I have the below data in SAS and I want to get the table with the number of customers buying a certain product in a certain time.
It does not count if a customer is repeated within the group.
Product customer interval
1 A Morning
1 A Morning
1 B Afternoon
1 A Evening
2 A Afternoon
2 B Morning
2 C Afternoon
What I want to get is the below table
Morning Afternoon Evening All
Product Customer number
1 1 1 1 2
2 1 2 0 3
I believe you have to remove duplicates to make this table.
This is easily done by using the nodupkey option in a proc sort:
proc sort data = have out = want nodupkey;
by product customer interval;
run;
Here's a format that will correctly order the interval categories by putting spaces in front of the categories you want first:
proc format;
value $interval
"Morning" = " Morning"
"Afternoon" = " Afternoon"
"Evening" = "Evening";
run;
And here's the tabulate statement:
proc tabulate data = want order = formatted;
class product interval;
tables product, interval = " " all / row = float misstext = "0" printmiss;
keylabel n = " ";
format interval $interval.;
run;
This returns the following table:
Morning Afternoon Evening All
Product
1 1 1 1 3
2 1 2 0 3
If there are missing values this will be more complicated.
Related
I have a dataset with varying observations per ID, and these participants are also in different treatment status (Group). I wonder if I can use proc means to quickly calculate the number of participants and visits to clinic per group status by using proc means? Ideally, I can use proc means sum function quickly capture those with 0 and 1 based on group status and gain the total number? However, I got stuck in how to proceed.
ID Visit Group
1 1 0
1 2 0
2 1 1
2 2 1
2 3 1
3 1 0
4 1 1
4 2 1
5 1 0
5 2 0
6 1 1
6 2 1
6 3 1
6 4 1
Specifically, I am interested in 1) the total number of participants in each group status. In this case we can 3 participants (ID:1,3,and 5)in the control group (0) and another 3 participants (ID:2,4,and 6) in the treatment group (1).
2) the total number of visits per group status. In this case, the total visits in the control group (0) will be 5 (2+1+2=5) and the total visits in the treatment group (1) will be 9 (3+2+4=9).
I wonder if proc means procedure can help quickly calculate such values? Thanks.
Yes, you can use proc means to get counts.
data have;
input ID$ Visit Group;
cards;
1 1 0
1 2 0
2 1 1
2 2 1
2 3 1
3 1 0
4 1 1
4 2 1
5 1 0
5 2 0
;
run;
proc means data=have n;
class group id;
var visit;
types group id group*id;
run;
If you want the sum of visit, add "sum" behind proc means data=have n and ;.
It looks like GROUP is assigned at the ID level and not the ID/VISIT level. In that case if you want to count the number of ID's in each group you need to first get down to one observation per ID.
proc sort data=have nodupkey out=unique_ids ;
by id;
run;
Now you can count how many ID's are in each group. The normal way is to use PROC FREQ.
proc freq data=unique_ids;
tables group;
run;
But you can count with PROC MEANS/SUMMARY also.
proc summary data=unique_ids nway;
class group;
output out=counts N=N_ids ;
run;
proc print data=counts;
var group n_ids;
run;
MEANS doesn't do a distinct count easily so SQL may be a simpler to understand option here.
proc sql;
create table want as
select group, count(*) as num_visits, count(distinct ID) as num_participants
from have
group by group
order by 1;
quit;
The google search has been difficult for this. I have two categorical variables, age and months, with 7 levels each. for a few levels, say age =7 and month = 7 there is no value and when I use proc sql the intersections that do not have entries do not show, eg:
age month value
1 1 4
2 1 12
3 1 5
....
7 1 6
...
1 7 8
....
5 7 44
6 7 5
THIS LINE DOESNT SHOW
what i want
age month value
1 1 4
2 1 12
3 1 5
....
7 1 6
...
1 7 8
....
5 7 44
6 7 5
7 7 0
this happens a few times in the data, where tha last groups dont have value so they dont show, but I'd like them to for later purposes
You have a few options available, both seem to work on the premise of creating the master data and then merging it in.
Another is to use a PRELOADFMT and FORMATs or CLASSDATA option.
And the last - but possibly the easiest, if you have all months in the data set and all ages, then use the SPARSE option within PROC FREQ. It creates all possible combinations.
proc freq data=have;
table age*month /out = want SPARSE;
weight value;
run;
First some sample data:
data test;
do age=1 to 7;
do month=1 to 12;
value = ceil(10*ranuni(1));
if ranuni(1) < .9 then
output;
end;
end;
run;
This leaves a few holes, notably, (1,1).
