/
workbook.twb
2017 lines (2016 loc) · 139 KB
/
workbook.twb
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<?xml version='1.0' encoding='utf-8' ?>
<!-- build 20192.19.0917.1648 -->
<workbook original-version='18.1' source-build='2019.2.4 (20192.19.0917.1648)' source-platform='mac' version='18.1' xmlns:user='http://www.tableausoftware.com/xml/user'>
<document-format-change-manifest>
<IntuitiveSorting />
<IntuitiveSorting_SP2 />
<SheetIdentifierTracking ignorable='true' predowngraded='true' />
<SortTagCleanup />
<WindowsPersistSimpleIdentifiers />
</document-format-change-manifest>
<preferences>
<preference name='ui.encoding.shelf.height' value='24' />
<preference name='ui.shelf.height' value='26' />
</preferences>
<datasources>
<datasource caption='Ask-A-Manager-Salary-Survey-2019' inline='true' name='federated.0mhrior040avue13ym7qz1xpdgjy' version='18.1'>
<connection class='federated'>
<named-connections>
<named-connection caption='Ask-A-Manager-Salary-Survey-2019' name='textscan.1a9vl1e05onvbi1fii5xy0zcwmss'>
<connection class='textscan' directory='/Users/klyment/Projects/askamanager_salary_survey/data/v1' filename='Ask-A-Manager-Salary-Survey-2019.csv' password='' server='' />
</named-connection>
</named-connections>
<relation connection='textscan.1a9vl1e05onvbi1fii5xy0zcwmss' name='Ask-A-Manager-Salary-Survey-2019.csv' table='[Ask-A-Manager-Salary-Survey-2019#csv]' type='table'>
<columns character-set='UTF-8' header='yes' locale='en_US' separator=','>
<column datatype='datetime' name='Timestamp' ordinal='0' />
<column datatype='string' name='Age' ordinal='1' />
<column datatype='string' name='Industry' ordinal='2' />
<column datatype='string' name='JobTitle' ordinal='3' />
<column datatype='string' name='Currency' ordinal='4' />
<column datatype='string' name='City' ordinal='5' />
<column datatype='string' name='State' ordinal='6' />
<column datatype='string' name='Country' ordinal='7' />
<column datatype='string' name='Location' ordinal='8' />
<column datatype='string' name='Experience' ordinal='9' />
<column datatype='integer' name='Base' ordinal='10' />
<column datatype='integer' name='HourlyRate' ordinal='11' />
<column datatype='string' name='Extras' ordinal='12' />
<column datatype='string' name='Notes' ordinal='13' />
<column datatype='string' name='AnnualSalary (Original)' ordinal='14' />
<column datatype='integer' name='AnnualSalary' ordinal='15' />
<column datatype='string' name='Location (Original)' ordinal='16' />
<column datatype='string' name='JobTitle (Original)' ordinal='17' />
<column datatype='string' name='Industry (Original)' ordinal='18' />
<column datatype='string' name='Industry (Clustered)' ordinal='19' />
</columns>
</relation>
<metadata-records>
<metadata-record class='capability'>
<remote-name />
<remote-type>0</remote-type>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias />
<aggregation>Count</aggregation>
<contains-null>true</contains-null>
<attributes>
<attribute datatype='string' name='character-set'>"UTF-8"</attribute>
<attribute datatype='string' name='collation'>"en_US"</attribute>
<attribute datatype='string' name='field-delimiter'>","</attribute>
<attribute datatype='string' name='header-row'>"true"</attribute>
<attribute datatype='string' name='locale'>"en_US"</attribute>
<attribute datatype='string' name='single-char'>""</attribute>
</attributes>
</metadata-record>
<metadata-record class='column'>
<remote-name>Timestamp</remote-name>
<remote-type>135</remote-type>
<local-name>[Timestamp]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>Timestamp</remote-alias>
<ordinal>0</ordinal>
<local-type>datetime</local-type>
<aggregation>Year</aggregation>
<contains-null>true</contains-null>
</metadata-record>
<metadata-record class='column'>
<remote-name>Age</remote-name>
<remote-type>129</remote-type>
<local-name>[Age]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>Age</remote-alias>
