356.Can SAS analytics reveal which programming languages are popular but surprisingly difficult to master?

Can SAS analytics reveal which programming languages are popular but surprisingly difficult to master?

options nocenter;

1.LANGUAGES MASTER DATASET CREATION

data languages_master;

    length Language_Name $15 Region $15 Introduced_Date $9;

    format Review_Year 4.;

    input Language_Name $ Speakers Region $ Difficulty_Level Countries_Count

        Introduced_Year;

    /* Store historical date safely as character */

    Introduced_Date = cats('01JAN', Introduced_Year);

    /* Review year after 5 years */

    Review_Year = Introduced_Year + 5;

datalines;

English 1500 Global 2 60 1600

Mandarin 1100 Asia 5 3 1200

Hindi 600 Asia 3 4 1500

Spanish 550 Europe 2 21 1400

French 300 Europe 3 29 1300

Arabic 310 MiddleEast 4 25 700

Russian 260 Europe 4 4 900

Portuguese 260 Europe 2 10 1500

Bengali 230 Asia 3 2 1200

Japanese 125 Asia 5 1 800

German 90 Europe 3 6 1100

Korean 80 Asia 4 2 900

Italian 65 Europe 2 4 1400

Turkish 75 Europe 3 2 1100

;

run;

proc print data=languages_master;

run;

OUTPUT:

Obs	Language_Name	Region	Introduced_Date	Review_Year	Speakers	Difficulty_Level	Countries_Count	Introduced_Year
1	English	Global	01JAN1600	1605	1500	2	60	1600
2	Mandarin	Asia	01JAN1200	1205	1100	5	3	1200
3	Hindi	Asia	01JAN1500	1505	600	3	4	1500
4	Spanish	Europe	01JAN1400	1405	550	2	21	1400
5	French	Europe	01JAN1300	1305	300	3	29	1300
6	Arabic	MiddleEast	01JAN700	705	310	4	25	700
7	Russian	Europe	01JAN900	905	260	4	4	900
8	Portuguese	Europe	01JAN1500	1505	260	2	10	1500
9	Bengali	Asia	01JAN1200	1205	230	3	2	1200
10	Japanese	Asia	01JAN800	805	125	5	1	800
11	German	Europe	01JAN1100	1105	90	3	6	1100
12	Korean	Asia	01JAN900	905	80	4	2	900
13	Italian	Europe	01JAN1400	1405	65	2	4	1400
14	Turkish	Europe	01JAN1100	1105	75	3	2	1100

2.DATES PRACTICE

data languages_dates;

    set languages_master;

    /* Reference year instead of SAS date */

    Reference_Year = 2026;

    /* Correct calculation using years */

    Years_Since_Intro = Reference_Year - Introduced_Year;

run;

proc print data=languages_dates;

run;

OUTPUT:

Obs	Language_Name	Region	Introduced_Date	Review_Year	Speakers	Difficulty_Level	Countries_Count	Introduced_Year	Reference_Year	Years_Since_Intro
1	English	Global	01JAN1600	1605	1500	2	60	1600	2026	426
2	Mandarin	Asia	01JAN1200	1205	1100	5	3	1200	2026	826
3	Hindi	Asia	01JAN1500	1505	600	3	4	1500	2026	526
4	Spanish	Europe	01JAN1400	1405	550	2	21	1400	2026	626
5	French	Europe	01JAN1300	1305	300	3	29	1300	2026	726
6	Arabic	MiddleEast	01JAN700	705	310	4	25	700	2026	1326
7	Russian	Europe	01JAN900	905	260	4	4	900	2026	1126
8	Portuguese	Europe	01JAN1500	1505	260	2	10	1500	2026	526
9	Bengali	Asia	01JAN1200	1205	230	3	2	1200	2026	826
10	Japanese	Asia	01JAN800	805	125	5	1	800	2026	1226
11	German	Europe	01JAN1100	1105	90	3	6	1100	2026	926
12	Korean	Asia	01JAN900	905	80	4	2	900	2026	1126
13	Italian	Europe	01JAN1400	1405	65	2	4	1400	2026	626
14	Turkish	Europe	01JAN1100	1105	75	3	2	1100	2026	926

3.PROC SQL – DATA QUERY & AGGREGATION

proc sql;

    create table language_summary as

    select Region,

           count(Language_Name) as Total_Languages,

           mean(Speakers) as Avg_Speakers

    from languages_dates

    group by Region;

quit;

proc print data=language_summary;

run;

OUTPUT:

Obs	Region	Total_Languages	Avg_Speakers
1	Asia	5	427.00
2	Europe	7	228.57
3	Global	1	1500.00
4	MiddleEast	1	310.00