I would use a series of SQL statements to get the levels, cross join those, and then left join the values on, doing a coalesce to put 0 when missing.
proc sql;
create table ages as
select distinct age from test;
create table months as
select distinct month from test;
create table want as
select a.age,
a.month,
coalesce(b.value,0) as value
from (
select age, month from ages, months
) as a
left join
test as b
on a.age = b.age
and a.month = b.month;
quit;
The group independent crossing of the classification variables requires a distinct selection of each level variable be crossed joined with the others -- this forms a hull that can be left joined to the original data. For the case of age*month having more than one item you need to determine if you want
rows with repeated age and month and original value
rows with distinct age and month with either
aggregate function to summarize the values, or
an indication of too many values
data have;
input age month value;
datalines;
1 1 4
2 1 12
3 1 5
7 1 6
1 7 8
5 7 44
6 7 5
8 8 1
8 8 11
run;
proc sql;
create table want1(label="Original class combos including duplicates and zeros for absent cross joins")
as
select
allAges.age
, allMonths.month
, coalesce(have.value,0) as value
from
(select distinct age from have) as allAges
cross join
(select distinct month from have) as allMonths
left join
have
on
have.age = allAges.age and have.month = allMonths.month
order by
allMonths.month, allAges.age
;
quit;
And a slight variation that marks duplicated class crossings
proc format;
value S_V_V .t = 'Too many source values'; /* single valued value */
quit;
proc sql;
create table want2(label="Distinct class combos allowing only one contributor to value, or defaulting to zero when none")
as
select distinct
allAges.age
, allMonths.month
, case
when count(*) = 1 then coalesce(have.value,0)
else .t
end as value format=S_V_V.
, count(*) as dup_check
from
(select distinct age from have) as allAges
cross join
(select distinct month from have) as allMonths
left join
have
on
have.age = allAges.age and have.month = allMonths.month
group by
allMonths.month, allAges.age
order by
allMonths.month, allAges.age
;
quit;
This type of processing can also be done in Proc TABULATE using the CLASSDATA= option.
I have data that's tracking a certain eye phenomena. Some patients have it in both eyes, and some patients have it in a single eye. This is what some of the data looks like:
EyeID PatientID STATUS Gender
1 1 1 M
2 1 0 M
3 2 1 M
4 3 0 M
5 3 1 M
6 4 1 M
7 4 0 M
8 5 1 F
9 6 1 F
10 6 0 F
11 7 1 F
12 8 1 F
13 8 0 F
14 9 1 F
As you can see from the data above, there are 9 patients total and all of them have the particular phenomena in one eye.
I need the count the number of patients with this eye phenomena.
To get the number of total patients in the dataset, I used:
PROC FREQ data=new nlevels;
tables PatientID;
run;
To count the number of patients with this eye phenomena, I used:
PROC SORT data=new out=new1 nodupkey;
by Patientid Status;
run;
proc freq data=new1 nlevels;
tables Status;
run;
However, it gave the correct number of patients with the phenomena (9), but not the correct number without (0).
I now need to calculate the gender distribution of this phenomena. I used:
proc freq data=new1;
tables gender*Status/chisq;
run;
However, in the cross table, it has the correct number of patients who have the phenomena (9), but not the correct number without (0). Does anyone have any thoughts on how to do this chi-square, where if the has this phenomena in at least 1 eye, then they are positive for this phenomena?
Thanks!
PROC FREQ is doing what you told it to: counting the status=0 cases.
In general here you are using sort of blunt tools to accomplish what you're trying to accomplish, when you probably should use a more precise tool. PROC SORT NODUPKEY is sort of overkill for example, and it doesn't really do what you want anyway.
To set up a dataset of has/doesn't have, for example, let's do a few things. First I add one more row - someone who actually doesn't have - so we see that working.
data have;
input eyeID patientID status gender $;
datalines;
1 1 1 M
2 1 0 M
3 2 1 M
4 3 0 M
5 3 1 M
6 4 1 M
7 4 0 M
8 5 1 F
9 6 1 F
10 6 0 F
11 7 1 F
12 8 1 F
13 8 0 F
14 9 1 F
15 10 0 M
;;;;
run;
Now we use the data step. We want a patient-level dataset at the end, where we have eye-level now. So we create a new patient-level status.
data patient_level;
set have;
by patientID;
retain patient_status;
if first.patientID then patient_status =0;
patient_status = (patient_Status or status);
if last.patientID then output;
keep patientID patient_Status gender;
run;
Now, we can run your second proc freq. Also note you have a nice dataset of patients.
title "Patients with/without condition in any eye";
proc freq data=patient_level;
tables patient_status;
run;
title;
You also may be able to do your chi-square analysis, though I'm not a statistician and won't dip my toe into whether this is an appropriate analysis. It's likely better than your first, anyway - as it correctly identifies has/doesn't have status in at least one eye. You may need a different indicator, if you need to know number of eyes.
title "Crosstab of gender by patient having/not having condition";
proc freq data=patient_level;
tables gender*patient_Status/chisq;
run;
title;
If your actual data has every single patient having the condition, of course, it's unlikely a chi-square analysis is appropriate.
