Help in plotting equiplot for the DHS dataset

[Wasim Khan]

Hello Everyone.
Recently i was working in a project of maternal inequality of BDHS 2011,2014,2017 data by measuring the changes in concentration indexes of ANC, SBA & PNC utilization. I wanted to plot an equiplot for a better understanding of the inequality. But i am new to this forum and have very little knowledge about this software. I have arranged my data set in the following format Graph Variables.dta
i wanted my graph to look like this
i know the command of equiplot but am unable to sort my datset according to my needs.
any help is highly appreciated.
Thanks in Advance.

[Wasim Khan]

* Example generated by -dataex-. For more info, type help dataex
clear
input double(Division_2011 Residence_2011 ANC_greater_or_4_2011 Wealth_Index_2011 Division_2014 Residence_2014 ANC_greater_or_4_2014 Wealth_Index_2014 Division_2017 Residence_2017 ANC_greater_or_4_2017 Wealth_Index_2017)
1 1 0 3 1 1 0 1 1 1 0 1
1 1 0 3 1 1 0 1 1 1 0 1
1 1 0 3 1 1 1 2 1 1 1 2
1 1 1 2 1 1 1 2 1 1 0 1
1 1 0 2 1 1 0 2 1 1 0 1
1 1 0 2 1 1 0 1 1 1 0 1
1 1 0 4 1 1 0 1 1 1 1 3
1 1 0 1 1 1 1 1 1 1 0 2
1 1 1 3 1 1 1 4 1 1 0 2
1 1 0 2 1 1 1 2 1 1 0 2
1 1 0 2 1 1 0 3 1 1 1 1
1 1 1 2 1 1 0 2 1 1 0 2
1 1 0 2 1 1 0 2 1 1 0 1
1 1 0 3 1 1 1 1 1 1 0 1
1 1 0 2 1 1 0 2 1 1 0 2
1 1 0 3 1 1 0 1 1 1 0 1
1 1 0 2 1 1 0 2 1 1 1 3
1 1 0 3 1 1 1 2 1 1 0 1
1 1 0 2 1 1 1 2 1 1 0 2
1 1 0 2 1 1 0 4 1 1 0 1
1 1 0 2 1 1 0 2 1 1 1 5
1 1 1 2 1 1 0 3 1 1 0 1
1 1 0 1 1 1 1 3 1 1 1 4
1 1 0 1 1 1 1 4 1 1 0 1
1 1 0 4 1 1 1 3 1 1 0 3
1 1 0 2 1 1 0 1 1 1 0 3
1 1 0 2 1 1 1 1 1 1 0 2
1 0 1 5 1 1 0 2 1 1 1 3
1 0 1 5 1 1 1 5 1 1 1 1
1 0 1 5 1 1 1 2 1 1 1 2
1 0 1 5 1 1 1 1 1 1 0 1
1 0 1 5 1 1 1 1 1 1 0 1
1 0 0 4 1 1 0 1 1 1 1 3
1 0 0 5 1 1 1 3 1 1 0 1
1 0 1 5 1 1 0 3 1 1 0 3
1 0 1 5 1 1 0 2 1 1 1 4
1 0 0 4 1 1 0 4 1 1 1 1
1 1 1 2 1 1 0 5 1 1 0 1
1 1 0 2 1 1 0 4 1 1 0 1
1 1 0 3 1 1 0 1 1 1 0 2
1 1 1 1 1 1 0 4 1 1 1 1
1 1 0 5 1 1 1 3 1 1 0 1
1 1 1 1 1 1 0 2 1 1 0 1
1 1 0 2 1 1 0 2 1 1 1 1
1 1 1 2 1 1 0 3 1 1 0 1
1 1 0 3 1 1 0 3 1 1 1 1
1 1 1 2 1 1 1 1 1 1 1 1
1 1 0 2 1 1 1 1 1 1 1 3
1 1 0 4 1 1 0 3 1 1 1 5
1 1 0 1 1 1 1 4 1 1 1 3
1 1 1 2 1 1 0 3 1 1 0 5
1 1 0 2 1 1 0 3 1 1 0 4
1 1 0 2 1 1 0 2 1 1 0 2
1 1 0 1 1 1 0 4 1 1 1 4
1 1 0 2 1 1 0 1 1 1 0 3
1 1 0 3 1 1 0 3 1 1 1 4
1 1 0 3 1 1 0 2 1 1 1 2
1 1 0 3 1 1 0 1 1 1 0 1
1 1 1 3 1 1 1 1 1 1 1 3
1 1 0 4 1 1 1 1 1 1 0 2
1 1 0 2 1 1 0 2 1 1 1 4
1 1 0 3 1 1 0 4 1 1 1 3
1 1 0 3 1 1 0 4 1 1 0 3
1 1 0 3 1 1 0 4 1 1 1 4
1 1 0 4 1 1 0 3 1 1 1 3
1 1 0 4 1 1 0 2 1 1 1 2
1 1 0 1 1 1 0 1 1 1 1 2
1 1 0 1 1 1 0 2 1 1 1 2
1 0 0 2 1 1 0 2 1 1 1 1
1 0 0 3 1 1 1 4 1 1 1 3
