BISMILLAH VISDAT A
Shalma Kaisya Candradewi, G1401221105
2024-03-08
library(readxl)## Warning: package 'readxl' was built under R version 4.3.2library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.3.3library(treemap)## Warning: package 'treemap' was built under R version 4.3.3library(reshape2)## Warning: package 'reshape2' was built under R version 4.3.3library(dplyr)## Warning: package 'dplyr' was built under R version 4.3.2##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(plyr)## Warning: package 'plyr' was built under R version 4.3.2## ------------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## ------------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following objects are masked from 'package:dplyr':
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## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarizedata.visdat <- read.csv("C:/Users/ACER/Downloads/ds_salaries (2).csv",sep = ",")
data.visdat## id work_year experience_level employment_type
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## job_title salary salary_currency
## 1 Data Scientist 70000 EUR
## 2 Machine Learning Scientist 260000 USD
## 3 Big Data Engineer 85000 GBP
## 4 Product Data Analyst 20000 USD
## 5 Machine Learning Engineer 150000 USD
## 6 Data Analyst 72000 USD
## 7 Lead Data Scientist 190000 USD
## 8 Data Scientist 11000000 HUF
## 9 Business Data Analyst 135000 USD
## 10 Lead Data Engineer 125000 USD
## 11 Data Scientist 45000 EUR
## 12 Data Scientist 3000000 INR
## 13 Data Scientist 35000 EUR
## 14 Lead Data Analyst 87000 USD
## 15 Data Analyst 85000 USD
## 16 Data Analyst 8000 USD
## 17 Data Engineer 4450000 JPY
## 18 Big Data Engineer 100000 EUR
## 19 Data Science Consultant 423000 INR
## 20 Lead Data Engineer 56000 USD
## 21 Machine Learning Engineer 299000 CNY
## 22 Product Data Analyst 450000 INR
## 23 Data Engineer 42000 EUR
## 24 BI Data Analyst 98000 USD
## 25 Lead Data Scientist 115000 USD
## 26 Director of Data Science 325000 USD
## 27 Research Scientist 42000 USD
## 28 Data Engineer 720000 MXN
## 29 Business Data Analyst 100000 USD
## 30 Machine Learning Manager 157000 CAD
## 31 Data Engineering Manager 51999 EUR
## 32 Big Data Engineer 70000 USD
## 33 Data Scientist 60000 EUR
## 34 Research Scientist 450000 USD
## 35 Data Analyst 41000 EUR
## 36 Data Engineer 65000 EUR
## 37 Data Science Consultant 103000 USD
## 38 Machine Learning Engineer 250000 USD
## 39 Data Analyst 10000 USD
## 40 Machine Learning Engineer 138000 USD
## 41 Data Scientist 45760 USD
## 42 Data Engineering Manager 70000 EUR
## 43 Machine Learning Infrastructure Engineer 44000 EUR
## 44 Data Engineer 106000 USD
## 45 Data Engineer 88000 GBP
## 46 ML Engineer 14000 EUR
## 47 Data Scientist 60000 GBP
## 48 Data Engineer 188000 USD
## 49 Data Scientist 105000 USD
## 50 Data Engineer 61500 EUR
## 51 Data Analyst 450000 INR
## 52 Data Analyst 91000 USD
## 53 AI Scientist 300000 DKK
## 54 Data Engineer 48000 EUR
## 55 Computer Vision Engineer 60000 USD
## 56 Principal Data Scientist 130000 EUR
## 57 Data Scientist 34000 EUR
## 58 Data Scientist 118000 USD
## 59 Data Scientist 120000 USD
## 60 Data Scientist 138350 USD
## 61 Data Engineer 110000 USD
## 62 Data Engineer 130800 USD
## 63 Data Scientist 19000 EUR
## 64 Data Scientist 412000 USD
## 65 Machine Learning Engineer 40000 EUR
## 66 Data Scientist 55000 EUR
## 67 Data Scientist 43200 EUR
## 68 Data Science Manager 190200 USD
## 69 Data Scientist 105000 USD
## 70 Data Scientist 80000 EUR
## 71 Data Scientist 55000 EUR
## 72 Data Scientist 37000 EUR
## 73 Research Scientist 60000 GBP
## 74 BI Data