setwd("~/Documents/0 - Montgomery College/0 - DATA 110/Datasets")
library(tidyverse)## ── Attaching packages ─────────────────────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 3.2.1 ✔ purrr 0.3.2
## ✔ tibble 2.1.3 ✔ dplyr 0.8.3
## ✔ tidyr 1.0.0 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0## ── Conflicts ────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphapfizer <- read_csv("pfizer.csv")## Parsed with column specification:
## cols(
## org_indiv = col_character(),
## first_plus = col_character(),
## first_name = col_character(),
## last_name = col_character(),
## city = col_character(),
## state = col_character(),
## category = col_character(),
## cash = col_double(),
## other = col_double(),
## total = col_double()
## )fda <- read_csv("fda.csv")## Parsed with column specification:
## cols(
## name_last = col_character(),
## name_first = col_character(),
## name_middle = col_character(),
## issued = col_character(),
## office = col_character()
## )We can View data at any time by clicking on its table icon in the Environment tab in the Grid view.
str(pfizer)## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 10087 obs. of 10 variables:
## $ org_indiv : chr "3-D MEDICAL SERVICES LLC" "AA DOCTORS, INC." "ABBO, LILIAN MARGARITA" "ABBO, LILIAN MARGARITA" ...
## $ first_plus: chr "STEVEN BRUCE" "AAKASH MOHAN" "LILIAN MARGARITA" "LILIAN MARGARITA" ...
## $ first_name: chr "STEVEN" "AAKASH" "LILIAN" "LILIAN" ...
## $ last_name : chr "DEITELZWEIG" "AHUJA" "ABBO" "ABBO" ...
## $ city : chr "NEW ORLEANS" "PASO ROBLES" "MIAMI" "MIAMI" ...
## $ state : chr "LA" "CA" "FL" "FL" ...
## $ category : chr "Professional Advising" "Expert-Led Forums" "Business Related Travel" "Meals" ...
## $ cash : num 2625 1000 0 0 1800 ...
## $ other : num 0 0 448 119 0 0 47 0 0 396 ...
## $ total : num 2625 1000 448 119 1800 ...
## - attr(*, "spec")=
## .. cols(
## .. org_indiv = col_character(),
## .. first_plus = col_character(),
## .. first_name = col_character(),
## .. last_name = col_character(),
## .. city = col_character(),
## .. state = col_character(),
## .. category = col_character(),
## .. cash = col_double(),
## .. other = col_double(),
## .. total = col_double()
## .. )head(pfizer)## # A tibble: 6 x 10
## org_indiv first_plus first_name last_name city state category cash
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 3-D MEDI… STEVEN BR… STEVEN DEITELZW… NEW … LA Profess… 2625
## 2 AA DOCTO… AAKASH MO… AAKASH AHUJA PASO… CA Expert-… 1000
## 3 ABBO, LI… LILIAN MA… LILIAN ABBO MIAMI FL Busines… 0
## 4 ABBO, LI… LILIAN MA… LILIAN ABBO MIAMI FL Meals 0
## 5 ABBO, LI… LILIAN MA… LILIAN ABBO MIAMI FL Profess… 1800
## 6 ABDULLAH… ABDULLAH ABDULLAH RAFFEE FLINT MI Expert-… 750
## # … with 2 more variables: other <dbl>, total <dbl>describe(pfizer)## vars n mean sd median trimmed mad min max
## org_indiv* 1 10087 NaN NA NA NaN NA Inf -Inf
## first_plus* 2 9884 NaN NA NA NaN NA Inf -Inf
## first_name* 3 9884 NaN NA NA NaN NA Inf -Inf
## last_name* 4 10087 NaN NA NA NaN NA Inf -Inf
## city* 5 10087 NaN NA NA NaN NA Inf -Inf
## state* 6 10087 NaN NA NA NaN NA Inf -Inf
## category* 7 10086 NaN NA NA NaN NA Inf -Inf
## cash 8 10086 3241.12 21815.80 0 814.66 0.00 0 1185466
## other 9 10084 266.47 861.06 41 121.95 60.79 0 27681
## total 10 10087 3506.57 21792.20 750 1111.64 947.38 0 1185466
## range skew kurtosis se
## org_indiv* -Inf NA NA NA
## first_plus* -Inf NA NA NA
## first_name* -Inf NA NA NA
## last_name* -Inf NA NA NA
## city* -Inf NA NA NA
## state* -Inf NA NA NA
## category* -Inf NA NA NA
## cash 1185466 30.20 1280.49 217.23
## other 27681 12.27 234.62 8.57
## total 1185466 30.26 1284.45 216.98str(fda)## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 272 obs. of 5 variables:
## $ name_last : chr "ADELGLASS" "ADKINSON" "ALLEN" "AMSTERDAM" ...
## $ name_first : chr "JEFFREY" "N." "MARK" "DANIEL" ...
## $ name_middle: chr "M." "FRANKLIN" "S." NA ...
## $ issued : chr "5/25/1999" "4/19/2000" "1/28/2002" "11/17/2004" ...
## $ office : chr "Center for Drug Evaluation and Research" "Center for Biologics Evaluation and Research" "Center for Devices and Radiological Health" "Center for Biologics Evaluation and Research" ...
## - attr(*, "spec")=
## .. cols(
## .. name_last = col_character(),
## .. name_first = col_character(),
## .. name_middle = col_character(),
## .. issued = col_character(),
## .. office = col_character()
## .. )head(fda)## # A tibble: 6 x 5
## name_last name_first name_middle issued office
## <chr> <chr> <chr> <chr> <chr>
## 1 ADELGLASS JEFFREY M. 5/25/1999 Center for Drug Evaluation an…
## 2 ADKINSON N. FRANKLIN 4/19/2000 Center for Biologics Evaluati…
## 3 ALLEN MARK S. 1/28/2002 Center for Devices and Radiol…
## 4 AMSTERDAM DANIEL <NA> 11/17/20… Center for Biologics Evaluati…
## 5 AMSTUTZ HARLAN C. 7/19/2004 Center for Devices and Radiol…
## 6 ANDERSON C. JOSEPH 2/25/2000 Center for Devices and Radiol…# describe not needed for fda because there are no continuous variables# print values for total in pfizer data
pfizer$total ## [1] 2625 1000 448 119 1800 750 47 825
## [9] 3000 396 1750 58 88 2000 189 2500
## [17] 38 4400 2074 218 1750 154 1000 4000
## [25] 1250 93 750 59 1250 1000 3000 41
## [33] 2400 12840 39 750 109 1062 390 71
## [41] 30 850 120 2000 99 1000 28 1500
## [49] 1750 2300 27 1000 66 1500 1000 174
## [57] 1000 33 2500 3000 611 300 669 2150
## [65] 1160 42 2000 1250 131 1250 117 780
## [73] 231 12300 100 750 381 950 663 4007
## [81] 1500 384 9000 395 7500 115 33 1000
## [89] 50 1000 32 85 1750 66 1375 48
## [97] 1172 190 4814 1144 2363 66 2500 1000
## [105] 10948 132 6500 6150 669 2000 234 1000
## [113] 99 1000 29 314 1750 1000 563 47
## [121] 384 316 7500 204 6750 7545 4485 1250
## [129] 1800 750 58 6702 3750 135 82 256
## [137] 6750 1500 270 79 2375 41 2500 2000
## [145] 1000 4000 1500 314 1500 60 1250 87
## [153] 3000 1000 5000 1000 97 462 19250 5299
## [161] 937 3829 3000 525 1000 478 296 7107
## [169] 1000 1250 49 593 7500 1750 186 1375
## [177] 25 60 1500 4500 236 500 175 3000
## [185] 318 110 3250 100 875 158 3000 1500
## [193] 61 244 669 7878 63 1500 253 974
## [201] 85 9250 169 40 1000 476 1500 25
## [209] 2206 1028 285 432 560 1250 34 32
## [217] 1000 3500 90 6000 401 2200 428 2600
## [225] 1000 2500 161 75 3500 2500 33 1250
## [233] 963 309 3000 750 262 481 213 36
## [241] 5000 124 1800 2000 167 2000 2000 44
## [249] 1200 79 267 3000 732 20500 1250 1849
## [257] 22250 240 2000 1000 8081 37750 9500 71
## [265] 4500 95 894 1375 128 1250 35 27
## [273] 85 1000 210 750 413 1374 29 1500
## [281] 167 123 1250 1000 80 1035 671 15520
## [289] 341 8500 6572 353 4500 32 1000 153
## [297] 2000 1000 1000 6000 2585 1765 2000 7000
## [305] 201 1250 804 4500 208 815 669 1000
## [313] 90 583 3175 221 1065 2000 27 2000
## [321] 49 2500 73 628 669 1500 396 617