<ordinal>1</ordinal>
<local-type>string</local-type>
<aggregation>Count</aggregation>
<scale>1</scale>
<width>1073741823</width>
<contains-null>true</contains-null>
<collation flag='0' name='LEN_RUS' />
</metadata-record>
<metadata-record class='column'>
<remote-name>Industry</remote-name>
<remote-type>129</remote-type>
<local-name>[Industry]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>Industry</remote-alias>
<ordinal>2</ordinal>
<local-type>string</local-type>
<aggregation>Count</aggregation>
<scale>1</scale>
<width>1073741823</width>
<contains-null>true</contains-null>
<collation flag='0' name='LEN_RUS' />
</metadata-record>
<metadata-record class='column'>
<remote-name>JobTitle</remote-name>
<remote-type>129</remote-type>
<local-name>[JobTitle]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>JobTitle</remote-alias>
<ordinal>3</ordinal>
<local-type>string</local-type>
<aggregation>Count</aggregation>
<scale>1</scale>
<width>1073741823</width>
<contains-null>true</contains-null>
<collation flag='0' name='LEN_RUS' />
</metadata-record>
<metadata-record class='column'>
<remote-name>Currency</remote-name>
<remote-type>129</remote-type>
<local-name>[Currency]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>Currency</remote-alias>
<ordinal>4</ordinal>
<local-type>string</local-type>
<aggregation>Count</aggregation>
<scale>1</scale>
<width>1073741823</width>
<contains-null>true</contains-null>
<collation flag='0' name='LEN_RUS' />
</metadata-record>
<metadata-record class='column'>
<remote-name>City</remote-name>
<remote-type>129</remote-type>
<local-name>[City]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>City</remote-alias>
<ordinal>5</ordinal>
<local-type>string</local-type>
<aggregation>Count</aggregation>
<scale>1</scale>
<width>1073741823</width>
<contains-null>true</contains-null>
<collation flag='0' name='LEN_RUS' />
</metadata-record>
<metadata-record class='column'>
<remote-name>State</remote-name>
<remote-type>129</remote-type>
<local-name>[State]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>State</remote-alias>
<ordinal>6</ordinal>
<local-type>string</local-type>
<aggregation>Count</aggregation>
<scale>1</scale>
<width>1073741823</width>
<contains-null>true</contains-null>
<collation flag='0' name='LEN_RUS' />
</metadata-record>
<metadata-record class='column'>
<remote-name>Country</remote-name>
<remote-type>129</remote-type>
<local-name>[Country]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>Country</remote-alias>
<ordinal>7</ordinal>
<local-type>string</local-type>
<aggregation>Count</aggregation>
<scale>1</scale>
<width>1073741823</width>
<contains-null>true</contains-null>
<collation flag='0' name='LEN_RUS' />
</metadata-record>
<metadata-record class='column'>
<remote-name>Location</remote-name>
<remote-type>129</remote-type>
<local-name>[Location]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>Location</remote-alias>
<ordinal>8</ordinal>
<local-type>string</local-type>
<aggregation>Count</aggregation>
<scale>1</scale>
<width>1073741823</width>
<contains-null>true</contains-null>
<collation flag='0' name='LEN_RUS' />
</metadata-record>
<metadata-record class='column'>
<remote-name>Experience</remote-name>
<remote-type>129</remote-type>
<local-name>[Experience]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>Experience</remote-alias>
<ordinal>9</ordinal>
<local-type>string</local-type>
<aggregation>Count</aggregation>
<scale>1</scale>
<width>1073741823</width>
<contains-null>true</contains-null>
<collation flag='0' name='LEN_RUS' />
</metadata-record>
<metadata-record class='column'>
<remote-name>Base</remote-name>
<remote-type>20</remote-type>
<local-name>[Base]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>Base</remote-alias>
<ordinal>10</ordinal>
<local-type>integer</local-type>
