates in SAS date format.
• The difference between dates c and d is calculated and stored in the variable diff.
• The RUN statement marks the end of the DATA step.
• This code snippet demonstrates the comparison of dates in SAS, including the calculation of date differences. SAS represents dates as the number of days since January 1, 1960, with January 1, 1960, being represented as 0. The resulting dataset "date_comparison" can be further analyzed or used for various tasks in SAS.

SAS Student Health Information Processing

This SAS code snippet processes student health information from the "sashelp.class" dataset to calculate Body Mass Index (BMI) and modify variable names.

Explanation:

• The DATA statement initiates the DATA step to create a new dataset named "class" by processing data from the existing dataset "sashelp.class".
• The SET statement reads data from the "sashelp.class" dataset.
• Several calculations are performed:
  • WeightKg: Calculates the weight in kilograms by multiplying the Weight variable by 0.454 (conversion factor from pounds to kilograms).
  • HeightM: Calculates the height in meters by multiplying the Height variable by 2.54/100 (conversion factor from inches to meters).
  • BMI: Calculates the Body Mass Index using the formula: weight (kg) / (height (m) ** 2).
• The RENAME statement changes the variable name Sex to Gender.
• The DROP statement removes the Weight variable from the dataset.
• The KEEP statement selects and retains specific variables in the dataset: Name, Sex, Age, BMI, WeightKg, and HeightM.
• The RUN statement marks the end of the DATA step.
• Although the variable Sex is renamed to Gender, it must still be referenced by its original name in subsequent processing steps. This is a limitation of SAS where the RENAME statement only changes the label of the variable, not its internal name.
• This code snippet showcases data processing techniques in SAS, including variable renaming, calculation of derived variables (BMI), and selecting specific variables for analysis or reporting purposes.

Processing Employee Compensation Data with "employee_compensation" Dataset

The DATA step creates a dataset named "employee_compensation" with variables name, salary, and bonus. Four observations are provided with corresponding values for each variable. Calculation of Net Salary Using "+" Operator The DATA step "testdata1" calculates the net salary by adding the salary and bonus using the "+" operator. The SET statement reads data from the "employee_compensation" dataset. Calculation of Net Salary Using SUM Function The DATA step "testdata2" calculates the net salary using the SUM function. The SET statement reads data from the "employee_compensation" dataset.

Explanation:

• In the "testdata1" DATA step, the "+" operator is used to calculate the net salary. However, when a missing value (.) is encountered in the "bonus" variable, SAS treats it as a missing value and sets the result of the addition to missing.
• In the "testdata2" DATA step, the SUM function is used to calculate the net salary. The SUM function treats missing values as zeros and continues the addition. Therefore, even when a missing value (.) is present in the "bonus" variable, the SUM function still calculates the sum of salary and bonus correctly.
• This demonstrates a difference in behavior between the "+" operator and the SUM function when handling missing values in SAS.
• These SAS code snippets illustrate how different methods of calculation can produce different results when dealing with missing values in SAS datasets.

SAS Numeric Functions Example with NULL Dataset

Explanation:

• Three separate DATA steps, each starting with DATA NULL, are used to demonstrate different numeric functions in SAS.
• In the first DATA step, variables a and b are assigned values, and the ABS function is used to calculate the absolute value of variable b. The PUT statement is then used to display the values of variables a and c.
• In the second DATA step, variable a is assigned a value, and the CEIL, FLOOR, and INT functions are used to calculate the ceiling, floor, and integer parts of variable a, respectively. The PUT statement is used to display the values of variables a, b, c, and d.
• In the third DATA step, variables a, b, and c are assigned values. The MIN and MAX functions are used to determine the minimum and maximum values among these variables, respectively. The PUT statement is used to display the values of variables d and e.
• These SAS code snippets showcase the usage of numeric functions such as ABS, CEIL, FLOOR, INT, MIN, and MAX, demonstrating their functionality and how they can be utilized in SAS programming for numerical calculations and comparisons.

SAS String Manipulation Example: Extracting Country Name using the "country_data" Dataset

This SAS code snippet demonstrates how to use the SCAN function to extract specific words or tokens from a string in SAS. In this example, the fourth word from the given string representing the name of a country is extracted and stored in a new variable for further analysis or processing.

Explanation:

• The DATA step creates a dataset named "country_data".
• A character variable 'a' is assigned the string ' United Kingdom of Great Britain '.
• The SCAN function is used to extract the fourth word from the string stored in variable 'a'.
• The result, representing the country name "Great", is stored in a new variable named "country".
• The RUN statement marks the end of the DATA step.

SAS String Manipulation Example: Finding Substrings using the "country_info" Dataset

This SAS code snippet demonstrates how to use the INDEXC and INDEXW functions to find substrings within a string in SAS. In this example, the position of the character "A" and the word "Emirates" within the given string representing a country name is determined for further analysis or processing.

Explanation:

• The DATA step creates a dataset named "country_info".
• A character variable 'a' is assigned the string ' United Arab Emirates '.
• The COMPRESS function removes extra spaces from the string stored in variable 'a'.
• The INDEXC function is used to find the position of the character "A" in the compressed string. The result is stored in variable 'b'.
• The INDEXW function is used to find the position of the word "Emirates" in the original string. The result is stored in variable 'c'.
• The RUN statement marks the end of the DATA step.

SAS Data Generation Example: Generating Numeric Data with "numeric_data" Dataset

This SAS code snippet demonstrates how to generate numeric data using a DO loop in SAS. In this example, variables x_value and y_value are assigned values from 1 to 10 iteratively, resulting in a dataset containing numeric data pairs for further analysis or processing.

Explanation:

• The DATA step creates a dataset named "numeric_data".
• Within a DO loop that iterates from 1 to 10, two variables x_value and y_value are assigned values equal to the loop index.
• The OUTPUT statement writes the values of x_value and y_value to the dataset for each iteration of the loop.
• The DROP statement removes the loop index variable 'index' from the dataset.
• The RUN statement marks the end of the DATA step.