## [329] 1250 74 168 2000 78 5500 1250 202
## [337] 875 528 291 4500 162 3000 1000 750
## [345] 42 1500 104 8777 709 2375 580 7000
## [353] 1038 2250 5813 1750 472 74 3000 461
## [361] 103 2500 656 387 110 2375 625 496
## [369] 1000 49 123 3000 4688 70000 1500 71
## [377] 6250 800 400 320 365 11250 411 2000
## [385] 1000 79 1250 138 292 252 1750 374
## [393] 7250 3300 2000 159 2000 124 885 2000
## [401] 208 2000 1000 122 4000 1752 1000 687
## [409] 2773 30500 3000 1144 500 2750 1153 376
## [417] 2000 834 916 2500 753 185 7400 6250
## [425] 1937 24750 1250 5872 15250 1035 25750 2500
## [433] 327 2589 722 16000 2170 52 500 587
## [441] 1000 88 71 3750 1043 792 2039 4000
## [449] 467 337 1000 3375 6500 239 347 2000
## [457] 237 2341 468 2329 1100 105000 756 1000
## [465] 97 1250 53 619 1050 316 300 669
## [473] 1000 151 1000 5250 295 3250 87 126
## [481] 262 1750 4108 2000 1986 11000 750 270
## [489] 33 1000 140 3750 283 1024 21000 273
## [497] 668 273 2000 500 63 1250 47 116
## [505] 85 382 2000 2500 3000 243 927 669
## [513] 66 111 2500 4000 172 1000 124 51
## [521] 2400 78 1000 371 76710 80621 3500 976
## [529] 343 3000 750 750 199 235 8000 184
## [537] 177 690 1000 134 1000 79 271 1000
## [545] 1560 174 2750 56 1250 85 186 67
## [553] 384 47 2000 4702 26400 484 2000 199
## [561] 1500 40 1000 80 230 3300 51 475
## [569] 2000 260 1000 758 27000 1250 1057 625
## [577] 9750 2206 44 2000 277 4614 3750 287
## [585] 182 2500 636 384 317 3000 56 173
## [593] 1750 136 3750 1250 149 1600 314 750
## [601] 27 750 216 251 5000 25 2000 37
## [609] 6600 127 1100 504 750 85 116693 105811
## [617] 19093 1172 1000 750 109 1250 39 1200
## [625] 87 173 10500 326 85 800 114 67
## [633] 2000 196 75 59 700 243 573 1805
## [641] 296 562 1413 11250 435 2000 149 1125
## [649] 2000 2000 168 875 1250 137 1368 6400
## [657] 5600 1000 149 725 623 136 736 1153
## [665] 493 2750 1750 779 625 625 52 822
## [673] 445 2150 241 3200 541 160 5000 129
## [681] 2000 1250 163 635 252 1115 459 520
## [689] 1000 275 3000 260 1750 1500 1250 25
## [697] 521 2000 251 905 669 63 3750 437
## [705] 583 13250 422 27 1250 86 532 384
## [713] 73 3250 80 2200 1000 114 1000 120
## [721] 423 7500 222 1000 195 1000 5748 375
## [729] 1250 121 929 804 1000 130 90 1750
## [737] 281 4250 570 261 4375 471 341 3500
## [745] 39 1750 750 1250 28 1000 1500 352
## [753] 1000 298 47 1650 537 7543 29725 500
## [761] 86 102 3000 109 6000 50000 1500 81
## [769] 397 533 625 355 1000 200 5500 600
## [777] 369 2250 1000 1250 194 55 1500 2250
## [785] 289 145 5000 283 258 13500 264 505
## [793] 3500 555 2500 1000 465 387 610 384
## [801] 1189 290 25000 68 3000 277 3000 27
## [809] 2500 313 1250 139 37 1000 84 252
## [817] 8250 33 18063 281 8500 68 3000 91
## [825] 3000 44 85 1500 67 1750 2500 155
## [833] 800 25 2500 1000 107 67 500 95
## [841] 2500 131 2000 220 2000 485 3085 1218
## [849] 777 231 4000 1500 750 183 5057 15682
## [857] 2464 4291 90 2000 88 2000 110 27
## [865] 4000 263 2000 1748 690 9600 63 1000
## [873] 374 4500 192 1000 58 1000 221 1250
## [881] 59 500 427 85 375 113 50 568
## [889] 1000 2000 1000 86 1000 289 82 28
## [897] 2750 625 77 625 1400 301 578 330
## [905] 1750 1000 84 2000 886 16000 218 1500
## [913] 54 1000 286 1074 549 115 2000 395
## [921] 6500 264 304 3600 236 4500 370 30
## [929] 2500 89 865 669 1000 1000 91 248
## [937] 500 215 4750 31 516 1000 1000 195
## [945] 1000 252 500 6375 318 1000 226 115
## [953] 7225 289 650 629 247 800 346 3300
## [961] 47 750 327 285 549 625 75 500
## [969] 104 747 384 750 32 750 71 56
## [977] 4500 164 2000 353 1000 88 19500 45
## [985] 1000 111 1050 1327 669 1428 975 164
## [993] 910 1000 1207 17000 85 31 8835 601
## [1001] 669 3500 2000 1750 750 84 47 1500
## [1009] 52 89 650 1250 723 560 426 387
## [1017] 120 1500 313 877 384 666 415 171
## [1025] 907 31000 1875 216 8500 0 1500 4000
## [1033] 1250 8575 2002 26250 644 46 1600 94
## [1041] 1000 1000 69 500 72 539 384 97
## [1049] 500 1310 19750 245 1000 750 203 492
## [1057] 455 945 638 258 51 5500 622 55
## [1065] 2200 190 4000 38 925 144 8000 639
## [1073] 1500 1167 669 299 500 290 358 2400
## [1081] 470 4250 5750 537 487 728 669 290
## [1089] 358 2400 853 1375 193 4125 684 390
## [1097] 1000 28 3150 856 3750 1465 104 2750
## [1105] 1000 2000 1925 2400 142 1250 37 669
## [1113] 1000 126 1250 84 2500 172 275 70
## [1121] 438 1000 625 269 6062 64 1500 101
## [1129] 1375 131 102 3500 134 500 264 11500
## [1137] 674 5000 25 625 96 4189 1000 254
## [1145] 1000 1000 26 1551 7950 22500 41975 8957
## [1153] 684 245 9500 218 66 675 64 3000
## [1161] 87 1172 5579 3000 594 320 3000 26
## [1169] 252 1750 185 3500 73 192 5000 532
## [1177] 1500 1000 186 1435 166 1000 61 465
## [1185] 3500 838 4500 11289 3500 44 2000 205
## [1193] 2000 85 1500 100 1000 528 1250 306
## [1201] 1250 1000 65278 1000 82 2250 366 3000
## [1209] 1619 8000 651 2000 10144 650 387 102
## [1217] 380 47 12000 221 2000 393 481 17000
## [1225] 559 2000 698 5500 464 63 6250 186
## [1233] 330 2200 138 1000 3000 1250 25 3000
## [1241] 208 2000 216 1250 37 47 178 2000
## [1249] 1522 669 1216 692 53 575 1012 669
## [1257] 456 350 500 374 1059 105 26500 50
## [1265] 1000 36 1000 2000 407 990 187 1750
## [1273] 1800 26 1000 750 750 124 44 1000
## [1281] 450 2000 1144 2500 1000 2000 58 314
## [1289] 6750 572 2500 250 1000 5000 1750 1500
## [1297] 2400 67 63 1500 178 1750 992 7250
## [1305] 6416 33 5000 1056 2850 174 1000 559
## [1313] 71 1750 1000 750 107 1500 533 30
## [1321] 625 2500 2000 189 1000 621 669 66
## [1329] 2400 790 144 3731 1000 522 500 229
## [1337] 875 777 264 103 1000 33 1000 237
## [1345] 112 1000 127 2000 28 2000 1500 367
## [1353] 800 25 33 3000 84 5500 147 984
## [1361] 62 1059 677 1000 427 10000 176 1000
## [1369] 148 500 165 750 167 2500 125 706
## [1377] 1500 68 1000 79 29 2250 81 2250
## [1385] 155 1750 1018 1500 336 230 11500 167
## [1393] 9500 416 2500 132 1650 308 10888 2000
## [1401] 126 1000 1750 165 50000 285 245 1750
## [1409] 1000 12500 960 6000 2619 9750 967 29000
## [1417] 1250 3000 1750 4000 81911 949 638 69
## [1425] 513 1000 116 235 5250 157 592 2050
## [1433] 78 99 1500 158 927 6500 1062 123
## [1441] 67 3500 120 1250 200 1000 91 954
## [1449] 643 236 625 100 3381 470 9750 31
## [1457] 5400 653 3000 1500 62 32 1000 230
## [1465] 1250 1000 75 66 550 94 394 376
## [1473] 2000 30 1000 67 220 315 1000 1825
## [1481] 235 47 3500 379 1750 4000 362 33
## [1489] 2500 105 33 1000 156 1000 1690 9200
## [1497] 776 387 3000 601 669 162 1200 50
## [1505] 625 137 176 2000 566 103 2500 502
## [1513] 813 2000 269 38 1300 196 1253 67
## [1521] 19000 344 3649 9500 709 13000 1000 26
## [1529] 4447 690 19050 2722 18500 951 20000 577
## [1537] 102 1000 27 1000 117 1131 282 2500
## [1545] 47 1750 2942 53 111 2500 772 560
## [1553] 557 110 2000 54 449 42 1750 52
## [1561] 3000 3000 41 