Analyst 150000 USD
## 75 Head of Data 235000 USD
## 76 Data Scientist 45000 EUR
## 77 BI Data Analyst 100000 USD
## 78 3D Computer Vision Researcher 400000 INR
## 79 ML Engineer 270000 USD
## 80 Data Analyst 80000 USD
## 81 Data Analytics Engineer 67000 EUR
## 82 Data Engineer 140000 USD
## 83 Applied Data Scientist 68000 CAD
## 84 Machine Learning Engineer 40000 EUR
## 85 Director of Data Science 130000 EUR
## 86 Data Engineer 110000 PLN
## 87 Data Analyst 50000 EUR
## 88 Data Analytics Engineer 110000 USD
## 89 Lead Data Analyst 170000 USD
## 90 Data Analyst 80000 USD
## 91 Marketing Data Analyst 75000 EUR
## 92 Data Science Consultant 65000 EUR
## 93 Lead Data Analyst 1450000 INR
## 94 Lead Data Engineer 276000 USD
## 95 Data Scientist 2200000 INR
## 96 Cloud Data Engineer 120000 SGD
## 97 AI Scientist 12000 USD
## 98 Financial Data Analyst 450000 USD
## 99 Computer Vision Software Engineer 70000 USD
## 100 Computer Vision Software Engineer 81000 EUR
## 101 Data Analyst 75000 USD
## 102 Data Engineer 150000 USD
## 103 BI Data Analyst 11000000 HUF
## 104 Data Analyst 62000 USD
## 105 Data Scientist 73000 USD
## 106 Data Analyst 37456 GBP
## 107 Research Scientist 235000 CAD
## 108 Data Engineer 115000 USD
## 109 Data Engineer 150000 USD
## 110 Data Engineer 2250000 INR
## 111 Machine Learning Engineer 80000 EUR
## 112 Director of Data Engineering 82500 GBP
## 113 Lead Data Engineer 75000 GBP
## 114 AI Scientist 12000 USD
## 115 Data Engineer 38400 EUR
## 116 Machine Learning Scientist 225000 USD
## 117 Data Scientist 50000 USD
## 118 Data Science Engineer 34000 EUR
## 119 Data Analyst 90000 USD
## 120 Data Engineer 200000 USD
## 121 Big Data Engineer 60000 USD
## 122 Principal Data Engineer 200000 USD
## 123 Data Analyst 50000 USD
## 124 Applied Data Scientist 80000 GBP
## 125 Data Analyst 8760 EUR
## 126 Principal Data Scientist 151000 USD
## 127 Machine Learning Scientist 120000 USD
## 128 Data Scientist 700000 INR
## 129 Machine Learning Engineer 20000 USD
## 130 Lead Data Scientist 3000000 INR
## 131 Machine Learning Developer 100000 USD
## 132 Data Scientist 42000 EUR
## 133 Applied Machine Learning Scientist 38400 USD
## 134 Computer Vision Engineer 24000 USD
## 135 Data Scientist 100000 USD
## 136 Data Analyst 90000 USD
## 137 ML Engineer 7000000 JPY
## 138 ML Engineer 8500000 JPY
## 139 Principal Data Scientist 220000 USD
## 140 Data Scientist 80000 USD
## 141 Data Analyst 135000 USD
## 142 Data Science Manager 240000 USD
## 143 Data Engineering Manager 150000 USD
## 144 Data Scientist 82500 USD
## 145 Data Engineer 100000 USD
## 146 Machine Learning Engineer 70000 EUR
## 147 Research Scientist 53000 EUR
## 148 Data Engineer 90000 USD
## 149 Data Engineering Manager 153000 USD
## 150 Cloud Data Engineer 160000 USD
## 151 Director of Data Science 168000 USD
## 152 Data Scientist 150000 USD
## 153 Data Scientist 95000 CAD
## 154 Data Scientist 13400 USD
## 155 Data Science Manager 144000 USD
## 156 Data Science Engineer 159500 CAD
## 157 Data Scientist 160000 SGD
## 158 Applied Machine Learning Scientist 423000 USD
## 159 Data Analytics Manager 120000 USD
## 160 Machine Learning Engineer 125000 USD
## 161 Head of Data 230000 USD
## 162 Head of Data Science 85000 USD
## 163 Data Engineer 24000 EUR
## 164 Data Science Consultant 54000 EUR
## 165 Director of Data Science 110000 EUR
## 166 Data Specialist 165000 USD
## 167 Data Engineer 80000 USD
## 168 Director of Data Science 250000 USD
## 169 BI Data Analyst 55000 USD
## 170 Data Architect 150000 USD
## 171 Data Architect 170000 USD
## 172 Data Engineer 60000 GBP
## 173 Data Analyst 60000 