Series and scatter plot in one graph

[Anita_n]

Dear all, I wish to plot a graph which has a series and a scatter plot in it :  Hier is the original data

Sector	2012	2013	2014	2015	2016	2017	2018	2019
Bank 1	825	846	846	847	865	862	850	868
Bank 2	810	829	845	845	853	848	846	872
Bank 3	834	822	836	833	850	856	844	854
Bank 4	797	808	825	789	820	823	832	853
Bank 5	849	851	851	851	858	862	842	861
Bank 6	838	841	825	840	845	848	829	853
Bank 7	787	803	798	828	845	843	840	849
Bank 8	792	800	815	821	835	834	832	849
Bank 9	795	807	820	825	843	834	839	855
Bank 10	834	837	826	842	839	853	838	868
Bank 11	810	833	837	838	852	858	849	871
Bank 12	807	820	807	815	845	837	848	846
Bank 13	811	800	804	805	831	831	827	847
Bank 14	804	827	829	829	837	836	823	854
Bank 15	784	798	798	790	830	828	818	852
Bank 16	818	811	824	815	848	837	833	851
Bank 17	795	786	794	803	817	817	819	856
Bank 18	823	829	811	831	837	850	827	866
Bank 19	775	775	769	783	806	813	800	832
Bank 20	757	771	789	804	813	822	809	852
Bank 21	767	759	757	704	751	768	771	811
Bank 22	772	761	755	758	791	792	787	826
Average sector	795	800	804	812	830	832	824	846
Bank A	774	785	805	833	839	843	827	836

I want the scatter plot  for all the 22 banks and the series for Bank A and Average sector . I tried doing that by tranposing the table to become this: 

