[Andrew Musau]

I see some values in September, so are you sure that your calender runs from 1Oct - 31Mar? Otherwise, you will still have gaps with some dates in September as there are no observations between Apr-Aug. I will drop these in my illustration below. Below, I use labmask from the Stata Journal.

net install gr0034, from(http://www.stata-journal.com/software/sj8-2)

* Example generated by -dataex-. To install: ssc install dataex
clear
input float(date year month) double kilos
22189 0 10 217441
22190 0 10 214775
22191 0 10 156388
22192 0 10 141009
22193 0 10 215526
22194 0 10 129711
22195 0 10 169852
22196 0 10 204643
22197 0 10 281729
22198 0 10 412143
22199 0 10 324678
22200 0 10 396663
22201 0 10 460965
22202 0 10 398581
22203 0 10 597762
22204 0 10 556337
22205 0 10 479245
22206 0 10 624982
22207 0 10 670106
22208 0 10 555994
22209 0 10 411386
22210 0 10 440721
22211 0 10 541893
22212 0 10 450873
22213 0 10 400985
22214 0 10 500747
22215 0 10 406071
22216 0 10 731186
22217 0 10 461157
22218 0 10 330291
22219 0 10 219186
22220 0 11 343361
22221 0 11 374581
22222 0 11 463049
22223 0 11 370848
22224 0 11 562520
22225 0 11 530294
22226 0 11 449209
22227 0 11 528547
22228 0 11 533763
22229 0 11 477947
22230 0 11 418616
22231 0 11 369379
22232 0 11 487571
22233 0 11 501288
22234 0 11 429535
22235 0 11 374690
22236 0 11 419362
22237 0 11 455256
22238 0 11 457892
22239 0 11 464516
22240 0 11 431156
22241 0 11 590224
22242 0 11 502984
22243 0 11 703208
22244 0 11 695350
22245 0 11 818355
22246 0 11 822102
22247 0 11 752457
22248 0 11 544013
22249 0 11 825585
22250 0 12 444274
22251 0 12 613058
22252 0 12 513498
22253 0 12 419795
22254 0 12 432261
22255 0 12 211717
22256 0 12 432832
22257 0 12 238591
22258 0 12 345336
22259 0 12 359076
22260 0 12 275059
22261 0 12 258255
22262 0 12 161090
22263 0 12 232518
22264 0 12 284216
22265 0 12 130504
22266 0 12 189958
22267 0 12 195392
22268 0 12 173444
22269 0 12 166438
22270 0 12 212410
22271 0 12 298384
22272 0 12 234968
22273 0 12 224480
22274 0 12 125155
22275 0 12 346394
22276 0 12 352458
22277 0 12 262452
22278 0 12 400913
22279 0 12 395405
22280 0 12 374545
22281 0  1  71661
22282 0  1 385205
22283 0  1 643502
22284 0  1 643509
22285 0  1 676360
22286 0  1 483264
22287 0  1 433171
22288 0  1 451833
22289 0  1 518764
22290 0  1 563800
22291 0  1 541312
22292 0  1 591733
22293 0  1 472663
22294 0  1 277247
22295 0  1 417866
22296 0  1 605605
22297 0  1 491667
22298 0  1 455746
22299 0  1 519541
22300 0  1 537373
22301 0  1 309939
22302 0  1 280627
22303 0  1 262320
22304 0  1 317909
22305 0  1 299978
22306 0  1 382633
22307 0  1 572021
22308 0  1 308382
22309 0  1 268690
22310 0  1 395303
22311 0  1 206577
22312 0  2 151037
22313 0  2 255222
22314 0  2  80864
22315 0  2  21935
22316 0  2  17483
22317 0  2   7812
22318 0  2  27121
22319 0  2   8382
22320 0  2  17019
22321 0  2   6675
22322 0  2   5969
22323 0  2   8100
22324 0  2   7308
22326 0  2  11820
22327 0  2  18388
22328 0  2  29949
22329 0  2  28561