<aggregation>Sum</aggregation>
<contains-null>true</contains-null>
</metadata-record>
<metadata-record class='column'>
<remote-name>HourlyRate</remote-name>
<remote-type>20</remote-type>
<local-name>[HourlyRate]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>HourlyRate</remote-alias>
<ordinal>11</ordinal>
<local-type>integer</local-type>
<aggregation>Sum</aggregation>
<contains-null>true</contains-null>
</metadata-record>
<metadata-record class='column'>
<remote-name>Extras</remote-name>
<remote-type>129</remote-type>
<local-name>[Extras]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>Extras</remote-alias>
<ordinal>12</ordinal>
<local-type>string</local-type>
<aggregation>Count</aggregation>
<scale>1</scale>
<width>1073741823</width>
<contains-null>true</contains-null>
<collation flag='0' name='LEN_RUS' />
</metadata-record>
<metadata-record class='column'>
<remote-name>Notes</remote-name>
<remote-type>129</remote-type>
<local-name>[Notes]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>Notes</remote-alias>
<ordinal>13</ordinal>
<local-type>string</local-type>
<aggregation>Count</aggregation>
<scale>1</scale>
<width>1073741823</width>
<contains-null>true</contains-null>
<collation flag='0' name='LEN_RUS' />
</metadata-record>
<metadata-record class='column'>
<remote-name>AnnualSalary (Original)</remote-name>
<remote-type>129</remote-type>
<local-name>[AnnualSalary (Original)]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>AnnualSalary (Original)</remote-alias>
<ordinal>14</ordinal>
<local-type>string</local-type>
<aggregation>Count</aggregation>
<scale>1</scale>
<width>1073741823</width>
<contains-null>true</contains-null>
<collation flag='0' name='LEN_RUS' />
</metadata-record>
<metadata-record class='column'>
<remote-name>AnnualSalary</remote-name>
<remote-type>20</remote-type>
<local-name>[AnnualSalary]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>AnnualSalary</remote-alias>
<ordinal>15</ordinal>
<local-type>integer</local-type>
<aggregation>Sum</aggregation>
<contains-null>true</contains-null>
</metadata-record>
<metadata-record class='column'>
<remote-name>Location (Original)</remote-name>
<remote-type>129</remote-type>
<local-name>[Location (Original)]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>Location (Original)</remote-alias>
<ordinal>16</ordinal>
<local-type>string</local-type>
<aggregation>Count</aggregation>
<scale>1</scale>
<width>1073741823</width>
<contains-null>true</contains-null>
<collation flag='0' name='LEN_RUS' />
</metadata-record>
<metadata-record class='column'>
<remote-name>JobTitle (Original)</remote-name>
<remote-type>129</remote-type>
<local-name>[JobTitle (Original)]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>JobTitle (Original)</remote-alias>
<ordinal>17</ordinal>
<local-type>string</local-type>
<aggregation>Count</aggregation>
<scale>1</scale>
<width>1073741823</width>
<contains-null>true</contains-null>
<collation flag='0' name='LEN_RUS' />
</metadata-record>
<metadata-record class='column'>
<remote-name>Industry (Original)</remote-name>
<remote-type>129</remote-type>
<local-name>[Industry (Original)]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>Industry (Original)</remote-alias>
<ordinal>18</ordinal>
<local-type>string</local-type>
<aggregation>Count</aggregation>
<scale>1</scale>
<width>1073741823</width>
<contains-null>true</contains-null>
<collation flag='0' name='LEN_RUS' />
</metadata-record>
<metadata-record class='column'>
<remote-name>Industry (Clustered)</remote-name>
<remote-type>129</remote-type>
<local-name>[Industry (Clustered)]</local-name>
<parent-name>[Ask-A-Manager-Salary-Survey-2019.csv]</parent-name>
<remote-alias>Industry (Clustered)</remote-alias>
<ordinal>19</ordinal>
<local-type>string</local-type>
<aggregation>Count</aggregation>
<scale>1</scale>
<width>1073741823</width>
<contains-null>true</contains-null>