SAS Data Processing Example: Calculating BMI

This SAS code snippet demonstrates how to calculate Body Mass Index (BMI) using data from the "sashelp.class" dataset. It calculates BMI values both in metric and imperial units and writes them to a new dataset for further analysis or reporting purposes.

Explanation:

• The DATA step creates a dataset named "student_health_data".
• The SET statement reads data from the "sashelp.class" dataset.
• Two calculations are performed:
  • Calculation of BMI in metric units: The weight in kilograms (weight_kg) is calculated by multiplying the weight variable by 0.454, and the height in meters (height_m) is calculated by multiplying the height variable by 2.54 and dividing by 100. The BMI is then calculated by dividing the weight in kilograms by the square of the height in meters (height_m * height_m).
  • Calculation of BMI in imperial units: The BMI is calculated directly using the weight and height variables, assuming weight is in pounds and height is in inches.
• The OUTPUT statement writes the calculated BMI values to the dataset.
• The RUN statement marks the end of the DATA step.

SAS Data Entry Example: Recording Employee Bonuses

This SAS code snippet demonstrates how to record employee bonuses in a dataset using manual data entry. It defines variables for employee name, bonus amount, and date of birth, assigns appropriate formats to the variables, and then manually enters the data for each employee into the dataset for further analysis or reporting.

Explanation:

• The DATA step creates a dataset named "employee_bonuses".
• The INPUT statement defines three variables: employee_name, bonus_amount, and date_of_birth.
• The INFORMAT statement specifies the format of the bonus_amount variable as a dollar amount and the date_of_birth variable as a date in the format DDMMMYY.
• Four observations are entered manually using the CARDS statement, providing values for each variable.
• The RUN statement marks the end of the DATA step.

Cars Project SAS Script Explanation

This SAS script, named "CarsProject.SAS", performs data import, manipulation, and analysis tasks related to car data from different regions.

1. Importing European Car Data:

   • The PROC IMPORT procedure imports data from an Excel file named "carseu.xlsx" located at '/home/u62422869/The Simplest Guide to SAS/'.
   • The OUT option specifies the output dataset as "cars_eu" and replaces it if it already exists.
   • The GETNAMES option is set to "yes" to indicate that variable names are included in the first row of the Excel file.

2. Data Formatting for European Cars:

   • The DATA step "cars_eu" formats the MSRP variable as a dollar amount.

3. Importing Asian Car Data:

   • The FILENAME statement assigns a fileref "carsasia" to a text file named "carsasiapipedelimited.txt" located at '/home/u62422869/The Simplest Guide to SAS/'.
   • The PROC IMPORT procedure imports data from the text file, specifying it as a delimited file with "|" as the delimiter.
   • The OUT option specifies the output dataset as "cars_asia" and replaces it if it already exists.
   • The GETNAMES option is set to "no" to indicate that variable names are not included in the data file.

4. Data Manipulation for Asian Cars:

   • The DATA step "cars_asia" formats the VAR5 variable as a dollar amount and renames the variables to meaningful names (Make, Model, Type, Origin, MSRP).

5. Printing European Cars with MSRP over $150,000:

   • The PROC PRINT procedure prints the dataset "cars_eu", showing only records where MSRP is greater than $150,000.

6. Printing Asian Cars with MSRP over $150,000:

   • The PROC PRINT procedure prints the dataset "cars_asia", showing only records where MSRP is greater than $150,000.

This SAS script demonstrates the process of importing, formatting, and analyzing car data from different regions, providing insights into high-value cars in both European and Asian markets.

SAS Data Import Example: Student Data

This SAS script imports student data from an external data file named "my_data_file.dat" located at '/home/u62422869/The Simplest Guide to SAS/'.

Explanation:

1. File Location Specification:

   • The FILENAME statement assigns a fileref "my_data" to the external data file "my_data_file.dat".

2. Data Import:

   • The DATA step "student_data" is initiated to create a dataset named "student_data".
   • The INFILE statement specifies the source file as "my_data".
   • The INPUT statement defines the variables and their positions within the input data lines.
     • Variable "Name" is read from columns 1 to 5.
     • Variable "Age" is read from columns 6 to 7.
     • Variable "Gender" is read from column 18.
     • Variable "Weight" is read from the current position (@18) in the input line.
   • The RUN statement marks the end of the DATA step.

This SAS script demonstrates the process of importing student data from an external file into a SAS dataset. It specifies the file location, defines the variables and their positions within the input data lines, and then reads the data accordingly to create the "student_data" dataset for further analysis or processing.

SAS Data Combination Example: Combining Student Data

This SAS script combines data from two datasets "student_data_1" and "student_data_2" into a single dataset named "combined_student_data".

Explanation:

1. Data Import and Definition (Dataset A):

   • The DATA step "student_data_1" creates a dataset to store student information.
   • The INPUT statement defines the variables and their types: student_name, gender, student_age, and student_weight.
   • The CARDS statement specifies the data values for each variable.
   • The RUN statement marks the end of the DATA step.

2. Data Import and Definition (Dataset B):

   • The DATA step "student_data_2" creates another dataset to store additional student information.
   • Similar to Dataset A, it defines the variables and their types: student_name, gender, student_age, and student_weight.
   • The CARDS statement specifies the data values for each variable.
   • The RUN statement marks the end of the DATA step.

3. Combining Data (Dataset C):

   • The DATA step "combined_student_data" combines the data from both datasets A and B.
   • The SET statement is used to concatenate the observations from datasets A and B.
   • The RUN statement marks the end of the DATA step.

This SAS script demonstrates how to combine data from multiple datasets into a single dataset in SAS. It imports and defines variables for student data in two separate datasets, and then combines them into a unified dataset for further analysis or processing.

SAS Data Append Example: Appending Student Information

This SAS script appends data from two datasets "student_info_1" and "student_info_2" into a single dataset named "combined_student_info".

Explanation:

1. Data Import and Definition (Dataset A):

   • The DATA step "student_info_1" creates a dataset to store student information.
   • The INPUT statement defines the variables and their types: student_name, gender, student_age, and student_weight.
   • The CARDS statement specifies the data values for each variable.
   • The RUN statement marks the end of the DATA step.

