I have a SAS dataset that I want to transpose with both character and numerical variables. Only interval and group are character variables, the rest are numerical variables. However, proc transpose converts all of the variables into character variables. How can I modify the below program so that numerical variables remain as numerical and character variables as character after the transpose procedure? Thank you.
proc transpose data=sourceh.test out=sourceh.test2;
var interval group cap rank volatility correlation significance;
run;
You can do it in two steps.
proc transpose data=sourceh.test out=nums prefix=num;
var _numeric_;
run;
proc transpose data=sourceh.test out=char prefix=char;
var _character_;
run;
FYI: the conversion of character to numeric that you are getting now can be a useful feature, I call it en masse VVALUE.
Related
I have a set of data which has multiple columns but only one observation.
I need to transpose the data to have multiple observations with 2 column of data.The very first column in my Data is the Status. I want this to be the 2nd column of data and all remaining columns observations labeled in a column called 'Category'
Proc tranpose data=RNAD_STG out=RNAD;by Status; Run;
I want it to look like this.
I've transposed from Observation to Variable before but the reverse has me stuck. What can I do to achieve my desired output?
The log should state: NOTE: No variables to transpose.
Adding in a VAR statement solves this issue, either listing all variables, or a shortcut list or a wildcard list for all character variables.
Proc tranpose data=RNAD_STG out=RNAD (rename=(col1=status _name_=category));
by Status;
var CH7--PPE2;
*var _character_;
Run;
I have table like first table on the picture.
It's information about banks deals on the FX market on daily basis (buy minus sell). I would like to calculate cumulative results like on the second table. The number of banks and their names, also as date are not fixed. I'm new in SAS and tried to find solutions, but didn't find anything useful. I will be glad for any help.
When data such as this is in a wide format, it can be more difficult to process in SAS compared to a long format. Long data formats have numerous benefits in the form of by-group processing, indexing, filtering, etc. Many SAS procedures are designed around this concept.
For more information on the examples below, check out SAS's example on the Program Data Vector and by-group processing. Mastering these concepts will help you with data step programming.
Here are two ways you can solve it:
1. Use a sum statement and by-group processing.
In this example, we will:
Convert the data from wide to long in order to convert the bank name to a character variable
Perform a cumulative sum on each bank
Convert back to long again
By converting the bank name into a character variable, we can use by-group processing on it.
/* Convert from wide to long */
proc transpose data=raw
out=raw_transposed
name=bank
;
by date;
run;
proc sort data=raw_transposed;
by bank date;
run;
/* Use by-group processing to get cumulative values by month for each bank */
data cumulative_long;
set raw_transposed;
by bank date;
/* Reset the cumulative sum for each bank */
if(first.bank) then call missing(cumulative);
cumulative+COL1;
run;
proc sort data=raw_transposed;
by date bank;
run;
/* Convert from long to wide */
proc transpose data=raw_transposed
out=want(drop=_NAME_)
;
by date;
id bank;
var COL1;
run;
The sum statement can be used as a shortcut of the following code:
data cumulative_long;
set raw_transposed;
by bank date;
retain cumulative;
if(first.bank) then cumulative = 0;
cumulative = cumulative + COL1;
run;
cumulative does not exist in the dataset: we are creating it here. This value will become missing whenever SAS moves on to read a new row. We want SAS to carry the last value forward. retain tells SAS to carry its last value forward until we change it.
2. Use macro variables and dictionary tables
A second option would be to read all of the bank names from a dictionary table to prevent transposing. We will:
Read the names of the banks from the special table dictionary.columns into a macro variable using PROC SQL
Use arrays to perform cumulative sums
This assumes the bank naming scheme is always prefixed with "Bank." If does not follow a regular pattern, you can exclude all other variables from the initial SQL query.
proc sql noprint;
select name
, cats(name, '_cume')
into :banks separated by ' '
, :banks_cume separated by ' '
from dictionary.columns
where memname = 'RAW'
AND libname = 'WORK'
AND upcase(name) LIKE 'BANK%'
;
quit;
data want;
set raw;
array banks[*] &banks.;
array banks_cume[*] &banks_cume.;
do i = 1 to dim(banks);
banks_cume[i]+banks[i];
end;
drop i;
run;
I often work with a large number of variables that have zero or empty values only, but I could not find a SAS command to drop these unwanted variables. I know we can use SAS/IML, but I encountered such cases many times and would like to have a macro that may help me without having to type the variable names to avoid errors. Here is my code for removing variables with zero values only. It works to produce a cleaned output data set y from a raw data set x without using the names of the variables. I hope others could have a better solution or help me to make mine better.
%Macro dropZeroV(x, y) ;
proc means data = &x. ;
var _numeric_;
output out = sumTab ; run;
proc transpose data = sumTab(drop = _TYPE_) out= sumt; var _Numeric_; id _STAT_; run;
%let Vlst =;
proc sql noprint;
select _NAME_ into : dropLst separated by ' '
from sumT
where Max=0 and Min =0;
data &y.;
set &x.; drop &dropLst.;
run;
proc print data = &y.; run;
%Mend dropZeroV;
Use STACKODS and ODS SUMMARY to get the table in the format needed in one step rather than multiple steps. This limits it to the sum, since if the sum = 0, all values are 0. You may also want to look at rounding to avoid any issues with numeric precision.