Sector	Year	Value
Bank 1	2012	825
Bank 2	2012	810
Bank 3	2012	834
Bank 4	2012	797
Bank 5	2012	849
Bank 6	2012	838
Bank 7	2012	787
Bank 8	2012	792
Bank 9	2012	795
Bank 10	2012	834
Bank 11	2012	810
Bank 12	2012	807
Bank 13	2012	811
Bank 14	2012	804
Bank 15	2012	784
Bank 16	2012	818
Bank 17	2012	795
Bank 18	2012	823
Bank 19	2012	775
Bank 20	2012	757
Bank 21	2012	767
Bank 22	2012	772
Average sector	2012	795
Bank A	2012	774
Bank 1	2013	846
Bank 2	2013	829
Bank 3	2013	822
Bank 4	2013	808
Bank 5	2013	851
Bank 6	2013	841
Bank 7	2013	803
Bank 8	2013	800
Bank 9	2013	807
Bank 10	2013	837
Bank 11	2013	833
Bank 12	2013	820
Bank 13	2013	800
Bank 14	2013	827
Bank 15	2013	798
Bank 16	2013	811
Bank 17	2013	786
Bank 18	2013	829
Bank 19	2013	775
Bank 20	2013	771
Bank 21	2013	759
Bank 22	2013	761
Average sector	2013	800
Bank A	2013	785
Bank 1	2014	846
Bank 2	2014	845
Bank 3	2014	836
Bank 4	2014	825
Bank 5	2014	851
Bank 6	2014	825
Bank 7	2014	798
Bank 8	2014	815
Bank 9	2014	820
Bank 10	2014	826
Bank 11	2014	837
Bank 12	2014	807
Bank 13	2014	804
Bank 14	2014	829
Bank 15	2014	798
Bank 16	2014	824
Bank 17	2014	794
Bank 18	2014	811
Bank 19	2014	769
Bank 20	2014	789
Bank 21	2014	757
Bank 22	2014	755
Average sector	2014	804
Bank A	2014	805
Bank 1	2015	847
Bank 2	2015	845
Bank 3	2015	833
Bank 4	2015	789
Bank 5	2015	851
Bank 6	2015	840
Bank 7	2015	828
Bank 8	2015	821
Bank 9	2015	825
Bank 10	2015	842
Bank 11	2015	838
Bank 12	2015	815
Bank 13	2015	805
Bank 14	2015	829
Bank 15	2015	790
Bank 16	2015	815
Bank 17	2015	803
Bank 18	2015	831
Bank 19	2015	783
Bank 20	2015	804
Bank 21	2015	704
Bank 22	2015	758
Average sector	2015	812
Bank A	2015	833
Bank 1	2016	865
Bank 2	2016	853
Bank 3	2016	850
Bank 4	2016	820
Bank 5	2016	858
Bank 6	2016	845
Bank 7	2016	845
Bank 8	2016	835
Bank 9	2016	843
Bank 10	2016	839
Bank 11	2016	852
Bank 12	2016	845
Bank 13	2016	831
Bank 14	2016	837
Bank 15	2016	830
Bank 16	2016	848
Bank 17	2016	817
Bank 18	2016	837
Bank 19	2016	806
Bank 20	2016	813
Bank 21	2016	751
Bank 22	2016	791
Average sector	2016	830
Bank A	2016	839
Bank 1	2017	862
Bank 2	2017	848
Bank 3	2017	856
Bank 4	2017	823
Bank 5	2017	862
Bank 6	2017	848
Bank 7	2017	843
Bank 8	2017	834
Bank 9	2017	834
Bank 10	2017	853
Bank 11	2017	858
Bank 12	2017	837
Bank 13	2017	831
Bank 14	2017	836
Bank 15	2017	828
Bank 16	2017	837
Bank 17	2017	817
Bank 18	2017	850
Bank 19	2017	813
Bank 20	2017	822
Bank 21	2017	768
Bank 22	2017	792
Average sector	2017	832
Bank A	2017	843
Bank 1	2018	850
Bank 2	2018	846
Bank 3	2018	844
Bank 4	2018	832
Bank 5	2018	842
Bank 6	2018	829
Bank 7	2018	840
Bank 8	2018	832
Bank 9	2018	839
Bank 10	2018	838
Bank 11	2018	849
Bank 12	2018	848
Bank 13	2018	827
Bank 14	2018	823
Bank 15	2018	818
Bank 16	2018	833
Bank 17	2018	819
Bank 18	2018	827
Bank 19	2018	800
Bank 20	2018	809
Bank 21	2018	771
Bank 22	2018	787
Average sector	2018	824
Bank A	2018	827
Bank 1	2019	868
Bank 2	2019	872
Bank 3	2019	854
Bank 4	2019	853
Bank 5	2019	861
Bank 6	2019	853
Bank 7	2019	849
Bank 8	2019	849
Bank 9	2019	855
Bank 10	2019	868
Bank 11	2019	871
Bank 12	2019	846
Bank 13	2019	847
Bank 14	2019	854
Bank 15	2019	852
Bank 16	2019	851
Bank 17	2019	856
Bank 18	2019	866
Bank 19	2019	832
Bank 20	2019	852
Bank 21	2019	811
Bank 22	2019	826
Average sector	2019	846
Bank A	2019	836

and using this code:

proc sgplot data=CombinedData;
    /* Scatter plot for all banks except "Bank A" and "Sector Average" */
    scatter x=Year y=Value / group=Group 
        markerattrs=(symbol=circlefilled size=10 color=gray) transparency=0.5; 

    /* Series plot for "Bank A" */
    series x=Year y=Value / group=Group 
        lineattrs=(thickness=2 color=red)  markerattrs=(size=6 )   legendlabel="Bank A"; 

    /* Series plot for "Sector Average" */
    series x=Year y=Value / group=Group 
        lineattrs=(thickness=2 color=yellow) 
        markerattrs=(size=6) legendlabel="Sector Average"; 

    /* Titles and Labels */
    title "Satisfaction with Face-to-Face User Support (2012-2019)";
    xaxis label="Year" values=(2012 to 2019 by 1);
    yaxis label="Satisfaction Value" values=(700 to 900 by 20) grid;
    
    /* Legend Position */
    keylegend / location=inside position=topright across=1;
run;

 but unfortunately am not getting it. I will be happly if someone can help.