2. Data Import and Definition (Dataset B):

   • The DATA step "student_info_2" creates another dataset to store additional student information.
   • Similar to Dataset A, it defines the variables and their types: student_name, gender, student_age, and student_weight.
   • The CARDS statement specifies the data values for each variable.
   • The RUN statement marks the end of the DATA step.

3. Appending Data to Combined Dataset (Dataset C):

   • The PROC APPEND procedure appends data from datasets A and B into a single dataset named "combined_student_info".
   • Two PROC APPEND statements are used to append data from datasets A and B separately.
   • The BASE option specifies the base dataset where the new observations will be added.
   • The DATA option specifies the dataset from which data will be appended.
   • The RUN statement marks the end of each PROC APPEND procedure.

This SAS script demonstrates how to append data from multiple datasets into a single dataset in SAS. It imports and defines variables for student data in two separate datasets, and then appends them into a unified dataset for further analysis or processing. Note that running the code multiple times may result in duplicated appended records.

SAS Data Deduplication Example: Removing Duplicate Student Records

This SAS script processes student records from the "student_records" dataset to remove duplicate entries and create a sorted dataset named "sorted_student_records".

Explanation:

1. Data Import and Definition (Dataset A):

   • The DATA step "student_records" creates a dataset to store student information.
   • The INPUT statement defines the variables and their types: student_name, student_gender, student_age, and student_weight.
   • The CARDS statement specifies the data values for each variable.
   • The RUN statement marks the end of the DATA step.

2. Sorting and Deduplicating Data:

   • The PROC SORT procedure sorts the "student_records" dataset by student_name.
   • The OUT option specifies the output dataset as "sorted_student_records".
   • The NODUPKEY option removes duplicate observations based on the sorted key variable (student_name).
   • The BY statement in PROC SORT specifies the variable by which the dataset should be sorted.
   • The RUN statement marks the end of the PROC SORT procedure.

This SAS script demonstrates how to remove duplicate records from a dataset in SAS using the PROC SORT procedure with the NODUPKEY option. It sorts the dataset by student name and removes duplicate entries, resulting in a sorted dataset with unique student records.

SAS Data Deduplication Example: Removing Duplicate Employee Records

This SAS script processes employee records from the "employee_data" dataset to remove duplicate entries and create a sorted dataset named "sorted_employee_data".

Explanation:

1. Data Import and Definition (Dataset A):

   • The DATA step "employee_data" creates a dataset to store employee information.
   • The INPUT statement defines the variables and their types: employee_name, employee_gender, employee_age, and employee_weight.
   • The CARDS statement specifies the data values for each variable.
   • The RUN statement marks the end of the DATA step.

2. Sorting and Deduplicating Data:

   • The PROC SORT procedure sorts the "employee_data" dataset by employee_name.
   • The OUT option specifies the output dataset as "sorted_employee_data".
   • The NODUP option removes duplicate observations based on all variables.
   • The BY statement in PROC SORT specifies the variable by which the dataset should be sorted.
   • The RUN statement marks the end of the PROC SORT procedure.

This SAS script demonstrates how to remove duplicate records from a dataset in SAS using the PROC SORT procedure with the NODUP option. It sorts the dataset by employee name and removes duplicate entries, resulting in a sorted dataset with unique employee records.

SAS Data Merge Example: Combining Employee Information

This SAS script merges two datasets, "employee_info" and "employee_height", based on the common variable "employee_name" to create a combined dataset named "combined_employee_data".

Explanation:

1. Data Import and Definition (Datasets A and B):

   • Two DATA steps, "employee_info" and "employee_height", create datasets to store employee information and height data, respectively.
   • The INPUT statements define the variables and their types: employee_name, employee_gender, employee_age, employee_weight in "employee_info", and employee_name, employee_height in "employee_height".
   • The CARDS statements specify the data values for each variable.
   • The RUN statements mark the end of the DATA steps.

2. Sorting Data (Datasets A and B):

   • Two PROC SORT procedures sort datasets "employee_info" and "employee_height" by the variable "employee_name".
   • The BY statement in PROC SORT specifies the variable by which the datasets should be sorted.
   • The RUN statements mark the end of the PROC SORT procedures.

3. Merging Datasets (Dataset C):

   • The DATA step "combined_employee_data" merges datasets "employee_info" and "employee_height" based on the common variable "employee_name".
   • The MERGE statement combines the observations from both datasets into a single dataset.
   • The BY statement specifies the variable by which the datasets should be merged.
   • The RUN statement marks the end of the DATA step.

This SAS script demonstrates how to merge two datasets based on a common variable using the MERGE statement. It combines employee information and height data into a single dataset for comprehensive analysis or reporting.

SAS Data Merge Example: Combining Height and Weight Information

This SAS script merges two datasets, "class_weight" and "class_height", based on the common variable "name" to create a combined dataset named "class_merged".

Explanation:

1. Data Import and Definition (Datasets A and B):

   • Two DATA steps, "class_weight" and "class_height", create datasets to store weight and height information, respectively.
   • The INPUT statements define the variables and their types: name, gender, age, and weight in "class_weight", and name and height in "class_height".
   • The CARDS statements specify the data values for each variable.
   • The RUN statements mark the end of the DATA steps.

2. Sorting Data (Datasets A and B):

   • Two PROC SORT procedures sort datasets "class_weight" and "class_height" by the variable "name".
   • The BY statement in PROC SORT specifies the variable by which the datasets should be sorted.
   • The RUN statements mark the end of the PROC SORT procedures.

3. Merging Datasets (Dataset C):

   • The DATA step "class_merged" merges datasets "class_weight" and "class_height" based on the common variable "name".
   • The MERGE statement combines the observations from both datasets into a single dataset.
   • The BY statement specifies the variable by which the datasets should be merged.
   • The IF statement ensures that only observations present in both datasets are included in the merged dataset.
   • The RUN statement marks the end of the DATA step.

This SAS script demonstrates how to merge two datasets based on a common variable using the MERGE statement. It combines weight and height information into a single dataset for comprehensive analysis or reporting.

