I have a data set contains a series variables named; PG_86xt, AG_86xt,... with same suffix _86xt. How can I remove such suffix while renaming these variables?
I know how to add prefix or suffix. But the logic of removing them seems to be a little bit different. I think proc dataset modify is still the way to go. But the length of substring before suffix (or after prefix) is unknown.
The example on how to add prefix or suffix
data one;
input id name :$10. age score1 score2 score3;
datalines;
1 George 10 85 90 89
2 Mary 11 99 98 91
3 John 12 100 100 100
4 Susan 11 78 89 100
;
run;
proc datasets library = work nolist;
modify one;
rename &suffixlist;
quit;
You can use the scan function to get the desired result.
By altering the example you have in the link to fit your example:
data one;
input id name :$10. age PG_86xt AG_86xt IG_86xt;
datalines;
1 George 10 85 90 89
2 Mary 11 99 98 91
3 John 12 100 100 100
4 Susan 11 78 89 100
;
run;
By filtering on only those column that fits your convention (XX_86xt), you could use the first part of the scan for renaming.
proc sql noprint;
select cats(name,'=',scan(name, 1, '_'))
into :suffixlist
separated by ' '
from dictionary.columns
where libname = 'WORK' and memname = 'ONE' and '86xt' = scan(name, 2, '_');
quit;
You can use the index function to find the (first) place in each variable name where the suffix / prefix starts, then use that to construct appropriate parameters for substr. It's a bit more work than the code in your example, but you'll get there.
Related
I´m trying to combine and sum certain observations of a dataset with different values for their common variables, in this case, I am trying to combine the deaths of three age intervals (85-90), (91-95), (95+) in one only (85+) age interval. Our teacher told us it is better if we do not create a new variable and use proc means, tabulate etc.
I have read every google page and all I can find is a proc means combining and summing by variable, but I don´t need the whole group summed, just some observations of the group.
Having the dataset like:
.
.
.
71 to 75 3
76 to 80 4
81 to 85 2
86 to 90 3
91 to 95 1
95+ 3
I would like to have it like
.
.
.
71 to 75 3
76 to 80 4
81 to 85 2
85+ 7
Thanks!
Create a custom format to map the existing literal categorizations into a new ones.
* A format to map literal agecat strings to broader categories;
proc format ;
value $age_cat_want (default=20)
'86 to 90' = '86+'
'91 to 95' = '86+'
'95+' = '86+'
;
This only works for concatenating categories, creating a coarser aggregation.
Example:
* A format to get you into the pickle you are in;
proc format;
value age_cat_have
71-75 = '71 to 75'
76-80 = '76 to 80'
81-84 = '81 to 85'
86-90 = '86 to 90'
91-95 = '91 to 95'
95-high = '95+'
;
data have;
input age ##;
agecat = put (age, age_cat_have.);
datalines;
71 72 73
76 77 78 79
82 83
87 86 86
94
99 101 113
;
proc freq data=have;
title "Original categories are character literals";
table agecat;
run;
* A format to map literal agecat strings to broader categories;
proc format ;
value $age_cat_want (default=20)
'86 to 90' = '86+'
'91 to 95' = '86+'
'95+' = '86+'
;
proc freq data=have;
title "New age categories via custom format $age_cat_want";
table agecat;
format agecat $age_cat_want.;
run;
Note: An existing literal categorization cannot be explicitly split. You would have to make presumptions about the age value distribution within each category and impute a specific age that could be applied to a different age mapping format.
I have large dataset of a few million patient encounters that include a diagnosis, timestamp, patientID, and demographic information.
We have found that a particular type of disease is frequently comorbid with a common condition.
I would like to count the number of this type of disease that each patient has, and then create a histogram showing how many people have 1,2,3,4, etc. additional diseases.
This is the format of the data.
PatientID Diagnosis Date Gender Age
1 282.1 1/2/10 F 25
1 282.1 1/2/10 F 87
1 232.1 1/2/10 F 87
1 250.02 1/2/10 F 41
1 125.1 1/2/10 F 46
1 90.1 1/2/10 F 58
2 140 12/15/13 M 57
2 282.1 12/15/13 M 41
2 232.1 12/15/13 M 66
3 601.1 11/19/13 F 58
3 231.1 11/19/13 F 76
3 123.1 11/19/13 F 29
4 601.1 12/30/14 F 81
4 130.1 12/30/14 F 86
5 230.1 1/22/14 M 60
5 282.1 1/22/14 M 46
5 250.02 1/22/14 M 53
Generally, I was thinking of a DO loop, but I'm not sure where to start because there are duplicates in the dataset, like with patient 1 (282.1 is listed twice). I'm not sure how to account for that. Any thoughts?