<collation flag='0' name='LEN_RUS' />
</metadata-record>
</metadata-records>
</connection>
<aliases enabled='yes' />
<column datatype='string' name='[Age]' role='dimension' type='nominal' />
<column caption='Annual Salary' datatype='integer' name='[AnnualSalary]' role='measure' type='quantitative' />
<column datatype='string' name='[City]' role='dimension' semantic-role='[City].[Name]' type='nominal' />
<column datatype='string' name='[Country]' role='dimension' semantic-role='[Country].[ISO3166_2]' type='nominal' />
<column caption='Hourly Rate' datatype='integer' name='[HourlyRate]' role='measure' type='quantitative' />
<column datatype='string' name='[Job Title (software)]' role='dimension' type='nominal'>
<calculation class='categorical-bin' column='[JobTitle]' new-bin='true'>
<bin default-name='false' value='"Software"'>
<value>".Net Software Developer"</value>
<value>"Advisory Software Engineer"</value>
<value>"Associate Senior Software Engineer"</value>
<value>"Associate Software Engineer"</value>
<value>"AVP of Software Development"</value>
<value>"Back-end software engineer"</value>
<value>"Chief Software Architect"</value>
<value>"Director of Software Engineering"</value>
<value>"Director, Software Development"</value>
<value>"Embedded software engineer"</value>
<value>"Freelance Software Engineer"</value>
<value>"Front-end Software Development"</value>
<value>"Global Director of Software Engineering"</value>
<value>"Go software engineering"</value>
<value>"Graphics software developer"</value>
<value>"Head of Software"</value>
<value>"Intermediate Software Engineer"</value>
<value>"iOS Software Developer"</value>
<value>"IT Department Head/Software Engineer"</value>
<value>"IT Software Development Advisor Sr"</value>
<value>"Jr software dev"</value>
<value>"Jr Software Engineer"</value>
<value>"Junior Software Developer"</value>
<value>"Lead Software Developer"</value>
<value>"Lead Software Engineer"</value>
<value>"Management Software Development"</value>
<value>"manager, software delivery"</value>
<value>"Principal Software Engineer"</value>
<value>"Scientific software developer"</value>
<value>"Senior data software engineer"</value>
<value>"Senior Embedded Software Engineer"</value>
<value>"Senior Manager Software Development"</value>
<value>"Senior Software Architect"</value>
<value>"Senior Software Engineer"</value>
<value>"Senior Software Manager"</value>
<value>"Senior Software Quality Engineer"</value>
<value>"Senior Software Support Manager"</value>
<value>"Senior Software Tester"</value>
<value>"Senior Staff Software Engineer"</value>
<value>"Software"</value>
<value>"Software Account Executive"</value>
<value>"Software Admin"</value>
<value>"Software application engineer"</value>
<value>"Software Architect"</value>
<value>"Software Build Engineer"</value>
<value>"Software Business Analyst"</value>
<value>"Software Configuration Manager"</value>
<value>"Software Consultant"</value>
<value>"Software craftsman"</value>
<value>"Software deployment consultant"</value>
<value>"Software Designer"</value>
<value>"Software Dev"</value>
<value>"Software Dev Senior Engineer"</value>
<value>"Software Developer"</value>
<value>"Software Director"</value>
<value>"Software Engineer"</value>
<value>"Software Implementation Engineer"</value>
<value>"Software Manager"</value>
<value>"Software Product Manager"</value>
<value>"Software Programmer"</value>
<value>"Software Project Manage Consultant"</value>
<value>"Software QA Analyst"</value>
<value>"Software QA Engineer"</value>
<value>"Software QA Tester"</value>
<value>"Software QA, manual"</value>
<value>"Software Quality Assurance Engineer"</value>
<value>"Software Quality Manager"</value>
<value>"Software R&D Manager"</value>
<value>"Software Research Engineer"</value>
<value>"Software Sales"</value>
<value>"Software Sales Executive"</value>