3000 146 50 350 109
## [1569] 31977 37732 21294 12400 10123 8600 3800 5500
## [1577] 2500 1750 35 231 4000 632 560 1500
## [1585] 57 53 1000 102 838 144 106 657
## [1593] 296 4950 1250 64 1000 104 1250 2138
## [1601] 17250 416 26 750 1500 2000 6393 1307
## [1609] 2250 187 622 1000 500 243 819 9000
## [1617] 195 143 1500 163 4500 3750 365 47
## [1625] 14000 210 1250 33 668 799 26 1000
## [1633] 2187 625 382 129 1405 807 669 726
## [1641] 2250 274 37190 875 88 475 912 4000
## [1649] 387 2400 282 384 999 750 551 2500
## [1657] 44 1250 2000 552 384 89 3500 316
## [1665] 1044 264 7388 1000 728 437 2800 289
## [1673] 4900 13734 7350 281 3000 101 1500 625
## [1681] 121 2500 1348 269 2250 6500 47220 1000
## [1689] 31500 11000 3250 2000 655 581 1000 295
## [1697] 65 1750 551 7250 1600 82 445 213
## [1705] 500 48 1000 8155 3416 30500 800 1250
## [1713] 88 413 234 1457 80 2000 1071 9499
## [1721] 5705 99 44500 1716 7000 119 1800 1000
## [1729] 36 242 2500 416 2500 49 1250 264
## [1737] 73 12000 590 90 725 155 2625 148
## [1745] 500 1000 149 69 3500 219 439 7500
## [1753] 272 932 7000 431 159 750 453 5500
## [1761] 1000 44 1000 30 1000 224 750 65
## [1769] 11250 700 55 3500 113 1500 73 1500
## [1777] 31 2300 3000 292 6750 60 1750 88
## [1785] 2750 7500 277 11000 1000 25 63 2100
## [1793] 281 194 384 4500 6500 113 1020 669
## [1801] 1074 506 1000 1000 1500 99 750 284
## [1809] 5000 176 1167 376 2000 409 6750 543
## [1817] 2000 63 66 5000 171 409 7000 706
## [1825] 13500 1000 85 875 518 196 5500 1000
## [1833] 165 363 294 505 25 7470 446 3500
## [1841] 1118 860 4000 95 625 2000 101 62
## [1849] 850 93 62 1700 720 549 538 330
## [1857] 1750 38 3750 283 29 1375 1750 60
## [1865] 3500 7000 7783 25500 2219 12000 42 2000
## [1873] 143 1000 48 460 273 2000 25278 50449
## [1881] 2500 221 750 107 1250 194 80 1000
## [1889] 728 47 5750 264 223 2300 1000 212
## [1897] 750 42 85 1352 6037 769 148 3400
## [1905] 738 290 903 384 2041 945 13726 1134
## [1913] 120 13500 554 60480 1000 96 197 10000
## [1921] 378 2813 4750 210 1000 90 798 10250
## [1929] 101 8000 3548 53500 5000 5750 129 503
## [1937] 1000 349 4000 78 234 742 1000 123
## [1945] 33 1500 2000 141 1250 84 795 358
## [1953] 2400 70 47 1000 134 4000 58 750
## [1961] 410 800 307 1875 2000 5438 617548 58
## [1969] 1500 500 2252 9000 8000 1250 143 6450
## [1977] 1750 670 560 31 206 5000 179 750
## [1985] 725 3959 374 2329 139 63 3500 43
## [1993] 317 1500 902 669 2000 1000 517 666
## [2001] 153 1700 1563 1000 28 600 125 506
## [2009] 706 878 4750 725 6750 1000 134 659
## [2017] 669 1092 86 4816 173 2500 1000 110
## [2025] 1500 1000 49 3000 130 3000 2000 2000
## [2033] 780 575 81 27 275 567 750 296
## [2041] 3500 33 1000 364 2000 79 7000 625
## [2049] 3000 1198 47 10500 75 38 1000 235
## [2057] 2000 32 498 509 973 669 2000 256
## [2065] 377 384 103104 522 719 549 128 2800
## [2073] 65 734 1500 97 72 3000 76 2250
## [2081] 360 799 278 1200 98 120 2500 3000
## [2089] 341 3500 172 738 1000 1000 3000 72
## [2097] 1500 50 1250 259 41 1500 85 1500
## [2105] 89 46 750 106 88 365 750 552
## [2113] 669 84 1000 1000 104 385 4500 555
## [2121] 4000 45 3000 4500 950 446 1000 3500
## [2129] 1295 7500 3000 617 1000 54 170 3500
## [2137] 399 1000 198 1800 132 2500 265 2500
## [2145] 2500 346 4500 210 500 97 38 750
## [2153] 164 157 387 90 1000 267 1020 560
## [2161] 55 1500 83 30 2000 345 2375 90
## [2169] 88 1500 665 79 669 602 62 2578
## [2177] 750 345 750 202 5750 284 74 750
## [2185] 428 292 1750 1000 48 242 2500 31
## [2193] 2400 2250 93 1000 54 3500 82 107
## [2201] 1250 1000 189 5000 335 694 549 591
## [2209] 10000 221 47 3500 216 2333 525 387
## [2217] 3875 2000 233 3000 165 1500 175 111
## [2225] 343 1750 940 26 1500 1054 8000 257
## [2233] 1500 137 2000 42 592 10500 472 325
## [2241] 280 3500 671 384 1000 109 1000 82
## [2249] 5500 1186 437 118 2800 1507 622 5000
## [2257] 684 58 5625 551 1000 2000 1000 337
## [2265] 1500 46 1000 556 6086 298 6500 1000
## [2273] 88 49 2000 223 1625 110 649 1500
## [2281] 1305 5000 500 99 1454 402 2000 1000
## [2289] 117 249 901 1000 348 2000 3750 1500
## [2297] 2250 90 3250 977 11700 139 3750 804
## [2305] 669 29 4500 1067 62 2094 9772 1000
## [2313] 33 74 2750 1000 84 85 3500 127
## [2321] 1250 189 1197 15150 611 1750 1625 80
## [2329] 3250 312 57460 857698 156000 10900 0 139
## [2337] 750 114 797 846 309 3000 1108 1250
## [2345] 403 3500 26 55 7500 3000 5000 616
## [2353] 2300 500 62 500 210 267 415 111
## [2361] 15104 394 560 177 1750 750 431 64
## [2369] 435 7000 35529 22000 26010 1500 174 750
## [2377] 95 30 99 2000 154 955 96 52000
## [2385] 393 2350 578 3750 669 1650 669 1000
## [2393] 5000 80 60 2500 49 1000 750 346
## [2401] 42 387 53 3500 750 800 625 37500
## [2409] 149 12000 729 2000 69 1000 2000 486
## [2417] 1080 33 2500 144 1250 2375 190 145
## [2425] 67 15000 1000 2500 659 1012 6200 1600
## [2433] 28 1000 28 88 2000 98 1076 12000
## [2441] 563 309 60 8700 536 564 2500 5750
## [2449] 830 638 6000 165 800 107 2075 1028
## [2457] 656 5375 10052 491 18813 2500 295 3000
## [2465] 2500 750 176 500 139 224 11094 834
## [2473] 3000 121 2750 101734 40 6000 322 2000
## [2481] 227 4000 4875 722 1500 3000 90 725
## [2489] 2000 1200 75 3553 1000 4500 3000 1400
## [2497] 197 5000 27 1000 599 669 2000 49
## [2505] 476 669 1250 98 1000 1000 89 529
## [2513] 5125 46 807 384 1636 1208 3000 110
## [2521] 694 2750 271 4000 222 15250 168 5222
## [2529] 3999 2000 199 2000 354 3000 44 12000
## [2537] 13600 7000 600 25 1000 129 750 25
## [2545] 1000 31 176 1500 525 7000 229 625
## [2553] 108 2000 1504 389 9300 50 2000 155
## [2561] 572 48 800 750 1250 258 2600 247
## [2569] 1000 53 1500 533 6000 9750 1750 44
## [2577] 770 638 6000 103 514 655 688 2500
## [2585] 81 180 2750 221 1000 184 1505 10500
## [2593] 141 956 669 5000 317 589 4500 1424
## [2601] 6500 1875 926 50 332 531 7500 638
## [2609] 1665 1218 1000 297 1000 203 2500 114
## [2617] 1500 110 1500 85 500 121 1250 92
## [2625] 260 3500 1000 425 487 1246 150 2400
## [2633] 1000 1317 14000 804 7750 893 11750 381
## [2641] 2183 8750 189 1000 669 36 79 1250
## [2649] 1000 1096 13000 1135 633 1000 84 1500
## [2657] 2250 1250 222 647 387 500 40 2340
## [2665] 561 273 2000 875 1500 140 68 1000
## [2673] 95 332 550 58 2000 739 3000 50
## [2681] 293 1000 25 1250 173 1000 73 2000
## [2689] 38 25 1250 27 1250 32 3049 660
## [2697] 7454 143 9833 174 750 3325 236 6250
## [2705] 114 119 1500 1000 42 2000 8325 500
## [2713] 1250 221 45 5250 130 6100 446 3500
## [2721] 383 2750 58 728 1250 761 3750 161
## [2729] 1500 83 2000 325 2000 204 1250 432
## [2737] 320 1200 532 384 130 14000 355 1931
## [2745] 15000 436 15750 2000 172 4000 708 1750
## [2753] 2250 454 1750 288 4000 284 5000 1000
## [2761] 2000 101 950 700 137 27 1000 116
## [2769] 3750 607 322 1000 200 790 202 5000
## [2777] 4800 25 500 1250 92 500 624 3000
## [2785] 346 700 122 764 7000 190 275 376
## [2793] 2000 750 93 2000 372 1500 73 159
## [2801] 700 367 318 1750 60 3000 629 625
## [2809] 61 1000 108 925 1750 860 1250 96
## [2817] 3593 282 8500 100 875 26 1000 2500
## [2825] 2500 413 79 1250 91 1500 3600 303
## [2833] 6750 26 309 2750 56 1200 126 4000
## [2841] 44 2000 170 3000 64 2500 165 2000
## [2849] 77 2000 315 458 10000 643 2000 350
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## [6577] 49 233 2000 1000 347 2000 2500 31
