-------------------------------------------------------------------------------
Finding your way around your data
-------------------------------------------------------------------------------
Searching
The lookfor command is helpful for quickly finding the relevant variables
in your dataset
. sysuse auto, clear
(1978 automobile data)
. lookfor foreign
Variable Storage Display Value
name type format label Variable label
-------------------------------------------------------------------------------
foreign byte %8.0g origin Car origin
lookfor only looks in the variable names and variable labels
Sometimes I know a term that should appear in the value labels
That is what lookfor2 (available from SSC) is for
. lookfor2 domestic
Variable Storage Display Value
name type format label Variable label
-------------------------------------------------------------------------------
foreign byte %8.0g origin Car origin
Once I have found a variable, I often want to see what variables are
close
This is what closedes is for (it is part of the lookfor2 package)
. closedesc headroom
Variable Storage Display Value
name type format label Variable label
-------------------------------------------------------------------------------
price int %8.0gc Price
mpg int %8.0g Mileage (mpg)
rep78 int %8.0g Repair record 1978
headroom float %6.1f Headroom (in.)
trunk int %8.0g Trunk space (cu. ft.)
weight int %8.0gc Weight (lbs.)
length int %8.0g Length (in.)
sometimes I want to look for variables by other characteristics. For
example, they have a certain value label, i.e. they are part of the same
batery of questions.
. ds, has(type numeric)
price headroom length gear_ratio
mpg trunk turn foreign
rep78 weight displacement
. ds, has(vallabel "*origin*")
foreign
-------------------------------------------------------------------------------
index >>
-------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Finding your way around your data
-------------------------------------------------------------------------------
Getting an overview
For getting a quick list of variables the desc command is useful
A similar command is codebook together with the compact option (the
default is too verbose for my taste)
. codebook, compact
Variable Obs Unique Mean Min Max Label
-------------------------------------------------------------------------------
make 74 74 . . . Make and model
price 74 74 6165.257 3291 15906 Price
mpg 74 21 21.2973 12 41 Mileage (mpg)
rep78 69 5 3.405797 1 5 Repair record 1978
headroom 74 8 2.993243 1.5 5 Headroom (in.)
trunk 74 18 13.75676 5 23 Trunk space (cu. ft.)
weight 74 64 3019.459 1760 4840 Weight (lbs.)
length 74 47 187.9324 142 233 Length (in.)
turn 74 18 39.64865 31 51 Turn circle (ft.)
displacement 74 31 197.2973 79 425 Displacement (cu. in.)
gear_ratio 74 36 3.014865 2.19 3.89 Gear ratio
foreign 74 2 .2972973 0 1 Car origin
-------------------------------------------------------------------------------
. desc
Contains data from C:\Program Files\Stata17\ado\base/a/auto.dta
Observations: 74 1978 automobile data
Variables: 12 13 Apr 2020 17:45
(_dta has notes)
-------------------------------------------------------------------------------
Variable Storage Display Value
name type format label Variable label
-------------------------------------------------------------------------------
make str18 %-18s Make and model
price int %8.0gc Price
mpg int %8.0g Mileage (mpg)
rep78 int %8.0g Repair record 1978
headroom float %6.1f Headroom (in.)
trunk int %8.0g Trunk space (cu. ft.)
weight int %8.0gc Weight (lbs.)
length int %8.0g Length (in.)
turn int %8.0g Turn circle (ft.)
displacement int %8.0g Displacement (cu. in.)
gear_ratio float %6.2f Gear ratio
foreign byte %8.0g origin Car origin
-------------------------------------------------------------------------------
Sorted by: foreign
The htmlcb package (available from SSC) can be used getting a more
detailed description of the data.
. htmlcb, saving(cb.html) replace
Output written to cb.html
-------------------------------------------------------------------------------
<< index >>
-------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Finding your way around your data
-------------------------------------------------------------------------------
Describing variables
An ugly, but extremely useful, graph for getting a good overview of a variable
is spikeplot
. sysuse nlsw88
(NLSW, 1988 extract)
. spikeplot hours
For categorical variables the fre command (from SSC by Ben Jann) is just
absolutely necessary
. fre union
union -- Union worker
----------------------------------------------------------------
| Freq. Percent Valid Cum.