<value>"Software specialist"</value>
<value>"Software Support Specialist"</value>
<value>"Software Systems Engineer"</value>
<value>"Software Team Lead"</value>
<value>"Software Test Automation Engineer"</value>
<value>"Software Test Engineer"</value>
<value>"Software Tester"</value>
<value>"Software Trainer"</value>
<value>"Software Training Specialist"</value>
<value>"Sr Software Architect"</value>
<value>"Sr Staff Software Engineer"</value>
<value>"Sr. Software Developer"</value>
<value>"Sr. Software Enginerr"</value>
<value>"Sr. Software Test Engineer"</value>
<value>"Staff Software Developer"</value>
<value>"Staff Software Engineer"</value>
<value>"Sysadmin/Software Developer"</value>
<value>"Tech Staff, Software Engineering"</value>
<value>"Test Engineering Software Analyst"</value>
<value>"VP of Software Development"</value>
<value>"VP software development"</value>
</bin>
</calculation>
</column>
<column caption='Job Title' datatype='string' name='[JobTitle]' role='dimension' type='nominal' />
<column datatype='integer' name='[Number of Records]' role='measure' type='quantitative' user:auto-column='numrec'>
<calculation class='tableau' formula='1' />
</column>
<column datatype='string' name='[State]' role='dimension' semantic-role='[State].[Name]' type='nominal' />
<column-instance column='[Age]' derivation='None' name='[none:Age:nk]' pivot='key' type='nominal' />
<column-instance column='[Country]' derivation='None' name='[none:Country:nk]' pivot='key' type='nominal' />
<column-instance column='[State]' derivation='None' name='[none:State:nk]' pivot='key' type='nominal' />
<group hidden='true' name='[Exclusions (Age,Country,State)]' name-style='unqualified' user:auto-column='exclude'>
<groupfilter function='crossjoin'>
<groupfilter function='level-members' level='[none:Age:nk]' />
<groupfilter function='level-members' level='[none:Country:nk]' />
<groupfilter function='level-members' level='[none:State:nk]' />
</groupfilter>
</group>
<drill-paths>
<drill-path name='Country, State, City'>
<field>[Country]</field>
<field>[State]</field>
<field>[City]</field>
</drill-path>
</drill-paths>
<layout dim-ordering='alphabetic' dim-percentage='0.694316' measure-ordering='alphabetic' measure-percentage='0.305684' show-structure='true' />
<semantic-values>
<semantic-value key='[Country].[Name]' value='"United States"' />
</semantic-values>
</datasource>
</datasources>
<worksheets>
<worksheet name='Counts per Industry'>
<table>
<view>
<datasources>
<datasource caption='Ask-A-Manager-Salary-Survey-2019' name='federated.0mhrior040avue13ym7qz1xpdgjy' />
</datasources>
<datasource-dependencies datasource='federated.0mhrior040avue13ym7qz1xpdgjy'>
<column datatype='string' name='[Industry]' role='dimension' type='nominal' />
<column datatype='integer' name='[Number of Records]' role='measure' type='quantitative' user:auto-column='numrec'>
<calculation class='tableau' formula='1' />
</column>
<column-instance column='[Industry]' derivation='None' name='[none:Industry:nk]' pivot='key' type='nominal' />
<column-instance column='[Number of Records]' derivation='Sum' name='[sum:Number of Records:qk]' pivot='key' type='quantitative' />
</datasource-dependencies>
<shelf-sorts>
<shelf-sort-v2 dimension-to-sort='[federated.0mhrior040avue13ym7qz1xpdgjy].[none:Industry:nk]' direction='DESC' is-on-innermost-dimension='true' measure-to-sort-by='[federated.0mhrior040avue13ym7qz1xpdgjy].[sum:Number of Records:qk]' shelf='rows' />
</shelf-sorts>
<aggregation value='true' />
</view>
<style />
<panes>
<pane selection-relaxation-option='selection-relaxation-allow'>
<view>
<breakdown value='auto' />
</view>
<mark class='Automatic' />
<encodings>
<text column='[federated.0mhrior040avue13ym7qz1xpdgjy].[sum:Number of Records:qk]' />
</encodings>
<style>
<style-rule element='mark'>
<format attr='mark-labels-show' value='true' />
