SAS for R Users

3Data Inspection and Cleaning

3.1 Introduction

Data Cleaning and Inspection is the next important part of the data analysis pipeline. It implies that before starting analysis, visualization or machine learning and its insights, you should have cleaned any data that has to be analyzed. Though Machine Learning, Exploratory Data Analysis and Data Visualization take up more time in analytical education, in an actual data science project much more time is spent in data inspection and cleaning.

3.2 Data Inspection

Data inspection helps us determine that data import has been executed correctly, that variables are in same length (rows) and breadth (columns) and that variables (columns) are in the same format as expected.

3.2.1 Data Inspection in SAS

Let’s try this in SAS

• Referring to a column is easier in SAS than in R

/* Refer to a column by using var in proc and keeping it by keep in data step*/

data import4 (keep=ozone2);
set import3;
run;

Creating a new variable which is twice the size of target column

data import4;
set import4;
ozone3=2*ozone2;
run;

Printing only that variable by using var (which is also used in other Procs)

proc print data=import3 (obs=5);
var ozone2;
run;

• Referring to a row is more complex in SAS than R

proc print data=import3 (obs=7);
run;
Obs  Wind  Temp  Month  Day  Ozone2   Solar_R2   var2
1    7.4   67    5      1    41.0000  190.000   .
2    8     72    5      2    36.0000  118.000   .
3    12.6  74    5      3    12.0000  149.000   .
4    11.5  62    5      4    18.0000  313.000   .
5 14.3 56 5 5 35.3017 185.932 ...