This document contains SAS code for performing various data analysis tasks such as data manipulation, descriptive statistics, frequency analysis, and time series analysis using PROC procedures like PROC MEANS, PROC FREQ, PROC STDIZE, and PROC PANEL. Some key tasks shown include importing data from Excel, generating lags and differences for panel data analysis, imputing missing values, creating new variables, and merging datasets.

1. PROC CONTENTS DATA=ATIFRAZA.PRACTICE;
RUN;
PROC MEANS DATA=ATIFRAZA.PRACTICE NMISS N MEAN STD SKEW KURT MIN
MAX CSS MAXDEC=3;
RUN;
DATA ATIFRAZA.PRACTICE;
SET ATIFRAZA.PRACTICE;
LABEL INVOICE = 'Pricce';
RUN;
PROC CONTENTS DATA=ATIFRAZA.PRACTICE;
RUN;
PROC UNIVARIATE DATA=ATIFRAZA.PRACTICE;
VAR CYLINDERS;
RUN;
PROC MEANS DATA=ATIFRAZA.PRACTICE NOPRINT;
VAR INVOICE CYLINDERS;
OUTPUT OUT=ATIFRAZA.abc
MAX = MaxInv MaxCyl
MAXID(INVOICE(MODEL) CYLINDERS(MODEL)) = MaxInvoice MaxCylinders
MIN = MinInv MinCyl
MINID(INVOICE(MODEL) CYLINDERS(MODEL)) = MinInvoice MinCylinders
;
RUN;
PROC PRINT DATA=ATIFRAZA.PRACTICE;
RUN;
CREATING CONTINGENCY TABLE
PROC FREQ DATA=ATIFRAZA.PRACTICE;
TABLE TYPE*ORIGIN/CHISQ;
TABLE TYPE*ORIGIN/ PLOT=FREQPLOT(TYPE=DOT);
TITLE 'TYPE AND ORIGIN TABLE';
RUN;
PROC FREQ DATA=ATIFRAZA.PRACTICE;
TABLE MAKE ORIGIN;
RUN;
PROC FREQ AND PLOT
PROC FREQ DATA=ATIFRAZA.PRACTICE;
TABLE MAKE/ PLOT=FREQPLOT(TYPE=DOT);
WEIGHT CYLINDERS;
RUN;
proc print data=atifraza.practice;
run;
PROC FREQ DATA=ATIFRAZA.PRACTICE;
TABLE Type*Origin/CHISQ;
TABLE Type*Origin/ PLOT=FREQPLOT(TYPE=DOT);
WEIGHT INVOICE;
TITLE 'TYPE AND ORIGIN TABLE';
RUN;
TIMED MEANS AND WINSORIZED

2. PROC UNIVARIATE DATA=ATIFRAZA.PRACTICE
WINSORIZED=10
TRIMMED=10
ROBUSTSCALE;
VAR CYLINDERS;
RUN;
STANDARDIZE DATA
PROC STDIZE DATA=atifraza.practice
OUT=ATIFRAZA.PRACTICEOK
REPONLY MISSING=MEAN;
VAR CYLINDERS;
BY NOTSORTED TYPE;
RUN;
PROC CORR DATA=atifraza.practiceOK pearson
PLOTS=matriX(HISTOGRAM);
RUN;
/* MERGING TWO TABLES */
data class;
input Name $ 1-25 Year $ 26-34 Major $ 36-50;
datalines;
Abbott, Jennifer first
Carter, Tom third Theater
Mendoza, Elissa fourth Mathematics
Tucker, Rachel first
Uhl, Roland second
Wacenske, Maurice third Theater
;
run;
proc print data=class;
title 'Acting Class Roster';
run;
data GRADE;
input Name $ 1-25 Year $ 26-34 GRADE $ 36-50;
datalines;
Abbott, Jenni first D
Carter, Tom third T
Mendoza, fourth M
Tucker, Ra first A
Uhl, Roland second B
Wacenske, Maurice third C
;
run;
proc print data=GRADE;
title 'Acting Class Roster';
run;
proc sort data=class;
by major;
run;
DATA MERGED;