22330 0  2  14797
22331 0  2  37500
22367 0  3  38110
22368 0  3  39812
22369 0  3 119093
22550 1  9  30572
22551 1  9  98512
22552 1  9 182601
22553 1  9 138817
22554 1 10 674764
22555 1 10 558345
22556 1 10 634476
22557 1 10 682916
22558 1 10 789773
22559 1 10 744648
22560 1 10 664479
22561 1 10 622090
22562 1 10 547615
22563 1 10 595351
22564 1 10 702597
22565 1 10 584840
22566 1 10 554814
22567 1 10 622070
22568 1 10 634559
22569 1 10 696918
22570 1 10 723898
22571 1 10 777443
22572 1 10 734257
22573 1 10 651609
22574 1 10 512716
22575 1 10 506459
22576 1 10 553743
22577 1 10 315231
22578 1 10 366495
22579 1 10 410387
22580 1 10 286126
22581 1 10 270082
22582 1 10 474542
22583 1 10 236578
22584 1 10 195127
22585 1 11 388184
22586 1 11 469820
22587 1 11 312197
22588 1 11 217251
22589 1 11 251247
22590 1 11 370173
22591 1 11 324401
22592 1 11 345120
22593 1 11 192244
22594 1 11 357838
22595 1 11 296727
22596 1 11 369587
22597 1 11 209974
22598 1 11 232645
22599 1 11 389248
22600 1 11 297196
22601 1 11 381549
22602 1 11 259946
22603 1 11 448258
22604 1 11 374545
22605 1 11 167131
22606 1 11 148915
22607 1 11 125862
22608 1 11 121988
22609 1 11 308078
22610 1 11 296548
22611 1 11 190741
22612 1 11 228828
22613 1 11 157088
22614 1 11 145906
22615 1 12 125843
22616 1 12 142378
22617 1 12 130781
22618 1 12  98331
22619 1 12 156848
22620 1 12  80604
22621 1 12 155936
22622 1 12 144203
22623 1 12 119197
22624 1 12 136930
22625 1 12 118505
22626 1 12 147002
22627 1 12 152242
22628 1 12 184725
22629 1 12 202855
22630 1 12 249227
22631 1 12 282639
22632 1 12 147041
22633 1 12 194177
22634 1 12 225637
22635 1 12 204281
22636 1 12 222460
22637 1 12 275612
22638 1 12 324776
22639 1 12 115118
22640 1 12 298012
22641 1 12 281119
22642 1 12 324554
22643 1 12 376365
22644 1 12 542017
22645 1 12 647953
22646 1  1 182062
22647 1  1 440316
22648 1  1 590459
22649 1  1 420291
22650 1  1 577376
22651 1  1 453879
22652 1  1 395481
22653 1  1 497996
22654 1  1 459708
22655 1  1 434478
22656 1  1 568668
22657 1  1 484064
22658 1  1 366658
22659 1  1 280233
22660 1  1 393004
22661 1  1 374622
22662 1  1 548439
22663 1  1 489415
22664 1  1 499840
22665 1  1 477615
22666 1  1 455946
22667 1  1 333159
22668 1  1 304074
22669 1  1 546346
22670 1  1 336759
22671 1  1 337529
22672 1  1 222791
22673 1  1 296321
22674 1  1 136709
22675 1  1 324511
22676 1  1 182665
22677 1  2 377622
22678 1  2 171317
22679 1  2 158570
22680 1  2 209780
22681 1  2 198458
22682 1  2 109427
22683 1  2 325986
22684 1  2 310593
22685 1  2 221924
22686 1  2 304629
22687 1  2 147061
22688 1  2 165274
22689 1  2 118849
22690 1  2 230544
22691 1  2 239814
22692 1  2 160647
22693 1  2 148698
22694 1  2 230646
22695 1  2 188020
22696 1  2 372197
22697 1  2 155793
22698 1  2 225878
22699 1  2 221937
22700 1  2  97887
22701 1  2  78709
22702 1  2 143795
22703 1  2 149986
22704 1  2 161023
end
format %td date
label values year year
label def year 0 "2020-2021", modify
label def year 1 "2021-2022", modify