SAS SQL Example: Creating a New Table from an Existing Dataset

This SAS SQL query creates a new table named "student_info" by selecting all records from the existing dataset "sashelp.class".

Explanation:

1. PROC SQL Statement:

   • The PROC SQL statement initiates the SQL procedure.

2. CREATE TABLE Statement:

   • The CREATE TABLE statement is used to define and create a new table named "student_info".
   • The AS keyword specifies that the new table will be created based on the result of a query.

3. SELECT Statement:

   • The SELECT statement retrieves all columns (represented by '*') from the existing dataset "sashelp.class".

4. FROM Clause:

   • The FROM clause specifies the source dataset "sashelp.class" from which data will be selected.

5. QUIT Statement:

   • The QUIT statement ends the SQL procedure.

This SAS SQL query demonstrates how to create a new table by selecting all records from an existing dataset using the PROC SQL procedure. It provides a way to store and manipulate data for further analysis or reporting purposes.

SAS SQL Example: Creating a New Table with Filtered Data

This SAS SQL query creates a new table named "male_students" by selecting all records from the existing dataset "sashelp.class" where the sex is 'M'.

Explanation:

1. PROC SQL Statement:

   • The PROC SQL statement initiates the SQL procedure.

2. CREATE TABLE Statement:

   • The CREATE TABLE statement is used to define and create a new table named "male_students".
   • The AS keyword specifies that the new table will be created based on the result of a query.

3. SELECT Statement:

   • The SELECT statement retrieves all columns (represented by '*') from the existing dataset "sashelp.class".

4. FROM Clause:

   • The FROM clause specifies the source dataset "sashelp.class" from which data will be selected.

5. WHERE Clause:

   • The WHERE clause filters the records based on the condition that sex is 'M'.

6. QUIT Statement:

   • The QUIT statement ends the SQL procedure.

This SAS SQL query demonstrates how to create a new table by selecting specific records from an existing dataset based on certain criteria using the PROC SQL procedure. It allows for the creation of targeted subsets of data for further analysis or reporting purposes.

SAS SQL Example: Creating a Summary Table with Grouping + AVG.

This SAS SQL query creates a new table named "average_age_by_sex" by calculating the average age and grouping the data by sex from the 'sashelp.class' dataset.

Explanation:

1. PROC SQL Statement:

   • The PROC SQL statement initiates the SQL procedure.

2. CREATE TABLE Statement:

   • The CREATE TABLE statement is used to define and create a new table named "average_age_by_sex".
   • The AS keyword specifies that the new table will be created based on the result of a query.

3. SELECT Statement:

   • The SELECT statement calculates the average age using the AVG() function and renames the result column as "average_age".
   • It also selects the 'sex' column from the 'sashelp.class' dataset.

4. FROM Clause:

   • The FROM clause specifies the source dataset "sashelp.class" from which data will be selected.

5. GROUP BY Clause:

   • The GROUP BY clause groups the data by the 'sex' column.

6. QUIT Statement:

   • The QUIT statement ends the SQL procedure.

This SAS SQL query demonstrates how to create a summary table by calculating aggregates and grouping data based on specific criteria from an existing dataset using the PROC SQL procedure. It provides insights into the distribution of average ages by sex.

SAS SQL Example: Creating a Summary Table with Grouping + MIN.

This SAS SQL query creates a new table named "minimum_msrp_by_origin" by calculating the minimum Manufacturer's Suggested Retail Price (MSRP) and grouping the data by origin from the 'sashelp.cars' dataset.

Explanation:

1. PROC SQL Statement:

   • The PROC SQL statement initiates the SQL procedure.

2. CREATE TABLE Statement:

   • The CREATE TABLE statement is used to define and create a new table named "minimum_msrp_by_origin".
   • The AS keyword specifies that the new table will be created based on the result of a query.

3. SELECT Statement:

   • The SELECT statement calculates the minimum MSRP using the MIN() function and renames the result column as "minimum_msrp".
   • It also selects the 'origin' column from the 'sashelp.cars' dataset.

4. FROM Clause:

   • The FROM clause specifies the source dataset "sashelp.cars" from which data will be selected.

5. GROUP BY Clause:

   • The GROUP BY clause groups the data by the 'origin' column.

6. QUIT Statement:

   • The QUIT statement ends the SQL procedure.

This SAS SQL query demonstrates how to create a summary table by calculating aggregates and grouping data based on specific criteria from an existing dataset using the PROC SQL procedure. It provides insights into the distribution of minimum MSRP values by origin.

SAS SQL Example: Creating a Table with Unique Gender-Age Combinations

This SAS SQL query creates a new table named "unique_gender_age" by selecting distinct combinations of gender and age from the 'sashelp.class' dataset. The resulting table is ordered by gender and age.

Explanation:

1. PROC SQL Statement:

   • The PROC SQL statement initiates the SQL procedure.

2. CREATE TABLE Statement:

   • The CREATE TABLE statement is used to define and create a new table named "unique_gender_age".
   • The AS keyword specifies that the new table will be created based on the result of a query.

3. SELECT Statement:

   • The SELECT statement retrieves distinct combinations of gender and age from the 'sashelp.class' dataset.
   • The DISTINCT keyword ensures that only unique combinations are selected.

4. FROM Clause:

   • The FROM clause specifies the source dataset "sashelp.class" from which data will be selected.

5. ORDER BY Clause:

   • The ORDER BY clause orders the result set by gender and age.

6. QUIT Statement:

   • The QUIT statement ends the SQL procedure.

This SAS SQL query demonstrates how to create a new table with unique combinations of gender and age from an existing dataset using the PROC SQL procedure. It provides a way to analyze and explore the distribution of gender-age combinations in the dataset.

SAS Data Processing Example: Transposing Student Information

This SAS snippet demonstrates data processing steps involving the creation of a dataset named STUDENT_INFO and its subsequent transposition using the PROC TRANSPOSE procedure.

