The script begins by creating two datasets (`height_and_weight` and `height_and_weight_20`) via DATA steps using `datalines`. It then uses `PROC PRINT` to generate comprehensive or selective list reports, `PROC SQL` as an alternative for similar queries, and `PROC FREQ` to obtain frequency tables for categorical variables, with a demonstration of the `MISSING` option.
Data Analysis
Type : CREATION_INTERNE
Data is entirely created within the SAS script via DATALINES statements in DATA steps.
1 Code Block
DATA STEP Data
Explanation :
Creates a SAS dataset named `height_and_weight` with variables `id` (character), `sex` (character), `ht_in` (numeric), and `wgt_lbs` (numeric) from provided in-line data.
Creates a SAS dataset named `height_and_weight` with variables `id` (character), `sex` (character), `ht_in` (numeric), and `wgt_lbs` (numeric) from provided in-line data.
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| 1 | DATA height_and_weight; |
| 2 | INPUT id $ sex $ ht_in wgt_lbs; |
| 3 | DATALINES; |
| 4 | 001 Male 71 190 |
| 5 | 002 Male 69 176 |
| 6 | 003 Female 64 130 |
| 7 | 004 Female 65 154 |
| 8 | ; |
| 9 | RUN; |
2 Code Block
PROC PRINT
Explanation :
Generates a list report displaying all observations and variables from the `height_and_weight` dataset.
Generates a list report displaying all observations and variables from the `height_and_weight` dataset.
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| 1 | PROC PRINT DATA = height_and_weight; |
| 2 | RUN; |
3 Code Block
PROC SQL
Explanation :
Performs an SQL query to select and display all columns from the `height_and_weight` dataset.
Performs an SQL query to select and display all columns from the `height_and_weight` dataset.
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| 1 | PROC SQL; |
| 2 | select * |
| 3 | from height_and_weight; |
| 4 | QUIT; |
4 Code Block
PROC PRINT
Explanation :
Displays a list report for the `id` and `ht_in` variables from the `height_and_weight` dataset, without including the observation number column (`noobs`).
Displays a list report for the `id` and `ht_in` variables from the `height_and_weight` dataset, without including the observation number column (`noobs`).
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| 1 | |
| 2 | PROC PRINT |
| 3 | DATA = height_and_weight noobs; |
| 4 | var id ht_in; |
| 5 | RUN; |
| 6 |
5 Code Block
PROC SQL
Explanation :
Performs an SQL query to select and display only the `id` and `ht_in` columns from the `height_and_weight` dataset.
Performs an SQL query to select and display only the `id` and `ht_in` columns from the `height_and_weight` dataset.
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| 1 | PROC SQL; |
| 2 | SELECT id, ht_in |
| 3 | FROM height_and_weight; |
| 4 | QUIT; |
6 Code Block
DATA STEP Data
Explanation :
Creates a second SAS dataset named `height_and_weight_20` with 20 observations, including missing values for the `sex` variable, from in-line data.
Creates a second SAS dataset named `height_and_weight_20` with 20 observations, including missing values for the `sex` variable, from in-line data.
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| 1 | DATA height_and_weight_20; |
| 2 | INPUT id $ sex $ ht_in wgt_lbs; |
| 3 | DATALINES; |
| 4 | 001 Male 71 190 |
| 5 | 002 Male 69 176 |
| 6 | 003 Female 64 130 |
| 7 | 004 Female 65 154 |
| 8 | 005 . 73 173 |
| 9 | 006 Male 69 182 |
| 10 | 007 Female 68 140 |
| 11 | 008 . 73 185 |
| 12 | 009 Female 71 157 |
| 13 | 010 Male 66 155 |
| 14 | 011 Male 71 213 |
| 15 | 012 Female 69 151 |
| 16 | 013 Female 66 147 |
| 17 | 014 Female 68 196 |
| 18 | 015 Male 75 212 |
| 19 | 016 Female 69 190 |
| 20 | 017 Female 66 194 |
| 21 | 018 Female 65 176 |
| 22 | 019 Female 65 176 |
| 23 | 020 Female 65 102 |
| 24 | RUN; |
7 Code Block
PROC FREQ
Explanation :
Generates univariate frequency tables for all variables in the `height_and_weight_20` dataset.
Generates univariate frequency tables for all variables in the `height_and_weight_20` dataset.
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| 1 | PROC FREQ DATA = height_and_weight_20; |
| 2 | RUN; |
8 Code Block
PROC FREQ
Explanation :
Generates a frequency table specifically for the `sex` variable in the `height_and_weight_20` dataset.
Generates a frequency table specifically for the `sex` variable in the `height_and_weight_20` dataset.
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| 1 | |
| 2 | PROC FREQ |
| 3 | DATA = height_and_weight_20; |
| 4 | TABLE sex; |
| 5 | RUN; |
| 6 |
9 Code Block
PROC FREQ
Explanation :
Generates a frequency table for the `sex` variable in the `height_and_weight_20` dataset, explicitly including missing values in the report.
Generates a frequency table for the `sex` variable in the `height_and_weight_20` dataset, explicitly including missing values in the report.
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| 1 | |
| 2 | PROC FREQ |
| 3 | DATA = height_and_weight_20; |
| 4 | TABLE sex / missing; |
| 5 | RUN; |
| 6 |
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Expert Advice
Stéphanie
Spécialiste Machine Learning et IA.
« This SAS script demonstrates the three pillars of data handling: Ingestion, Selective Reporting, and Data Quality Auditing. By juxtaposing procedural SAS with declarative SQL, the script highlights the versatility required for effective data manipulation. »