4.PROC MEANS – DESCRIPTIVE STATISTICS

proc means data=languages_dates mean min max;

    var Speakers Countries_Count Difficulty_Level;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Minimum	Maximum
Speakers Countries_Count Difficulty_Level	396.0714286 12.3571429 3.2142857	65.0000000 1.0000000 2.0000000	1500.00 60.0000000 5.0000000

5.PROC RANK – LANGUAGE POPULARITY RANKING

proc rank data=languages_dates out=language_rank descending;

    var Speakers;

    ranks Popularity_Rank;

run;

proc print data=language_rank;

run;

OUTPUT:

Obs	Language_Name	Region	Introduced_Date	Review_Year	Speakers	Difficulty_Level	Countries_Count	Introduced_Year	Reference_Year	Years_Since_Intro	Popularity_Rank
1	English	Global	01JAN1600	1605	1500	2	60	1600	2026	426	1.0
2	Mandarin	Asia	01JAN1200	1205	1100	5	3	1200	2026	826	2.0
3	Hindi	Asia	01JAN1500	1505	600	3	4	1500	2026	526	3.0
4	Spanish	Europe	01JAN1400	1405	550	2	21	1400	2026	626	4.0
5	French	Europe	01JAN1300	1305	300	3	29	1300	2026	726	6.0
6	Arabic	MiddleEast	01JAN700	705	310	4	25	700	2026	1326	5.0
7	Russian	Europe	01JAN900	905	260	4	4	900	2026	1126	7.5
8	Portuguese	Europe	01JAN1500	1505	260	2	10	1500	2026	526	7.5
9	Bengali	Asia	01JAN1200	1205	230	3	2	1200	2026	826	9.0
10	Japanese	Asia	01JAN800	805	125	5	1	800	2026	1226	10.0
11	German	Europe	01JAN1100	1105	90	3	6	1100	2026	926	11.0
12	Korean	Asia	01JAN900	905	80	4	2	900	2026	1126	12.0
13	Italian	Europe	01JAN1400	1405	65	2	4	1400	2026	626	14.0
14	Turkish	Europe	01JAN1100	1105	75	3	2	1100	2026	926	13.0

6.MACRO – AUTOMATED DIFFICULTY CLASSIFICATION

%macro difficulty_class;

data language_class;

    set language_rank;

    length Difficulty_Group $12;

    if Difficulty_Level <=2 then Difficulty_Group='Easy';

    else if Difficulty_Level=3 then Difficulty_Group='Medium';

    else Difficulty_Group='Hard';

run;

proc print data=language_class;

run;

%mend;

%difficulty_class;

OUTPUT:

Obs	Language_Name	Region	Introduced_Date	Review_Year	Speakers	Difficulty_Level	Countries_Count	Introduced_Year	Reference_Year	Years_Since_Intro	Popularity_Rank	Difficulty_Group
1	English	Global	01JAN1600	1605	1500	2	60	1600	2026	426	1.0	Easy
2	Mandarin	Asia	01JAN1200	1205	1100	5	3	1200	2026	826	2.0	Hard
3	Hindi	Asia	01JAN1500	1505	600	3	4	1500	2026	526	3.0	Medium
4	Spanish	Europe	01JAN1400	1405	550	2	21	1400	2026	626	4.0	Easy
5	French	Europe	01JAN1300	1305	300	3	29	1300	2026	726	6.0	Medium
6	Arabic	MiddleEast	01JAN700	705	310	4	25	700	2026	1326	5.0	Hard
7	Russian	Europe	01JAN900	905	260	4	4	900	2026	1126	7.5	Hard
8	Portuguese	Europe	01JAN1500	1505	260	2	10	1500	2026	526	7.5	Easy
9	Bengali	Asia	01JAN1200	1205	230	3	2	1200	2026	826	9.0	Medium
10	Japanese	Asia	01JAN800	805	125	5	1	800	2026	1226	10.0	Hard
11	German	Europe	01JAN1100	1105	90	3	6	1100	2026	926	11.0	Medium
12	Korean	Asia	01JAN900	905	80	4	2	900	2026	1126	12.0	Hard
13	Italian	Europe	01JAN1400	1405	65	2	4	1400	2026	626	14.0	Easy
14	Turkish	Europe	01JAN1100	1105	75	3	2	1100	2026	926	13.0	Medium

7.PROC UNIVARIATE – DISTRIBUTION ANALYSIS

proc univariate data=language_class;

    var Speakers;

    histogram Speakers;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Speakers

Moments
N	14	Sum Weights	14
Mean	396.071429	Sum Observations	5545
Std Deviation	424.305671	Variance	180035.302
Skewness	1.81572518	Kurtosis	2.88471787
Uncorrected SS	4536675	Corrected SS	2340458.93
Coeff Variation	107.128573	Std Error Mean	113.400461