Explanation:

1. DATA Step (Creating Dataset):

   • The DATA statement initiates the creation of a dataset named STUDENT_INFO.
   • Variables STUDENT_NAME, STUDENT_GENDER, STUDENT_AGE, and STUDENT_WEIGHT are defined to store information about students.
   • The INPUT statement specifies the format for reading data values.
   • The CARDS statement provides the actual data values for the variables.

2. PROC TRANSPOSE Step:

   • The PROC TRANSPOSE procedure is used to transpose the dataset STUDENT_INFO.
   • It transposes the variables STUDENT_GENDER, STUDENT_AGE, and STUDENT_WEIGHT.
   • The ID statement specifies STUDENT_NAME as the identifier variable.
   • The resulting transposed dataset is named T_STUDENT_INFO.

This SAS code snippet illustrates how to organize and manipulate student information, demonstrating the creation of a dataset and its transformation through transposition for further analysis or reporting purposes.

SAS Data Processing Example: Transposing Patient Vitals

This SAS snippet demonstrates data processing steps involving the creation of a dataset named PATIENT_VITALS and its subsequent transposition using the PROC TRANSPOSE procedure.

Explanation:

1. DATA Step (Creating Dataset):

   • The DATA statement initiates the creation of a dataset named PATIENT_VITALS.
   • Variables PATIENT_ID, PATIENT_NAME, TEST_TYPE, and TEST_VALUE are defined to store patient vital information.
   • The INPUT statement specifies the format for reading data values.
   • The CARDS statement provides the actual data values for the variables.

2. PROC SORT Step:

   • The PROC SORT procedure is used to sort the dataset PATIENT_VITALS.
   • It sorts the data by PATIENT_ID and PATIENT_NAME.

3. PROC TRANSPOSE Step:

   • The PROC TRANSPOSE procedure transposes the dataset PATIENT_VITALS.
   • It transposes the variable TEST_VALUE.
   • The ID statement specifies TEST_TYPE as the identifier variable.
   • The BY statement specifies PATIENT_ID and PATIENT_NAME for grouping.
   • The resulting transposed dataset is named T_PATIENT_VITALS.

This SAS code snippet illustrates how to organize and manipulate patient vital information, demonstrating the creation of a dataset and its transformation through transposition for further analysis or reporting purposes.

SAS Data Processing Example: Employee Total Pay Calculation

This SAS snippet demonstrates data processing steps involving the calculation of total pay for each employee from the dataset EMPLOYEE_PAY.

Explanation:

1. DATA Step (Creating Dataset):

   • The DATA statement initiates the creation of a dataset named EMPLOYEE_PAY.
   • Variables EMPLOYEE_NAME, PAY_PERIOD, and PAY_AMOUNT are defined to store employee pay information.
   • The INPUT statement specifies the format for reading data values.
   • The CARDS statement provides the actual data values for the variables.

2. PROC SORT Step:

   • The PROC SORT procedure is used to sort the dataset EMPLOYEE_PAY.
   • It sorts the data by EMPLOYEE_NAME.

3. DATA Step (Calculating Total Pay):

   • The DATA statement creates a new dataset named EMPLOYEE_TOTAL_PAY.
   • It reads data from the existing dataset EMPLOYEE_PAY.
   • The BY statement is used for grouping by EMPLOYEE_NAME.
   • The RETAIN statement initializes and retains the value of the variable TOTAL_PAY.
   • The IF statement conditionally assigns the value of TOTAL_PAY based on whether it is the first observation for a given EMPLOYEE_NAME.

This SAS code snippet illustrates how to calculate total pay for each employee across different pay periods, providing insights into overall compensation patterns.

SAS Data Processing Example: Employee Salary Details

This SAS code snippet demonstrates the processing of employee salary data to convert numeric values to character values with specific formats.

Explanation:

1. Creating Dataset and Defining Variables:

   • The DATA statement creates a dataset named EMPLOYEE_SALARY with variables WORKER, PAY, and WORKER_ID.
   • The INPUT statement specifies the format of the data to be read, including dollar and SSN formats for PAY and WORKER_ID, respectively.
   • The CARDS statement provides the data values for WORKER, PAY, and WORKER_ID.

2. Converting Numeric Values to Character Values:

   • The DATA statement creates a new dataset named SALARY_DETAILS by reading data from the existing EMPLOYEE_SALARY dataset.
   • The SET statement specifies the dataset to read from.
   • The PUT function is used to convert numeric values to character values for PAY and WORKER_ID.
   • The PUT function formats the variables with appropriate dollar and SSN formats, specified by dollar10.2 and ssn11. formats, respectively.

This SAS code snippet demonstrates the conversion of numeric employee salary and ID values to character values with specific formats for reporting or display purposes.

SAS Data Import Example: Employee Bonus Information

This SAS code snippet demonstrates the import of employee bonus information into two datasets: EMPLOYEE_BONUS and EMPLOYEE_BONUS1.

Explanation:

1. Creating Dataset and Defining Variables:

   • The DATA statement creates a dataset named EMPLOYEE_BONUS with variables EMPLOYEE_NAME, BONUS_AMOUNT, and DATE_OF_BIRTH.
   • The INPUT statement specifies the format of the data to be read, including dollar and date formats for BONUS_AMOUNT and DATE_OF_BIRTH, respectively.
   • The CARDS statement provides the data values for EMPLOYEE_NAME, BONUS_AMOUNT, and DATE_OF_BIRTH.

2. Duplicate Dataset Creation:

   • Another dataset named EMPLOYEE_BONUS1 is created as a duplicate of EMPLOYEE_BONUS using the same DATA statement, INPUT statement, and CARDS statement.
   • Both datasets contain the same bonus information for different employees.

This SAS code snippet demonstrates the process of importing employee bonus information into SAS datasets for further analysis or processing.

SAS Data Visualization: Employee Gender Distribution

This SAS code snippet processes employee data and visualizes the gender distribution using various charts.

Explanation:

1. Creating Dataset:

   • The DATA statement creates a dataset named EMPLOYEES_INFO, which contains information about employees, including their name, gender, age, and weight.
   • The CARDS section provides the data values, with each record separated by a comma.
   • The INPUT statement specifies the format of the data to be read from the CARDS section.