1 0 0 1 1 1 1 3 1 1 0 3
1 0 0 1 1 1 0 3 1 1 1 2
1 0 0 3 1 1 0 1 1 1 0 2
1 0 0 1 1 1 0 2 1 1 0 3
1 0 0 1 1 1 0 4 1 1 0 2
1 1 0 3 1 1 0 3 1 1 0 2
1 1 0 1 1 1 0 3 1 1 1 2
1 1 0 2 1 1 0 2 1 1 1 2
1 1 1 3 1 1 0 2 1 1 1 4
1 1 0 1 1 1 1 4 1 1 1 4
1 1 0 2 1 1 1 3 1 1 1 4
1 1 0 3 1 1 0 3 1 1 1 2
1 1 0 3 1 1 1 4 1 1 0 2
1 1 0 3 1 1 1 2 1 1 1 4
1 1 0 1 1 1 1 3 1 1 1 3
1 1 0 2 1 1 0 4 1 1 1 4
1 1 0 3 1 1 0 5 1 1 0 1
1 1 0 1 1 1 0 2 1 1 0 2
1 1 0 3 1 1 0 3 1 1 0 2
1 1 0 4 1 1 0 5 1 1 0 2
1 1 0 2 1 1 1 3 1 1 0 1
1 1 1 3 1 1 1 4 1 1 0 5
1 1 0 3 1 1 0 3 1 1 0 1
1 1 1 3 1 1 0 3 1 1 1 3
1 1 1 2 1 1 1 4 1 1 0 1
1 1 0 4 1 1 0 5 1 1 0 1
1 1 0 3 1 1 1 4 1 1 0 2
1 1 1 3 1 1 0 1 1 1 0 1
1 1 1 3 1 1 0 2 1 1 0 1
1 1 0 3 1 1 0 2 1 1 0 1
end
label values Division_2011 Division_2011
label def Division_2011 1 "Barisal", modify
label values Residence_2011 Residence_2011
label def Residence_2011 0 "Urban", modify
label def Residence_2011 1 "Rural", modify
label values ANC_greater_or_4_2011 ANC_greater_or_4_2011
label def ANC_greater_or_4_2011 0 "No", modify
label def ANC_greater_or_4_2011 1 "Yes", modify
label values Wealth_Index_2011 Wealth_Index_2011
label def Wealth_Index_2011 1 "Poorest", modify
label def Wealth_Index_2011 2 "Poorer", modify
label def Wealth_Index_2011 3 "Middle", modify
label def Wealth_Index_2011 4 "Richer", modify
label def Wealth_Index_2011 5 "Richest", modify
label values Division_2014 Division_2014
label def Division_2014 1 "Barisal", modify
label values Residence_2014 Residence_2014
label def Residence_2014 1 "Rural", modify
label values ANC_greater_or_4_2014 ANC_greater_or_4_2014
label def ANC_greater_or_4_2014 0 "Less Than 4", modify
label def ANC_greater_or_4_2014 1 "4 or more", modify
label values Wealth_Index_2014 Wealth_index_2014
label def Wealth_index_2014 1 "Poorest", modify
label def Wealth_index_2014 2 "Poorer", modify
label def Wealth_index_2014 3 "Middle", modify
label def Wealth_index_2014 4 "Richer", modify
label def Wealth_index_2014 5 "Richest", modify
label values Division_2017 Division_2017
label def Division_2017 1 "Barisal", modify
label values Residence_2017 Residence_2017
label def Residence_2017 1 "Rural", modify
label values ANC_greater_or_4_2017 ANC_greater_or_4_2017
label def ANC_greater_or_4_2017 0 "3 or less", modify
label def ANC_greater_or_4_2017 1 "4 or more", modify
label values Wealth_Index_2017 Wealth_index_2017
label def Wealth_index_2017 1 "Poorest", modify
label def Wealth_index_2017 2 "Poorer", modify
label def Wealth_index_2017 3 "Middle", modify
label def Wealth_index_2017 4 "Richer", modify
label def Wealth_index_2017 5 "Richest", modify