<format attr='mark-labels-cull' value='true' />
</style-rule>
</style>
</pane>
</panes>
<rows>[federated.0mhrior040avue13ym7qz1xpdgjy].[none:Industry:nk]</rows>
<cols>[federated.0mhrior040avue13ym7qz1xpdgjy].[sum:Number of Records:qk]</cols>
</table>
<simple-id uuid='{0ACC5749-BDA1-49BE-9340-F290A651165F}' />
</worksheet>
<worksheet name='USA per Age'>
<table>
<view>
<datasources>
<datasource caption='Ask-A-Manager-Salary-Survey-2019' name='federated.0mhrior040avue13ym7qz1xpdgjy' />
</datasources>
<datasource-dependencies datasource='federated.0mhrior040avue13ym7qz1xpdgjy'>
<column datatype='string' name='[Age]' role='dimension' type='nominal' />
<column datatype='integer' name='[Base]' role='measure' type='quantitative' />
<column datatype='string' name='[Country]' role='dimension' semantic-role='[Country].[ISO3166_2]' type='nominal' />
<column datatype='string' name='[State]' role='dimension' semantic-role='[State].[Name]' type='nominal' />
<column-instance column='[Base]' derivation='Avg' name='[avg:Base:qk]' pivot='key' type='quantitative' />
<column-instance column='[Age]' derivation='None' name='[none:Age:nk]' pivot='key' type='nominal' />
<column-instance column='[Country]' derivation='None' name='[none:Country:nk]' pivot='key' type='nominal' />
<column-instance column='[State]' derivation='None' name='[none:State:nk]' pivot='key' type='nominal' />
</datasource-dependencies>
<filter class='categorical' column='[federated.0mhrior040avue13ym7qz1xpdgjy].[Exclusions (Age,Country,State)]'>
<groupfilter function='except' user:ui-domain='database' user:ui-enumeration='exclusive' user:ui-marker='enumerate'>
<groupfilter function='crossjoin'>
<groupfilter function='level-members' level='[none:Age:nk]' />
<groupfilter function='level-members' level='[none:Country:nk]' />
<groupfilter function='level-members' level='[none:State:nk]' />
</groupfilter>
<groupfilter function='reorder-dimensionality'>
<groupfilter function='crossjoin'>
<groupfilter function='member' level='[none:Country:nk]' member='"USA"' />
<groupfilter function='union'>
<groupfilter function='crossjoin'>
<groupfilter function='union'>
<groupfilter function='member' level='[none:Age:nk]' member='"35-44"' />
<groupfilter function='member' level='[none:Age:nk]' member='"55-64"' />
</groupfilter>
<groupfilter function='member' level='[none:State:nk]' member='"NJ"' />
</groupfilter>
<groupfilter function='crossjoin'>
<groupfilter function='member' level='[none:Age:nk]' member='"65 or over"' />
<groupfilter function='union'>
<groupfilter function='member' level='[none:State:nk]' member='"CA"' />
<groupfilter function='member' level='[none:State:nk]' member='"NY"' />
</groupfilter>
</groupfilter>
<groupfilter function='crossjoin'>
<groupfilter function='member' level='[none:Age:nk]' member='"under 18"' />
<groupfilter function='union'>
<groupfilter function='member' level='[none:State:nk]' member='"MA"' />
<groupfilter function='member' level='[none:State:nk]' member='"OR"' />
</groupfilter>
</groupfilter>
</groupfilter>
</groupfilter>
<order>
<hierarchy name='[none:Age:nk]' />
<hierarchy name='[none:Country:nk]' />
<hierarchy name='[none:State:nk]' />
</order>
</groupfilter>
</groupfilter>
</filter>
<manual-sort column='[federated.0mhrior040avue13ym7qz1xpdgjy].[none:Age:nk]' direction='ASC'>
<dictionary>
<bucket>"under 18"</bucket>
<bucket>"18-24"</bucket>
<bucket>"25-34"</bucket>
<bucket>"35-44"</bucket>
<bucket>"45-54"</bucket>
<bucket>"55-64"</bucket>
<bucket>"65 or over"</bucket>
</dictionary>
</manual-sort>
<filter class='categorical' column='[federated.0mhrior040avue13ym7qz1xpdgjy].[none:Country:nk]'>
<groupfilter function='member' level='[none:Country:nk]' member='"USA"' user:ui-domain='relevant' user:ui-enumeration='inclusive' user:ui-marker='enumerate' />
</filter>
<slices>
<column>[federated.0mhrior040avue13ym7qz1xpdgjy].[none:Country:nk]</column>