USD
## 174 Principal Data Scientist 235000 USD
## 175 Research Scientist 51400 EUR
## 176 Data Engineering Manager 174000 USD
## 177 Data Scientist 58000 MXN
## 178 Data Scientist 30400000 CLP
## 179 Machine Learning Engineer 81000 USD
## 180 Data Scientist 420000 INR
## 181 Big Data Engineer 1672000 INR
## 182 Data Scientist 76760 EUR
## 183 Data Engineer 22000 EUR
## 184 Finance Data Analyst 45000 GBP
## 185 Machine Learning Scientist 12000 USD
## 186 Data Engineer 4000 USD
## 187 Data Analytics Engineer 50000 USD
## 188 Data Science Consultant 59000 EUR
## 189 Data Engineer 65000 EUR
## 190 Machine Learning Engineer 74000 USD
## 191 Data Science Manager 152000 USD
## 192 Machine Learning Engineer 21844 USD
## 193 Big Data Engineer 18000 USD
## 194 Data Science Manager 174000 USD
## 195 Research Scientist 120500 CAD
## 196 Data Scientist 147000 USD
## 197 BI Data Analyst 9272 USD
## 198 Machine Learning Engineer 1799997 INR
## 199 Data Science Manager 4000000 INR
## 200 Data Science Consultant 90000 USD
## 201 Data Scientist 52000 EUR
## 202 Machine Learning Infrastructure Engineer 195000 USD
## 203 Data Scientist 32000 EUR
## 204 Research Scientist 50000 USD
## 205 Data Scientist 160000 USD
## 206 Data Scientist 69600 BRL
## 207 Machine Learning Engineer 200000 USD
## 208 Data Engineer 165000 USD
## 209 Data Engineer 20000 USD
## 210 Data Analytics Manager 120000 USD
## 211 Machine Learning Engineer 21000 EUR
## 212 Research Scientist 48000 EUR
## 213 Data Engineer 48000 GBP
## 214 Big Data Engineer 435000 INR
## 215 Machine Learning Engineer 21000 EUR
## 216 Principal Data Engineer 185000 USD
## 217 Computer Vision Engineer 180000 DKK
## 218 Data Scientist 76760 EUR
## 219 Machine Learning Engineer 75000 EUR
## 220 Data Analytics Manager 140000 USD
## 221 Machine Learning Engineer 180000 PLN
## 222 Data Scientist 85000 GBP
## 223 Data Scientist 2500000 INR
## 224 Data Scientist 40900 GBP
## 225 Machine Learning Scientist 225000 USD
## 226 Principal Data Scientist 416000 USD
## 227 Data Scientist 110000 CAD
## 228 Data Scientist 75000 EUR
## 229 Data Scientist 135000 USD
## 230 Data Analyst 90000 CAD
## 231 Big Data Engineer 1200000 INR
## 232 ML Engineer 256000 USD
## 233 Director of Data Engineering 200000 USD
## 234 Data Analyst 200000 USD
## 235 Data Architect 180000 USD
## 236 Head of Data Science 110000 USD
## 237 Research Scientist 80000 CAD
## 238 Data Scientist 39600 EUR
## 239 Data Scientist 4000 USD
## 240 Data Engineer 1600000 INR
## 241 Data Scientist 130000 CAD
## 242 Data Analyst 80000 USD
## 243 Data Engineer 110000 USD
## 244 Data Scientist 165000 USD
## 245 AI Scientist 1335000 INR
## 246 Data Engineer 52500 GBP
## 247 Data Scientist 31000 EUR
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## 445 215300 US 0 US L
## 446 76940 GR 100 GR M
## 447 209100 US 100 US L
## 448 154600 US 100 US L
## 449 180000 US 100 US M
## 450 21983 PT 100 PT L
## 451 80000 US 100 US M
## 452 78791 CA 100 CA M
## 453 196979 CA 50 CA L
## 454 120000 US 100 US S
## 455 125000 US 0 US M
## 456 37236 US 50 US L
## 457 105000 US 100 US M
## 458 87932 DE 0 DE M
## 459 18442 IN 100 IN M
## 460 31615 IN 100 IN L
## 461 58255 PT 50 PT L
## 462 100000 US 50 US L
## 463 54957 DE 50 DE L
## 464 18442 IN 100 IN M
## 465 162674 DE 100 DE M
## 466 120000 US 100 US M
## 467 144000 US 50 US L
## 468 104890 US 100 US M
## 469 100000 US 100 US M
## 470 140000 US 100 US M
## 471 135000 US 100 US M
## 472 50000 US 100 US M
## 473 220000 US 100 US M
## 474 140000 US 100 US M
## 475 183228 GB 0 GB M
## 476 91614 GB 0 GB M
## 477 185100 US 100 US M
## 478 220000 