-------------------+--------------------------------------------
Valid 0 Nonunion | 1417 63.09 75.45 75.45
1 Union | 461 20.53 24.55 100.00
Total | 1878 83.62 100.00
Missing . | 368 16.38
Total | 2246 100.00
----------------------------------------------------------------
-------------------------------------------------------------------------------
<< index >>
-------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Finding your way around your data
-------------------------------------------------------------------------------
Describing variables (special cases)
Sometimes I have a number of variables that together represent a concept
For those cases I find groups (from SSC by Nick Cox) extremely helpful
. groups married never_married
+----------------------------------------------+
| married never_married Freq. Percent |
|----------------------------------------------|
| Single Has been married 570 25.38 |
| Single Never married 234 10.42 |
| Married Has been married 1442 64.20 |
+----------------------------------------------+
. tab married never_married
| Never married
Married | Has been Never mar | Total
-----------+----------------------+----------
Single | 570 234 | 804
Married | 1,442 0 | 1,442
-----------+----------------------+----------
Total | 2,012 234 | 2,246
With two variables we can get away with tab, but what if I also had
variables living_together, and divorsed?
For panel data I sometimes want to see the entire history of a person if
a certain condition is met.
In this example we are looking at the retrospectively reported
educational history of a respondent
And we find some persons who after primary school immediately entered a
university of applied sciences.
. use education.dta
(cleaned up spell data from NEPS)
. lany ID_t sort start finish if start == 8 & sort == 2, by(ID_t) sepby(ID_t)
(2 real changes made)
(8 real changes made)
+----------------------------------------+
| ID_t sort start finish |
|----------------------------------------|
36. | 8000814 1 enter Gym finish Ab |
37. | 8000814 2 enter Fac finish Fa |
|----------------------------------------|
38. | 8000985 1 enter Gru finish Re |
39. | 8000985 2 enter Fac finish Fa |
|----------------------------------------|
40. | 8001265 1 enter Gym finish Ab |
41. | 8001265 2 enter Fac finish Fa |
|----------------------------------------|
42. | 8008101 1 enter Gru finish Re |
43. | 8008101 2 enter Fac finish Fa |
|----------------------------------------|
44. | 8008488 1 enter Rea finish Re |
45. | 8008488 2 enter Fac finish Fa |
|----------------------------------------|
46. | 8010928 1 enter Gru finish Ha |
47. | 8010928 2 enter Fac finish Fa |
|----------------------------------------|
48. | 8011551 1 enter Gym finish Ab |
49. | 8011551 2 enter Fac finish Fa |
|----------------------------------------|
50. | 8012361 1 enter Gru finish Ha |
51. | 8012361 2 enter Fac finish Fa |
52. | 8012361 3 enter voc finish Re |
53. | 8012361 4 enter Gym finish Ab |
+----------------------------------------+
-------------------------------------------------------------------------------
<< index >>
-------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Working with your data -- Workflow
-------------------------------------------------------------------------------
Workflow
An excellent resource for improving ones workflow in Stata is (Long 2009)
The mkproject package from SSC can help setting up a new projct:
It creates the different folders and initial .do files with a
commands in them that most .do files should have (sometimes called
boilerplate code)
It also creates a project, which you can fill with files so you can
have easy access to them.
Additional .do files with the standard commands already in them can
be created witht the boilerplate command, which is part of mkproject
If you typed mkproject proj, it would create the following folders and
files:
proj/
├─ proj.stpr
├─ admin/
├─ docu/
├─ research_log.txt
├─ posted/
│ ├─ data/
├─ work/
│ ├─ proj_main.do
│ ├─ proj_ana01.do
│ ├─ proj_dta01.do
After that it will open the do-file editor with the project open like so
The newest version of mkproject also has a git option that will setup a
git repository.
It als has a smclpres option, for setting up the folder structure and a
.do file with boilerplate code for a smclpres presentation like this.