I want to ask a complicated (for me) question about SAS programming. I think I can explain better by using simple example. So, I have the following dataset:
Group Category
A 1
A 1
A 2
A 1
A 2
A 3
B 1
B 2
B 2
B 1
B 3
B 2
I want to count the each category for each group. I can do it by using PROC FREQ. But it is not better way for my dataset. It will be time consuming for me as my dataset is too large and I have a huge number of groups. So, if I use PROC FREQ, firstly I need to create new datasets for each group and then use PROC FREQ for each group. In sum, I need to create the following dataset:
CATEGORIES
Group 1 (first category) 2 3
A 3 2 1
B 2 3 1
So, the number of first category in group A is 3. The number of first category in group B is 2 and so on. I think I can explain it. Thanks for your helps.
There is more than one way to do this in SAS. My bias is proc sql, so:
proc sql;
select grp,
sum(case when category = 1 then 1 else 0 end) as cat_1,
sum(case when category = 2 then 1 else 0 end) as cat_2,
sum(case when category = 3 then 1 else 0 end) as cat_3
from t
group by grp;
Either proc freq or proc summary will do the job of producing frequency counts:
data example;
length group category $1;
input group category;
cards;
A 1
A 1
A 2
A 1
A 2
A 3
B 1
B 2
B 2
B 1
B 3
B 2
;
run;
proc freq data=example;
table group*category;
run;
proc summary data=example nway;
class group category;
output out=example_frequency (drop=_type_);
run;
proc summary will produce a dataset in a 'long' format. If you need to transpose it (I'd suggest not doing so: you'll probably find working with the long format easier in most circumstances) you can use proc transpose:
proc transpose data=example_frequency out=example_matrix (drop=_name_);
by group;
id category;
var _freq_;
run;
Lets say I have data which looks like:
ID A1Q A2Q B1Q B2Q Continued
23 1 2 2 3
24 1 2 3 3
To understand the table it translates into, Person with ID 23 had answers 1,2,2,4 for the questions A1,A2,B1,B2 respectively. I want to know how to know the percentage of students who answered 1, 2 or 3 in the entire dataset.
I have tried using
PROC FREQ data = test.one;
tables A2Q-A2Q;
tables B1Q-B2Q;
RUN;
But this does not get me what I want. It separately analyzes each question and the output is long. I just need it into one table that tells me this percentage answered 1, this percentage answered 2 and etc.
The output could be:
Question: 1 2 3
Percentage A1Q 40% 40% 20%
Percentage A2Q 60% 20% 20%
Total Percentage 30% 30% 40%
So it would translate such that 40% people chose 1, 40% chose 2, and 30% chose 3 for question A1Q. The total percentage is out of all the people that gave answers, 30% chose 1 30% chose 2 and 40% chose 3.
You'd still need to work on it a little bit and transpose the final results but this could be a start... also if you have lots of questions, consider wrapping this up in a macro program.
data quest;
input ID A1Q A2Q B1Q B2Q;
datalines;
21 2 3 1 2
22 3 2 2 3
23 1 2 2 3
24 1 2 3 3
25 2 1 3 3
run;
options missing = 0;
proc freq data=quest;
table A1Q / nocol nocum nofreq out = freq1(rename=(A1Q=Answer Count=A1Q));
table A2Q / nocol nocum nofreq out = freq2;
table B1Q / nocol nocum nofreq out = freq3;
table B2Q / nocol nocum nofreq out = freq4;
run;
proc sql;
create table results as
select freq1.Answer,
freq1.Percent as pctA1Q,
freq2.Percent as pctA2Q,
freq3.Percent as pctB1Q,
freq4.Percent as pctB2Q
from freq1
left join freq2
on freq1.Answer = freq2.A2Q
left join freq3
on freq1.Answer = freq3.B1Q
left join freq4
on freq1.Answer = freq4.B2Q;
quit;
My suggestion would be to transpose your data and then do a proc freq or proc tabulate. I would recommend proc tabulate so you can format your output, since it looks like you have questions that are grouped.
data long;
set have;
array qs(*) a1q--b2q; *list first and last variable and everything in between will be included;
do i=1 to dim(qs);
question=vname(qs(i));
response=qs(i);
output;
end;
keep id question response;
run;
proc freq data=long;
table question*response/list;
run;