US 100 US M
## 479 200000 US 100 US M
## 480 120000 US 100 US M
## 481 120000 AE 100 AE S
## 482 65000 AE 100 AE S
## 483 324000 US 100 US M
## 484 216000 US 100 US M
## 485 210000 US 100 US M
## 486 120000 US 100 US M
## 487 230000 US 100 US M
## 488 100000 DZ 50 DZ M
## 489 100000 CA 100 US M
## 490 31875 TN 100 CZ M
## 491 200000 MY 100 US M
## 492 75000 CA 100 CA S
## 493 35590 PL 100 PL L
## 494 78791 CA 100 CA M
## 495 100000 BR 100 US M
## 496 153000 US 50 US M
## 497 58035 PK 100 DE M
## 498 165000 US 100 US M
## 499 93427 FR 50 FR L
## 500 52396 CA 100 CA L
## 501 62651 NL 100 NL L
## 502 32974 EE 100 EE S
## 503 40000 JP 100 MY L
## 504 87425 AU 100 AU L
## 505 115000 US 100 US M
## 506 86703 AU 50 AU M
## 507 75000 BO 100 US L
## 508 64849 AT 0 AT L
## 509 120000 US 100 US L
## 510 157000 US 100 US L
## 511 150000 AU 100 AU S
## 512 70912 CA 50 CA L
## 513 65000 US 100 US S
## 514 71444 IE 100 IE S
## 515 20000 PK 0 PK M
## 516 48000 RU 100 US S
## 517 152500 US 100 US M
## 518 68147 FR 100 FR M
## 519 122346 CH 0 CH L
## 520 380000 US 100 US L
## 521 69336 CA 100 CA M
## 522 10000 PT 100 LU M
## 523 20000 GR 100 GR S
## 524 405000 US 100 US L
## 525 135000 US 100 US L
## 526 177000 US 100 US L
## 527 78000 US 100 US M
## 528 135000 US 100 US M
## 529 100000 US 100 US M
## 530 90320 US 100 US M
## 531 85000 CA 0 CA M
## 532 75000 CA 0 CA M
## 533 214000 US 100 US M
## 534 192600 US 100 US M
## 535 266400 US 100 US M
## 536 213120 US 100 US M
## 537 112900 US 100 US M
## 538 155000 US 100 US M
## 539 141300 US 0 US M
## 540 102100 US 0 US M
## 541 115934 US 100 US M
## 542 81666 US 100 US M
## 543 206699 US 0 US M
## 544 99100 US 0 US M
## 545 130000 US 100 US M
## 546 115000 US 100 US M
## 547 110500 US 100 US M
## 548 130000 US 100 US M
## 549 99050 US 100 US M
## 550 160000 US 100 US M
## 551 205300 US 0 US L
## 552 140400 US 0 US L
## 553 176000 US 100 US M
## 554 144000 US 100 US M
## 555 200100 US 100 US M
## 556 160000 US 100 US M
## 557 145000 US 100 US M
## 558 70500 US 0 US M
## 559 205300 US 0 US M
## 560 140400 US 0 US M
## 561 205300 US 0 US M
## 562 184700 US 0 US M
## 563 175100 US 100 US M
## 564 140250 US 100 US M
## 565 116150 US 100 US M
## 566 54000 US 0 US M
## 567 170000 US 100 US M
## 568 65438 GB 0 GB M
## 569 80000 US 100 US M
## 570 140000 US 100 US M
## 571 210000 US 100 US M
## 572 140000 US 100 US M
## 573 100000 US 100 US M
## 574 69000 US 100 US M
## 575 210000 US 100 US M
## 576 140000 US 100 US M
## 577 210000 US 100 US M
## 578 150075 US 100 US M
## 579 100000 US 100 US M
## 580 25000 US 100 US M
## 581 126500 US 100 US M
## 582 106260 US 100 US M
## 583 220110 US 100 US M
## 584 160080 US 100 US M
## 585 105000 US 100 US M
## 586 110925 US 100 US M
## 587 45807 GB 0 GB M
## 588 140000 US 100 US M
## 589 99000 US 0 US M
## 590 60000 US 100 US M
## 591 192564 US 100 US M
## 592 144854 US 100 US M
## 593 230000 US 100 US M
## 594 150000 US 100 US M
## 595 150260 US 100 US M
## 596 109280 US 100 US M
## 597 210000 US 100 US M
## 598 170000 US 100 US M
## 599 160000 US 100 US M
## 600 130000 US 100 US M
## 601 67000 CA 0 CA M
## 602 52000 CA 0 CA M
## 603 154000 US 100 US M
## 604 126000 US 100 US M
## 605 129000 US 0 US M
## 606 150000 US 100 US M
## 607 200000 IN 100 US L#VISUALISASI PROPORSI
salary_data <- c(data.visdat$salary_currency)
salary_data## [1] "EUR" "USD" "GBP" "USD" "USD" "USD" "USD" "HUF" "USD" "USD" "EUR" "INR"
