I have the following piece of result, which i need to add. Seems like a simple request, but i have spent a few days already trying to find the solution to this problem.
Data have:
Measure Jan_total Feb_total
Startup 100 200
Switcher 300 500
Data want:
Measure Jan_total Feb_total
Startup 100 200
Switcher 300 500
Total 400 700
I want individually placed vertical sum results of each column under the respective column please.
Can someone help me arrive at the solution for this request, please?
To do this in data step code, you would do something like:
data want;
set have end=end; * Var 'end' will be true when we get to the end of 'have'.;
jan_sum + jan_total; * These 'sum statements' accumulate the totals from each observation.;
feb_sum + feb_total;
output; * Output each of the original obbservations.;
if end then do; * When we reach the end of the input...;
measure = 'Total'; * ...update the value in Measure...;
jan_total = jan_sum; * ...move the accumulated totals to the original vars...;
feb_total = feb_sum;
output; * ...and output them in an additional observation.
end;
drop jan_sum feb_sum; * Get rid of the accumulator variables (this statement can go anywhere in the step).;
run;
You could do this many other ways. Assuming that you actually have columns for all the months, you might re-write the data step code to use arrays, or you might use PROC SUMMARY or PROC SQL to calculate the totals and add the resulting totals back using a much shorter data step, etc.
proc means noprint
data = have;
output out= want
class measure;
var Jan_total Feb_total;
run;
It depends on if this is for display or for a data set. It usually makes no sense to have a total in the data set and it's just used for reporting.
PROC PRINT has a SUM statement that will add the totals to the end of a report. PROC TABULATE also provides another mechanism for reporting like this.
example from here.
options obs=10 nobyline;
proc sort data=exprev;
by sale_type;
run;
proc print data=exprev noobs label sumlabel
n='Number of observations for the order type: '
'Number of observations for the data set: ';
var country order_date quantity price;
label sale_type='Sale Type'
price='Total Retail Price* in USD'
country='Country' order_date='Date' quantity='Quantity';
sum price quantity;
by sale_type;
format price dollar7.2;
title 'Retail and Quantity Totals for #byval(sale_type) Sales';
run;
options byline;
Results:
Related
I am being asked to provide summary statistics including corresponding confidence interval (CI) with its width for the population mean. I need to print 85% 90% and 99%. I know I can either use univariate or proc means to return 1 interval of your choice but how do you print all 3 in a table? Also could someone explain the difference between univariate, proc means and proc sql and when they are used?
This is what I did and it only printed 85% confidence.
proc means data = mydata n mean clm alpha = 0.01 alpha =0.1 alpha = 0.15;
var variable;
RUN;
To put all three values in one table you can execute your step three times and put the results in one table by using an append step.
For shorter code and easier usage you can define a macro for this purpose.
%macro clm_val(TAB=, VARIABLE=, CONF=);
proc means
data = &TAB. n mean clm
alpha = &CONF.;
ods output summary=result;
var &VARIABLE.;
run;
data result;
length conf $8;
format conf_interval percentn8.0;
conf="&CONF.";
conf_interval=1-&CONF.;
set result;
run;
proc append data = result
base = all_results;
quit;
%mend;
%clm_val(TAB=sashelp.class, VARIABLE=age, CONF=0.01);
%clm_val(TAB=sashelp.class, VARIABLE=age, CONF=0.1);
%clm_val(TAB=sashelp.class, VARIABLE=age, CONF=0.15);
The resulting table looks like this:
For an if-query I would like to create a macro varibale giving the respective frequency of the underlying time
series. I tried to get some descriptive statistics from proc time series. However, they unfortunately do not include the figure for the frequency.
The underlying times series does not necessarily conclude all periods of the frequency. That excludes a selected count by proc sql from my point of view.
Does anyone know an efficient procedure to determine the frequency without computing the frequency on my own (in a data step or a proc sql code)?
You can use the outspectra statement to help learn what kind of seasonality it has. Based on the data, give PROC TIMESERIES your best guess of day, month, etc. In the example below, we know we want to forecast by month but we do not know what seasonality it has.
proc timeseries data=sashelp.air outspectra=spectra;
id date interval=month;
var air;
run;
Plot this spectra dataset in proc sgplot and you'll see something that looks like this:
proc sgplot data=spectra;
where NOT missing(period);
series x=period y=p;
run;
This line will naturally increase over time, but we're looking for a bumps in the line. Notice the large bump somewhere between 0 and 24 months and the several smaller bumps before it. Let's zoom in on that by filtering out the longer periods.
proc sgplot data=spectra;
where period < 24 and NOT missing(period);
series x=period y=p;
run;
It's pretty clear that there is a strong seasonality of 12, with potentially smaller cycles at 3 and 6 months. From this plot, we can conclude that our seasonality should be 12 based on our spectra plot.