Target diagnoses to count would be 282.1, 232.1, 250.02. In this example, patient 1 would have a count of 3, patient 2 would have 2, etc.
Edit:
This is what I have used, but the output is showing each PatientID on multiple lines in the output.
PROC SQL;
create table want as
select age, gender, patientID,
count(distinct diagnosis_description) as count
from dz_prev
where diagnosis in (282.1, 232.1)
group by patientID;
quit;
This is what the output table looks like. Why is this patientID showing up so many times?
Obs AGE GENDER PATIENTID count
1 55 Male 107828695 1
2 54 Male 107828695 1
3 54 Male 107828695 1
4 54 Male 107828695 1
5 54 Male 107828695 1
If you include variables that are neither grouping variables or summary statistics then SAS will happily re-merge your summary statistics back with all of the source records. That is why you are getting multiple records. AGE can usually vary if your dataset covers many years. And GENDER can also vary if your data is messy. So for a quick analysis you might try something like this.
create table want as
select patientID
, min(age) as age_at_onset
, min(gender) as gender
, count(distinct diagnosis_description) as count
from dz_prev
where diagnosis in (282.1, 232.1)
group by patientID
;
I think you can get what you want with an SQL statement
PROC SQL NOPRINT;
create table want as
select PatientID,
count(distinct Diagnosis) as count
from have
where Diagnosis in (282.1, 232.1, 250.02)
group by PatientID;
quit;
This filters to only the diagnoses you are interested in, counts the distinct times they are seen, by the PatientID, and saves the results to a new table.
i need help in finding how to convert datavalues in a column to reverse order into new column or same column.I mean first datavalue in column should be the last value in column and vice versa.
example:
name age
karl 40
lowry 56
jim 29
robert 34
samuel 60
harry 47
the output i need should look like this.
name age
harry 47
samuel 60
robert 34
jim 29
lowry 56
karl 40
i need reverse order of the datavalues on variables age and name or only on one variable.
First create a variable of the observation number:
data temp;
set have;
ObsNum = _n_;
run;
Then use that variable to sort the dataset:
proc sort data=temp out=want (drop=ObsNum);
by descending ObsNum;
run;
I am new to SAS, so this might be a silly type of question.
Assume there are several datasets with similar structure but different column names. I want to get new datasets with the same number of rows but only a subset of columns.
In the following example, Data_A and Data_B are original datasets and SubA and SubBare what I want. What is the efficient way of deriving SubA and SubB?
DATA A_auto;
LENGTH A_make $ 20;
INPUT A_make $ 1-17 A_price A_mpg A_rep78 A_hdroom A_trunk A_weight A_length A_turn A_displ A_gratio A_foreign;
CARDS;
AMC Concord 4099 22 3 2.5 11 2930 186 40 121 3.58 0
AMC Pacer 4749 17 3 3.0 11 3350 173 40 258 2.53 0
Audi Fox 6295 23 3 2.5 11 2070 174 36 97 3.70 1
;
RUN;
DATA B_auto;
LENGTH make $ 20;
INPUT B_make $ 1-17 B_price B_mpg B_rep78 B_hdroom B_trunk B_weight B_length B_turn B_displ B_gratio B_foreign;
CARDS;
Toyota Celica 5899 18 5 2.5 14 2410 174 36 134 3.06 1
Toyota Corolla 3748 31 5 3.0 9 2200 165 35 97 3.21 1
VW Scirocco 6850 25 4 2.0 16 1990 156 36 97 3.78 1
;
RUN;
DATA SubA;
set A_auto;
keep A_make A_price;
RUN;
DATA SubB;
set B_auto;
keep B_make B_price;
RUN;
Here's my new answer. This introduces quite a few concepts, but all are necessary to complete this task.
First of all I would store the required part variable names (the suffixes that are common to all datasets) in a new dataset. This keeps them all in one place and makes it easier to change if required.
The next step is to create a regular expression (regex) search string that combines all the names, separated by a pipe (|), which is the regex symbol for or. I've also added a $ symbol to end of the names, this ensures only variables ending with the part names will be selected.
select into :[macroname] is the method to create macro variables within proc sql
Then I set up a macro to extract the specific variable names for the current dataset and use those names to create a view (like my original answer)
The dictionary library referenced in the proc sql is a metadata library that contains information on all active libraries, tables, columns etc, so is a good source of identifying what the actual variable names are called (based on the regex search string created earlier).