## [13] "EUR" "USD" "USD" "USD" "JPY" "EUR" "INR" "USD" "CNY" "INR" "EUR" "USD"
## [25] "USD" "USD" "USD" "MXN" "USD" "CAD" "EUR" "USD" "EUR" "USD" "EUR" "EUR"
## [37] "USD" "USD" "USD" "USD" "USD" "EUR" "EUR" "USD" "GBP" "EUR" "GBP" "USD"
## [49] "USD" "EUR" "INR" "USD" "DKK" "EUR" "USD" "EUR" "EUR" "USD" "USD" "USD"
## [61] "USD" "USD" "EUR" "USD" "EUR" "EUR" "EUR" "USD" "USD" "EUR" "EUR" "EUR"
## [73] "GBP" "USD" "USD" "EUR" "USD" "INR" "USD" "USD" "EUR" "USD" "CAD" "EUR"
## [85] "EUR" "PLN" "EUR" "USD" "USD" "USD" "EUR" "EUR" "INR" "USD" "INR" "SGD"
## [97] "USD" "USD" "USD" "EUR" "USD" "USD" "HUF" "USD" "USD" "GBP" "CAD" "USD"
## [109] "USD" "INR" "EUR" "GBP" "GBP" "USD" "EUR" "USD" "USD" "EUR" "USD" "USD"
## [121] "USD" "USD" "USD" "GBP" "EUR" "USD" "USD" "INR" "USD" "INR" "USD" "EUR"
## [133] "USD" "USD" "USD" "USD" "JPY" "JPY" "USD" "USD" "USD" "USD" "USD" "USD"
## [145] "USD" "EUR" "EUR" "USD" "USD" "USD" "USD" "USD" "CAD" "USD" "USD" "CAD"
## [157] "SGD" "USD" "USD" "USD" "USD" "USD" "EUR" "EUR" "EUR" "USD" "USD" "USD"
## [169] "USD" "USD" "USD" "GBP" "USD" "USD" "EUR" "USD" "MXN" "CLP" "USD" "INR"
## [181] "INR" "EUR" "EUR" "GBP" "USD" "USD" "USD" "EUR" "EUR" "USD" "USD" "USD"
## [193] "USD" "USD" "CAD" "USD" "USD" "INR" "INR" "USD" "EUR" "USD" "EUR" "USD"
## [205] "USD" "BRL" "USD" "USD" "USD" "USD" "EUR" "EUR" "GBP" "INR" "EUR" "USD"
## [217] "DKK" "EUR" "EUR" "USD" "PLN" "GBP" "INR" "GBP" "USD" "USD" "CAD" "EUR"
## [229] "USD" "CAD" "INR" "USD" "USD" "USD" "USD" "USD" "CAD" "EUR" "USD" "INR"
## [241] "CAD" "USD" "USD" "USD" "INR" "GBP" "EUR" "TRY" "GBP" "USD" "USD" "USD"
## [253] "USD" "INR" "USD" "CAD" "USD" "EUR" "USD" "EUR" "USD" "EUR" "INR" "INR"
## [265] "EUR" "USD" "USD" "USD" "TRY" "EUR" "USD" "BRL" "EUR" "USD" "EUR" "USD"
## [277] "USD" "USD" "TRY" "EUR" "USD" "USD" "EUR" "USD" "USD" "INR" "EUR" "USD"
## [289] "EUR" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD"
## [301] "GBP" "GBP" "USD" "USD" "GBP" "USD" "USD" "USD" "USD" "USD" "USD" "GBP"
## [313] "GBP" "GBP" "GBP" "USD" "GBP" "USD" "USD" "USD" "USD" "USD" "USD" "USD"
## [325] "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD"
## [337] "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD"
## [349] "USD" "USD" "USD" "USD" "USD" "USD" "GBP" "GBP" "USD" "USD" "USD" "USD"
## [361] "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD"
## [373] "USD" "EUR" "EUR" "CAD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD"
## [385] "INR" "USD" "GBP" "USD" "USD" "GBP" "GBP" "USD" "USD" "USD" "USD" "USD"
## [397] "EUR" "GBP" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "GBP" "USD" "USD"
## [409] "GBP" "USD" "GBP" "GBP" "EUR" "EUR" "GBP" "GBP" "USD" "USD" "USD" "USD"
## [421] "USD" "USD" "USD" "USD" "USD" "USD" "USD" "EUR" "USD" "GBP" "EUR" "EUR"
## [433] "EUR" "EUR" "GBP" "GBP" "EUR" "EUR" "USD" "USD" "EUR" "EUR" "GBP" "GBP"
## [445] "USD" "EUR" "USD" "USD" "USD" "EUR" "USD" "CAD" "CAD" "USD" "USD" "CNY"
## [457] "USD" "EUR" "INR" "INR" "EUR" "USD" "EUR" "INR" "EUR" "USD" "USD" "USD"
## [469] "USD" "USD" "USD" "USD" "USD" "USD" "GBP" "GBP" "USD" "USD" "USD" "USD"
## [481] "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "EUR" "USD" "USD"
## [493] "PLN" "CAD" "USD" "USD" "EUR" "USD" "EUR" "CAD" "EUR" "EUR" "USD" "AUD"
## [505] "USD" "AUD" "USD" "EUR" "USD" "USD" "USD" "CAD" "USD" "EUR" "USD" "USD"
## [517] "USD" "EUR" "CHF" "USD" "CAD" "USD" "USD" "USD" "USD" "USD" "USD" "USD"
## [529] "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD"
## [541] "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD"
## [553] "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD"
## [565] "USD" "USD" "USD" "GBP" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD"
## [577] "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "GBP" "USD"
## [589] "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD" "USD"
## [601] "USD" "USD" "USD" "USD" "USD" "USD" "USD"library(d3Tree)## Warning: package 'd3Tree' was built under R version 4.3.3data_frame <- data.frame(salary_currency = salary_data)
data_frame## salary_currency