gen doy= doy(date)
gen time= doy if inrange(month(date), 10, 12)
bys year (date): replace time= cond(doy==1,  time[_n-1]+1, time[_n-1]+ doy - doy[_n-1]) if missing(time)
drop if missing(time)
bys year (time): gen time1= time[1]
replace time = time-time1+1
gen mask= string(date, "%tdDm")
labmask time, values(mask)
xtset year time
set scheme s1color
xtline kilos, overlay xlab(1(30)181,val) xtitle("") leg( order(1 "2000-2001" 2 "2001-2002") col(1) pos(1) ring(0))

Res.:

[Marine Jouvin]

Thanks a lot Andrew, it worked perfectly fine this time !

Many thanks for the help on that!

[Marine Jouvin]

I reopen this topic as I'm trying to do something very similar than before. I want to have a two-way line graph plotting values for variable "kilos" over the period october-march, but this time for different sub-groups. This is the current code I'm using :

        
        
        collapse (mean) kilos (max) cooperative, by (date_transaction_num year section_status)
        
        generate month = month(date_transaction_num)
        generate day = day(date_transaction_num)
        generate year2 = year(date_transaction_num)
    
        keep if month==10 | month==11 | month==12 | month==1 | month==2 | month==3
        
        gen doy = doy(date_transaction_num)
        gen time = doy if inrange(month(date_transaction_num), 10, 12)
        bys year2 (date_transaction_num): replace time = cond(doy == 1, time[_n-1] + 1, time[_n-1] + doy - doy[_n-1]) if missing(time)
        drop if missing(time)
        bys year2 (time): gen time1 = time[1]
        replace time = time - time1 + 1
        gen mask = string(date_transaction_num, "%tdDm")
    
        set scheme tab2, perm
    
        graph twoway line kilos time if year == 0 & section_status==0 || ///
               line kilos time if year  == 0 & section_status==1 || ///
               line kilos time if year  == 0 & section_status==2 , ///
               xlab(1 "01 Oct" 32 "01 Nov" 62 "01 Dec" 93 "01 Jan" 123 "01 Feb" 151 "01 Mar" ) ///
               xtitle("") ytitle("Volume")  ///
               yscale(range(0, 1500)) ylab(0(500)1500, angle(horizontal)) ///
               legend(order(1 "Control" 2 "Spillover" 3 "Treated") col(1) pos(1) ring(0)) ///
               saving(part1, replace)

I'm not sure about changes that need to be make to have values plotted from October to March in this part of the code :

        gen doy = doy(date_transaction_num)
        gen time = doy if inrange(month(date_transaction_num), 10, 12)
        bys year2 (date_transaction_num): replace time = cond(doy == 1, time[_n-1] + 1, time[_n-1] + doy - doy[_n-1]) if missing(time)
        drop if missing(time)
        bys year2 (time): gen time1 = time[1]
        replace time = time - time1 + 1
        gen mask = string(date_transaction_num, "%tdDm")

Here is data example :