You can turn this into a macro to help identify the season if you'd like. Simply search for the largest bump within a reasonable timeframe. In our case we'll choose 36 because we do not suspect that we have any seasonality > 36 months.
proc sort data=spectra;
by period;
run;
data identify_period;
set spectra;
by period;
where NOT missing(period) AND period LE 36;
delta = abs(p - lag(p) );
run;
proc sql;
select period, max(delta) as max_delta
from identify_period
having delta = max(delta)
;
quit;
Output:
PERIOD max_delta
12 163712
I don't know how to do this without data step logic, but you could wrap the data step in a macro as follows:
%macro get_frequency(data,date_variable,output_variable);
proc sort data=&data (keep=&date_variable) out=__tempsorted;
by &date_variable;
run;
data _null_;
set __tempsorted end=lastobs;
prevdate=lag(&date_variable);
if _n_ > 1 then do;
interval_number+1;
interval_total + (&date_variable - prevdate);
end;
if lastobs then do;
average_interval = interval_total/interval_number;
frequency = round(365.25/average_interval);
call symput ("&output_variable",left(put(frequency,best32.)));
end;
run;
proc datasets nolist;
delete __tempsorted;
run;
quit;
%mend get_frequency;
Then you can call the macro on your original data set timeseries to examine the variable date and create a new macro variable frequency1 with the required frequency.
data work.timeseries;
input date date. value;
format date date9.;
datalines;
01Oct18 3000
01Nov18 4000
01Dec18 6500
01Jan19 7000
01Feb19 4000
01Mar19 5000
01Apr19 7500
01May19 4800
01Jun19 4500
;
run;
%get_frequency(timeseries,date,freqency1)
%put &=frequency1;
This seems to work on your sample data where each date is the first of the month. If your dates are evenly distributed (e.g. always near month start/end, or always near mid-month etc.) then this macro should work ok. Obviously if you have multiple observations per date then it will give the completely incorrect frequency.
I have a dataset with visitors and weather variables. I'm trying to forecast visitors based on the weather variables. Since the dataset only consists of visitors in season there is missing values and gaps for every year. When running proc reg in sas it's all okay but the issue comes when i'm using proc VARMAX. I cannot run the regression due to missing values. How can i tackle this?
proc varmax data=tivoli4 printall plots=forecast(all);
id obs interval=day;
model lvisitors = rain sunshine averagetemp
dfebruary dmarch dmay djune djuly daugust doctober dnovember ddecember
dwednesday dthursday dfriday dsaturday dsunday
d_24Dec2016 d_05Dec2013 d_24Dec2017 d_24Dec2014 d_24Dec2015 d_24Dec2019
d_24Dec2018 d_24Sep2012 d_06Jul2015
d_08feb2019 d_16oct2014 d_15oct2019 d_20oct2016 d_15oct2015 d_22sep2017 d_08jul2015
d_20Sep2019 d_08jul2016 d_16oct2013 d_01aug2012 d_18oct2012 d_23dec2012 d_30nov2013 d_20sep2014 d_17oct2012 d_17jun2014
dFrock2012 dFrock2013 dFrock2014 dFrock2015 dFrock2016 dFrock2017 dFrock2018 dFrock2019
dYear2015 dYear2016 dYear2017
/p=7 q=2 Method=ml dftest;
garch p=1 q=1 form=ccc OUTHT=CONDITIONAL;
restrict
ar(3,1,1)=0, ar(4,1,1)=0, ar(5,1,1)=0,
XL(0,1,13)=0, XL(0,1,14)=0, XL(0,1,13)=0, XL(0,1,27)=0, XL(0,1,38)=0, XL(0,1,42)=0;
output lead=10 out=forecast;
run;
As with any forecast, you will first need to prepare your time-series. You should first run through your data through PROC TIMESERIES to fill-in or impute missing values. The impute choice that is most appropriate is dependent on your variables. The below code will:
Sum lvisitors by day and set missing values to 0
Set missing values of averagetemp to average
Set missing values of rain, sunshine, and your variables starting with d to 0 (assuming these are indicators)
Code:
proc timeseries data=have out=want;
id obs interval = day
setmissing = 0
notsorted
;
var lvisitors / accumulate=total;
crossvar averagetemp / accumulate=none setmissing=average;
crossvar rain sunshine d: / accumulate=none;
run;
Important Time Interval Consideration
Depending on your data, this could bias your error rate and estimates since you always know no one will be around in the off-season. If you have many missing values for off-season data, you will want to remove those rows.