## 1 EUR
## 2 USD
## 3 GBP
## 4 USD
## 5 USD
## 6 USD
## 7 USD
## 8 HUF
## 9 USD
## 10 USD
## 11 EUR
## 12 INR
## 13 EUR
## 14 USD
## 15 USD
## 16 USD
## 17 JPY
## 18 EUR
## 19 INR
## 20 USD
## 21 CNY
## 22 INR
## 23 EUR
## 24 USD
## 25 USD
## 26 USD
## 27 USD
## 28 MXN
## 29 USD
## 30 CAD
## 31 EUR
## 32 USD
## 33 EUR
## 34 USD
## 35 EUR
## 36 EUR
## 37 USD
## 38 USD
## 39 USD
## 40 USD
## 41 USD
## 42 EUR
## 43 EUR
## 44 USD
## 45 GBP
## 46 EUR
## 47 GBP
## 48 USD
## 49 USD
## 50 EUR
## 51 INR
## 52 USD
## 53 DKK
## 54 EUR
## 55 USD
## 56 EUR
## 57 EUR
## 58 USD
## 59 USD
## 60 USD
## 61 USD
## 62 USD
## 63 EUR
## 64 USD
## 65 EUR
## 66 EUR
## 67 EUR
## 68 USD
## 69 USD
## 70 EUR
## 71 EUR
## 72 EUR
## 73 GBP
## 74 USD
## 75 USD
## 76 EUR
## 77 USD
## 78 INR
## 79 USD
## 80 USD
## 81 EUR
## 82 USD
## 83 CAD
## 84 EUR
## 85 EUR
## 86 PLN
## 87 EUR
## 88 USD
## 89 USD
## 90 USD
## 91 EUR
## 92 EUR
## 93 INR
## 94 USD
## 95 INR
## 96 SGD
## 97 USD
## 98 USD
## 99 USD
## 100 EUR
## 101 USD
## 102 USD
## 103 HUF
## 104 USD
## 105 USD
## 106 GBP
## 107 CAD
## 108 USD
## 109 USD
## 110 INR
## 111 EUR
## 112 GBP
## 113 GBP
## 114 USD
## 115 EUR
## 116 USD
## 117 USD
## 118 EUR
## 119 USD
## 120 USD
## 121 USD
## 122 USD
## 123 USD
## 124 GBP
## 125 EUR
## 126 USD
## 127 USD
## 128 INR
## 129 USD
## 130 INR
## 131 USD
## 132 EUR
## 133 USD
## 134 USD
## 135 USD
## 136 USD
## 137 JPY
## 138 JPY
## 139 USD
## 140 USD
## 141 USD
## 142 USD
## 143 USD
## 144 USD
## 145 USD
## 146 EUR
## 147 EUR
## 148 USD
## 149 USD
## 150 USD
## 151 USD
## 152 USD
## 153 CAD
## 154 USD
## 155 USD
## 156 CAD
## 157 SGD
## 158 USD
## 159 USD
## 160 USD
## 161 USD
## 162 USD
## 163 EUR
## 164 EUR
## 165 EUR
## 166 USD
## 167 USD
## 168 USD
## 169 USD
## 170 USD
## 171 USD
## 172 GBP
## 173 USD
## 174 USD
## 175 EUR
## 176 USD
## 177 MXN
## 178 CLP
## 179 USD
## 180 INR
## 181 INR
## 182 EUR
## 183 EUR
## 184 GBP
## 185 USD
## 186 USD
## 187 USD
## 188 EUR
## 189 EUR
## 190 USD
## 191 USD
## 192 USD
## 193 USD
## 194 USD
## 195 CAD
## 196 USD
## 197 USD
## 198 INR
## 199 INR
## 200 USD
## 201 EUR
## 202 USD
## 203 EUR
## 204 USD
## 205 USD
## 206 BRL
## 207 USD
## 208 USD
## 209 USD
## 210 USD
## 211 EUR
## 212 EUR
## 213 GBP
## 214 INR
## 215 EUR
## 216 USD
## 217 DKK
## 218 EUR
## 219 EUR
## 220 USD
## 221 PLN
## 222 GBP
## 223 INR
## 224 GBP
## 225 USD
## 226 USD
## 227 CAD
## 228 EUR
## 229 USD
## 230 CAD
## 231 INR
## 232 USD
## 233 USD
## 234 USD
## 235 USD
## 236 USD
## 237 CAD
## 238 EUR
## 239 USD
## 240 INR
## 241 CAD
## 242 USD
## 243 USD
## 244 USD
## 245 INR
## 246 GBP
## 247 EUR
## 248 TRY
## 249 GBP
## 250 USD
## 251 USD
## 252 USD
## 253 USD
## 254 INR
## 255 USD
## 256 CAD
## 257 USD
## 258 EUR
## 259 USD
## 260 EUR
## 261 USD
## 262 EUR
## 263 INR
## 264 INR
## 265 EUR
## 266 USD
## 267 USD
## 268 USD
## 269 TRY
## 270 EUR
## 271 USD
## 272 BRL
## 273 EUR
## 274 USD
## 275 EUR
## 276 USD
## 277 USD
## 278 USD
## 279 TRY
## 280 EUR
## 281 USD
## 282 USD
## 283 EUR
## 284 USD
## 285 USD
## 286 INR
## 287 EUR
## 288 USD
## 289 EUR
## 290 USD
## 291 USD
## 292 USD
## 293 USD
## 294 USD
## 295 USD
## 296 USD
## 297 USD
## 298 USD
## 299 USD
## 300 USD
## 301 GBP
## 302 GBP
## 303 USD
## 304 USD
## 305 GBP
## 306 USD
## 307 USD
## 308 USD
## 309 USD
## 310 USD
## 311 USD
## 312 