This is my dataset and i found this code in net that i am sharing Template_Practice2.docx
by this approach i tried to reshape my data into long format by year

egen id = seq(), from(1)
reshape long Division_ Residence_ ANC_greater_or_4_ Wealth_Index_, i(id) j(year)
(j = 2011 2014 2017)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations            7,319   ->   21,957      
Number of variables                  13   ->   6           
j variable (3 values)                     ->   year
xij variables:
Division_2011 Division_2014 Division_2017 ->   Division_
Residence_2011 Residence_2014 Residence_2017-> Residence_
ANC_greater_or_4_2011 ANC_greater_or_4_2014 ANC_greater_or_4_2017->ANC_greater_o
> r_4_
Wealth_Index_2011 Wealth_Index_2014 Wealth_Index_2017->Wealth_Index_
-----------------------------------------------------------------------------

Here is my new dataset

* Example generated by -dataex-. For more info, type help dataex
clear
input int(id year) double(Division_ Residence_ ANC_greater_or_4_ Wealth_Index_)
 1 2011 1 1 0 3
 1 2014 1 1 0 1
 1 2017 1 1 0 1
 2 2011 1 1 0 3
 2 2014 1 1 0 1
 2 2017 1 1 0 1
 3 2011 1 1 0 3
 3 2014 1 1 1 2
 3 2017 1 1 1 2
 4 2011 1 1 1 2
 4 2014 1 1 1 2
 4 2017 1 1 0 1
 5 2011 1 1 0 2
 5 2014 1 1 0 2
 5 2017 1 1 0 1
 6 2011 1 1 0 2
 6 2014 1 1 0 1
 6 2017 1 1 0 1
 7 2011 1 1 0 4
 7 2014 1 1 0 1
 7 2017 1 1 1 3
 8 2011 1 1 0 1
 8 2014 1 1 1 1
 8 2017 1 1 0 2
 9 2011 1 1 1 3
 9 2014 1 1 1 4
 9 2017 1 1 0 2
10 2011 1 1 0 2
10 2014 1 1 1 2
10 2017 1 1 0 2
11 2011 1 1 0 2
11 2014 1 1 0 3
11 2017 1 1 1 1
12 2011 1 1 1 2
12 2014 1 1 0 2
12 2017 1 1 0 2
13 2011 1 1 0 2
13 2014 1 1 0 2
13 2017 1 1 0 1
14 2011 1 1 0 3
14 2014 1 1 1 1
14 2017 1 1 0 1
15 2011 1 1 0 2
15 2014 1 1 0 2
15 2017 1 1 0 2
16 2011 1 1 0 3
16 2014 1 1 0 1
16 2017 1 1 0 1
17 2011 1 1 0 2
17 2014 1 1 0 2
17 2017 1 1 1 3
18 2011 1 1 0 3
18 2014 1 1 1 2
18 2017 1 1 0 1
19 2011 1 1 0 2
19 2014 1 1 1 2
19 2017 1 1 0 2
20 2011 1 1 0 2
20 2014 1 1 0 4
20 2017 1 1 0 1
21 2011 1 1 0 2
21 2014 1 1 0 2
21 2017 1 1 1 5
22 2011 1 1 1 2
22 2014 1 1 0 3
22 2017 1 1 0 1
23 2011 1 1 0 1
23 2014 1 1 1 3
23 2017 1 1 1 4
24 2011 1 1 0 1
24 2014 1 1 1 4
24 2017 1 1 0 1
25 2011 1 1 0 4
25 2014 1 1 1 3
25 2017 1 1 0 3
26 2011 1 1 0 2
26 2014 1 1 0 1
26 2017 1 1 0 3
27 2011 1 1 0 2
27 2014 1 1 1 1
