mlmgaq.blogg.se - Import data sas university edition

The program will import the CSV file into SAS and convert it to a SAS dataset. The GETNAMES option tells the procedure that the first row of the CSV file contains the values that will be used to label the variables in the TRAIN dataset.Įxecute the code by pressing F3, or by clicking on the little running man.

The REPLACE option tells the procedure that if the TRAIN file already exists in the work library then it should be overwritten.

The OUT = option gives the name of the output file, as we are not specifying a LIBREF this dataset is created in the work library.

The DBMS option tells the procedure the type of file it will be reading.

Here the DATAFILE option tells the procedure the name and location of the input file (note how this is relative to the shared folder you setup when you installed SAS University Edition).

Enter the following code into the code window as shown below. To import the CSV file, we will use the PROC IMPORT procedure. Now launch the virtual machine and log onto the SAS University Edition.ĭownload the train.csv and test.csv file from the Kaggle website and store them within the shared folder you setup when installed SAS unviersity edition, usually this will be : C:\SASUniversityEdition\myfolders\ .

If you haven’t done this yet that you can follow the tutorial here.

Outputting a dataset to a CSV file in preparation for submitting it to Kaggleīefore you get started you will need to install a virtual machine and then the SAS university edition files.

Using the KEEP statement to only keep variables that you want in your dataset.

Using IF…THEN.ELSE logic to modify a dataset.

Calculating the proportion of men and women who survived using PROC FREQ.

Reading a CSV file into SAS using PROC IMPORT.

It explains how to use the SAS University Edition to do the following: It should be useful both for people who want to learn SAS, but also for those who want to use SAS to enter the Kaggle competition. The tutorial is designed to be roughly equivalent to the first excel lesson available on the Kaggle website. This is the first of our tutorials on using SAS university edition to explore the data from the Kaggle Titanic: Machine Learning from Disaster edition.