GBP
## 313 GBP
## 314 GBP
## 315 GBP
## 316 USD
## 317 GBP
## 318 USD
## 319 USD
## 320 USD
## 321 USD
## 322 USD
## 323 USD
## 324 USD
## 325 USD
## 326 USD
## 327 USD
## 328 USD
## 329 USD
## 330 USD
## 331 USD
## 332 USD
## 333 USD
## 334 USD
## 335 USD
## 336 USD
## 337 USD
## 338 USD
## 339 USD
## 340 USD
## 341 USD
## 342 USD
## 343 USD
## 344 USD
## 345 USD
## 346 USD
## 347 USD
## 348 USD
## 349 USD
## 350 USD
## 351 USD
## 352 USD
## 353 USD
## 354 USD
## 355 GBP
## 356 GBP
## 357 USD
## 358 USD
## 359 USD
## 360 USD
## 361 USD
## 362 USD
## 363 USD
## 364 USD
## 365 USD
## 366 USD
## 367 USD
## 368 USD
## 369 USD
## 370 USD
## 371 USD
## 372 USD
## 373 USD
## 374 EUR
## 375 EUR
## 376 CAD
## 377 USD
## 378 USD
## 379 USD
## 380 USD
## 381 USD
## 382 USD
## 383 USD
## 384 USD
## 385 INR
## 386 USD
## 387 GBP
## 388 USD
## 389 USD
## 390 GBP
## 391 GBP
## 392 USD
## 393 USD
## 394 USD
## 395 USD
## 396 USD
## 397 EUR
## 398 GBP
## 399 USD
## 400 USD
## 401 USD
## 402 USD
## 403 USD
## 404 USD
## 405 USD
## 406 GBP
## 407 USD
## 408 USD
## 409 GBP
## 410 USD
## 411 GBP
## 412 GBP
## 413 EUR
## 414 EUR
## 415 GBP
## 416 GBP
## 417 USD
## 418 USD
## 419 USD
## 420 USD
## 421 USD
## 422 USD
## 423 USD
## 424 USD
## 425 USD
## 426 USD
## 427 USD
## 428 EUR
## 429 USD
## 430 GBP
## 431 EUR
## 432 EUR
## 433 EUR
## 434 EUR
## 435 GBP
## 436 GBP
## 437 EUR
## 438 EUR
## 439 USD
## 440 USD
## 441 EUR
## 442 EUR
## 443 GBP
## 444 GBP
## 445 USD
## 446 EUR
## 447 USD
## 448 USD
## 449 USD
## 450 EUR
## 451 USD
## 452 CAD
## 453 CAD
## 454 USD
## 455 USD
## 456 CNY
## 457 USD
## 458 EUR
## 459 INR
## 460 INR
## 461 EUR
## 462 USD
## 463 EUR
## 464 INR
## 465 EUR
## 466 USD
## 467 USD
## 468 USD
## 469 USD
## 470 USD
## 471 USD
## 472 USD
## 473 USD
## 474 USD
## 475 GBP
## 476 GBP
## 477 USD
## 478 USD
## 479 USD
## 480 USD
## 481 USD
## 482 USD
## 483 USD
## 484 USD
## 485 USD
## 486 USD
## 487 USD
## 488 USD
## 489 USD
## 490 EUR
## 491 USD
## 492 USD
## 493 PLN
## 494 CAD
## 495 USD
## 496 USD
## 497 EUR
## 498 USD
## 499 EUR
## 500 CAD
## 501 EUR
## 502 EUR
## 503 USD
## 504 AUD
## 505 USD
## 506 AUD
## 507 USD
## 508 EUR
## 509 USD
## 510 USD
## 511 USD
## 512 CAD
## 513 USD
## 514 EUR
## 515 USD
## 516 USD
## 517 USD
## 518 EUR
## 519 CHF
## 520 USD
## 521 CAD
## 522 USD
## 523 USD
## 524 USD
## 525 USD
## 526 USD
## 527 USD
## 528 USD
## 529 USD
## 530 USD
## 531 USD
## 532 USD
## 533 USD
## 534 USD
## 535 USD
## 536 USD
## 537 USD
## 538 USD
## 539 USD
## 540 USD
## 541 USD
## 542 USD
## 543 USD
## 544 USD
## 545 USD
## 546 USD
## 547 USD
## 548 USD
## 549 USD
## 550 USD
## 551 USD
## 552 USD
## 553 USD
## 554 USD
## 555 USD
## 556 USD
## 557 USD
## 558 USD
## 559 USD
## 560 USD
## 561 USD
## 562 USD
## 563 USD
## 564 USD
## 565 USD
## 566 USD
## 567 USD
## 568 GBP
## 569 USD
## 570 USD
## 571 USD
## 572 USD
## 573 USD
## 574 USD
## 575 USD
## 576 USD
## 577 USD
## 578 USD
## 579 USD
## 580 USD
## 581 USD
## 582 USD
## 583 USD
## 584 USD
## 585 USD
## 586 USD
## 587 GBP
## 588 USD
## 589 USD
## 590 USD
## 591 USD
## 592 USD
## 593 USD
## 594 USD
## 595 USD
## 596 USD
## 597 USD
## 598 USD
## 599 USD
## 600 USD
## 601 USD
## 602 USD
## 603 USD
## 604 USD
## 605 USD
## 606 USD
## 607 USDtable(data.visdat$salary_currency)##
## AUD BRL CAD CHF CLP CNY DKK EUR GBP HUF INR JPY MXN PLN SGD TRY USD
## 2 2 18 1 1 2 2 95 44 2 27 3 2 3 2 3 398datavis <- data.frame(
jenis = c("AUD","BRL","CAD","CHF","CLP","CNY","DKK","EUR","GBP","HUF","INR","JPY","MXN","PLN","SGD","TRY","USD"),
jumlah = c(2, 2, 18, 1, 1, 2, 2, 95, 44, 2, 27, 3, 2, 3, 2, 3, 398)
