📊 PROC RANK in SAS – Rank, Percentile, and Group Your Data Easily

Introduction

In data analysis, ranking values is essential for identifying top performers, segmenting data, and calculating percentiles. PROC RANK in SAS makes this process easy by assigning ranks, percentiles, or group numbers to numeric variables.

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🔧 Syntax of PROC RANK

PROC RANK DATA=input_dataset OUT=output_dataset 
           RANKS=rank_variable <TIES=LOW|HIGH|MEAN|DENSE>;
    VAR variable_to_rank;
    BY group_variable;
    <GROUPS=n>;
RUN;

Key Options Explained:

Option	Description
DATA=	Input dataset
OUT=	Output dataset with new rank variable
RANKS=	Name of the new variable that stores the rank
TIES=	Specifies how tied values are handled (default is MEAN)
BY	Perform ranking within each BY-group
VAR	Variable to rank
GROUPS=	Divide data into equal-sized groups (like quantiles or deciles)

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📌 Example 1: Basic Ranking

proc rank data=sashelp.class out=ranked_class;
    var height;
    ranks height_rank;
run;

Explanation:
Ranks students in sashelp.class by their height, storing the result in height_rank.

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📌 Example 2: Ranking within Groups

proc sort data=sashelp.class out=sorted_class;
    by sex;
run;

proc rank data=sorted_class out=ranked_sex;
    by sex;
    var weight;
    ranks weight_rank;
run;

Explanation:
Ranks weight within each sex group.

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📌 Example 3: Create Percentile or Quantile Groups

proc rank data=sashelp.class out=grouped_class groups=4;
    var age;
    ranks age_quartile;
run;

Explanation:
Divides age into 4 quartile groups (0 to 3).

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📌 TIES= Option in Action

proc rank data=sashelp.class out=ranked_ties ties=low;
    var height;
    ranks height_rank;
run;

TIES= Options:

• LOW – Lowest rank for all ties
• HIGH – Highest rank for all ties
• MEAN – Average rank (default)
• DENSE – No gaps between ranks

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✅ When to Use PROC RANK

• Ranking top N values
• Creating quantile-based bins (e.g., deciles, quartiles)
• Calculating percentiles
• Segmenting customers or products
• Normalizing scorecards

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🧠 Tips for Using PROC RANK

• Always sort the dataset before using BY.
• Use GROUPS= for percentiles or bucketing.
• For multiple variables, use multiple VAR and RANKS pairs.
• Combine with PROC SQL or PROC PRINT for better reporting.

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📎 Final Thoughts

PROC RANK is a powerful yet simple procedure in SAS that enables effective data ranking and segmentation. It’s especially useful in scoring, customer segmentation, and exploratory data analysis.

✅ All About PROC SUMMARY in SAS: A Comprehensive Guide with Practical Examples

📘 Introduction

PROC SUMMARY in SAS is a powerful procedure used to generate summary statistics for numeric variables. It is often considered functionally equivalent to PROC MEANS but with more flexibility in generating customized outputs and silent summaries.

In this guide, you'll learn:

• Complete syntax of PROC SUMMARY
• All available options and statements
• Grouped examples
• How it differs from PROC MEANS
• Output datasets and tips

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🛠 Syntax of PROC SUMMARY

PROC SUMMARY <options>;
  VAR variable(s);
  CLASS variable(s);
  BY variable(s);
  OUTPUT OUT=dataset <stat-options>;
RUN;

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⚙️ Common Options in PROC SUMMARY

Option	Description
DATA=	Specifies the input dataset
N	Count of non-missing values
MEAN	Mean or average value
STD	Standard deviation
MIN	Minimum value
MAX	Maximum value
SUM	Total sum
MAXDEC=	Maximum number of decimals
NWAY	Outputs only rows with all CLASS variables present
CHARTYPE	Adds a TYPE variable to output

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📄 Statements in PROC SUMMARY

Statement	Purpose
VAR	Specifies numeric variables to analyze
CLASS	Group summary statistics by categorical variables
BY	BY-group processing; data must be sorted
OUTPUT	Outputs summary statistics to a dataset

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✅ PROC SUMMARY vs PROC MEANS

Feature	PROC MEANS	PROC SUMMARY
Displays output	Yes (default)	No (unless PRINT)
Flexibility	Moderate	High
Use in production	For display/review	For data pipelines
Output dataset	Optional	Common

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🧪 Examples of PROC SUMMARY

Example 1: Basic Summary Statistics

proc summary data=sashelp.class print;
run;

Note: Use PRINT to display output.

