I think above are enough demonstrations for data transformation.Īnd as always, next step is to look on the descriptive statistics of the data, and here's how to do it,įrom the above numerical output, we see that the p-value = 0.2627 is greater than $\alpha = 0.05$, hence there is no sufficient evidence to conclude that the average volume of palay production is not equal to 15000. Now, what about playing with the variables of the data? Say we want to view a specific column only, assuming observations from row 15 to 20 of the Benguet variable, how is that? Well, I humbly present to you the following code,įor viewing multiple columns, simply enumerate the name of the variables using either keep - keeps the variables to be returned, or drop - drops the variables, excluded in the printing. If you want to start and end on specific row, you can do the following. Having that habit, here's how to do it in SAS®, in this case, first five observations, In R, we always start by viewing the head of the data or the first few observations of the data, and we code it as head(data).
Now that we have the data in our workspace, let's do some transformation on it. To do this, hover to the sidebar, click on Folders tab, and there you will find the "up arrow" for upload. Working in SAS® Studio, requires you to upload your data into it.
To reproduce this article, please click here to download the data. We'll use again the Volume of Palay Production (1994 to 2013 quarterly) from Cordillera Administrative Region (CAR) Philippines. Our goal here is to address the basics in order to proceed with the analysis, and thus we have the following:ġ.
So let's welcome this software by doing analysis on it. If you've been following this blog, I have been promoting free software (R, Python, and C/C++) for analysis, and the introduction of SAS® University Edition will only mean one thing, a new topic to discuss on succeeding posts.
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