-------------------------------------------------------------------------------
<< index >>
-------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Working with your data -- Workflow
-------------------------------------------------------------------------------
Settings for your project
Larger projects need to be split up over different files
But I often need some settings throughout the project, that could be
some file names,
a list of variables, or something like that
I want to define them once, but I don't want to use globals
Instead, I typically have one .do file that defines my settings as
locals, and use include at the top of all my other .do files
-------------------------------------------------------------------------------
<< index >>
-------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Working with your data -- Workflow
-------------------------------------------------------------------------------
Between program and .do file
You can run a .do file called child.do by typing do child.do
You can also run that .do file by typing do child.do something else
This will make the following local macros available at the beginning of
child.do:
`0' containing: something else
`1' containing: something
`2' containing: else
child.do could do some complicated/fiddly manipulations of a variable,
maybe multiple levels of if conditions
You want to apply those to multiple variable, say var1 and var2
If you were programming you would have put it in a sub-routine.
Now you can create another .do file (e.g. parent.do ) that contains the
lines:
foreach var of varlist var2 var2 {
do child.do `var'
}
To me this is the ".do file equivalent" of a sub-routine.
Inside child.do I would start with args in order to give the local macros
`1', `2', etc. more meaningful names
-------------------------------------------------------------------------------
<< index >>
-------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Working with your data -- Working with ID variables
-------------------------------------------------------------------------------
getting at parts of a variable
Say we have an ID variable where the eight digit is the continent, the
seventh and sixth digit the country within that continent, the fifth
digit the cite, fourth digit the village, and the first three the
household within the village.
You want the country, i.e. the last three digits
If I divide by 100,000, then I am getting close; all I need to do is get
rid of the bit after the decimal point, i.e. round down
That can be done with the floor() function
gen country = floor(ID/100000)
-------------------------------------------------------------------------------
<< index >>
-------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Working with your data -- Working with ID variables
-------------------------------------------------------------------------------
typing large numbers
If I have to type large numbers, like 100,000, I alway worry that I
miscount the zeros
Instead one can also type 1e5
So, for the previous slide I would actually write:
gen country = floor(ID/1e5)
-------------------------------------------------------------------------------
<< index >>
-------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Working with your data -- Working with ID variables
-------------------------------------------------------------------------------
Even and odd
Sometimes I work with data where the odd observations in a unit are the
origins and the following even observations are the destinations
so I want to find out if a number is even or odd
gen origin = mod(sort,2)
-------------------------------------------------------------------------------
<< index >>
-------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Working with your data -- Adding more meta data
-------------------------------------------------------------------------------
notes
Notes can be attached to the data and to variables
You can add multiple notes to the same variable or dataset
notes can be longer than labels
. sysuse auto
(1978 automobile data)
. note
_dta:
1. From Consumer Reports with permission
. note foreign : this is american data, so foreign is non-US
. note: this is example data that comes with Stata
. note
_dta:
1. From Consumer Reports with permission
2. this is example data that comes with Stata
foreign:
1. this is american data, so foreign is non-US
. note foreign
foreign:
1. this is american data, so foreign is non-US
-------------------------------------------------------------------------------
<< index >>
-------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Working with your data -- Adding more meta data
-------------------------------------------------------------------------------
char
A char is a more general version of notes
notes are numbered, but chars can have any name
. char rep78[Description] repair status
. char list rep78[]
rep78[Description]: repair status
For people making data available to others, and who want to add meta data
to the data there are two options:
They could add the meta data as notes. This makes it probably a bit
easier for potential users to find that information, but at the same
time can interfere with the user who may also want to use notes.
They could add the meta data as chars. This is less likely to
interfere with how the user may want to use the data, but makes it a
bit harder for the user to find that information.
-------------------------------------------------------------------------------
<< index >>
-------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Some commands can be slow
-------------------------------------------------------------------------------
collapse can be slow
for some datasets commands like collapse can be uncomfortably slow
. set rmsg on
r; t=0.00 9:19:08
. import delimited using "time_series_covid19_confirmed_global.csv", clear
(1,028 vars, 289 obs)
r; t=0.78 9:19:09
. drop lat v4
r; t=0.00 9:19:09
. collapse (sum) v*, by(countryregion)
r; t=10.38 9:19:19
. set rmsg off
The gtools package (from SSC, by Mauricio Caceres Bravo) contains various
commands that are faster than the official Stata version.