Since PROC VARMAX does not support custom time intervals, you can instead create a simple time identifier. You can alternatively turn this into a format for proc format and converttime_id at the end.
data want;
set have;
time_id+1;
run;
proc varmax data=want;
id time_id interval=day;
...
output lead=10 out=myforecast;
run;
data myforecast;
merge myforecast
want(keep=time_id date)
;
by time_id;
run;
Or, if you made a format:
data myforecast;
set myforecast;
date = put(time_id, timeid.);
drop time_id;
run;
I have three columns in a dataset: spend, age_bucket, and multiplier. The data looks something like...
spend age_bucket multiplier
10 18-24 2x
120 18-24 2x
1 35-54 3x
I'd like a dataset with the columns as the age buckets, the rows as the multipliers, and the entries as the sum (or other aggregate function) of the spend column. Is there a proc to do this? Can I accomplish it easily using proc SQL?
There are a few ways to do this.
data have;
input spend age_bucket $ multiplier $;
datalines;
10 18-24 2x
120 18-24 2x
1 35-54 3x
10 35-54 2x
;
proc summary data=have;
var spend;
class age_bucket multiplier;
output out=temp sum=;
run;
First you can use PROC SUMMARY to calculate the aggregation, sum in this case, for the variable in question. The CLASS statement gives you things to sum by. This will calculate the N-Way sums and the output data set will contain them all. Run the code and look at data set temp.
Next you can use PROC TRANSPOSE to pivot the table. We need to use a BY statement so a PROC SORT is necessary. I also filter to the aggregations you care about.
proc sort data=temp(where=(_type_=3));
by multiplier;
run;
proc transpose data=temp out=want(drop=_name_);
by multiplier;
var spend;
id age_bucket;
idlabel age_bucket;
run;
In traditional mode 35-54 is not a valid SAS variable name. SAS will convert your columns to proper names. The label on the variable will retain the original value. Just be aware if you need to reference the variable later, the name has changed to be valid.
I have data on exam results for 2 years for a number of students. I have a column with the year, the students name and the mark. Some students don't appear in year 2 because they don't sit any exams in the second year. I want to show whether the performance of students persists or whether there's any pattern in their subsequent performance. I can split the data into two halves of equal size to account for the 'first-half' and 'second-half' marks. I can also split the first half into quintiles according to the exam results using 'proc rank'
I know the output I want is a 5 X 5 table that has the original 5 quintiles on one axis and the 5 subsequent quintiles plus a 'dropped out' category as well, so a 5 x 6 matrix. There will obviously be around 20% of the total number of students in each quintile in the first exam, and if there's no relationship there should be 16.67% in each of the 6 susequent categories. But I don't know how to proceed to show whether this is the case of not with this data.
How can I go about doing this in SAS, please? Could someone point me towards a good tutorial that would show how to set this up? I've been searching for terms like 'performance persistence' etc, but to no avail. . .
I've been proceeding like this to set up my dataset. I've added a column with 0 or 1 for the first or second half of the data using the first procedure below. I've also added a column with the quintile rank in terms of marks for all the students. But I think I've gone about this the wrong way. Shoudn't I be dividing the data into quintiles in each half, rather than across the whole two periods?
Proc rank groups=2;
var yearquarter;
ranks ExamRank;
run;
Proc rank groups=5;
var percentageResult;
ranks PerformanceRank;
run;
Thanks in advance.
Why are you dividing the data into quintiles?
I would leave the scores as they are, then make a scatterplot with
PROC SGPLOT data = dataset;
x = year1;
y = year2;
loess x = year1 y = year2;
run;
Here's a fairly basic example of the simple tabulation. I transpose your quintile data and then make a table. Here there is basically no relationship, except that I only allow a 5% DNF so you have more like 19% 19% 19% 19% 19% 5%.
data have;
do i = 1 to 10000;
do year = 1 to 2;
if year=2 and ranuni(7) < 0.05 then call missing(quintile);
else quintile = ceil(5*ranuni(7));
output;
end;
end;
run;
proc transpose data=have prefix=year out=have_t;
by i;
var quintile;
id year;
run;
proc tabulate data=have_t missing;
class year1 year2;
tables year1,year2*rowpctn;
run;
PROC CORRESP might be helpful for the analysis, though it doesn't look like it exactly does what you want.
proc corresp data=have_t outc=want outf=want2 missing;
tables year1,year2;
run;