You won't need the proc print in your code, I just put it in to show everything is working as expected.
Let me know if this works for you
/* create intial datasets */
DATA A_auto;
LENGTH A_make $ 20;
INPUT A_make $ 1-17 A_price A_mpg A_rep78 A_hdroom A_trunk A_weight A_length A_turn A_displ A_gratio A_foreign;
CARDS;
AMC Concord 4099 22 3 2.5 11 2930 186 40 121 3.58 0
AMC Pacer 4749 17 3 3.0 11 3350 173 40 258 2.53 0
Audi Fox 6295 23 3 2.5 11 2070 174 36 97 3.70 1
;
RUN;
DATA B_auto;
LENGTH B_make $ 20;
INPUT B_make $ 1-17 B_price B_mpg B_rep78 B_hdroom B_trunk B_weight B_length B_turn B_displ B_gratio B_foreign;
CARDS;
Toyota Celica 5899 18 5 2.5 14 2410 174 36 134 3.06 1
Toyota Corolla 3748 31 5 3.0 9 2200 165 35 97 3.21 1
VW Scirocco 6850 25 4 2.0 16 1990 156 36 97 3.78 1
;
RUN;
/* create dataset containing partial name of variables to keep */
data keepvars;
input part_name $ :20.;
datalines;
_make
_price
;
run;
/* create regular expression search string from partial names */
proc sql noprint;
select
cats(part_name,'$') /* '$' matches end of string */
into
:name_str separated by '|' /* '|' is an 'or' search operator in regular expressions */
from
keepvars;
quit;
%put &name_str.; /* print search string to log */
/* macro to create views from datasets */
%macro create_views (dsname, vwname); /* inputs are dataset name being read in and view name being created */
/* extract specific variable names to be kept, based on search string */
proc sql noprint;
select
name
into
:vars separated by ' '
from
dictionary.columns
where
libname = 'WORK'
and memname = upper("&dsname.")
and prxmatch("/&name_str./",strip(name))>0; /* prxmatch is regular expression search function */
quit;
%put &vars.; /* print variables to keep to log */
/* create views */
data &vwname. / view=&vwname.;
set &dsname. (keep=&vars.);
run;
/* test view by printing */
proc print data=&vwname.;;
run;
%mend create_views;
/* run macro for each dataset */
%create_views(A_auto, SubA);
%create_views(B_auto, SubB);
This is my input dataset:
Ref Col_A0 Col_01 Col_02 Col_aa Col_03 Col_04 Col_bb
NYC 10 0 44 55 66 34 44
CHG 90 55 4 33 22 34 23
TAR 10 8 0 25 65 88 22
I need to calculate the % of Col_A0 for a specific reference.
For example % col_A0 would be calculated as
10/(10+0+44+55+66+34+44)=.0395 i.e. 3.95%
So my output should be
Ref %Col_A0 %Rest
NYC 3.95% 96.05%
CHG 34.48% 65.52%
TAR 4.58% 95.42%
I can do this part but the issue is column variables.
Col_A0 and Ref are fixed columns so they will be there in the input every time. But the other columns won't be there. And there can be some additional columns too like Col_10, col_11 till col_30 and col_cc till col_zz.
For example the input data set in some scenarios can be just:
Ref Col_A0 Col_01 Col_02 Col_aa Col_03
NYC 10 0 44 55 66
CHG 90 55 4 33 22
TAR 10 8 0 25 65
So is there a way I can write a SAS code which checks to see if the column exists or not. Or if there is any other better way to do it.
This is my current SAS code written in Enterprise Guide.
PROC SQL;
CREATE TABLE output123 AS
select
ref,
(col_A0/(Sum(Col_A0,Col_01,Col_02,Col_aa,Col_03,Col_04,Col_bb)) FORMAT=PERCENT8.2 AS PERCNT_ColA0,
(1-(col_A0/(Sum(Col_A0,Col_01,Col_02,Col_aa,Col_03,Col_04,Col_bb))) FORMAT=PERCENT8.2 AS PERCNT_Rest
From Input123;
quit;
Scenarios where all the columns are not there I get an error. And if there are additional columns then I miss those. Please advice.
Thanks
I would not use SQL, but would use regular datastep.
data want;
set have;
a0_prop = col_a0/sum(of _numeric_);
run;
If you wanted to do this in SQL, the easiest way is to keep (or transform) the dataset in vertical format, ie, each variable a separate row per ID. Then you don't need to know how many variables there are to figure it out.
If you always want to sum all the numeric columns then just do :
col_A0 / sum(of _numeric_)