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Example 2: Specifying Variables

proc summary data=sashelp.class print;
  var height weight;
run;

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Example 3: Using CLASS Statement

proc summary data=sashelp.class print;
  class sex;
  var height weight;
run;

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Example 4: Creating Output Dataset

proc summary data=sashelp.class n mean maxdec=1;
  class sex;
  var height weight;
  output out=summary_stats;
run;

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Example 5: Custom Output Variable Names

proc summary data=sashelp.class;
  class sex;
  var weight;
  output out=summary_data
    mean=mean_weight
    max=max_weight
    min=min_weight;
run;

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Example 6: Using BY Statement (sorted data)

proc sort data=sashelp.class out=sorted;
  by sex;
run;

proc summary data=sorted print;
  by sex;
  var height;
run;

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🔄 Output Options in PROC SUMMARY

You can customize the summary stats by combining the following keywords in the OUTPUT statement:

Keyword	Description
N=	Assigns name to N (count)
MEAN=	Assigns name to mean
STD=	Standard deviation
SUM=	Assigns name to sum
MIN=	Assigns name to minimum
MAX=	Assigns name to maximum

Example:

output out=myout n=n_obs mean=avg std=stdev;

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🧠 Tips for PROC SUMMARY

• Always use PRINT if you want to display results.
• Use NWAY if you need only fully classified combinations.
• Use meaningful output variable names with =.
• Great for creating reusable summary datasets.

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📦 Summary Table

Feature	Description
Primary Use	Summary statistics
Shows Output	No (by default)
Supports Groups	Yes (CLASS or BY)
Custom Output	Yes (OUTPUT statement)
Output Dataset	Yes
Flexible Output	Highly customizable

 

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📊 PROC MEANS in SAS – A Complete Guide with Syntax, Options & Examples

📘 Introduction

The PROC MEANS procedure in SAS is one of the most frequently used procedures for generating descriptive statistics. It helps compute means, medians, standard deviations, minimums, maximums, and more for numeric variables.

This blog post explores everything about PROC MEANS:

• Syntax and arguments
• Available statistics
• Options and statements
• Multiple grouped examples
• Tips for better analysis

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🔧 Syntax of PROC MEANS

PROC MEANS <options>;
  VAR variable(s);
  CLASS variable(s);
  BY variable(s);
  OUTPUT OUT=dataset <output-options>;
RUN;

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🧾 Commonly Used Options in PROC MEANS

Option	Description
N	Count of non-missing values
MEAN	Average value
STD	Standard deviation
MIN	Minimum value
MAX	Maximum value
MEDIAN	Median value
SUM	Sum of values
MAXDEC=	Maximum number of decimals
DATA=	Specifies input dataset
NWAY	Forces output only for combinations of all class variables
CHARTYPE	Adds type variable in output
Q1, Q3	1st and 3rd quartiles

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🧠 Key Statements in PROC MEANS

Statement	Purpose
VAR	Specifies numeric variables to analyze
CLASS	Performs group-wise analysis (similar to GROUP BY)
BY	Performs BY-group processing (requires sorted data)
OUTPUT	Saves results to a new dataset

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🧪 PROC MEANS Examples

✅ Example 1: Basic Summary Statistics

proc means data=sashelp.class;
run;

Output: N, Mean, Std, Min, Max for all numeric variables.

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✅ Example 2: Specify Variables and Options

proc means data=sashelp.class mean std maxdec=2;
  var age height weight;
run;

Output: Mean and standard deviation for specified variables with 2 decimals.

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✅ Example 3: Using CLASS Statement

proc means data=sashelp.class n mean median maxdec=1;
  class sex;
  var height weight;
run;

Output: Summary by gender.

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✅ Example 4: Using BY Statement

proc sort data=sashelp.class out=sorted;
  by sex;
run;

proc means data=sorted n mean std;
  by sex;
run;

Note: BY requires pre-sorting.

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✅ Example 5: Saving Output to a Dataset

proc means data=sashelp.class n mean max min;
  var height weight;
  class sex;
  output out=class_summary mean=mean_height mean_weight;
run;

Output Dataset: class_summary with mean of height and weight by sex.

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✅ Example 6: Percentiles and Custom Statistics

proc means data=sashelp.class n mean median q1 q3;
  var weight;
run;

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📌 When to Use CLASS vs BY in PROC MEANS

Feature	CLASS	BY
Sorting	Not required	Requires sorting
Output	Summary by group	Separate table per group
Flexibility	More user-friendly for reporting	Ideal for structured data

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🧠 Tips for Using PROC MEANS Effectively

• Use MAXDEC= to format output.
• CLASS is easier to use than BY for grouped summaries.
• Combine with OUTPUT statement to reuse summary data.
• Filter data using WHERE before calling PROC MEANS.