PROC MEANS + PROC TRANSPOSE go to :
ods select none;
proc means data= &x. stackods sum;
var _numeric_;
ods output summary = sumT;
run;
I have data that looks like this and has 500 variables with a target:
var1 var2 var3 var4 ... var500 target
The names of the variables are not sequential as above so I don't think I can use something like var1:var500. I want to loop through the variables to create graphs. Some of the variables are continous and some are nominal.
for var1 through var500
if nominal then create graphtypeA var[i] * target
else if continous then create graphtypeB var[i] * target
end;
I can easily create a second table that has the data type in it to check against. Arrays seem like they might be useful to peform this task of looping through variables. Something like:
data work.mydata;
set archive.mydata;
array myarray{501] myarray1 - myarray501
do i=1 to 500;
proc sgpanel;
panelby myarray[501];
histogram myarray[i];
end;
run;
This doesn't work though and it doens't check to see what type of variable it is. If we assume I have another sas.dataset that has varname and vartype (continuous, nominal) how can I loop through to create the desired graphs for the given vartype? Thanks in advance.
Basically, you need to loop over some variables, apply some logic to determine the variable type, then produce output depending on the variable type. While there are many approaches to this problem, one solution is to select your variables into a macro variable, loop over this "list" (not a formal data structure) of variables, and use macro control logic to designate different subroutines for numeric and character variables.
I'll use the sashelp.cars data set to illustrate. In this example the variable origin is your 'Target' variable and the variables Make, Type, Horsepower, and Cylinders are the numeric and character variables.
* get some data;
data set1 (keep = Make Type Origin Horsepower Cylinders);
set sashelp.cars;
run;
* create dataset of variable names and types;
proc contents data = set1
out = vars
noprint;
run;
* get variable names and variable types (1=numeric, 2=character)
* into two macro variable "lists" where each entry is seperated
* by a space;
proc sql noprint;
select name, type
into :varname separated by ' ', :vartype separated by ' '
from vars
where name <> "Make";
quit;
* put the macro variables to the log to confirm they are what
* you expect
%put &varname;
%put &vartype;
Now, use a macro to loop over the values in the macro variable list. The countw function counts the number of variables, and uses this number as the loop iterator limit. The scan function reads in each variable name and type by its relative position in the respective macro variable lists. For each variable the type is then evaluated and a plot is produced depending on whether it is character or numeric. In this example, a histogram with density plot is produced for numeric variables and a bar chart of frequency counts is produced for character variables.
The loop logic is general, and Proc sgpanel and Proc sgplot cab be modified or replaced with other desired data step processing or procedures.
* turn on options that are useful for
* macro debugging, turn them off
* when using in production;
options mlogic mprint symbolgen;
%macro plotter;
%do i = 1 %to %sysfunc(countw(&varname));
%let nextvar = %scan(&varname, &i, %str( ));
%let nextvartype = %scan(&vartype, &i, %str( ));
%if &nextvartype. = 1 %then %do;
proc sgpanel data=set1 noautolegend;
title "&nextvar. Distribution";
panelby Origin;
histogram &nextvar.;
density &nextvar.;
run;
%end;
%if &nextvartype. = 2 %then %do;
proc sgplot data=set1;
title "&nextvar. Count by Origin";
vbar &nextvar. /group= origin;
run;
%end;
%end;
%mend plotter;
*call the macro;
%plotter;
Unfortunately it is not possible to use arrays outside a data step in the way that you propose here, at least not in any very efficient way. However, there are quite a few options available to you. One would be just to call your graphing proc once and tell it to graph every numeric variable in your dataset, e.g. like so:
proc univariate data = sashelp.class;
var _NUMERIC_;
histogram;
run;
If the variables you want to graph that are of the same type are adjacent in the column order of your dataset, you can use a double-dash list, e.g.
proc univariate data = sashelp.class;
var age--weight;
histogram;
run;
In general you should seek to avoid calling procs or running data steps separately for every variable - it is nearly always more efficient to call them just once and process everything in one go.
So I have multiple continuous variables that I have used proc rank to divide into 10 groups, ie for each observation there is now a "GPA" and a "GRP_GPA" value, ditto for Hmwrk_Hrs and GRP_Hmwrk_Hrs. But for each of the new group columns the values are between 1 - 10. Is there a way to change that value so that rather than 1 for instance it would be 1.2-2.8 if those were the min and max values within the group? I know I can do it by hand using proc format or if then or case in sql but since I have something like 40 different columns that would be very time intensive.
It's not clear from your question if you want to store the min-max values or just format the rank columns with them. My solution below formats the rank column and utilises the ability of SAS to create formats from a dataset. I've obviously only used 1 variable to rank, for your data it will be a simple matter to wrap a macro around the code and run for each of your 40 or so variables. Hope this helps.
/* create ranked dataset */
proc rank data=sashelp.steel groups=10 out=want;
var steel;
ranks steel_rank;
run;
/* calculate minimum and maximum values per rank */
proc summary data=want nway;
class steel_rank;
var steel;
output out=want_min_max (drop=_:) min= max= / autoname;
run;
/* create dataset with formatted values */
data steel_rank_fmt;
set want_min_max (rename=(steel_rank=start));
retain fmtname 'stl_fmt' type 'N';
label=catx('-',steel_min,steel_max);
run;
/* create format from previous dataset */
proc format cntlin=steel_rank_fmt;
run;
/* apply formatted value to rank column */
proc datasets lib=work nodetails nolist;
modify want;
format steel_rank stl_fmt10.;
quit;
In addition to Keith's good answer, you can also do the following:
proc rank data = sashelp.cars groups = 10 out = test;
var enginesize;
ranks es;
run;
proc sql ;
select *, catx('-',min(enginesize), max(enginesize)) as esrange, es from test
group by es
order by make, model
;
quit;