cumulative frequency by county year sex

[Mahdie Raj]

Hi everyone,
I have a dataset that looks like the below. I am looking for T which is a cumulative frequency of L by county, year and sex. For example, at age 0, T would be the sum of Ls from age 0 to age 85. Also, at age 85 (last age group), the T will be L. I tried following codes but got wrong numbers. Thanks.

by county year sex: summarize L // to gen the sum
by county year sex: gen double T = r(sum) - sum(L) + L







clear
input county year sex age L
Adams 2010 Female 0 99462.915
Adams 2010 Female 1 397713.5
Adams 2010 Female 5 497141.87
Adams 2010 Female 10 497141.87
Adams 2010 Female 15 497141.87
Adams 2010 Female 20 495568.23
Adams 2010 Female 25 493969.22
Adams 2010 Female 30 492575.72
Adams 2010 Female 35 491064.88
Adams 2010 Female 40 487533.79
Adams 2010 Female 45 478392.74
Adams 2010 Female 50 466395.58
Adams 2010 Female 55 454064.89
Adams 2010 Female 60 436749.32
Adams 2010 Female 65 405525.36
Adams 2010 Female 70 363477.66
Adams 2010 Female 75 316775.15
Adams 2010 Female 80 256369.22
Adams 2010 Female 85 277742.66
Adams 2010 Male 0 99520.477
Adams 2010 Male 1 397958.55
Adams 2010 Male 5 497448.19
Adams 2010 Male 10 497448.19
Adams 2010 Male 15 496294.4
Adams 2010 Male 20 494913.49
Adams 2010 Male 25 489758.75
Adams 2010 Male 30 483261
Adams 2010 Male 35 474619.84
Adams 2010 Male 40 461946.18
Adams 2010 Male 45 441946.16
Adams 2010 Male 50 410951.42
Adams 2010 Male 55 388334.25
Adams 2010 Male 60 365335.6
Adams 2010 Male 65 322609.76
Adams 2010 Male 70 267814.1
Adams 2010 Male 75 210542.98
Adams 2010 Male 80 141499.02
Adams 2010 Male 85 109716.39
Adams 2015 Female 0 100000
Adams 2015 Female 1 400000
Adams 2015 Female 5 500000
Adams 2015 Female 10 499040.66
Adams 2015 Female 15 496185.44
Adams 2015 Female 20 494711.49
Adams 2015 Female 25 493168.27
Adams 2015 Female 30 488535.46
Adams 2015 Female 35 483812.26
Adams 2015 Female 40 482228.28
Adams 2015 Female 45 481019.43
Adams 2015 Female 50 470029.95
Adams 2015 Female 55 450034.19
Adams 2015 Female 60 423027.81
Adams 2015 Female 65 385458.09
Adams 2015 Female 70 342320.79
Adams 2015 Female 75 293442.65
Adams 2015 Female 80 221900.41
Adams 2015 Female 85 310688.43
Adams 2015 Male 0 98778.815
Adams 2015 Male 1 394801.11
Adams 2015 Male 5 493501.39
Adams 2015 Male 10 493501.39
Adams 2015 Male 15 489662.8
Adams 2015 Male 20 481991.82
Adams 2015 Male 25 475695.34
Adams 2015 Male 30 466366.11
Adams 2015 Male 35 459636.47
Adams 2015 Male 40 457340.43
Adams 2015 Male 45 445977.45
Adams 2015 Male 50 423741.06
Adams 2015 Male 55 399166.61
Adams 2015 Male 60 370458.09
Adams 2015 Male 65 338345.95
Adams 2015 Male 70 291721.69
Adams 2015 Male 75 225543.46
Adams 2015 Male 80 139951.64
Adams 2015 Male 85 110494.66
Allen 2010 Female 0 98998.286
Allen 2010 Female 1 395735.46
Allen 2010 Female 5 494669.32
Allen 2010 Female 10 494669.32
Allen 2010 Female 15 494669.32
Allen 2010 Female 20 494669.32
Allen 2010 Female 25 493393.72
Allen 2010 Female 30 492137.37
Allen 2010 Female 35 490602.82
Allen 2010 Female 40 486948.93
Allen 2010 Female 45 483047.17
Allen 2010 Female 50 475027.64