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🧾 Summary Table

Feature	Description
Procedure Name	PROC MEANS
Primary Use	Descriptive statistics
Key Outputs	N, Mean, Std, Min, Max, Median, etc.
Common Options	MAXDEC=, NWAY, CHARTYPE
Supports Grouping	Yes – via CLASS and BY

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🔄 Mastering PROC TRANSPOSE in SAS: Convert Rows to Columns and Vice Versa

PROC TRANSPOSE is a powerful SAS procedure used to reshape data by converting rows into columns or columns into rows. Whether you're preparing datasets for reporting or statistical analysis, PROC TRANSPOSE can simplify your task with just a few lines of code.

In this blog, you'll learn:

• What is PROC TRANSPOSE?
• When and why to use it
• Syntax and options
• Multiple real-world examples
• Tips and tricks for efficient use

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🔍 What is PROC TRANSPOSE in SAS?

PROC TRANSPOSE is used to pivot data—turning variables (columns) into observations (rows), or vice versa. It's especially useful for:

• Summarizing repeated measures
• Restructuring long or wide datasets
• Preparing data for visualizations or modeling

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📚 Basic Syntax of PROC TRANSPOSE

proc transpose data=input_data out=output_data <options>;
   by variable(s);         * Optional: groups data;
   id variable;            * Optional: names for new columns;
   var variable(s);        * Variables to transpose;
run;

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📌 Key Options Explained

Option	Description
BY	Groups data before transposing
VAR	Specifies variables to transpose
ID	Uses values of a variable as new column names
NAME=	Renames the default _NAME_ column
LABEL=	Renames the default _LABEL_ column

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✅ Example 1: Transposing Without BY or ID

🔹 Input Data

data sales;
   input Quarter $ Sales;
   datalines;
Q1 100
Q2 120
Q3 140
Q4 160
;

🔹 Transpose Code

proc transpose data=sales out=sales_transposed;
   var Sales;
run;

🔹 Output

NAME	COL1	COL2	COL3	COL4
Sales	100	120	140	160

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✅ Example 2: Transposing with ID to Use Column Names

proc transpose data=sales out=sales_wide;
   id Quarter;
   var Sales;
run;

🔹 Output

NAME	Q1	Q2	Q3	Q4
Sales	100	120	140	160

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✅ Example 3: Transpose with BY Grouping

🔹 Input Data

data student_scores;
   input Student $ Subject $ Score;
   datalines;
John Math 85
John English 78
John Science 92
Anna Math 88
Anna English 91
Anna Science 84
;

🔹 Transpose Code

proc sort data=student_scores;
   by Student;
run;

proc transpose data=student_scores out=scores_wide;
   by Student;
   id Subject;
   var Score;
run;

🔹 Output

Student	English	Math	Science
John	78	85	92
Anna	91	88	84

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✅ Example 4: Transposing Multiple Variables

data patient_data;
   input ID $ Visit $ Height Weight;
   datalines;
P1 Visit1 170 65
P1 Visit2 171 66
P2 Visit1 160 60
P2 Visit2 161 61
;

🔹 Code

proc sort data=patient_data;
   by ID;
run;

proc transpose data=patient_data out=trans_height prefix=Height_;
   by ID;
   id Visit;
   var Height;
run;

proc transpose data=patient_data out=trans_weight prefix=Weight_;
   by ID;
   id Visit;
   var Weight;
run;

data final_transposed;
   merge trans_height trans_weight;
   by ID;
run;

🔹 Output

ID	Height_Visit1	Height_Visit2	Weight_Visit1	Weight_Visit2
P1	170	171	65	66
P2	160	161	60	61

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🧠 Tips for Using PROC TRANSPOSE

• Always SORT your data before using BY.
• Use the PREFIX= option to create meaningful column names.
• Combine multiple transpositions for complex reshaping.
• Use NAME= and LABEL= to rename the default variables _NAME_ and _LABEL_.

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🔎 When to Use PROC TRANSPOSE

Use Case	PROC TRANSPOSE?
Convert long to wide format	✅ Yes
Convert wide to long format	✅ Yes (reverse)
Reshape repeated measures	✅ Yes
Change actual data values	❌ No
Merge multiple reshaped tables	✅ Yes

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📈 Conclusion

PROC TRANSPOSE is an essential tool in any SAS programmer’s toolkit. It simplifies the process of reshaping data for reporting, analysis, and modeling. With a good understanding of BY, ID, and VAR options, you can handle almost any data transformation challenge.