27 2017 1 1 0 2
28 2011 1 0 1 5
28 2014 1 1 0 2
28 2017 1 1 1 3
29 2011 1 0 1 5
29 2014 1 1 1 5
29 2017 1 1 1 1
30 2011 1 0 1 5
30 2014 1 1 1 2
30 2017 1 1 1 2
31 2011 1 0 1 5
31 2014 1 1 1 1
31 2017 1 1 0 1
32 2011 1 0 1 5
32 2014 1 1 1 1
32 2017 1 1 0 1
33 2011 1 0 0 4
33 2014 1 1 0 1
33 2017 1 1 1 3
34 2011 1 0 0 5
end
label values Division_ Division_2017
label def Division_2017 1 "Barisal", modify
label values Residence_ Residence_2017
label def Residence_2017 0 "Urban", modify
label def Residence_2017 1 "Rural", modify
label values ANC_greater_or_4_ ANC_greater_or_4_2017
label def ANC_greater_or_4_2017 0 "3 or less", modify
label def ANC_greater_or_4_2017 1 "4 or more", modify
label values Wealth_Index_ Wealth_index_2017
label def Wealth_index_2017 1 "Poorest", modify
label def Wealth_index_2017 2 "Poorer", modify
label def Wealth_index_2017 3 "Middle", modify
label def Wealth_index_2017 4 "Richer", modify
label def Wealth_index_2017 5 "Richest", modify

Now for the equiplot when i try to reshape the data into wide format by dividing into wealth quantiles for division & year it is what i get

drop if missing( Wealth_Index_ )
reshape wide ANC_greater_or_4_, i( Division_ year ) j( Wealth_Index_ )

. reshape wide ANC_greater_or_4_, i( Division_ year ) j( Wealth_Index_ )
(j = 1 2 3 4 5)
values of variable Wealth_Index_ not unique within Division_ year
    Your data are currently long. You are performing a reshape wide. You
    specified i(Division_ year) and j(Wealth_Index_). There are observations
    within i(Division_ year) with the same value of j(Wealth_Index_). In the
    long data, variables i() and j() together must uniquely identify the
    observations.

         long                                wide
        +---------------+                   +------------------+
        | i   j   a   b |                   | i   a1 a2  b1 b2 |
        |---------------| <--- reshape ---> |------------------|
        | 1   1   1   2 |                   | 1   1   3   2  4 |
        | 1   2   3   4 |                   | 2   5   7   6  8 |
        | 2   1   5   6 |                   +------------------+
        | 2   2   7   8 |
        +---------------+
    Type reshape error for a list of the problem variables.
r(9);

If anyone can give solution then it would be really benificial