## [6585] 3750 87 3793 1278 1000 125 3500 1774
## [6593] 33 1500 9575 1000 1000 562 2750 117
## [6601] 38 2250 417 308 86 5316 27 2000
## [6609] 105 2000 800 71 3000 62 1500 1000
## [6617] 625 632 1500 76 7952 246 1686 1000
## [6625] 28 140 5750 598 287 2188 1046 56
## [6633] 2500 728 560 1094 384 55 2000 27
## [6641] 2000 57 1000 114 2050 2000 140 96
## [6649] 50162 2000 61 74 5000 186 1294 2070
## [6657] 500 62 1250 327 13728 565 42 1750
## [6665] 1000 85 1000 1000 2000 206 754 18000
## [6673] 465 3500 248 5750 242 94 2750 42
## [6681] 1000 125 3500 29 5750 280 8568 221
## [6689] 2850 374 1600 2250 264 1500 1250 80
## [6697] 1000 2000 641 1000 978 7250 155 10500
## [6705] 426 750 75 2750 66 4500 59 750
## [6713] 55 750 3000 86 1000 82 1000 277
## [6721] 899 318 1500 118 121 1000 1000 78
## [6729] 5500 106 49 2625 1670 1250 241 3500
## [6737] 80 939 23500 589 1000 1250 1250 520
## [6745] 4500 800 168 3000 57 827 9750 217
## [6753] 9000 32 2500 75 830 777 519 384
## [6761] 750 81 248 7500 1986 11500 2000 700
## [6769] 137 1250 625 28 1000 759 13750 137
## [6777] 3750 165 47 1250 166 1000 105 550
## [6785] 275 750 42 406 384 625 28 130
## [6793] 63 3500 1405 688 2000 1000 39 1500
## [6801] 92 4000 255 77 1750 247 797 549
## [6809] 2500 82 26 500 83 584 560 27
## [6817] 2500 58 1000 69 117 7500 347 223
## [6825] 1875 625 180 48349 443 1000 1750 136
## [6833] 1250 71 399 5000 397 3250 119 976
## [6841] 8500 291 25000 2000 112 972 4750 298
## [6849] 3350 11000 973 15000 1000 172 277 2400
## [6857] 750 96 2150 25 191 79 2500 156
## [6865] 1250 1250 638 3500 1750 3946 1250 49
## [6873] 878 376 2000 1000 3000 144 2625 3500
## [6881] 64 1750 26 500 49 5000 426 49
## [6889] 2000 5000 1250 44 1600 193 37 800
## [6897] 750 1088 290 62 1000 26 2500 39
## [6905] 2000 151 47 1000 177 1250 16000 5000
## [6913] 39 1000 131 66 1750 3000 2000 2000
## [6921] 117 158 1500 1000 96 134 6750 168
## [6929] 750 136 1000 81 1043 2750 1750 566
## [6937] 442 258 3000 168 601 669 4000 398
## [6945] 500 100 1000 1650 225 1500 239 893
## [6953] 669 652 14034 28900 884 669 114 5500
## [6961] 25 1750 680 423 29 79 2300 508
## [6969] 10500 235 31 79 2500 40 750 21100
## [6977] 269 13000 750 54 1000 601 273 2000
## [6985] 1756 1500 1199 13500 1090 384 77 1250
## [6993] 1085 4800 3165 331 1141 4500 991 290
## [7001] 25039 44 800 6111 155 16662 933 669
## [7009] 3000 745 1750 367 2750 625 50 1000
## [7017] 1500 314 497 384 1000 766 292 1750
## [7025] 318 4175 3040 2435 48350 1476 7000 178
## [7033] 1600 248 5125 29 247 4500 232 1000
## [7041] 90 508 387 366 9500 525 1000 354
## [7049] 683 669 1500 79922 335 3000 192 800
## [7057] 83 2400 1000 89 532 3500 61407 229
## [7065] 3000 451 1000 1845 472 500 39 3000
## [7073] 114 1000 93 925 47 517 5400 307
## [7081] 3000 249 1000 380 2000 53 6000 488
## [7089] 2000 221 2500 1000 3000 244 24250 28500
## [7097] 1000 159 6500 1051 56 37588 43 2000
## [7105] 560 1000 91 2500 113 2500 239 767
## [7113] 1000 562 1000 643 1468 22750 891 3500
## [7121] 536 1000 833 6500 25 302 625 549
## [7129] 1000 5250 50 3774 22500 6850 1750 3198
## [7137] 402 6000 45 275 216 127 1500 47
## [7145] 1000 54 4790 938 7500 163 1250 101
## [7153] 187 2000 514 536 236 3000 237 4284
## [7161] 22500 793 10500 818 107 380 15000 80
## [7169] 750 57 768 280 3500 242 78 3500
## [7177] 89 839 139 4500 1500 1500 247 38
## [7185] 2000 456 387 99 2000 37 69 2500
## [7193] 412 459 151 500 150 1000 1000 3500
## [7201] 978 750 55 4250 271 1000 460 500
## [7209] 68 500 57 1000 80 1715 1375 27410
## [7217] 1044 3250 500 2000 155 44 88 1000
## [7225] 45 51 3000 750 185 3000 360 1000
## [7233] 1500 42 7200 1303 536 866 139 17000
## [7241] 800 207 750 575 306 1750 4000 2000
## [7249] 147 750 1175 17500 2500 442 13250 750
## [7257] 750 45 2250 426 753 1500 415 6250
## [7265] 686 384 500 1750 221 88 2549 1000
## [7273] 76 1535 663 10000 545 262 500 1750
## [7281] 385 1000 104 594 243 5000 203 671
## [7289] 85 3500 1078 6250 1000 1250 1000 625
## [7297] 2000 109 198 2250 43 170 1750 121
## [7305] 1250 102 159 4500 136 1000 119 2500
## [7313] 133 2000 8625 1000 4000 178 77 4125
## [7321] 55 33 2000 267 3750 160 1500 944
## [7329] 124 1000 10156 2294 40500 47 3000 458
## [7337] 11250 53153 56710 174631 323122 2000 20188 38500
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## [7353] 2000 30 4750 535 449 1000 114 7250
## [7361] 554 669 648 236 3695 640 456 3000
## [7369] 69176 2000 1000 268 1000 27 3750 219
## [7377] 41 1500 44 2000 722 489 1750 75
## [7385] 2500 750 62951 968 454 813 2586 14000
## [7393] 297 28750 27 625 136 301 669 668
## [7401] 318 1750 226 4500 18229 1500 35 3000
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## [7425] 2500 270 13000 327 1750 1000 555 2625
## [7433] 2000 1000 71 2000 2000 80 407 459
## [7441] 589 262 1750 1250 85 2500 459 560
## [7449] 121 5250 47 111 2500 219 1750 26
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## [7465] 1076 88 9560 1500 184 6754 10500 3796
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## [7481] 1000 121 12735 129 2500 1500 32 652
## [7489] 236 8250 839 1000 406 1000 167 330
## [7497] 6000 386 142 56 5055 2500 43 104
## [7505] 7750 994 560 2000 2750 43 1000 2134
## [7513] 60750 61 1375 95 6750 1500 61 741
## [7521] 5500 44 2000 90 1750 1000 92 3375
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## [7537] 359 3000 660 242 3000 624 958 273
## [7545] 2000 750 209 2000 504 2000 83 1150
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## [7569] 2250 83 593 63 1250 99 1924 87
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## [7585] 1250 1657 32 1250 439 1250 3000 72