2. Creating Charts:

   • PROC CHART is used to create different types of charts based on the EMPLOYEES_INFO dataset.
   • The VBAR statement generates a vertical bar chart to show the distribution of genders.
   • The HBAR statement generates a horizontal bar chart to visualize the gender distribution.
   • The PIE statement creates a pie chart to display the proportion of each gender in the dataset.

These SAS code snippets demonstrate the process of visualizing employee gender distribution using various chart types for effective data exploration and analysis.

SAS Data Visualization: Age and Course Distribution

This SAS code snippet processes student class data and creates vertical bar charts to visualize the distribution of age and course numbers.

Explanation:

1. Creating Dataset:

   • The DATA statement creates a dataset named CLASS, which contains information about students, including their name, gender, age, course number, and major.
   • The INPUT statement specifies the format of the data to be read.

2. Creating Charts:

   • PROC CHART is used to create vertical bar charts based on the CLASS dataset.
   • The first chart (vbar AGE) visualizes the distribution of ages among students using vertical bars.
   • The second chart (vbar COURSENO) displays the distribution of course numbers discretely using vertical bars.

These SAS code snippets demonstrate the process of visualizing age and course distribution among students using vertical bar charts for effective data analysis and exploration.

SAS Data Visualization: Course Enrollment

This SAS code snippet processes student enrollment data and creates a horizontal bar chart to visualize the distribution of course enrollment.

Explanation:

1. Creating Dataset:

   • The DATA statement creates a dataset named ENROLLMENT_DATA, containing information about students, including their name, gender, age, course ID, and major.
   • The INPUT statement specifies the format of the data to be read.

2. Creating Chart:

   • PROC CHART is used to create a horizontal bar chart based on the ENROLLMENT_DATA dataset.
   • The HBAR statement generates a horizontal bar chart, with course IDs plotted discretely along the y-axis.

This SAS code snippet demonstrates the process of visualizing the distribution of course enrollment among students using a horizontal bar chart for effective data analysis and exploration.

SAS Data Visualization: Course Enrollment Analysis

This SAS code snippet processes student enrollment data and creates a vertical bar chart to analyze the distribution of student ages across different courses, majors, and genders.

Explanation:

1. Creating Dataset:

   • The DATA statement creates a dataset named STUDENT_INFO, containing information about students, including their name, gender, age, course number, and major.
   • The INPUT statement specifies the format of the data to be read.

2. Creating Chart:

   • PROC CHART is used to create a vertical bar chart based on the STUDENT_INFO dataset.
   • The VBAR statement generates a vertical bar chart, with course numbers plotted discretely along the x-axis.
   • Grouping is done by major, and subgrouping is done by gender.
   • The SUMVAR option calculates the mean age for each combination of course, major, and gender.

This SAS code snippet demonstrates the process of visualizing the distribution of student ages across different courses, majors, and genders using a vertical bar chart for insightful analysis and decision-making.

SAS Data Visualization: Course Enrollment Analysis

This SAS code snippet processes student enrollment data and creates a horizontal bar chart to analyze the distribution of student ages across different courses, majors, and genders.

Explanation:

1. Creating Dataset:

   • The DATA statement creates a dataset named STUDENT_DATA, containing information about students, including their name, gender, age, course number, and major.
   • The INPUT statement specifies the format of the data to be read.

2. Creating Chart:

   • PROC CHART is used to create a horizontal bar chart based on the STUDENT_DATA dataset.
   • The HBAR statement generates a horizontal bar chart, with course numbers plotted discretely along the y-axis.
   • Grouping is done by major, and subgrouping is done by gender.
   • The SUMVAR option calculates the mean age for each combination of course, major, and gender.

This SAS code snippet demonstrates the process of visualizing the distribution of student ages across different courses, majors, and genders using a horizontal bar chart for insightful analysis and decision-making.

SAS Data Visualization: Age vs. Weight Analysis

This SAS code snippet processes personal information data and creates scatter plots to analyze the relationship between age and weight.

Explanation:

1. Creating Dataset:

   • The DATA statement creates a dataset named PERSONAL_INFO, containing information about individuals, including their name, gender, age, and weight.
   • The INPUT statement specifies the format of the data to be read.

2. Creating Scatter Plots:

   • PROC PLOT is used to create scatter plots based on the PERSONAL_INFO dataset.
   • The first scatter plot (without symbols) plots age against weight.
   • The second scatter plot (with '*' symbols) also plots age against weight but uses asterisks as symbols.

These SAS code snippets demonstrate the process of visualizing the relationship between age and weight using scatter plots, allowing for insights into potential correlations or patterns.

SAS Data Filtering Example: Filtering by Gender

This SAS code snippet processes personal information data and prints selected records based on gender.

Explanation:

1. Creating Dataset:

   • The DATA statement creates a dataset named PERSON_INFO, containing information about individuals, including their name, gender, age, and weight.
   • The INPUT statement specifies the format of the data to be read.

2. Printing Selected Data:

   • PROC PRINT is used to display selected data from the PERSON_INFO dataset.
   • The VAR statement specifies the variables to be displayed.
   • The WHERE statement filters the records based on gender, displaying only those where the gender is 'M' (Male).

This SAS code snippet demonstrates how to filter and display specific records from a dataset based on certain criteria, such as gender.

SAS Reporting Examples: Tabular Reports

1. Course Information Report:

• The first PROC REPORT generates a tabular report of student course information.
• Columns include student name, gender, age, course code, and major.
• Each column is defined using DEFINE statements.

2. Mean Age by Gender Report:

• The second PROC REPORT generates a tabular report showing the mean age by gender.
• The STUDENT_GENDER column is grouped, and the STUDENT_AGE column is analyzed to calculate the mean.

3. Cars Dataset Report:

• The third PROC REPORT generates a tabular report of selected variables from the SASHELP.CARS dataset.
• Columns include car make, model, type, origin, and MSRP.
• Column widths and formatting options are specified using DEFINE statements.

