I am trying to determine if a customer lives in a rural or urban county. So i was wondering if there is a PROC formula to identify if a particular county is urban or rural using zip-code?
Thanks!
You'll need to find not a PROC, but a data source that links zip codes to this indicator. Once you have that data imported in SAS, you can merge it with your customer data.
Related
I have a question about the function "Analyse in Excel" or "Analyse in Excel" in German when a PBI (Power BI) report has been published.
I read in a flat table in PBI and create some measures in PBI. Basically, it's about account numbers and the limits. A calculation is not necessary or possible here.
If I now want to analyse the data in Excel Pivot Table, I can only display the measures as values. An analysis of account numbers and limits is not possible, as limits are not measures.
What do I have to do to be able to select original data as values?
Thank you very much for your feedback and best regards
Andi
Try adding a measure from the table you are wanting to analyze and then double clicking on the measure value. This will pop open a new sheet and drillthrough to the rows detail behind that cell. It may give you the detail you are wanting. I also believe it will give you proper data types on columns so you can do Excel analysis.
Sorry! I do not get it.
To make it clear - I stripped down a very easy example of my problem:
I'm loading a flat file with account, currency, date and balance information.
The respective Power BI looks like:
After publishing the report into the cloud I would analyse the data within Excel
However, when I try to bring the "balance" information as value in, I'm receiving the following message:
The balance is not a measure in Power BI. Any idea what I can do?
Thank you and best regards
Andi
Basically I have a big Excel dataset about 500x500 with economic information from various companies.
Each row is representing a different company and in columns we have the information. A little bit of it is qualitative like ZIP code, type, etc. But most of it is quantitative. For each of the quantitative info, we have info for 5 years, so we have one column for each year and for each information i.e. Debt 2019, Debt 2020, etc.
So my question is which is the best way to preprocess this data to work with it and how should it be done. Either doing the preprocessing with Excel, running a Script on PowerBI, using Query, SQL, ...
The objective is to have a report which will be accessible online and the user will type the name of the company and it will show them the dashboard with the information of that company (only that one), so they can navigate through it.
The structure and which information is shown is the same for each company, the only thing that changes is the "numbers" that each company has. So it has to be possible to change which data is showing (to use the one from the company they want).
It also needs to be able to show comparative data to other groups of companies or to the total.
I want to have it right from the start, because then changes get complicated.
I thought about doing sort of a "relational model" with one "table" for each company with the quantitative data (with one row for each year and each column one info point) and then a general table with the qualitative data (with rows being each company and the columns the info). But I am not really sure.
I know how to use Power BI but I have never used it for something this big. I would like to know which way to organize this data is better and some info on how to do it.
Many thanks to everyone.
I thought about doing sort of a "relational model" with one "table" for each company with the quantitative data (with one row for each year and each column one info point) and then a general table with the qualitative data (with rows being each company and the columns the info).
Yes, do that.
General guidance is to use Power Query in PowerBI to transform the data into a star schema model. See Understand star schema and the importance for Power BI
So that would typically result in one table that has the "dimension" data for each company, a date table, and a "fact" table at the grain of (CompanyId,Date) with the quantitative data.
This is my imported table in SAS
enter image description here
I want to create a new column titled YTD that sums the months of the year. The new table should look like this
enter image description here
It would be idea if the code was able to accommodate new months moving forward as well.
I do realize that this data set is not ideally structured, but this is what I have to work with.
Thanks
For the data structure imaged you can create a view that performs a sum of all numeric variables
data want / view=want;
set have;
YTD = sum (of _numeric_);
run;
I am new to SAS and data analytics in general. So sorry if my question sound too dumb.
I have a dataset of brand medicine with three variables. Variable 1 contains the drug name, variable two contains whether that drug is BRANDED, Generic or Brand-Generic and variable 3 contains the total sale of that drug.
What I want is percent split the BRANDED, GENERIC AND BRANDED GENERIC drugs among total drug sale. The final output should look like
Branded : 35%
Generic : 25%
Branded-Generic : 40%
Any help with a sas code which would do that is greatly appreciated thank you.
So you want a % sale split! You can try using SQL (proc sql) to get your desired answer.
proc sql;
create table want as
select drug_type, sum(total_sale) as tot_sale
from have
group by drug_type;
create table want as
select *, tot_sale/sum(tot_sale) as percent_sale format=percent10.2
from want;
quit;
I created a table 'want' that will have total sale for each drug type. Using that table, I created a column that has the calculated sale percentage and formatted it to a percent (for easy view).
Of course, there are other ways of doing it, like using proc summary or proc freq or even a data step. But as a beginner, I guess starting out with SQL would be a good decision.
Proc MI is used to impute missing values in a SAS dataset. Is there a way to obtain a SAS code from Proc MI procedure, so that we can score datasets with missing value without having to use Proc MI procedure? This is needed so that dataset in production environment can be score consistently. I dont want to use Proc MI in production environment.
Thanks!
I don't believe this is possible, at least not in the way that it is for proc import. If you want to use a purely deterministic imputation algorithm, you will need to write your own data step to do it.
I don't know how complicated your data set is, but if you score a data set that covers all the possibilities you will see in the production data, then you can use your scored data set as a lookup table for the production data.