Allen 2010 Female 55 463434.94
Allen 2010 Female 60 446807.87
Allen 2010 Female 65 417953.94
Allen 2010 Female 70 376568.96
Allen 2010 Female 75 318026.09
Allen 2010 Female 80 246704.78
Allen 2010 Female 85 292923.37
Allen 2010 Male 0 99861.203
Allen 2010 Male 1 399409.11
Allen 2010 Male 5 498913.85
Allen 2010 Male 10 498312.45
Allen 2010 Male 15 496906.96
Allen 2010 Male 20 495458.29
Allen 2010 Male 25 494799.24
Allen 2010 Male 30 491774.86
Allen 2010 Male 35 486020.68
Allen 2010 Male 40 479873.09
Allen 2010 Male 45 472502.27
Allen 2010 Male 50 462894.52
Allen 2010 Male 55 447626.36
Allen 2010 Male 60 421867.13
Allen 2010 Male 65 386638.79
Allen 2010 Male 70 341341.01
Allen 2010 Male 75 282815.87
Allen 2010 Male 80 201613.88
Allen 2010 Male 85 210143.54
Allen 2015 Female 0 99363.818
Allen 2015 Female 1 397291.62
Allen 2015 Female 5 496614.52
Allen 2015 Female 10 496614.52
Allen 2015 Female 15 496614.52
Allen 2015 Female 20 495527.63
Allen 2015 Female 25 492755.43
Allen 2015 Female 30 490709.92
Allen 2015 Female 35 488423.83
Allen 2015 Female 40 483557.78
Allen 2015 Female 45 476718.8
Allen 2015 Female 50 465965.89
Allen 2015 Female 55 450750.13
Allen 2015 Female 60 431975.26
Allen 2015 Female 65 406729.75
Allen 2015 Female 70 372265.05
Allen 2015 Female 75 321212.93
Allen 2015 Female 80 257781.94
Allen 2015 Female 85 365861.18
Allen 2015 Male 0 99414.375
Allen 2015 Male 1 397151.85
Allen 2015 Male 5 495412.17
Allen 2015 Male 10 494434.28
Allen 2015 Male 15 493148.19
Allen 2015 Male 20 491568.21
Allen 2015 Male 25 488567.72
Allen 2015 Male 30 483143.99
Allen 2015 Male 35 477731.49
Allen 2015 Male 40 473009.72
Allen 2015 Male 45 466005.75
Allen 2015 Male 50 451956.23
Allen 2015 Male 55 431394.33
Allen 2015 Male 60 405535.77
Allen 2015 Male 65 370253.17
Allen 2015 Male 70 324727.86
Allen 2015 Male 75 258190.87
Allen 2015 Male 80 182925.58
Allen 2015 Male 85 181263.53

end

[Clyde Schechter]

rangestat (sum) cum_L = L, interval(age 0 .) by(county year sex)

-rangestat- is written by Robert Picard, Nick Cox, and Roberto Ferrer, and is available from SSC.

In the future, when showing data examples, please use the -dataex- command to do so. If you are running version 15.1 or a fully updated version 14.2, it is already part of your official Stata installation. If not, run -ssc install dataex- to get it. Either way, run -help dataex- to read the simple instructions for using it. -dataex- will save you time; it is easier and quicker than typing out tables. It includes complete information about aspects of the data that are often critical to answering your question but cannot be seen from tabular displays or screenshots. It also makes it possible for those who want to help you to create a faithful representation of your example to try out their code, which in turn makes it more likely that their answer will actually work in your data.



When asking for help with code, always show example data. When showing example data, always use -dataex-.

Added: Actually you don't have to get -rangestat- to do this. All native Stata commands:

gsort county year sex -age
by county year sex, sort: gen double cum2 = sum(L)