SAS Frequency Analysis Example

1. Gender Frequency Table:

• The first PROC FREQ generates a frequency table for the GENDER variable from the CLASS dataset.
• It counts the number of occurrences of each gender category.

2. Course Number Frequency Table:

• The second PROC FREQ generates a frequency table for the COURSENO variable from the CLASS dataset.
• It counts the number of occurrences of each course number.

Descriptive Statistics for Age Variable

• The PROC MEANS procedure calculates basic statistics such as mean, median, minimum, maximum, and standard deviation for the AGE variable from the CLASS dataset.

Descriptive Statistics for Age Variable

• The PROC UNIVARIATE procedure calculates basic statistics such as mean, median, minimum, maximum, and standard deviation for the AGE variable from the CLASS dataset.

This SAS snippet uses the ODS OUTPUT statement to save the frequency table generated by PROC FREQ into a dataset named myFreq.

Explanation:

• The DATA step creates a dataset named CLASS with variables: NAME, GENDER, AGE, COURSE, and MAJOR.
• The PROC FREQ procedure is used to generate frequency tables for the GENDER variable.
• ODS (Output Delivery System) is a system in SAS that controls the output formats of procedures.
• ODS TRACE ON and ODS TRACE OFF are used to trace ODS destinations, which helps in debugging and diagnosing issues related to ODS output.
• The ODS OUTPUT statement saves the output of PROC FREQ into a dataset named myFreq.

This SAS snippet saves the summary statistics generated by PROC MEANS into a dataset named myMeans.

Explanation:

• The DATA step creates a dataset named CLASS with variables: NAME, GENDER, AGE, COURSE, and MAJOR.
• PROC MEANS is used to calculate summary statistics for the AGE variable.
• ODS TRACE ON and ODS TRACE OFF are used to trace ODS destinations, which helps in debugging and diagnosing issues related to ODS output.
• The ODS OUTPUT statement saves the output of PROC MEANS into a dataset named myMeans.

This SAS snippet saves the basic measures statistics generated by PROC UNIVARIATE into a dataset named MYUNIVBASIC.

Explanation:

• The DATA step creates a dataset named CLASS with variables: NAME, GENDER, AGE, COURSE, and MAJOR.
• PROC UNIVARIATE is used to analyze the AGE variable, providing various statistical measures.
• ODS TRACE ON and ODS TRACE OFF are used to trace ODS destinations, which helps in debugging and diagnosing issues related to ODS output.
• The ODS OUTPUT statement saves the output of PROC UNIVARIATE into datasets named MYUNIVMOMENTS and MYUNIVBASIC.

Filtering Dataset by Macro Variables

Explanation:

• The DATA step creates a dataset named CLASS with variables: NAME, GENDER, AGE, COURSENO, and MAJOR.
• Macro variables VAR and VALUE are defined to hold the variable name and value to filter the dataset.
• PROC PRINT is used to display observations from the CLASS dataset where the value of the variable specified by the macro variable VAR matches the value specified by the macro variable VALUE.

Macro Variable Manipulation

Explanation:

• Macro variable B is assigned the value 10 using the %LET statement.
• Macro variable A is assigned the value of macro variable B.
• The %PUT statement displays the value of macro variable B, which is 10.
• The %PUT statement displays the value of macro variable A, which is B.
• The && operator resolves a single ampersand at runtime, so &&A resolves to &B, and the %PUT statement displays the value of macro variable B again.
• The &&& operator resolves a double ampersand at runtime, so &&&A resolves to &10, and the %PUT statement displays the value 10.

Macro Variable Cascading

Explanation:

• Macro variables are assigned values in a cascading manner: APPLE is assigned the value "banana", BANANA is assigned "cherry", CHERRY is assigned "plum", and PLUM is assigned "elderberry".
• The %PUT statements display the values of macro variable APPLE in different configurations using the double ampersand operator.
• The first %PUT statement displays the direct value of APPLE, which is "banana".
• The subsequent %PUT statements use the double ampersand operator to resolve the macro variable APPLE in a cascading manner.

Custom Macro for Reporting

Explanation:

• This SAS macro, named MYREPORT, is defined to generate summary statistics and print data for a specified dataset within a given library.
• The macro takes three arguments: LIB, DSN, and STATVAR.
• LIB represents the library name, DSN represents the dataset name, and STATVAR represents the variable for which summary statistics are calculated.
• Inside the macro, PROC MEANS is used to calculate summary statistics (mean, min, max, etc.) for the specified variable in the dataset.
• PROC PRINT is used to display the dataset contents after the summary statistics have been calculated.
• The macro is invoked twice:
  • First invocation: Calls MYREPORT with arguments (SASHELP, CLASS, AGE).
  • Second invocation: Calls MYREPORT with arguments (SASHELP, CARS, MSRP).
• This approach enables easy generation of summary reports for different datasets and variables by simply calling the MYREPORT macro with appropriate parameters.

Custom Macro for Reporting

Explanation:

• The macro MYREPORT is defined to generate summary statistics and print data for a specified dataset within a given library.
• It takes three arguments: LIB, DSN, and STATVAR.
  • LIB represents the library name.
  • DSN represents the dataset name.
  • STATVAR represents the variable for which summary statistics are calculated.
• Inside the macro, PROC MEANS calculates summary statistics (mean, min, max, etc.) for the specified variable in the dataset.
• Then, PROC PRINT displays the dataset contents after the summary statistics have been calculated.
• The macro is invoked four times:
  1. First invocation: Calls MYREPORT with arguments (SASHELP, CLASS, AGE).
  2. Second invocation: Calls MYREPORT with arguments (SASHELP, CLASS, HEIGHT).
  3. Third invocation: Calls MYREPORT with arguments (SASHELP, CLASS, WEIGHT).
  4. Fourth invocation: Calls MYREPORT with arguments (SASHELP, CARS, MSRP).
• This approach allows for the easy generation of summary reports for different datasets and variables by simply calling the MYREPORT macro with appropriate parameters.