## [7593] 2500 1200 89 278 159 700 41 1000
## [7601] 119 750 65 412 85 4250 686 8250
## [7609] 221 1250 1000 26 875 3000 4750 1000
## [7617] 1000 76 548 500 66 800 202 1000
## [7625] 64 1050 669 67 3000 52 818 2250
## [7633] 650 3250 1600 79 1250 108 1135 1500
## [7641] 712 15000 1346 655 1000 183 1250 112
## [7649] 2801 790 1000 124 3500 2000 144 2618
## [7657] 9000 3000 887 669 1000 61 2250 33
## [7665] 501 384 316 213 80 723 914 1000
## [7673] 185 1087 292 1750 839 459 4438 392
## [7681] 6000 306 302 750 550 7500 328 3000
## [7689] 353 2500 80 1197 286 11000 40 1000
## [7697] 1810 300 10000 659 292 1750 4000 195
## [7705] 367 2250 1250 28 1250 336 1000 2000
## [7713] 875 30 79 2500 90 3500 69 1000
## [7721] 117 3000 2500 2000 71 1375 69 83
## [7729] 257 2600 43046 5000 50 1000 44 2000
## [7737] 10039 100 3000 1000 74 114 1000 82
## [7745] 2000 225 97 3000 337 589 17750 188
## [7753] 350 47 594 76 3000 243 5717 964
## [7761] 1531 1000 373 3500 111 1000 66 90
## [7769] 79 2500 5500 1250 146500 4001 12962 1000
## [7777] 125 792 726 128 89 1000 25 3637
## [7785] 362 669 1750 1000 229 1250 1250 96
## [7793] 5423 4250 1189 8825 921 103 809 335
## [7801] 9000 2153 11880 508 1000 111 164 79
## [7809] 2500 827 30 2000 281 500 104 15830
## [7817] 1000 105 1000 91 3000 122 519 1000
## [7825] 82 1000 1000 84 442 11500 290 500
## [7833] 252745 1021 120 2500 313 8750 408 875
## [7841] 3000 219 2325 178 60 2500 174 2000
## [7849] 156 2250 1750 93 1250 555 1000 88
## [7857] 3750 93 717 242 2500 3750 15000 607
## [7865] 719 669 1000 55 1000 199 954 1750
## [7873] 280 2500 1250 8075 8000 1794 33000 111
## [7881] 3000 1000 625 622 750 1000 1000 92
## [7889] 86 1075 34 99 607 839 6000 1134
## [7897] 12000 1000 50 3000 82 2000 32 1000
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## [7913] 575 191 500 124 4500 171 162 1000
## [7921] 2000 54 203 3000 66 750 243 1000
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## [7937] 1250 44 2000 204 875 3750 14200 1000
## [7945] 1000 212 692 343 3000 551 12500 47
## [7953] 1000 92 96 100000 95 3000 297 3000
## [7961] 1000 79 625 77 598 33250 768 1750
## [7969] 640 775 1599 236 4500 2000 2500 95
## [7977] 4459 36500 490 2500 1916 82 47 1500
## [7985] 27 1000 50 1477 786 7000 12000 12000
## [7993] 1029 358 3375 1250 74 2500 65 506
## [8001] 236 683 335 2813 750 80 2500 346
## [8009] 2000 44 1000 42 678 25 8400 51
## [8017] 4500 106 140 4000 213 792 337 1750
## [8025] 2000 107 40 3500 7250 523 2250 5206
## [8033] 21500 718 11000 1000 1250 146 3500 86
## [8041] 1250 4500 466 43 1750 31 1250 62
## [8049] 2000 286 1000 806 27 1500 1500 29
## [8057] 814 560 1500 266 1000 578 261 371
## [8065] 669 1031 290 25080 2000 52 55 500
## [8073] 132 1000 28 740 332 1750 1250 100
## [8081] 44202 1250 1500 1000 63 1000 2813 2000
## [8089] 232 1145 290 162901 750 875 1617 5500
## [8097] 733 14500 10370 1000 102 1125 25 1000
## [8105] 104 227 4000 742 4000 923 638 90
## [8113] 2500 57 99 1000 110 2148 13000 1048
## [8121] 4125 500 67 2122 25564 1728 30 15963
## [8129] 877 342 1750 578 669 2000 173 1500
## [8137] 1000 29 65 500 3000 113 5402 1873
## [8145] 19650 2100 1638 42000 899 44 4500 150
## [8153] 38 1000 30 654 6250 340 159 5000
## [8161] 31 625 56 2000 224 66 2000 418
## [8169] 10000 86 750 225 1000 101 77 3375
## [8177] 35 8500 3000 100 1000 44 47 2500
## [8185] 127 365 1500 124 79 1000 111 1000
## [8193] 93 3500 198 31 2000 324 301 182
## [8201] 2500 503 3300 316 1000 33 1000 86
## [8209] 2000 281 1000 1000 1136 5000 89 1250
## [8217] 100 94 1875 713 252 2625 2000 249
## [8225] 554 2000 44 1000 28 1000 88 6000
## [8233] 183 1250 257 2000 190 1000 512 79
## [8241] 2500 6000 482 199 5750 750 7159 53500
## [8249] 8265 2237 3000 56 506 506 153 37500
## [8257] 1500 292 1000 93 859 564 459 1431
## [8265] 4350 130 1000 26 1500 466 427 50
## [8273] 578 1500 88 1250 1000 122 2625 307
## [8281] 1075 63 83 1700 145 3500 486 540
## [8289] 2500 267 66 220 750 6457 251 3500
## [8297] 625 100 161 4375 1423 333 3140 1000
## [8305] 1000 103 1800 705 47 2000 793 1750
## [8313] 2813 8878 29250 1700 62000 1000 297 3447
## [8321] 290 25431 1000 60 7250 324 1000 91
## [8329] 128 6000 353 2625 80 111 2500 1000
## [8337] 118 1000 176 343 4000 2000 35 2000
## [8345] 142 31 1000 513 409 519 78 1250
## [8353] 48 193 3000 85 422 300 669 7878
## [8361] 1000 2000 165 1414 68 2000 38 380
## [8369] 79 669 111 500 1250 4502 39 25262
## [8377] 118 4500 135 3000 63 5500 246 26
## [8385] 126 5700 431 390 3000 593 3250 272
## [8393] 136 2500 73 2000 5500 305 1000 1983
## [8401] 1000 84 1088 613 1750 880 560 10179
## [8409] 601 669 500 1274 7500 346 70 1000
## [8417] 1750 105 1000 60 573 223 1000 332
## [8425] 800 1750 2200 48 806 384 728 590
## [8433] 625 49 1750 114 1000 90 725 172
## [8441] 4500 142 57 2000 500 2000 918 669
## [8449] 193 2375 436 1500 199 31138 66 84
## [8457] 2600 368 3000 172 60 625 720 390
## [8465] 581 50 1250 123 33 7000 105 102
## [8473] 4000 29 2172 419 3750 659 381 9625
## [8481] 414 2500 28 714 861 669 819 384
## [8489] 4000 45 49 6000 3000 712 100 2750
## [8497] 55 1250 2000 426 384 384 5200 193
## [8505] 1200 54 3000 408 387 1056 352 3000
## [8513] 51 1375 60 1500 2940 37 1000 111
## [8521] 1893 31500 738 60 2500 114 3750 123
## [8529] 299 2500 1153 4000 486 6000 750 3175
## [8537] 354 5549 56500 7000 276 237 6375 40
## [8545] 2500 206 6313 5000 1375 66 3250 114
## [8553] 35 63 79 2500 74 2750 113 500
## [8561] 5000 196 484 390 27 1000 145 32
## [8569] 3000 125 750 103 1000 26 179 914
## [8577] 46494 37500 1750 49 350 2500 1000 2750
## [8585] 6188 3143 750 144 625 82 567 128
## [8593] 5875 2000 750 92 1000 107 2750 1750
## [8601] 78 2500 329 7000 1164 669 139 80
## [8609] 5392 47 7000 1000 49 299 85 2500
## [8617] 683 4000 27 1250 90 671 387 750
## [8625] 347 18000 214 1000 2000 1068 95 2938
## [8633] 4000 26 2000 70 47 625 6025 153
## [8641] 4000 1250 2000 1053 183 111 3500 899
## [8649] 384 20000 1100 129 527 416 625 1200
## [8657] 170 2400 1050 62 11272 29902 59638 934
## [8665] 1000 67 1500 198 2500 75 3376 2422
## [8673] 17750 486 7000 2730 574 1000 52 55
## [8681] 2500 145 2000 89 2250 200 1750 83
## [8689] 5000 116 90 1400 500 502 3000 291
## [8697] 3000 1500 96 1250 44 5750 82 2500
## [8705] 1750 1250 4564 4600 103 125 1000 88
## [8713] 449 2000 315 669 1000 4800 40 500
## [8721] 62 67 1000 185 555 1000 3600 653
## [8729] 262 1750 2100 750 279 164 27 2300
## [8737] 295 168 2500 12000 473 35 2609 3750
## [8745] 40 6187 750 354 37 8500 201 223
## [8753] 390 473 121 2000 137 67 1000 1746
## [8761] 3100 11500 15413 1250 159 4000 119 7090
## [8769] 50500 1799 185 9000 387 1750 1000 91
## [8777] 4000 44 1563 38 1200 70 4500 1000
## [8785] 112 750 875 1596 19500 938 5225 251
## [8793] 7000 854 549 4419 165 1250 2000 1500
## [8801] 1000 1000 263 15000 868 7000 1000 72
## [8809] 1750 2391 916 11600 5455 42500 1831 3900
## [8817] 1500 27 1500 203 134 4000 77 3500
## [8825] 549 140 7000 320 384 1627 35 2000
## [8833] 27 1750 217 724 56 2528 785 7700
## [8841] 445 425 487 869 4000 172 9500 769
## [8849] 318 1750 1000 3000 82 1000 115 11977
## [8857] 99 11875 1919 26625 1750 83 33 3375
## [8865] 1017 252 1750 2000 56 1000 35 845
## [8873] 290 1000 85 150 4000 243 691 167
## [8881] 44 1250 56 97 2375 106 754 290
## [8889] 4500 38 2000 373 1312 721 4147 576
## [8897] 387 255 8000 103 4000 601 669 500
## [8905] 82 3500 80 132 5500 228 151 2188
## [8913] 306 415 357 638 487 415 800 215
## [8921] 4419 38750 1301 7189 63 2000 154 2300
## [8929] 144 2000 1000 1000 2000 2750 169 