* Example generated by -dataex-. To install: ssc install dataex
clear
input float(year date_transaction_num section_status) double kilos_cert long cooperative float(month day year2 doy time time1) str5 mask
0 22189 1  297.7955010224949 15 10  1 2020 275  1 275 "01Oct"
0 22189 2 353.01869158878503 15 10  1 2020 275  1 275 "01Oct"
0 22190 1  315.0296803652968 15 10  2 2020 276  2 275 "02Oct"
0 22190 2  312.3481781376518 15 10  2 2020 276  2 275 "02Oct"
0 22191 1  278.7792642140468 15 10  3 2020 277  3 275 "03Oct"
0 22191 2  299.2186234817814 15 10  3 2020 277  3 275 "03Oct"
0 22192 1 255.53284671532847 17 10  4 2020 278  4 275 "04Oct"
0 22192 2 226.82530120481928 15 10  4 2020 278  4 275 "04Oct"
0 22193 1  350.3415300546448 17 10  5 2020 279  5 275 "05Oct"
0 22193 2  316.0567375886525 22 10  5 2020 279  5 275 "05Oct"
0 22194 1  336.3065326633166 18 10  6 2020 280  6 275 "06Oct"
0 22194 2              333.9 22 10  6 2020 280  6 275 "06Oct"
0 22195 1  398.6985815602837 22 10  7 2020 281  7 275 "07Oct"
0 22195 2  317.3891891891892 22 10  7 2020 281  7 275 "07Oct"
0 22196 0           421.8125 19 10  8 2020 282  8 275 "08Oct"
0 22196 1  307.2365038560411 22 10  8 2020 282  8 275 "08Oct"
0 22196 2 313.07142857142856 15 10  8 2020 282  8 275 "08Oct"
0 22197 0 1085.7142857142858  3 10  9 2020 283  9 275 "09Oct"
0 22197 1             414.75 22 10  9 2020 283  9 275 "09Oct"
0 22197 2 348.72586872586874 20 10  9 2020 283  9 275 "09Oct"
0 22198 0  769.0515463917526 21 10 10 2020 284 10 275 "10Oct"
0 22198 1  309.1232876712329 15 10 10 2020 284 10 275 "10Oct"
0 22198 2  437.7694805194805 22 10 10 2020 284 10 275 "10Oct"
0 22199 0  439.6273764258555 21 10 11 2020 285 11 275 "11Oct"
0 22199 1 312.63471502590676 18 10 11 2020 285 11 275 "11Oct"
0 22199 2  443.5045045045045 22 10 11 2020 285 11 275 "11Oct"
0 22200 0   522.411214953271 21 10 12 2020 286 12 275 "12Oct"
0 22200 1  322.8980044345898 18 10 12 2020 286 12 275 "12Oct"
0 22200 2  398.6291208791209 20 10 12 2020 286 12 275 "12Oct"
0 22201 0 484.81781376518217 21 10 13 2020 287 13 275 "13Oct"
0 22201 1 315.50905432595573 18 10 13 2020 287 13 275 "13Oct"
0 22201 2 381.02208835341366 20 10 13 2020 287 13 275 "13Oct"
0 22202 0 405.81052631578945 21 10 14 2020 288 14 275 "14Oct"
0 22202 1  253.0014598540146 22 10 14 2020 288 14 275 "14Oct"
0 22202 2 423.34520547945203 22 10 14 2020 288 14 275 "14Oct"
0 22203 0 465.49590163934425 21 10 15 2020 289 15 275 "15Oct"
0 22203 1 292.96437994722953 18 10 15 2020 289 15 275 "15Oct"
0 22203 2  402.3343373493976 20 10 15 2020 289 15 275 "15Oct"
0 22204 0  401.8550724637681 21 10 16 2020 290 16 275 "16Oct"
0 22204 1  445.2257495590829 18 10 16 2020 290 16 275 "16Oct"
0 22204 2  404.6120857699805 20 10 16 2020 290 16 275 "16Oct"
0 22205 0 440.06984667802385 21 10 17 2020 291 17 275 "17Oct"
0 22205 1          500.78125 18 10 17 2020 291 17 275 "17Oct"
0 22205 2 371.56129032258065 20 10 17 2020 291 17 275 "17Oct"
0 22206 0  433.5587529976019 21 10 18 2020 292 18 275 "18Oct"
0 22206 1  291.7133995037221 22 10 18 2020 292 18 275 "18Oct"
0 22206 2  392.3996383363472 22 10 18 2020 292 18 275 "18Oct"
0 22207 0  411.5290322580645 21 10 19 2020 293 19 275 "19Oct"