Macro Operations and Functions

Explanation:

• The macro variables a, b, c, d, and e are defined using the %LET statement to store different values and manipulate them using macro functions.
• %UPCASE: Converts the value of macro variable a to uppercase and assigns it to variable b.
• %LOWCASE: Converts the value of macro variable a to lowercase and assigns it to variable c.
• %SUBSTR: Extracts a substring from the value of macro variable a starting at position 1 with a length of 6 characters, and assigns it to variable d.
• %LENGTH: Calculates the length of the value of macro variable a and assigns it to variable e.
• The PUT statement is used to display the values of variables b, c, d, and e, showing the results of the string manipulations and length calculation.
• In the second part, numerical values are manipulated:
  • Macro variables x and y are defined with decimal values.
  • %SYSEVALF: Calculates the sum of x and y and assigns it to variable z.
• Finally, the result of the arithmetic operation is displayed using the PUT statement.

Cars Analysis Project Overview

Part 1

Importing and Analyzing Car Data

• Data Import from Excel: The SAS file carseu.xlsx is imported into the SAS dataset cars_eu using PROC IMPORT with the XLSX engine.
• Formatting Data: The MSRP variable in the cars_eu dataset is formatted as dollar8.
• Importing and Parsing Text File: The piped-delimited text file carsasiapipedelimited.txt is imported into the SAS dataset cars_asia, with appropriate data parsing.
• Data Filtering and Analysis: The PROC PRINT procedure is used to display records from cars_eu and cars_asia where MSRP is greater than $150,000.

Part 2

Merging and Sorting Car Data

• Library Assignment: The SAS library MyLib is assigned to the directory.
• Importing and Merging Data: The SAS dataset carsusa from the library MyLib is merged with cars_eu and cars_asia into the dataset cars_all.
• Sorting Data: The datasets cars_all and cars_mpg are sorted by model and make.
• Combining Sorted Data: The datasets cars_all and cars_mpg are merged into combined_cars_all based on model and make.

Part 3

Frequency Analysis and Statistical Calculation

• Frequency Count: PROC FREQ calculates the frequency count of car makes and origins.
• Formatting Frequency Table: The frequency count table is formatted, and percentages are rounded.
• Transpose and Print: The transposed frequency table is printed to display frequency counts by car make and origin.
• Statistical Calculation: A macro myStat calculates the frequency count and percentage of hybrid cars based on car make and type.

Project Heavy Weight - Analyzing Student Health Data

Importing and Preprocessing Data

• Data Import: Excel sheets for Statistics (STAT) and History (HIST) classes and student height data (STUDHT) are imported into SAS.
• Merging Data: The STAT and HIST datasets are stacked to create a single dataset called CLASS, then merged with STUDHT.
• Unit Conversion: Weight and height are converted to metric units (kilograms and meters, respectively).
• BMI Calculation: BMI is calculated using weight (kg) divided by the square of height (m).
• Status Assignment: Students are categorized into Underweight, Healthy, Overweight, or Obese based on BMI values.
• Pie Chart Generation: A pie chart is generated to visualize the distribution of student health statuses.

Frequency Distribution Analysis

• Frequency Table: A frequency distribution table is created for gender and health status variables.
• Formatting: Counts and percentages are concatenated and formatted for reporting.
• Transposition: The frequency table is transposed to display health status across gender.
• Final Report: A report titled "Report of Frequency Table" summarizes the distribution of health statuses by gender.

Analysing American Airline Data

Importing and Preprocessing Data

• Data Import: The airline data is imported from the airlines.txt file using the FILENAME statement, INFILE, and INPUT statements to align columns.
• Currency Format: Dollar formats (dollar10.2) are applied to currency variables like Total Assets.
• Rating Assignment: A new variable called RATING is created based on TotalOpCost using a defined format.
• Charts: Vertical bar charts and horizontal bar charts are created to visualize the distribution of ratings and average operating costs, respectively.

SQL Operations

• Subset Creation: A subset of data containing airlines whose names start with 'P' is created using PROC SQL.
• Macro Variable Creation: A macro variable called FASTPLANE is created using PROC SQL to store the name of the airline with the highest speed.

Project Completion Questions

• Data Import Code: The code to import the data involves using FILENAME statement, INFILE, and INPUT statements to read the text file.
• Currency Variable Format: Dollar10.2 format is used to display currency variables.
• Airlines Selection: PROC SQL is used to select airlines starting with the letter 'P' using the SUBSTR function.
• Proc Chart Option: The DISCRETE option is used in the PROC CHART procedure to create vertical bars for the RATING variable.
• Number of Airlines Rated 5: 7 airlines are rated 5.
• Mean Operating Cost of Airlines Rated 5: The mean operating cost of airlines rated 5 is 43.7857.
• Highest Speed: The FASTPLANE macro variable stores the highest speed at which an airline has flown their planes (216).
• SQL Code for FASTPLANE: PROC SQL is used to find the maximum speed value and store it in the FASTPLANE macro variable.

Solutions to exercises in Udemy course by Aslam Khan: "The Simplest Guide to SAS Programming- SAS Base-SAS Advanced"

Exercise 19: This code creates a vertical bar chart displaying the mean MSRP (Manufacturer's Suggested Retail Price) of cars for each make using the SASHELP.CARS dataset. The second step involves visually inspecting the chart to identify the car with the highest mean MSRP, which corresponds to the tallest bar in the chart.

Exercise 20: This code generates a report using PROC REPORT on the SASHELP.CARS dataset. It includes columns for Make, Type, Origin, Sum Retail Price, and Mean Retail Price. The Sum Retail Price column displays the total MSRP of all cars within each Make, Type, and Origin group, while the Mean Retail Price column shows the average MSRP for the same groups.

Usage

Clone the repository to your local machine. Open the desired SAS file in SAS Studio, SAS Enterprise Guide, or any other SAS environment. Run the code to execute the specified data processing or analysis tasks. Customize the code as needed for your specific use case. Future Updates

This repository will be regularly updated with additional SAS code snippets for various data science tasks and analyses. Stay tuned for more examples and templates to support your data science projects.

License

This repository is licensed under the MIT License. Feel free to use the code provided here for educational, research, or commercial purposes. Contributions are welcome!