Basic Statistical Measures
Location		Variability
Mean	396.0714	Std Deviation	424.30567
Median	260.0000	Variance	180035
Mode	260.0000	Range	1435
		Interquartile Range	460.00000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	3.492679	Pr > |t|	0.0040
Sign	M	7	Pr >= |M|	0.0001
Signed Rank	S	52.5	Pr >= |S|	0.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	1500
99%	1500
95%	1500
90%	1100
75% Q3	550
50% Median	260
25% Q1	90
10%	75
5%	65
1%	65
0% Min	65

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
65	13	310	6
75	14	550	4
80	12	600	3
90	11	1100	2
125	10	1500	1

The UNIVARIATE Procedure

8.PROC APPEND – ADD NEW LANGUAGE RECORDS

data new_languages;

    length Language_Name $15 Region $15 Introduced_Date $9;

    format Introduced_Year 4.;

    input Language_Name $ Speakers Region $ Difficulty_Level Countries_Count

        Introduced_Date $;

    /* Extract year safely */

    Introduced_Year = input(substr(Introduced_Date, 6, 4), 4.);

     Review_Year = Introduced_Year + 5;

datalines;

Swahili 80 Africa 3 5 01JAN1500

Thai    70 Asia   4 1 01JAN1300

;

run;

proc print data=new_languages;

run;

OUTPUT:

Obs	Language_Name	Region	Introduced_Date	Introduced_Year	Speakers	Difficulty_Level	Countries_Count	Review_Year
1	Swahili	Africa	01JAN1500	1500	80	3	5	1505
2	Thai	Asia	01JAN1300	1300	70	4	1	1305

proc append base=languages_master 

            data=new_languages;

run;

proc print data=new_languages;

run;

OUTPUT:

Obs	Language_Name	Region	Introduced_Date	Introduced_Year	Speakers	Difficulty_Level	Countries_Count	Review_Year
1	Swahili	Africa	01JAN1500	1500	80	3	5	1505
2	Thai	Asia	01JAN1300	1300	70	4	1	1305

9.PROC TRANSPOSE – DATA RESTRUCTURING

proc transpose data=languages_master out=transposed_languages;

    by Region NotSorted;

    var Speakers Countries_Count;

run;

proc print data=transposed_languages;

run;

OUTPUT:

Obs	Region	_NAME_	COL1	COL2
1	Global	Speakers	1500	.
2	Global	Countries_Count	60	.
3	Asia	Speakers	1100	600
4	Asia	Countries_Count	3	4
5	Europe	Speakers	550	300
6	Europe	Countries_Count	21	29
7	MiddleEast	Speakers	310	.
8	MiddleEast	Countries_Count	25	.
9	Europe	Speakers	260	260
10	Europe	Countries_Count	4	10
11	Asia	Speakers	230	125
12	Asia	Countries_Count	2	1
13	Europe	Speakers	90	.
14	Europe	Countries_Count	6	.
15	Asia	Speakers	80	.
16	Asia	Countries_Count	2	.
17	Europe	Speakers	65	75
18	Europe	Countries_Count	4	2
19	Africa	Speakers	80	.
20	Africa	Countries_Count	5	.
21	Asia	Speakers	70	.
22	Asia	Countries_Count	1	.
23	Africa	Speakers	80	.
24	Africa	Countries_Count	5	.
25	Asia	Speakers	70	.
26	Asia	Countries_Count	1	.
27	Africa	Speakers	80	.
28	Africa	Countries_Count	5	.
29	Asia	Speakers	70	.
30	Asia	Countries_Count	1	.

10.PROC MERGE – COMBINING DATASETS

proc sort data=languages_master; by Language_Name; run;

proc print data=languages_master;

run;

OUTPUT:

Obs	Language_Name	Region	Introduced_Date	Review_Year	Speakers	Difficulty_Level	Countries_Count	Introduced_Year
1	Arabic	MiddleEast	01JAN700	705	310	4	25	700
2	Bengali	Asia	01JAN1200	1205	230	3	2	1200
3	English	Global	01JAN1600	1605	1500	2	60	1600
4	French	Europe	01JAN1300	1305	300	3	29	1300
5	German	Europe	01JAN1100	1105	90	3	6	1100
6	Hindi	Asia	01JAN1500	1505	600	3	4	1500
7	Italian	Europe	01JAN1400	1405	65	2	4	1400
8	Japanese	Asia	01JAN800	805	125	5	1	800
9	Korean	Asia	01JAN900	905	80	4	2	900
10	Mandarin	Asia	01JAN1200	1205	1100	5	3	1200
11	Portuguese	Europe	01JAN1500	1505	260	2	10	1500
12	Russian	Europe	01JAN900	905	260	4	4	900
13	Spanish	Europe	01JAN1400	1405	550	2	21	1400
14	Swahili	Africa	01JAN1500	.	80	3	5	1500
15	Swahili	Africa	01JAN1500	.	80	3	5	1500
16	Swahili	Africa	01JAN1500	1505	80	3	5	1500
17	Thai	Asia	01JAN1300	.	70	4	1	1300
18	Thai	Asia	01JAN1300	.	70	4	1	1300
19	Thai	Asia	01JAN1300	1305	70	4	1	1300
20	Turkish	Europe	01JAN1100	1105	75	3	2	1100

proc sort data=language_rank; by Language_Name; run;

proc print data=language_rank;

run;