0 22207 1 415.83716075156576 22 10 19 2020 293 19 275 "19Oct"
0 22207 2  394.4795081967213 22 10 19 2020 293 19 275 "19Oct"
0 22208 0 510.61538461538464 21 10 20 2020 294 20 275 "20Oct"
0 22208 1  270.0464326160815 22 10 20 2020 294 20 275 "20Oct"
0 22208 2 341.62027491408935 22 10 20 2020 294 20 275 "20Oct"
0 22209 0 415.14018691588785 21 10 21 2020 295 21 275 "21Oct"
0 22209 1  337.6624203821656 22 10 21 2020 295 21 275 "21Oct"
0 22209 2 289.42419080068146 20 10 21 2020 295 21 275 "21Oct"
0 22210 0              456.6 21 10 22 2020 296 22 275 "22Oct"
0 22210 1 322.18535469107553 22 10 22 2020 296 22 275 "22Oct"
0 22210 2  363.6455223880597 22 10 22 2020 296 22 275 "22Oct"
0 22211 0  398.6479481641469 21 10 23 2020 297 23 275 "23Oct"
0 22211 1 276.01480263157896 22 10 23 2020 297 23 275 "23Oct"
0 22211 2 344.66031195840554 22 10 23 2020 297 23 275 "23Oct"
0 22212 0 427.74666666666667 21 10 24 2020 298 24 275 "24Oct"
0 22212 1 265.66037735849056 22 10 24 2020 298 24 275 "24Oct"
0 22212 2  282.2112676056338 22 10 24 2020 298 24 275 "24Oct"
0 22213 0  523.4765342960288 21 10 25 2020 299 25 275 "25Oct"
0 22213 1 277.12152777777777 22 10 25 2020 299 25 275 "25Oct"
0 22213 2  259.9608355091384 17 10 25 2020 299 25 275 "25Oct"
0 22214 0  512.4808743169399 21 10 26 2020 300 26 275 "26Oct"
0 22214 1  328.6489028213166 22 10 26 2020 300 26 275 "26Oct"
0 22214 2  307.5866261398176 22 10 26 2020 300 26 275 "26Oct"
0 22215 0  343.5924369747899 21 10 27 2020 301 27 275 "27Oct"
0 22215 1  296.7197898423818 22 10 27 2020 301 27 275 "27Oct"
0 22215 2  368.6361556064073 22 10 27 2020 301 27 275 "27Oct"
0 22216 0  442.0842391304348 21 10 28 2020 302 28 275 "28Oct"
0 22216 1 234.50181488203268 22 10 28 2020 302 28 275 "28Oct"
0 22216 2           264.6925 22 10 28 2020 302 28 275 "28Oct"
0 22217 0  467.4109947643979 21 10 29 2020 303 29 275 "29Oct"
0 22217 1  285.9777015437393 18 10 29 2020 303 29 275 "29Oct"
0 22217 2  328.2295081967213 22 10 29 2020 303 29 275 "29Oct"
0 22218 0  592.3365384615385 21 10 30 2020 304 30 275 "30Oct"
0 22218 1  353.4674418604651 20 10 30 2020 304 30 275 "30Oct"
0 22218 2  400.2278481012658 22 10 30 2020 304 30 275 "30Oct"
0 22219 0  570.2567567567568 21 10 31 2020 305 31 275 "31Oct"
0 22219 1  375.9054545454545 22 10 31 2020 305 31 275 "31Oct"
0 22219 2 391.16748768472905 22 10 31 2020 305 31 275 "31Oct"
0 22220 0 474.37692307692305 19 11  1 2020 306 32 275 "01Nov"
0 22220 1 294.65408805031444 18 11  1 2020 306 32 275 "01Nov"
0 22220 2 391.38197424892707 20 11  1 2020 306 32 275 "01Nov"
0 22221 0  396.2637362637363 21 11  2 2020 307 33 275 "02Nov"
0 22221 1 364.96882494004797 18 11  2 2020 307 33 275 "02Nov"
0 22221 2 357.15584415584414 20 11  2 2020 307 33 275 "02Nov"
0 22222 0 288.11897106109325 21 11  3 2020 308 34 275 "03Nov"
0 22222 1  343.9373814041746 22 11  3 2020 308 34 275 "03Nov"
0 22222 2 377.39700374531833 22 11  3 2020 308 34 275 "03Nov"
0 22223 0  318.9439655172414 21 11  4 2020 309 35 275 "04Nov"
0 22223 1  401.2074074074074 18 11  4 2020 309 35 275 "04Nov"
0 22223 2  429.4698795180723 22 11  4 2020 309 35 275 "04Nov"
0 22224 0  287.5677083333333 21 11  5 2020 310 36 275 "05Nov"
0 22224 1 325.60382513661204 22 11  5 2020 310 36 275 "05Nov"
end