3. MERGE CLASS(drop=year) GRADE (drop=year
RENAME=(NAME=ndName));
by major;
RUN;
PROC PRINT DATA=MERGED;
RUN;
/* adding engines of only chevrolet in make using where
statement */
proc sort data=ATIFRAZA.PRACTICE out=WORK.SORTTEMP;
where MAKE='Chevrolet';
;
by Make;
run;
proc print data=WORK.SORTTEMP label;
var Cylinders;
by Make;
sum Cylinders;
run;
proc delete data=work.SORTTEMP;
run;
/* ADDING A NEW VARIABLE WITH THE HELP OR PREVIOUS VARIABLES
ALSO DROPPING AND RENAMING IN SAME COMMAND */
DATA DUMMY;
SET ATIFRAZA.PRACTICE (rename=(Horsepower=HP) DROP=TYPE
MAKE ORIGIN);
AVERAGE=(INVOICE+MSRP)/2;
RUN;
/* CREATING A NEW VARIABLE AND SETTING ITS VALUE ACCORDING TO
SCORE IN ANOTHER VARIABLE */
DATA TESTOFRANKVARIABLE;
SET ATIFRAZA.PRACTICE;
if CYLINDERS>8 then STATUS="LARGE";
if CYLINDERS<=8 then STATUS="MEDIUM";
if CYLINDERS<=6 then STATUS="SMALL";
if CYLINDERS=. then STATUS="UNKNOWN";
RUN;
/* TOTAL (MEAN STD) BY CLASS I.E. TOTAL OF ITEMS BY CLASSES */
proc means data=atifraza.cake;
var PresentScore TasteScore;
class flavor;
output out=Cake_Desc mean=Avg_PS Avg_TS Std=SD_PS SD_TS
SUM=TOTALPS TOTALTS;
run;
/* SCANNING A VARIABLE AND THEN PUTTING NAME INVESTED */
DATA ATIFRAZA.MARKS;
INPUT S_NO $ NAME $10. DATE MMDDYY10. MARKS GRADE $;
FORMAT DATE MMDDYY10.;
DATALINES;
01 ALI RAZA 09122000 80 A
02 AHMED KHAN 02242011 75 B

4. 03 BINA ALI 08302006 60 C
04 MARIA ZIA 07212004 89 A
;
RUN;
DATA ATIFRAZA.MARKS2;
SET ATIFRAZA.MARKS;
F=SCAN(NAME,+1);
L=SCAN(NAME,+2);
NAMES=L||(",")||F;
RUN;
/*PANEL DATA*/
/* create lags and differentials in panel data */
PROC SORT DATA=ATIFRAZA.CPII;
BY COUNTRY;
RUN;
PROC PRINT DATA=ATIFRAZA.CPII;
RUN;
PROC TRANSPOSE DATA=ATIFRAZA.CPII OUT=ATIFRAZA.CPIITRANS;
BY COUNTRY;
RUN;
DATA ATIFRAZA.CPITRANS (RENAME=(COL1=CPI _NAME_=YEARS));
SET ATIFRAZA.CPIITRANS;
RUN;
DATA ATIFRAZA.ESPAK;
SET ATIFRAZA.CPITRANS;
IF COUNTRY='PAK';
/* CREATING DIFFERENCIAL AND LAGS IN A TIME SERIES DATA */
LAG_PK =LAG(CPI);
DCPI_PK =DIF(CPI);
RUN;
PROC PANEL DATA=ATIFRAZA.CPITRANS;
ID COUNTRY YEARS;
LAG CPI(1)/OUT=ATIFRAZA.LAGPANEL;
RUN;

5. /* SAAD FILE */
/*to generate data*/
data practice.sales;
input date mmddyy10. name $ 12-15 sales 17-22 expense 24-29;
datalines;
02/01/2010 ABC 10000 9000
03/01/2010 DEF 20000 19000
04/01/2010 JIK 15000 12000
;
run;
/*to add new variable*/
data practice.sales;
set practice.sales;
loss=sales-expense;
run;
/*to define format*/
proc print data=practice.sales;
format date mmddyy10. sales dollar. expense dollar. loss dollar.;
run;
/*to identify the contents*/
proc contents data=practice.sales;
run;
/*to define labels*/
data practice.sales;
set practice.sales;
label name='Person name';
label sales= 'Sales ($)';
label expense= 'Expense ($)';
label loss='Loss ($)';
run;
proc contents data=practice.sales;
run;
/*for descriptive analysis*/
proc means data=practice.sales;
run;
/*for selective descriptive analysis*/
proc means data=practice.sales noprint n missing mean STD skew kurt
nmiss max min;
var sales expense loss;
output out=meansales;
run;
proc print data=meansales;
run;
/*for frequency analysis*/
proc freq data=practice.sales;
table sales;
run;
/*cross tabulation*/
proc freq data=practice.sales;

6. table sales*expense;
run;
/*to generate new variables using existing ones*/
data practice.tsales;
set practice.sales;
MC=sales+expense;
Mult=sales*expense;
div=sales/expense;
logs= log(sales);
loge= log(expense);
logl= log(loss);
run;
/*regression analysis for one ind.variable*/
proc reg data=practice.tsales;
model logs=loge;
run;
/*regression analysis for several ind.variables*/
proc reg data=practice.tsales;
model logs=loge logl;
run;
/*anova analysis*/
proc anova data=practice.tsales;
class loge;
model logs=loge;
run;
/*to drop a variable*/
data practice.lsales;
set practice.tsales;
drop logl;
run;
/*to identify obs with max and min value*/
proc means data=practice.tsales noprint;
var sales expense;
output out=practice.meansales
mean= avs avex
std= sds sdex
max= maxs maxex
maxid(sales(name) expense(name))=maxidsl maxidex
min= mins minex
minid(sales(name) expense(name))=minidsl minidex
;
run;
proc print data=practice.meansales;
run;
/*univariate analysis*/
proc univariate data=practice.tsales;
var sales;
run;
proc univariate data=practice.tsales winsorized=.1
trimmed=
.1 .01