43984
## [8937] 4975 1222 1375 380 1375 331 7000 363
## [8945] 2750 273 1000 115 1000 97 2200 60
## [8953] 4000 104 47 6500 1000 2000 288 79
## [8961] 2500 1000 63 1000 110 3000 205 52
## [8969] 2000 266 1000 750 49 1600 2600 34514
## [8977] 21540 2000 208 2000 282565 28593 24440 4862
## [8985] 14800 57970 54288 171986 5067 231 6000 3000
## [8993] 3500 2504 72416 12000 75609 123 3750 15494
## [9001] 13000 47945 2000 91933 1000 3153 16623 22157
## [9009] 110 6500 6000 90 3375 739 390 52
## [9017] 2000 1938 551 236 1000 127 251 1000
## [9025] 8750 81390 5067 1000 1250 2500 233 141
## [9033] 2500 138 1750 1000 35 25 825 193
## [9041] 625 1000 100 1250 75 655 1000 121
## [9049] 500 750 1038 2800 947 487 1219 79
## [9057] 1218 1037 453 1250 32 1750 232 1250
## [9065] 107 39 4000 87 550 4647 1750 694
## [9073] 10000 4000 394 2500 691 6000 313 1250
## [9081] 389 500 7470 274 1561 459 33 3250
## [9089] 756 273 2000 4090 7750 1109 11600 2750
## [9097] 198 257 9000 122 1250 173 1250 33
## [9105] 47 1000 32 400 1750 1100 40 500
## [9113] 104 177786 90 1250 187 1375 43 727
## [9121] 236 5000 202 3000 158 1000 1000 330
## [9129] 387 2000 101 159 495 110 2500 1250
## [9137] 213 1000 27 2000 1000 52 182 42
## [9145] 8125 437 1377 669 625 92 111 1000
## [9153] 3500 11256 127 10000 1250 52 1750 938
## [9161] 223 1600 3000 64 29206 1111 669 5235
## [9169] 21546 11551 43273 20000 238232 16336 7384 45010
## [9177] 31 2000 93 676 669 35 47 1000
## [9185] 88 5000 505 553 1000 156 535 42
## [9193] 2000 750 50 561 6750 78 2500 500
## [9201] 335 92000 42895 1500 43 2000 295 787
## [9209] 1000 638 1500 115 221 625 580 425
## [9217] 487 140 519 415 47 1000 530 500
## [9225] 84 800 725 560 1000 158 4000 1000
## [9233] 1021 341 1750 80 2813 750 238 278
## [9241] 3000 700 600 600 81 3000 136 15446
## [9249] 7800 1365 1035 23500 2500 3500 1000 55937
## [9257] 232991 66096 31137 50400 73973 77670 50000 24759
## [9265] 8898 9426 70420 224617 33062 3687 51580 39632
## [9273] 6247 32207 123375 2710 29255 44488 4530 137642
## [9281] 16000 24214 18808 28994 8737 88087 185687 6800
## [9289] 17808 33515 18750 5000 117819 7802 7084 3600
## [9297] 150778 287095 13334 672263 6500 2500 14820 780
## [9305] 15891 3600 49018 5944 140656 21841 16237 162900
## [9313] 94800 23100 479314 1250 64385 11000 224997 46660
## [9321] 131863 2000 750 1173 12500 2802 33 1000
## [9329] 55 1144 540 1750 34 1000 2000 220
## [9337] 7750 1000 99 1750 677 459 6024 750
## [9345] 1250 1000 319 10000 417 694 27 1500
## [9353] 46 750 151 317 5075 493 2500 49
## [9361] 714 213 750 36 3750 120 119 1800
## [9369] 31 1250 2500 761 12677 117780 67473 80
## [9377] 9500 358 2000 875 902 669 750 77
## [9385] 1750 45 27 6000 688 5000 934 669
## [9393] 1750 1078 280 101 3500 750 51 1000
## [9401] 26 648 1153 17500 548 11500 669 4800
## [9409] 352 62 3250 85 2000 198 1250 313
## [9417] 1000 104 2000 148 350 7000 167 1500
## [9425] 76 1850 27 1000 235 1500 572 94
## [9433] 85 3000 106 3375 2000 81 180 1750
## [9441] 344 711 233 2500 842 726 1000 975
## [9449] 263 245 102 1000 38102 3300 3000 509
## [9457] 11250 393 3375 132 2750 237 457 242
## [9465] 3500 510 327 3750 322 1000 232 85
## [9473] 502 2000 110 123 7500 647 1500 1100
## [9481] 115 1250 25 1500 97 245 3250 108
## [9489] 1250 343 6500 215 1000 41 247 1000
## [9497] 72133 1500 3250 475 65 60 1000 105
## [9505] 655 387 175 31 2000 163 3000 747
## [9513] 4500 113 5500 82 750 67 1100 86
## [9521] 500 65 3200 267 381 290 187 13500
## [9529] 876 135 5125 287 5000 33 1000 800
## [9537] 980 560 97 1000 104 2000 205 2000
## [9545] 240 99 485 3376 195 750 1469 794
## [9553] 301 1500 220 875 875 800 78 2000
## [9561] 55 1304 318 5428 353 4500 500 270
## [9569] 669 39 1000 88302 18112 1250 163 7497
## [9577] 420 7000 391 2975 115 3000 326 2000
## [9585] 515 1000 2250 3000 143 246 560 55
## [9593] 71 2750 3000 750 30001 208 5500 9871
## [9601] 75400 3679 10600 162 604 5000 252 505
## [9609] 384 170 1000 111 4500 1000 102 1000
## [9617] 2070 26000 1086 9500 23215 594 323 321
## [9625] 54000 28 2000 394 2000 8356 5161 1250
## [9633] 107 74 2000 489 1500 140 143 7500
## [9641] 220 43 4000 104 1500 84 638 96
## [9649] 2000 7000 201 1000 1914 1250 138 589
## [9657] 264 4200 1000 224 66 2000 67 210
## [9665] 500 750 128 94 42 1500 8750 1075
## [9673] 34 1000 594 182 425 487 315 724
## [9681] 638 128 3000 1000 124 8500 9749 648
## [9689] 459 160 800 176 750 101 1000 35
## [9697] 1200 582 669 1750 81 1048 395 489
## [9705] 3600 2489 29750 468 4165 409 474 1500
## [9713] 221 15000 543 292 1750 140 2500 40
## [9721] 3800 580 96 27216 37097 63 1750 39
## [9729] 702 1500 129 66 1750 8750 153 3000
## [9737] 192 577 2000 509 2000 750 10000 79
## [9745] 1500 37 3333 1000 196 1500 800 85621
## [9753] 18000 10000 26 1000 1142 27 2000 350
## [9761] 181 2905 959 3225 1337 188 4000 1000
## [9769] 800 76 1250 125 159 1000 2000 106
## [9777] 41 3000 4000 296 1000 8000 1250 26
## [9785] 2699 3700 750 27 2661 38750 711 1250
## [9793] 134 548 78 486 254 2000 195 2000
## [9801] 450 132 3500 35442 2000 1000 71 143
## [9809] 134 1375 1000 126 1000 87 86 3000
## [9817] 756 487 1173 924 2500 669 974 259
## [9825] 13793 458 309 3000 743 487 234 3000
## [9833] 198 4000 187 1500 625 70 1358 3500
## [9841] 775 2000 1000 90 476 2006 900 67
## [9849] 8091 251 708 312 2250 50 1000 2000
## [9857] 79 1750 44 138 320 3000 600 2000
## [9865] 47 1000 114 1020 151 10000 748 669
## [9873] 78 500 847 11250 213 88 1775 148
## [9881] 242 485 390 306 1750 2625 5250 33
## [9889] 996 318 2625 143 1150 79 1075 94
## [9897] 1100 760 1000 259 93 2000 42 66
## [9905] 99 950 91 625 313 137 621 228
## [9913] 3500 3000 131 1250 108 55 2500 33
## [9921] 4000 224 688 12750 872 3433 1000 750
## [9929] 797 2940 2000 81 132853 486 10000 1000
## [9937] 180 1625 96 589 290 165 102 1000
## [9945] 111 5750 40 100 78 1000 28 891
## [9953] 33 122 1250 1000 50 55 1000 33
## [9961] 2000 41 3500 5000 31 600 1000 628
## [9969] 3000 348 381 1800 750 99 625 181
## [9977] 2000 108 1250 51 1000 256 1190 264
## [9985] 750 184 205 2500 2813 1000 126 2000
## [9993] 83 347 750 66 63 4000 356 4750
## [10001] 313 1750 116 1576 148 1140 750 234
## [10009] 2500 93 2500 1500 33 1000 102 750
## [10017] 333 1500 871 1500 216 7500 2000 195
## [10025] 250 9000 306 1250 700 802 66 4750
## [10033] 100 946 11500 585 2000 207 1066 3500
## [10041] 441 3000 500 64 1000 93 418 119
## [10049] 1800 2000 72 2000 326 1250 58 1625
## [10057] 26 1500 516 560 60 1750 1254 697
## [10065] 2000 100 2000 189 12500 1000 100 2000
## [10073] 72 750 66 821 415 47 348 2100
## [10081] 214 500 1750 65 61 2000 28# The output will be the first 10,000 values for that column.# convert total to numeric variable
pfizer$total <- as.numeric(pfizer$total)
str(pfizer)## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 10087 obs. of 10 variables:
## $ org_indiv : chr "3-D MEDICAL SERVICES LLC" "AA DOCTORS, INC." "ABBO, LILIAN MARGARITA" "ABBO, LILIAN MARGARITA" ...
## $ first_plus: chr "STEVEN BRUCE" "AAKASH MOHAN" "LILIAN MARGARITA" "LILIAN MARGARITA" ...
## $ first_name: chr "STEVEN" "AAKASH" "LILIAN" "LILIAN" ...
## $ last_name : chr "DEITELZWEIG" "AHUJA" "ABBO" "ABBO" ...
## $ city : chr "NEW ORLEANS" "PASO ROBLES" "MIAMI" "MIAMI" ...
## $ state : chr "LA" "CA" "FL" "FL" ...
## $ category : chr "Professional Advising" "Expert-Led Forums" "Business Related Travel" "Meals" ...
## $ cash : num 2625 1000 0 0 1800 ...
## $ other : num 0 0 448 119 0 0 47 0 0 396 ...
## $ total : num 2625 1000 448 119 1800 ...
## - attr(*, "spec")=
## .. cols(
## .. org_indiv = col_character(),
## .. first_plus = col_character(),
## .. first_name = col_character(),
## .. last_name = col_character(),
## .. city = col_character(),
## .. state = col_character(),
## .. category = col_character(),
## .. cash = col_double(),
## .. other = col_double(),
## .. total = col_double()
## .. )# summary of pfizer data
summary(pfizer) ## org_indiv first_plus first_name
## Length:10087 Length:10087 Length:10087
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## last_name city state
## Length:10087 Length:10087 Length:10087
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## category cash other total
## Length:10087 Min. : 0 Min. : 0.0 Min. : 0
## Class :character 1st Qu.: 0 1st Qu.: 0.0 1st Qu.: 191
## Mode :character Median : 0 Median : 41.0 Median : 750
## Mean : 3241 Mean : 266.5 Mean : 3507
## 3rd Qu.: 2000 3rd Qu.: 262.0 3rd Qu.: 2000
## Max. :1185466 Max. :27681.0 Max. :1185466
## NA's :1 NA's :3Now we will use dplyr to manipulate the data, using operations and functions:
. Sort: Largest to smallest, oldest to newest, alphabetical etc.
. Filter: Select a defined subset of the data.
. Summarize/Aggregate: Deriving one value from a series of other values to produce a summary statistic. Examples include: count, sum, mean, median, maximum, minimum etc. Often you’ll group data into categories first, and then aggregate by group.
. Join: Merging entries from two or more datasets based on common field(s), e.g. unique ID number, last name and first name.
. select - Choose which columns to include.
. filter - Filter the data.
. arrange - Sort the data, by size for continuous variables, by date, or alphabetically.
. group_by - Group the data by a categorical variable.
. mutate - Create new column(s) in the data, or change existing column(s).
. rename - Rename column(s).
. bind_rows - Merge two data frames into one, combining data from columns with the same name.
. summarize - Summarize, or aggregate (for each group if following group_by). Often used in conjunction with functions including:
o mean Calculate the mean, or average
o median Calculate the median
o max Find the maximum value
o min Find the minimum value
o sum Add all the values together
o n Count the number of records
There are also various functions to join data, which we will explore below.
Filter and sort the data in specific ways. For each of the following examples, copy the code that follows into your script, and view the results. Notice how we create a new object to hold the processed data.
# Find doctors in California who were paid $10,000 or more by Pfizer to run "Expert-Led Forums."
ca_expert_10000 <- pfizer %>%
filter(state == "CA" & total >= 10000 & category == "Expert-Led Forums")
# Notice the use of == to find values that match the specified text, >= for greater than or equal to, and the Boolean operator &.# Add a sort (using arrange funcion) to the end of the code to list the doctors in descending order by the payments received.
ca_expert_10000 <- pfizer %>%
filter(state == "CA" & total >= 10000 & category == "Expert-Led Forums") %>%
arrange(desc(total))
# If you arrange without the desc function, the sort will be from smallest to largest.# Find doctors in California or New York who were paid $10,000 or more by Pfizer to run "Expert-Led Forums."
ca_ny_expert_10000 <- pfizer %>%
filter((state == "CA" | state == "NY") & total >= 10000 & category == "Expert-Led Forums") %>%
arrange(desc(total))
# Notice the use of the | Boolean operator, and the brackets around that part of the query. This ensures that this part of the query is run first. See what happens if you exclude them.# Find doctors in states other than California who were paid $10,000 or more by Pfizer to run "Expert-Led Forums."
not_ca_expert_10000 <- pfizer %>%
filter(state != "CA" & total >= 10000 & category=="Expert-Led Forums") %>%
arrange(desc(total))
# Notice the use of the != operator to exclude doctors in California.# Find the 20 doctors across the four largest states (CA, TX, FL, NY) who were paid the most for professional advice.
ca_ny_tx_fl_prof_top20 <- pfizer %>%
filter((state=="CA" | state == "NY" | state == "TX" | state == "FL") & category == "Professional Advising") %>%
arrange(desc(total)) %>%
head(20)
# Notice the use of head, which grabs a defined number of rows from the start of a data frame.# Filter the data for all payments for running Expert-Led Forums or for Professional Advising, and arrange alphabetically by doctor (last name, then first name)
expert_advice <- pfizer %>%
filter(category == "Expert-Led Forums" | category == "Professional Advising") %>%
arrange(last_name, first_name)
#Notice that you can sort by multiple variables, separated by commas.# The grepl function finds values containing a particular string of text. This can simplify the code used to filter based on text.
# The search string comes after the | symbol. In this case, the pattern is "Professional".
# Grepl is case-sensitive, capitals matter
# use pattern matching with grepl to filter text
expert_advice <- pfizer %>%
filter(grepl("Expert|Professional", category)) %>%
arrange(last_name, first_name)# You can add the ! Boolean operator to grepl.
not_expert_advice <- pfizer %>%
filter(!grepl("Expert|Professional", category)) %>%
arrange(last_name, first_name)# The bind_rows function appends one data frame to another, here recreating the unfiltered data from the two data frames above.
# merge/append data frames
pfizer2 <- bind_rows(expert_advice, not_expert_advice) readr can write data to CSV and other text files.
# write expert_advice data to a csv file
write_csv(expert_advice, "expert_advice.csv", na="")
# na="" ensures that any empty cells in the data frame are saved as blanks - R represents null values as NA, so if you don't include this, any null values will appear as NA in the saved file.# calculate total payments by state
state_sum <- pfizer %>%
group_by(state) %>%
summarize(sum = sum(total)) %>%
arrange(desc(sum))
# Notice the use of group_by followed by summarize to group and summarize data, here using the function sum.# As above, but for each state also calculate the median payment, and the number of payments
state_summary <- pfizer %>%
group_by(state) %>%
summarize(sum = sum(total), median = median(total), count = n()) %>%
arrange(desc(sum))
# Notice the use of multiple summary functions, sum, median, and n. (You don't specify a variable for n because it is simply counting the number of rows in the data.)# As above, but group by state and category
state_category_summary <- pfizer %>%
group_by(state, category) %>%
summarize(sum = sum(total), median = median(total), count = n()) %>%
arrange(state, category)
# As for arrange, you can group_by by multiple variables, separated by commas.Now let’s see how to work with dates, using the FDA warning letters data.