At the end, I want something similar to this graph (the only difference is that here values run from October to December) :


Any idea ?

[Andrew Musau]

Do you have data that spans October to March? Show us the result of

contract year date_transaction_num
dataex date_transaction_num if !year, count(160)

[Marine Jouvin]

Thanks for the answer Andrew, here is the code example :

* Example generated by -dataex-. To install: ssc install dataex
clear
input float date_transaction_num
22189
22190
22191
22192
22193
22194
22195
22196
22197
22198
22199
22200
22201
22202
22203
22204
22205
22206
22207
22208
22209
22210
22211
22212
22213
22214
22215
22216
22217
22218
22219
22220
22221
22222
22223
22224
22225
22226
22227
22228
22229
22230
22231
22232
22233
22234
22235
22236
22237
22238
22239
22240
22241
22242
22243
22244
22245
22246
22247
22248
22249
22250
22251
22252
22253
22254
22255
22256
22257
22258
22259
22260
22261
22262
22263
22264
22265
22266
22267
22268
22269
22270
22271
22272
22273
22274
22275
22276
22277
22278
22279
22280
end
format %td date_transaction_num

[Andrew Musau]

The data that you show is from 1st October - 31 December. You state that you want October to March, but then what should be shown for the months January to March? Should this just be empty?

[Marine Jouvin]

Sorry, the first data example was wrong! Here is the right one :

* Example generated by -dataex-. To install: ssc install dataex
clear
input float date_transaction_num
22189
22190
22191
22192
22193
22194
22195
22196
22197
22198
22199
22200
22201
22202
22203
22204
22205
22206
22207
22208
22209
22210
22211
22212
22213
22214
22215
22216
22217
22218
22219
22220
22221
22222
22223
22224
22225
22226
22227
22228
22229
22230
22231
22232
22233
22234
22235
22236
22237
22238
22239
22240
22241
22242
22243
22244
22245
22246
22247
22248
22249
22250
22251
22252
22253
22254
22255
22256
22257
22258
22259
22260
22261
22262
22263
22264
22265
22266
22267
22268
22269
22270
22271
22272
22273
22274
22275
22276
22277
22278
22279
22280
22281
22282
22283
22284
22285
22286
22287
22288
22289
22290
22291
22292
22293
22294
22295
22296
22297
22298
22299
22300
22301
22302
22303
22304
22305
22306
22307
22308
22309
22310
22311
22312
22313
22314
22315
22316
22317
22318
22319
22320
22321
22322
22323
22324
22326
22327
22328
22329
22330
22331
22367
22368
22369
end
format %td date_transaction_num

[Andrew Musau]

Then you just map day of the year to time, starting October 1st= 1. You will have a day adjustment due to leap years, but that should not have an effect for purposes of graphing over a 180 day period. Here, the groups will constitute your panel identifier.

gen doy= cond(inrange(month(date_transaction_num), 10, 12) & !mod(year(date_transaction_num), 4), ///  
    doy(date_transaction_num)-274, cond(inrange(month(date_transaction_num), 10, 12) & mod(year(date_transaction_num), 4), ///
       doy(date_transaction_num)-273, doy(date_transaction_num) + 92))
 
gen mask= string(date, "%tdDm")
labmask doy, values(mask)

*CALL YOUR PANEL IDENTIFIER "WHICH"
xtset which doy

Res.:

. lab list
doy:
           1 01Oct
           2 02Oct
           3 03Oct
           4 04Oct
           5 05Oct
           6 06Oct
           7 07Oct
           8 08Oct
           9 09Oct
          10 10Oct
          11 11Oct
          12 12Oct
          13 13Oct
          14 14Oct
          15 15Oct
          16 16Oct
          17 17Oct
          18 18Oct
          19 19Oct
          20 20Oct
          21 21Oct
          22 22Oct
          23 23Oct
          24 24Oct
          25 25Oct
          26 26Oct
          27 27Oct
          28 28Oct
          29 29Oct
          30 30Oct
          31 31Oct
          32 01Nov
          33 02Nov
          34 03Nov
          35 04Nov
          36 05Nov
          37 06Nov
          38 07Nov
          39 08Nov
          40 09Nov
          41 10Nov
          42 11Nov
          43 12Nov
          44 13Nov
          45 14Nov
          46 15Nov
          47 16Nov
          48 17Nov
          49 18Nov
          50 19Nov
          51 20Nov
          52 21Nov
          53 22Nov
          54 23Nov
          55 24Nov
          56 25Nov
          57 26Nov
          58 27Nov
          59 28Nov
          60 29Nov
          61 30Nov
          62 01Dec
          63 02Dec
          64 03Dec
          65 04Dec
          66 05Dec
          67 06Dec
          68 07Dec
          69 08Dec
          70 09Dec
          71 10Dec
          72 11Dec
          73 12Dec
          74 13Dec
          75 14Dec
          76 15Dec
          77 16Dec
          78 17Dec
          79 18Dec
          80 19Dec
          81 20Dec
          82 21Dec
          83 22Dec
          84 23Dec
          85 24Dec
          86 25Dec
          87 26Dec
          88 27Dec
          89 28Dec
          90 29Dec
          91 30Dec
          92 31Dec
          93 01Jan
          94 02Jan
          95 03Jan
          96 04Jan
          97 05Jan
          98 06Jan
          99 07Jan
         100 08Jan
         101 09Jan
         102 10Jan
         103 11Jan
         104 12Jan
         105 13Jan
         106 14Jan
         107 15Jan
         108 16Jan
         109 17Jan
         110 18Jan
         111 19Jan
         112 20Jan
         113 21Jan
         114 22Jan
         115 23Jan
         116 24Jan
         117 25Jan
         118 26Jan
         119 27Jan
         120 28Jan
         121 29Jan
         122 30Jan
         123 31Jan
         124 01Feb
         125 02Feb
         126 03Feb
         127 04Feb
         128 05Feb
         129 06Feb
         130 07Feb
         131 08Feb
         132 09Feb
         133 10Feb
         134 11Feb
         135 12Feb
         136 13Feb
         138 15Feb
         139 16Feb
         140 17Feb
         141 18Feb
         142 19Feb
         143 20Feb
         179 28Mar
         180 29Mar
         181 30Mar