I was also wondering if there is a better way to illustrate this data than using this method 

[PaigeMiller]

unfortunately am not getting it

Explain what is wrong. Show us screen captures of these plots. Explain what you would like to see.

Please provide data as working SAS data step code (examples and instructions). Please do not provide data as copy and paste from Excel or similar. Please do not provide data as Excel files.

[Ksharp · Accepted solution]

data have;
infile cards expandtabs truncover;
input Sector & $40.	Year	Value;
cards;
Bank 1	   2012	825
Bank 2	   2012	810
Bank 3	   2012	834
Bank 4	   2012	797
Bank 5	   2012	849
Bank 6	   2012	838
Bank 7	   2012	787
Bank 8	   2012	792
Bank 9	   2012	795
Bank 10	   2012	834
Bank 11	   2012	810
Bank 12	   2012	807
Bank 13	   2012	811
Bank 14	   2012	804
Bank 15	   2012	784
Bank 16	   2012	818
Bank 17	   2012	795
Bank 18	   2012	823
Bank 19	   2012	775
Bank 20	   2012	757
Bank 21	   2012	767
Bank 22	   2012	772
Average sector  	2012	795
Bank A	  2012	774
Bank 1	  2013	846
Bank 2	  2013	829
Bank 3	  2013	822
Bank 4	  2013	808
Bank 5	  2013	851
Bank 6	  2013	841
Bank 7	  2013	803
Bank 8	  2013	800
Bank 9	  2013	807
Bank 10	  2013	837
Bank 11	  2013	833
Bank 12	  2013	820
Bank 13	  2013	800
Bank 14	  2013	827
Bank 15	  2013	798
Bank 16	  2013	811
Bank 17	  2013	786
Bank 18	  2013	829
Bank 19	  2013	775
Bank 20	  2013	771
Bank 21	  2013	759
Bank 22	  2013	761
Average sector   	2013	800
Bank A	  2013	785
Bank 1	  2014	846
Bank 2	  2014	845
Bank 3	  2014	836
Bank 4	  2014	825
Bank 5	  2014	851
Bank 6	  2014	825
Bank 7	  2014	798
Bank 8	  2014	815
Bank 9	  2014	820
Bank 10	  2014	826
Bank 11	  2014	837
Bank 12	  2014	807
Bank 13	  2014	804
Bank 14	  2014	829
Bank 15	  2014	798
Bank 16	  2014	824
Bank 17	  2014	794
Bank 18	  2014	811
Bank 19	  2014	769
Bank 20	  2014	789
Bank 21	  2014	757
Bank 22	  2014	755
Average sector	  2014	804
Bank A	   2014	805
Bank 1	   2015	847
Bank 2	   2015	845
Bank 3	   2015	833
Bank 4	   2015	789
Bank 5	   2015	851
Bank 6	   2015	840
Bank 7	   2015	828
Bank 8	   2015	821
Bank 9	   2015	825
Bank 10	   2015	842
Bank 11	   2015	838
Bank 12	   2015	815
Bank 13	   2015	805
Bank 14	   2015	829
Bank 15	   2015	790
Bank 16	   2015	815
Bank 17	   2015	803
Bank 18	   2015	831
Bank 19	   2015	783
Bank 20	   2015	804
Bank 21	   2015	704
Bank 22	   2015	758
Average sector	  2015	812
Bank A	   2015	833
Bank 1	   2016	865
Bank 2	   2016	853
Bank 3	   2016	850
Bank 4	   2016	820
Bank 5	   2016	858
Bank 6	   2016	845
Bank 7	   2016	845
Bank 8	   2016	835
Bank 9	   2016	843
Bank 10	   2016	839
Bank 11	   2016	852
Bank 12	   2016	845
Bank 13	   2016	831
Bank 14	   2016	837
Bank 15	   2016	830
Bank 16	   2016	848
Bank 17	   2016	817
Bank 18	   2016	837
Bank 19	   2016	806
Bank 20	   2016	813
Bank 21	   2016	751
Bank 22	   2016	791
Average sector	   2016	830
Bank A	   2016	839
Bank 1	   2017	862
Bank 2	   2017	848
Bank 3	   2017	856
Bank 4	   2017	823
Bank 5	   2017	862
Bank 6	   2017	848
Bank 7	   2017	843
Bank 8	   2017	834
Bank 9	   2017	834
Bank 10	   2017	853
Bank 11	   2017	858
Bank 12	   2017	837
Bank 13	   2017	831
Bank 14	   2017	836
Bank 15	   2017	828
Bank 16	   2017	837
Bank 17	   2017	817
Bank 18	   2017	850
Bank 19	   2017	813
Bank 20	   2017	822
Bank 21	   2017	768
Bank 22	   2017	792
Average sector	  2017	832
Bank A	   2017	843
Bank 1	   2018	850
Bank 2	   2018	846
Bank 3	   2018	844
Bank 4	   2018	832
Bank 5	   2018	842
Bank 6	   2018	829
Bank 7	   2018	840
Bank 8	   2018	832
Bank 9	   2018	839
Bank 10	   2018	838
Bank 11	   2018	849
Bank 12	   2018	848
Bank 13	   2018	827
Bank 14	   2018	823
Bank 15	   2018	818
Bank 16	   2018	833
Bank 17	   2018	819
Bank 18	   2018	827
Bank 19	   2018	800
Bank 20	   2018	809
Bank 21	   2018	771
Bank 22	   2018	787
Average sector	   2018	824
Bank A	   2018	827
Bank 1	   2019	868
Bank 2	   2019	872
Bank 3	   2019	854
Bank 4	   2019	853
Bank 5	   2019	861
Bank 6	   2019	853
Bank 7	   2019	849
Bank 8	   2019	849
Bank 9	   2019	855
Bank 10	   2019	868
Bank 11	   2019	871
Bank 12	   2019	846
Bank 13	   2019	847
Bank 14	   2019	854
Bank 15	   2019	852
Bank 16	   2019	851
Bank 17	   2019	856
Bank 18	   2019	866
Bank 19	   2019	832
Bank 20	   2019	852
Bank 21	   2019	811
Bank 22	   2019	826
Average sector	  2019	846
Bank A	   2019	836
;