7. robustscale;
var sales;
run;
proc print data=practice.tsales;
run;
/*correlation analysis*/
proc corr data=practice.tsales;
run;
proc corr data=practice.tsales kendall spearman pearson fisher;
run;
proc corr data=practice.tsales noprint alpha;
run;
proc corr data=practice.tsales csscp cov;
run;
proc corr data=practice.tsales plots=matrix(histogram);
run;
/*mean analysis usin class*/
proc means data=practice.heart;
class sex;
run;
/*frequency analysis using by statement*/
proc freq data=practice.heart;
by sex;
table AgeAtDeath*DeathCause/missing;
run;
/*to find missing values*/
proc means data=practice.heart nmiss n;
run;
/*sorting data using by*/
proc sort data=practice.heart;
by sex status;
run;
/*freq analysis different functions*/
proc freq data=practice.heart;
table AgeAtDeath/missprint;
run;
proc freq data=practice.heart order=data;
table BP_Status*Smoking_Status/chisq;
weight smoking;
run;
/* analysis of cars1 data*/
proc contents data=quiz.cars1;
run;

8. proc means data=quiz.cars1 n nmiss;
run;
/*replace mising values with 0*/
/*oneway*/
data quiz.cars2;
set quiz.cars1;
array change Invoice;
do over change;
if change=. then change=0;
end;
run ;
proc means data=quiz.cars2 n nmiss ;
run;
/*2ndway*/
proc stdize data=quiz.cars1 out=quiz.cars5 reponly missing=0;
var cylinders invoice;
run;
proc means data=quiz.cars5 n nmiss;
run;
/*if variable is not mentioned it will replace all missing values
present in data*/
proc stdize data=quiz.cars1 out=quiz.cars4 reponly missing=0;
run;
proc means data=quiz.cars4 n nmiss ;
run;
/*replace mising values with mean value*/
proc stdize data=quiz.cars2 out=Quiz.cars3 missing=mean reponly;
var Cylinders;
run;
proc means data=quiz.cars3 n nmiss;
run;
proc contents data=quiz.cars3;
run;
/* to identify outliers Box Plot, Histogram, Maxid, subgroup analysis
and
Univatiate anlaysis and scatter plot of that specific variable */
proc sgplot data= quiz.cars3;
vbox invoice;
run;
proc sgplot data=QUIZ.CARS3;
histogram Invoice /;
density Invoice;
yaxis grid;
discretelegend "DENSITY" / location=inside position=topright
across=1;

9. run;
proc univariate data=quiz.cars3;
var invoice;
run;
proc means data=quiz.cars3 noprint;
var invoice;
output out=quiz.minicar
max= maxinv
maxid(invoice(model))=maxidinv
min= minin
minid(invoice(model))=minidinv;
run;
proc sgpanel data=quiz.cars3;
panelby type;
vbox Invoice;
run;
Panel data:
data d26oct.cpi;
input country $ y2001 y2002 y2003;
datalines;
PAK 100 111 119
IND 99 107 116
SRL 88 95 101
BND 77 88 96
BHT 96 84 95
AFG 97 103 107
;
run;
proc sort data=d26oct.cpi;
by country;
run;
proc transpose data=d26oct.cpi out=d26oct.transdat;
by country;
run;
proc print data=d26oct.transdat;
run;
data d26oct.paneldat (rename=(col1=cpi _name_=year));
set d26oct.transdat;
run;
data d26oct.TSPAK;
set d26oct.paneldat;
if country="PAK";
dcpi_pk= dif(cpi);

10. lcpi_pk= lag(cpi);
run;
proc panel data=d26oct.transdat;
id country _name_;
lag col1 (1)/ out=d26oct.panllagdat;
run;
/** Import an XLSX file. **/
PROC IMPORT DATAFILE="/home/msaadbaloch0/Practice/WDI.xlsx"
OUT=practice.wdi
DBMS=XLSX
REPLACE;
RUN;
/** Print the results. **/
PROC PRINT DATA=practice.wdi; RUN;
/*Taking lags without moving the lastone in the next country----better
method*/
PROC SORT DATA=practice.wdi;
BY country;
run;
proc transpose data=practice.wdi out=practice.transwdi;
by country;
run;
proc contents data=practice.transwdi;
run;
data practice.panelwdi (rename=(col1=GDP col2=INF col3=FDI
_name_=year));
set practice.transwdi;
drop _LABEL_;
run;
data practice.panelwdifor;
set practice.panelwdi;
format FDI dollar10. GDP dollar10. INF dollar10.;
run;
proc contents data=practice.panelwdifor;
run;
/*to create lag*/
data practice.panelogdata;
set practice.panelwdifor;
by country;

11. Lag_GDP=lag(GDP);
Lag_FDI=lag(FDI);
Lag_INF=lag(INF);
if first.country then do;
Lag_GDP=.;
Lag_FDI=.;
Lag_INF=.;
end;
run;
/*to create differnce*/
data practice.paneldifdata;
set practice.panelwdifor;
by country;
dif_GDP=dif(GDP);
dif_INF=dif(INF);
dif_FDI=dif(FDI);
if first.country then do;
dif_GDP=.;
dif_INF=.;
dif_FDI=.;
end;
run;