[Andrew Musau]

Thinking about this question again, you don't have the same months repeated over multiple years. Your sample period is October 2000- March 2001 and you have multiple variables. Unless I am missing something, this calls for nothing special, different from your original question.

webuse sp500, clear
keep date open high low
replace date= date -90
keep if date<=td(30 mar 2001)
rename (open high low) var=
reshape long var, i(date) j(which) string
*I ASSUME THIS IS THE STRUCTURE OF YOUR DATA
encode which, gen(Which)
xtset Which date
xtline var, overlay ///
xlab(`=td(1oct2000)' `=td(1nov2000)'  `=td(1dec2000)' `=td(1jan2001)' `=td(1feb2001)' `=td(1mar2001)' `=td(30mar2001)', ///
format("%tdDm"))  ytitle(Some Var) xtitle("")

[Marine Jouvin]

Thanks for your answers Andrew !

Actually, I have data running from October 1st, 2020, to March 27th, 2023 for three groups. Below is a dataex example, which displays only values for the first year (2020) and first months (October and November) :

* Example generated by -dataex-. To install: ssc install dataex
clear
input float(year date_transaction_num section_status) double kilos_cert long cooperative float(month day year2)
0 22189 1  297.7955010224949 15 10  1 2020
0 22189 2 353.01869158878503 15 10  1 2020
0 22190 1  315.0296803652968 15 10  2 2020
0 22190 2  312.3481781376518 15 10  2 2020
0 22191 1  278.7792642140468 15 10  3 2020
0 22191 2  299.2186234817814 15 10  3 2020
0 22192 1 255.53284671532847 17 10  4 2020
0 22192 2 226.82530120481928 15 10  4 2020
0 22193 1  350.3415300546448 17 10  5 2020
0 22193 2  316.0567375886525 22 10  5 2020
0 22194 1  336.3065326633166 18 10  6 2020
0 22194 2              333.9 22 10  6 2020
0 22195 1  398.6985815602837 22 10  7 2020
0 22195 2  317.3891891891892 22 10  7 2020
0 22196 0           421.8125 19 10  8 2020
0 22196 1  307.2365038560411 22 10  8 2020
0 22196 2 313.07142857142856 15 10  8 2020
0 22197 0 1085.7142857142858  3 10  9 2020
0 22197 1             414.75 22 10  9 2020
0 22197 2 348.72586872586874 20 10  9 2020
0 22198 0  769.0515463917526 21 10 10 2020
0 22198 1  309.1232876712329 15 10 10 2020
0 22198 2  437.7694805194805 22 10 10 2020
0 22199 0  439.6273764258555 21 10 11 2020
0 22199 1 312.63471502590676 18 10 11 2020
0 22199 2  443.5045045045045 22 10 11 2020
0 22200 0   522.411214953271 21 10 12 2020
0 22200 1  322.8980044345898 18 10 12 2020
0 22200 2  398.6291208791209 20 10 12 2020
0 22201 0 484.81781376518217 21 10 13 2020
0 22201 1 315.50905432595573 18 10 13 2020
0 22201 2 381.02208835341366 20 10 13 2020
0 22202 0 405.81052631578945 21 10 14 2020
0 22202 1  253.0014598540146 22 10 14 2020
0 22202 2 423.34520547945203 22 10 14 2020
0 22203 0 465.49590163934425 21 10 15 2020
0 22203 1 292.96437994722953 18 10 15 2020
0 22203 2  402.3343373493976 20 10 15 2020
0 22204 0  401.8550724637681 21 10 16 2020
0 22204 1  445.2257495590829 18 10 16 2020
0 22204 2  404.6120857699805 20 10 16 2020
0 22205 0 440.06984667802385 21 10 17 2020
0 22205 1          500.78125 18 10 17 2020
0 22205 2 371.56129032258065 20 10 17 2020
0 22206 0  433.5587529976019 21 10 18 2020
0 22206 1  291.7133995037221 22 10 18 2020