OUTPUT:

Obs	Language_Name	Region	Introduced_Date	Review_Year	Speakers	Difficulty_Level	Countries_Count	Introduced_Year	Reference_Year	Years_Since_Intro	Popularity_Rank
1	Arabic	MiddleEast	01JAN700	705	310	4	25	700	2026	1326	5.0
2	Bengali	Asia	01JAN1200	1205	230	3	2	1200	2026	826	9.0
3	English	Global	01JAN1600	1605	1500	2	60	1600	2026	426	1.0
4	French	Europe	01JAN1300	1305	300	3	29	1300	2026	726	6.0
5	German	Europe	01JAN1100	1105	90	3	6	1100	2026	926	11.0
6	Hindi	Asia	01JAN1500	1505	600	3	4	1500	2026	526	3.0
7	Italian	Europe	01JAN1400	1405	65	2	4	1400	2026	626	14.0
8	Japanese	Asia	01JAN800	805	125	5	1	800	2026	1226	10.0
9	Korean	Asia	01JAN900	905	80	4	2	900	2026	1126	12.0
10	Mandarin	Asia	01JAN1200	1205	1100	5	3	1200	2026	826	2.0
11	Portuguese	Europe	01JAN1500	1505	260	2	10	1500	2026	526	7.5
12	Russian	Europe	01JAN900	905	260	4	4	900	2026	1126	7.5
13	Spanish	Europe	01JAN1400	1405	550	2	21	1400	2026	626	4.0
14	Turkish	Europe	01JAN1100	1105	75	3	2	1100	2026	926	13.0

data language_final;

    merge languages_master(in=a)

          language_rank(in=b);

    by Language_Name;

    if a;

run;

proc print data=language_final;

run;

OUTPUT:

Obs	Language_Name	Region	Introduced_Date	Review_Year	Speakers	Difficulty_Level	Countries_Count	Introduced_Year	Reference_Year	Years_Since_Intro	Popularity_Rank
1	Arabic	MiddleEast	01JAN700	705	310	4	25	700	2026	1326	5.0
2	Bengali	Asia	01JAN1200	1205	230	3	2	1200	2026	826	9.0
3	English	Global	01JAN1600	1605	1500	2	60	1600	2026	426	1.0
4	French	Europe	01JAN1300	1305	300	3	29	1300	2026	726	6.0
5	German	Europe	01JAN1100	1105	90	3	6	1100	2026	926	11.0
6	Hindi	Asia	01JAN1500	1505	600	3	4	1500	2026	526	3.0
7	Italian	Europe	01JAN1400	1405	65	2	4	1400	2026	626	14.0
8	Japanese	Asia	01JAN800	805	125	5	1	800	2026	1226	10.0
9	Korean	Asia	01JAN900	905	80	4	2	900	2026	1126	12.0
10	Mandarin	Asia	01JAN1200	1205	1100	5	3	1200	2026	826	2.0
11	Portuguese	Europe	01JAN1500	1505	260	2	10	1500	2026	526	7.5
12	Russian	Europe	01JAN900	905	260	4	4	900	2026	1126	7.5
13	Spanish	Europe	01JAN1400	1405	550	2	21	1400	2026	626	4.0
14	Swahili	Africa	01JAN1500	.	80	3	5	1500	.	.	.
15	Swahili	Africa	01JAN1500	.	80	3	5	1500	.	.	.
16	Swahili	Africa	01JAN1500	1505	80	3	5	1500	.	.	.
17	Thai	Asia	01JAN1300	.	70	4	1	1300	.	.	.
18	Thai	Asia	01JAN1300	.	70	4	1	1300	.	.	.
19	Thai	Asia	01JAN1300	1305	70	4	1	1300	.	.	.
20	Turkish	Europe	01JAN1100	1105	75	3	2	1100	2026	926	13.0

To Visit My Previous Online Streaming Flatform Dataset:Click Here

To Visit My Previous Statistical Evaluation Of Clinical Trials:Click Here

To Visit My Previous Unlocking Retail Insights Dataset:Click Here

To Visit My Previous Sas Interview Questions-1:Click Here  

Follow Us On : 

 

--->FOLLOW OUR BLOG FOR MORE INFORMATION.

--->PLEASE DO COMMENTS AND SHARE OUR BLOG.