proc sort data=have;by Sector year;run;
data have2;
merge have(where=(Sector not in ('Average sector' 'Bank A')))
      have(where=(s in ('Average sector' 'Bank A')) rename=(Sector=s year=y value=v));
run;
proc sgplot data=have2 noautolegend;
    /* Scatter plot for all banks except "Bank A" and "Sector Average" */
    scatter x=Year y=Value / group=sector 
        markerattrs=(symbol=circlefilled size=10 color=gray) transparency=0.5; 

    /* Series plot for "Bank A" */
    series x=y y=v / group=s 
        lineattrs=(thickness=2) markers markerattrs=(symbol=circlefilled size=6 )   curvelabel curvelabelloc=outside; 

    /* Titles and Labels */
    title "Satisfaction with Face-to-Face User Support (2012-2019)";
    xaxis label="Year" values=(2012 to 2019 by 1);
    yaxis label="Satisfaction Value" values=(700 to 900 by 20) grid;
    
run;

But I prefer to use  spaghetti plot:

https://blogs.sas.com/content/sastraining/2016/10/25/the-perfect-storm-for-state-fair-attendance/

https://blogs.sas.com/content/iml/2016/06/02/create-spaghetti-plots-in-sas.html

proc sgplot data=have2 noautolegend;
    /* Scatter plot for all banks except "Bank A" and "Sector Average" */
    series x=Year y=Value / group=sector  lineattrs=(thickness=2 pattern=solid color=grey) transparency=0.8; 

    /* Series plot for "Bank A" */
    series x=y y=v / group=s  lineattrs=(thickness=2)     curvelabel curvelabelloc=outside; 

    /* Titles and Labels */
    title "Satisfaction with Face-to-Face User Support (2012-2019)";
    xaxis label="Year" values=(2012 to 2019 by 1);
    yaxis label="Satisfaction Value" values=(700 to 900 by 20) grid;
    
run;