<column>[federated.0mhrior040avue13ym7qz1xpdgjy].[Exclusions (Age,Country,State)]</column>
</slices>
<aggregation value='true' />
</view>
<style />
<panes>
<pane selection-relaxation-option='selection-relaxation-allow'>
<view>
<breakdown value='auto' />
</view>
<mark class='Circle' />
<encodings>
<lod column='[federated.0mhrior040avue13ym7qz1xpdgjy].[none:State:nk]' />
</encodings>
<reference-line axis-column='[federated.0mhrior040avue13ym7qz1xpdgjy].[avg:Base:qk]' boxplot-mark-exclusion='false' boxplot-whisker-type='standard' enable-instant-analytics='true' formula='average' id='refline0' label-type='automatic' probability='95' scope='per-cell' symmetric='false' value-column='[federated.0mhrior040avue13ym7qz1xpdgjy].[avg:Base:qk]' z-order='1' />
<style>
<style-rule element='mark'>
<format attr='size' value='0.25' />
<format attr='mark-labels-cull' value='true' />
<format attr='mark-labels-show' value='false' />
</style-rule>
</style>
</pane>
</panes>
<rows>[federated.0mhrior040avue13ym7qz1xpdgjy].[avg:Base:qk]</rows>
<cols>([federated.0mhrior040avue13ym7qz1xpdgjy].[none:Country:nk] / [federated.0mhrior040avue13ym7qz1xpdgjy].[none:Age:nk])</cols>
</table>
<simple-id uuid='{F46B6250-D695-4D25-B6D3-3B00E0CFC961}' />
</worksheet>
</worksheets>
<windows source-height='30'>
<window class='worksheet' name='Counts per Industry'>
<cards>
<edge name='left'>
<strip size='160'>
<card type='pages' />
<card type='filters' />
<card type='marks' />
</strip>
</edge>
<edge name='top'>
<strip size='2147483647'>
<card type='columns' />
</strip>
<strip size='2147483647'>
<card type='rows' />
</strip>
<strip size='31'>
<card type='title' />
</strip>
</edge>
</cards>
<viewpoint>
<zoom type='fit-width' />
<highlight>
<color-one-way>
<field>[federated.0mhrior040avue13ym7qz1xpdgjy].[none:Industry:nk]</field>
</color-one-way>
</highlight>
</viewpoint>
<simple-id uuid='{D85551E8-6F36-49C3-A9F6-4C35E0F23671}' />
</window>
<window class='worksheet' maximized='true' name='USA per Age'>
<cards>
<edge name='left'>
<strip size='160'>
<card type='pages' />
<card type='filters' />
<card type='marks' />
</strip>
</edge>
<edge name='top'>
<strip size='2147483647'>
<card type='columns' />
</strip>
<strip size='2147483647'>
<card type='rows' />
</strip>
<strip size='31'>
<card type='title' />
</strip>
</edge>
</cards>
<viewpoint>
<selection-collection>
<tuple-selection>
<tuple-reference>
<tuple-descriptor>
<pane-descriptor>
<x-fields>
<field>[federated.0mhrior040avue13ym7qz1xpdgjy].[none:Country:nk]</field>
<field>[federated.0mhrior040avue13ym7qz1xpdgjy].[none:Age:nk]</field>
</x-fields>
<y-fields>
<field>[federated.0mhrior040avue13ym7qz1xpdgjy].[avg:Base:qk]</field>
</y-fields>
</pane-descriptor>
<columns>
<field>[federated.0mhrior040avue13ym7qz1xpdgjy].[avg:Base:qk]</field>
<field>[federated.0mhrior040avue13ym7qz1xpdgjy].[none:Age:nk]</field>
<field>[federated.0mhrior040avue13ym7qz1xpdgjy].[none:Country:nk]</field>
<field>[federated.0mhrior040avue13ym7qz1xpdgjy].[none:State:nk]</field>
</columns>
</tuple-descriptor>
<tuple>
<value>333954.4411764706</value>
<value>"35-44"</value>
<value>"USA"</value>
<value>"NJ"</value>
</tuple>
</tuple-reference>
</tuple-selection>
</selection-collection>
<highlight>
<color-one-way>
<field>[federated.0mhrior040avue13ym7qz1xpdgjy].[Job Title (software)]</field>
<field>[federated.0mhrior040avue13ym7qz1xpdgjy].[attr:State:nk]</field>
<field>[federated.0mhrior040avue13ym7qz1xpdgjy].[none:Age:nk]</field>
<field>[federated.0mhrior040avue13ym7qz1xpdgjy].[none:Country:nk]</field>
<field>[federated.0mhrior040avue13ym7qz1xpdgjy].[none:State:nk]</field>
</color-one-way>
</highlight>
</viewpoint>
<simple-id uuid='{19407C3B-EECB-4B40-90B7-D1070EC0353F}' />
</window>
</windows>
<thumbnails>
<thumbnail height='384' name='Counts per Industry' width='384'>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