0 22206 2  392.3996383363472 22 10 18 2020
0 22207 0  411.5290322580645 21 10 19 2020
0 22207 1 415.83716075156576 22 10 19 2020
0 22207 2  394.4795081967213 22 10 19 2020
0 22208 0 510.61538461538464 21 10 20 2020
0 22208 1  270.0464326160815 22 10 20 2020
0 22208 2 341.62027491408935 22 10 20 2020
0 22209 0 415.14018691588785 21 10 21 2020
0 22209 1  337.6624203821656 22 10 21 2020
0 22209 2 289.42419080068146 20 10 21 2020
0 22210 0              456.6 21 10 22 2020
0 22210 1 322.18535469107553 22 10 22 2020
0 22210 2  363.6455223880597 22 10 22 2020
0 22211 0  398.6479481641469 21 10 23 2020
0 22211 1 276.01480263157896 22 10 23 2020
0 22211 2 344.66031195840554 22 10 23 2020
0 22212 0 427.74666666666667 21 10 24 2020
0 22212 1 265.66037735849056 22 10 24 2020
0 22212 2  282.2112676056338 22 10 24 2020
0 22213 0  523.4765342960288 21 10 25 2020
0 22213 1 277.12152777777777 22 10 25 2020
0 22213 2  259.9608355091384 17 10 25 2020
0 22214 0  512.4808743169399 21 10 26 2020
0 22214 1  328.6489028213166 22 10 26 2020
0 22214 2  307.5866261398176 22 10 26 2020
0 22215 0  343.5924369747899 21 10 27 2020
0 22215 1  296.7197898423818 22 10 27 2020
0 22215 2  368.6361556064073 22 10 27 2020
0 22216 0  442.0842391304348 21 10 28 2020
0 22216 1 234.50181488203268 22 10 28 2020
0 22216 2           264.6925 22 10 28 2020
0 22217 0  467.4109947643979 21 10 29 2020
0 22217 1  285.9777015437393 18 10 29 2020
0 22217 2  328.2295081967213 22 10 29 2020
0 22218 0  592.3365384615385 21 10 30 2020
0 22218 1  353.4674418604651 20 10 30 2020
0 22218 2  400.2278481012658 22 10 30 2020
0 22219 0  570.2567567567568 21 10 31 2020
0 22219 1  375.9054545454545 22 10 31 2020
0 22219 2 391.16748768472905 22 10 31 2020
0 22220 0 474.37692307692305 19 11  1 2020
0 22220 1 294.65408805031444 18 11  1 2020
0 22220 2 391.38197424892707 20 11  1 2020
0 22221 0  396.2637362637363 21 11  2 2020
0 22221 1 364.96882494004797 18 11  2 2020
0 22221 2 357.15584415584414 20 11  2 2020
0 22222 0 288.11897106109325 21 11  3 2020
0 22222 1  343.9373814041746 22 11  3 2020
0 22222 2 377.39700374531833 22 11  3 2020
0 22223 0  318.9439655172414 21 11  4 2020
0 22223 1  401.2074074074074 18 11  4 2020
0 22223 2  429.4698795180723 22 11  4 2020
0 22224 0  287.5677083333333 21 11  5 2020
0 22224 1 325.60382513661204 22 11  5 2020
end
format %td date_transaction_num
label values year year
label def year 0 "2020-2021", modify
label values section_status section_status
label def section_status 0 "Control", modify
label def section_status 1 "Spillover", modify
label def section_status 2 "Treated", modify

I have a value for the variable "kilos_cert" for each group (section_status) running from October to March for each year (quasi-daily data, even if sometimes there are no values for some day).

[Andrew Musau]

If you want a graph similar to #16, then each group has 4 series: 2000, 2001, 2002 and 2003. So if you want to overlay all lines in one graph, the graph will not be readable. You could create a graph for each group using the -if- qualifier. Taking it from #23:

preserve
keep if group==1
xtset year doy
xtline ..., title(group 1)
restore, preserve
keep if group==2
*AND SO ON