. set rmsg on
r; t=0.00 9:19:19
. import delimited using "time_series_covid19_confirmed_global.csv", clear
(1,028 vars, 289 obs)
r; t=0.35 9:19:20
. drop lat v4
r; t=0.00 9:19:20
. gcollapse (sum) v*, by(countryregion)
r; t=5.22 9:19:25
. set rmsg off
-------------------------------------------------------------------------------
<< index >>
-------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Some commands can be slow
-------------------------------------------------------------------------------
reshape can be slow
Similarly, reshape can be painfully slow
. set rmsg on
r; t=0.00 9:19:25
. import delimited using "time_series_covid19_confirmed_global.csv", clear
(1,028 vars, 289 obs)
r; t=0.21 9:19:25
. drop lat v4
r; t=0.00 9:19:25
. reshape long v, i(provincestate countryregion) j(date)
(j = 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 3
> 1 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 1
> 06 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 12
> 5 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
> 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 1
> 83 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 20
> 2 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221
> 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 2
> 60 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 27
> 9 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298
> 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 3
> 37 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 35
> 6 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375
> 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 4
> 14 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 43
> 3 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452
> 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 4
> 91 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 51
> 0 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529
> 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 5
> 68 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 58
> 7 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606
> 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 6
> 45 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 66
> 4 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683
> 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 7
> 22 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 74
> 1 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760
> 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 7
> 99 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 81
> 8 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837
> 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 8
> 76 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 89
> 5 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914
> 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 9
> 53 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 97
> 2 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991
> 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
> 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1
> 024 1025 1026 1027 1028)
Data Wide -> Long
-----------------------------------------------------------------------------
Number of observations 289 -> 295,936
Number of variables 1,026 -> 4
j variable (1024 values) -> date
xij variables:
v5 v6 ... v1028 -> v
-----------------------------------------------------------------------------
r; t=67.45 9:20:33
. set rmsg off
fastreshape (available from SSC, by Michael Droste) is faster
. set rmsg on
r; t=0.00 9:20:33
. import delimited using "time_series_covid19_confirmed_global.csv", clear
(1,028 vars, 289 obs)
r; t=0.28 9:20:33
. drop lat v4
r; t=0.00 9:20:33
. fastreshape long v, i(provincestate countryregion) j(date)
pos: 0, len: 1
(note: j = 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 2
> 9 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 1
> 43 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 16
> 2 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181
> 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 2
> 20 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 23
> 9 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258
> 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 2
> 97 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 31
> 6 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335
> 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 3
> 74 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 39
> 3 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412
> 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 4
> 51 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 47
> 0 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489
> 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 5
> 28 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 54
> 7 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566
> 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 6
> 05 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 62
> 4 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643
> 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 6
> 82 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 70
> 1 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
> 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 7
> 59 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 77
> 8 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797
> 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 8
> 36 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 85
> 5 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874
> 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 9
> 13 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 93
> 2 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951
> 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 9
> 90 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 100
> 7 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022
> 1023 1024 1025 1026 1027 1028)
(variable v was int, now long to accommodate using data's values)
Data wide -> long
------------------------------------------------------------------------------
Number of obs. 289 -> 295936
Number of variables 1027 -> 5
j variable (1024 values) -> date
xij variables:
v5 v6 ... v1028 -> v
------------------------------------------------------------------------------
r; t=8.08 9:20:41
. set rmsg off
tolong (available from SSC, by Rafal Raciborski) is even faster, but less
general
. set rmsg on
r; t=0.00 9:20:42
. import delimited using "time_series_covid19_confirmed_global.csv", clear
(1,028 vars, 289 obs)
r; t=0.37 9:20:42
. drop lat v4
r; t=0.00 9:20:42
. tolong v, i(provincestate countryregion) j(date)
r; t=0.42 9:20:42
. set rmsg off
-------------------------------------------------------------------------------
<< index
-------------------------------------------------------------------------------
References
J. Scott Long (2009), The workflow of data analysis using Stata. College
Station, TX: Stata Press.
-------------------------------------------------------------------------------
index
-------------------------------------------------------------------------------