Filter the data for letters sent from the start of 2005 onwards. FDA sent warning letters from the start of 2005 onwards
You will have to fix “issued”" to be read as a date. If you look back at str(fda), it was read in as a chr (character). To coerce it to be a date, use the command,
fda$issued <- as.Date(fda$issued, "%m/%d/%Y")Check it out by running. Alternatively use the package “lubridate”
#install.packages("lubridate")
library(lubridate)##
## Attaching package: 'lubridate'## The following object is masked from 'package:base':
##
## datefda$issued <- ymd(fda$issued)fda$issued## [1] "1999-05-25" "2000-04-19" "2002-01-28" "2004-11-17" "2004-07-19"
## [6] "2000-02-25" "2000-07-19" "2002-10-30" "2004-01-21" "2000-01-14"
## [11] "2006-11-08" "2000-11-30" "2002-09-27" "2004-06-01" "2004-06-01"
## [16] "2000-08-02" "1997-07-30" "1999-04-30" "2004-08-31" "2005-06-07"
## [21] "1998-11-06" "2008-03-20" "2009-01-14" "2001-07-25" "2006-03-27"
## [26] "1997-11-21" "2009-04-09" "2000-11-30" "2009-11-24" "2009-03-30"
## [31] "2003-06-25" "2003-12-11" "2002-06-11" "2001-12-11" "2007-10-26"
## [36] "2007-03-29" "2009-02-02" "2010-03-17" "2004-06-01" "2002-01-02"
## [41] "2005-05-16" "2000-03-29" "2004-12-10" "2005-05-26" "2004-11-17"
## [46] "2006-11-22" "2008-05-28" "2009-03-02" "1999-07-22" "2005-06-13"
## [51] "2002-06-12" "2003-06-19" "2006-06-16" "2004-07-14" "2002-09-23"
## [56] "2000-05-24" "2009-11-09" "2001-03-09" "1997-10-07" "2008-03-19"
## [61] "1997-06-24" "1997-02-03" "2010-04-01" "2000-03-23" "2009-03-02"
## [66] "2006-04-21" "1998-03-20" "2003-03-17" "2004-05-14" "2005-12-21"
## [71] "2000-04-07" "1999-11-17" "1998-02-09" "1997-08-14" "2001-12-04"
## [76] "2004-02-05" "2001-07-27" "2004-06-14" "2006-08-22" "1999-05-19"
## [81] "2003-10-09" "2009-05-20" "2001-12-21" "2006-03-21" "2002-03-21"
## [86] "2003-07-25" "2007-01-24" "2003-09-29" "2009-04-20" "2002-10-04"
## [91] "2008-09-03" "2009-11-24" "2003-07-08" "2003-07-25" "1998-10-14"
## [96] "2008-05-01" "2006-07-06" "2009-06-26" "2008-04-21" "2005-08-23"
## [101] "2006-03-06" "2006-02-24" "2005-04-11" "2002-02-13" "2001-11-15"
## [106] "2003-04-14" "1997-10-09" "1997-10-10" "2002-01-16" "2002-06-05"
## [111] "2001-07-06" "2009-05-19" "2002-09-12" "2006-07-10" "2008-10-01"
## [116] "2005-06-06" "2004-01-16" "2004-01-16" "2002-09-27" "2008-05-30"
## [121] "2008-07-23" "2000-04-28" "2001-02-22" "2003-06-24" "1998-12-17"
## [126] "2001-04-27" "2004-08-02" "2005-06-10" "2005-10-27" "2009-07-01"
## [131] "2001-02-23" "2007-01-12" "1999-11-24" "2005-06-23" "2004-07-30"
## [136] "2003-11-07" "2004-07-26" "1999-03-01" "2009-09-15" "1997-06-11"
## [141] "2004-12-15" "2005-10-18" "2000-06-21" "2005-05-16" "2001-07-31"
## [146] "1997-10-03" "2006-11-03" "2006-10-12" "2000-09-27" "2000-04-17"
## [151] "2010-03-08" "2007-07-03" "1999-09-21" "2009-06-12" "2008-10-06"
## [156] "2005-05-13" "2000-05-24" "2004-07-28" "2005-01-27" "2005-07-06"
## [161] "2005-07-06" "2004-07-19" "2000-09-20" "1997-01-10" "2009-10-23"
## [166] "2004-07-02" "1998-08-04" "1999-09-28" "2000-08-15" "1999-08-06"
## [171] "2006-10-03" "2002-11-27" "2004-11-22" "2008-05-16" "2005-10-07"
## [176] "2010-01-28" "2008-04-23" "2001-07-06" "2005-02-24" "2005-02-24"
## [181] "1998-10-14" "2007-01-08" "2001-05-23" "2006-08-18" "2005-09-29"
## [186] "2001-08-30" "2009-03-11" "2008-12-01" "2004-07-13" "2003-06-17"
## [191] "2008-06-06" "2004-11-05" "2009-03-03" "1997-10-14" "2008-01-09"
## [196] "1999-10-27" "2000-11-30" "2007-10-26" "2004-03-25" "2008-11-13"
## [201] "2009-05-20" "2004-10-04" "2006-12-04" "2006-12-04" "2001-08-02"
## [206] "2000-07-18" "2005-04-11" "2005-10-03" "2005-10-11" "2001-04-03"
## [211] "2008-11-13" "2008-11-13" "2001-08-30" "2008-07-23" "2006-11-07"
## [216] "2001-02-23" "2008-03-19" "2006-12-20" "2004-05-13" "2002-03-26"
## [221] "2004-05-27" "2004-03-25" "2003-01-23" "1999-10-04" "2004-07-13"
## [226] "2004-09-10" "2003-05-27" "2006-02-20" "2000-07-19" "2006-06-28"
## [231] "2009-01-03" "2006-07-06" "2003-06-18" "2009-01-21" "2010-02-04"
## [236] "2003-03-17" "2002-08-07" "2005-02-15" "2000-06-20" "2006-07-14"
## [241] "2003-10-23" "2005-12-19" "2007-09-04" "2003-03-31" "2000-04-03"
## [246] "2007-07-27" "1997-10-02" "2007-05-30" "1999-04-09" "1996-11-19"
## [251] "1997-11-20" "2009-12-03" "2005-10-14" "2005-03-29" "2007-12-05"
## [256] "2008-09-05" "2008-03-06" "2005-03-16" "2006-02-07" "2000-01-28"
## [261] "1998-07-07" "2005-05-01" "2003-04-08" "2006-09-28" "1997-11-04"
## [266] "2000-06-15" "2009-02-18" "2005-04-11" "2003-07-30" "1999-03-26"
## [271] "2008-08-27" "2009-06-16"str(fda)## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 272 obs. of 5 variables:
## $ name_last : chr "ADELGLASS" "ADKINSON" "ALLEN" "AMSTERDAM" ...
## $ name_first : chr "JEFFREY" "N." "MARK" "DANIEL" ...
## $ name_middle: chr "M." "FRANKLIN" "S." NA ...
## $ issued : Date, format: "1999-05-25" "2000-04-19" ...
## $ office : chr "Center for Drug Evaluation and Research" "Center for Biologics Evaluation and Research" "Center for Devices and Radiological Health" "Center for Biologics Evaluation and Research" ...
## - attr(*, "spec")=
## .. cols(
## .. name_last = col_character(),
## .. name_first = col_character(),
## .. name_middle = col_character(),
## .. issued = col_character(),
## .. office = col_character()
## .. )post2005 <- fda %>%
filter(issued >= "2005-01-01") %>%
arrange(issued)
#Notice that operators like >= can be used for dates, as well as for numbers.# count the letters by year
letters_year <- fda %>%
mutate(year = format(issued, "%Y")) %>%
group_by(year) %>%
summarize(letters=n())
# This code introduces dplyr's mutate function to create a new column in the data. The new variable year is the four-digit year "%Y (see here for more on time and date formats in R), extracted from the issued dates using the format function. Then the code groups by year and counts the number of letters for each one.# add new columns showing how many days and weeks elapsed since each letter was sent
fda <- fda %>%
mutate(days_elapsed = Sys.Date() - issued,
weeks_elapsed = difftime(Sys.Date(), issued, units = "weeks"))
# Notice in the first line that this code changes the fda data frame, rather than creating a new object. The function Sys.Date returns the current date, and if you subtract another date, it will calculate the difference in days. To calculate date and time differences using other units, use the difftime function.
# Notice also that you can mutate multiple columns at one go, separated by commas.Here is an animation for the different types of joins: https://github.com/gadenbuie/tidyexplain
There are a number of join functions in dplyr to combine data from two data frames. Here are the most useful:
. inner_join() returns values from both tables only where there is a match
. left_join() returns all the values from the first-mentioned table, plus those from the second table that match
. semi_join() filters the first-mentioned table to include only values that have matches in the second table
. anti_join() filters the first-mentioned table to include only values that have no matches in the second table.
To illustrate, these joins will find doctors paid by Pfizer to run expert led forums who had also received a warning letter from the FDA:
# join to identify doctors paid to run Expert-led forums who also received a warning letter
expert_warned_inner <- inner_join(pfizer, fda, by=c("first_name" = "name_first", "last_name" = "name_last")) %>%
filter(category=="Expert-Led Forums")
expert_warned_semi <- semi_join(pfizer, fda, by=c("first_name" = "name_first", "last_name" = "name_last")) %>%
filter(category=="Expert-Led Forums")
# The code in by=c() defines how the join should be made. If instructions on how to join the tables are not supplied, dplyr will look for columns with matching names, and perform the join based on those.
# The difference between the two joins above is that the first contains all of the columns from both data frames, while the second gives only columns from the pfizer data frame.In practice, you may wish to inner_join and then use dplyr’s select function to select the columns that you want to retain, for example:
# as above, but select desired columns from data
expert_warned <- inner_join(pfizer, fda, by=c("first_name" = "name_first", "last_name" = "name_last")) %>%
filter(category=="Expert-Led Forums") %>%
select(first_plus, last_name, city, state, total, issued)
expert_warned <- inner_join(pfizer, fda, by=c("first_name" = "name_first", "last_name" = "name_last")) %>%
filter(category=="Expert-Led Forums") %>%
select(2:5,10,12)
# Notice that you can select by columns' names, or by their positions, where 1 is the first column, 3 is the third, and so on.