)treemap(datavis,
index="jenis",
vSize="jumlah",
type="index",
title="Proporsi salary currency",
fontcolor.labels = "white",
fontsize.labels = 10,
fontsize.title=13,
border.lwds = 0,
border.col = "white")
table(data.visdat$employment_type)##
## CT FL FT PT
## 5 4 588 10library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.3.2## Warning: package 'tibble' was built under R version 4.3.2## Warning: package 'tidyr' was built under R version 4.3.2## Warning: package 'readr' was built under R version 4.3.2## Warning: package 'purrr' was built under R version 4.3.2## Warning: package 'stringr' was built under R version 4.3.2## Warning: package 'forcats' was built under R version 4.3.2## Warning: package 'lubridate' was built under R version 4.3.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.0
## ✔ readr 2.1.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ plyr::arrange() masks dplyr::arrange()
## ✖ purrr::compact() masks plyr::compact()
## ✖ plyr::count() masks dplyr::count()
## ✖ plyr::desc() masks dplyr::desc()
## ✖ plyr::failwith() masks dplyr::failwith()
## ✖ dplyr::filter() masks stats::filter()
## ✖ plyr::id() masks dplyr::id()
## ✖ dplyr::lag() masks stats::lag()
## ✖ plyr::mutate() masks dplyr::mutate()
## ✖ plyr::rename() masks dplyr::rename()
## ✖ plyr::summarise() masks dplyr::summarise()
## ✖ plyr::summarize() masks dplyr::summarize()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsdata.vis <- data_frame(
employment_type = c("Casual/Temporary", "Full-Time(permanent)", "Full-Time", "Part-Time"),
nilai = c(5, 4, 588, 10)
)## Warning: `data_frame()` was deprecated in tibble 1.1.0.
## ℹ Please use `tibble()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.ggplot(data.vis, aes(x = 2, y = nilai, fill = employment_type)) +
geom_bar(stat = "identity", color = "white", width = 1) +
coord_polar(theta = "y") +
geom_text(aes(label = paste0(employment_type, ": ", nilai),
y = nilai),
position = position_stack(vjust = 0.5),
color = "white") +
scale_fill_manual(values = c("Casual/Temporary" = "red",
"Full-Time(permanent)" = "yellow",
"Full-Time" = "green",
"Part-Time" = "blue")) + # Sesuaikan warna fill
theme_void() +
xlim(0.5, 2.5) +
labs(title = "Persentase") +
theme(legend.position = "right")
data.visdatnew1 <- subset(data.visdat[,c("company_size", "experience_level")],company_size == "L")
data.visdatnew1 <- data.visdatnew1[,"experience_level"]
data.visdatnew2 <-subset(data.visdat[,c("company_size", "experience_level")], company_size == "M")
data.visdatnew2 <- data.visdatnew2[,"experience_level"]
data.visdatnew3 <-subset(data.visdat[,c("company_size", "experience_level")], company_size == "S")
data.visdatnew3 <- data.visdatnew3[,"experience_level"]kategori.data.visdatnew1 <- table(data.visdatnew1)
kategori.data.visdatnew2 <- table(data.visdatnew2)
kategori.data.visdatnew3 <- table(data.visdatnew3)data.data.visdatnew1 <- data.frame(value=(names(kategori.data.visdatnew1)), frequency = as.numeric(kategori.data.visdatnew1))
data.data.visdatnew2 <- data.frame(value=(names(kategori.data.visdatnew2)), frequency = as.numeric(kategori.data.visdatnew2))
data.data.visdatne3 <- data.frame(value=(names(kategori.data.visdatnew3)), frequency = as.numeric(kategori.data.visdatnew3))data.baru1 <- data.frame(data.data.visdatnew1, data.data.visdatnew2, data.data.visdatne3[,"frequency"])
colnames(data.baru1) <- c("experience_level", "L", "M", "S")data.compzize <- melt(data.baru1, id.vars = "experience_level"){r} table(data.visdat$job_title)
{r} Research.Scientist <- subset(data.visdat, job_title == “Research Scientist”) mean.data.rs <- mean(Research.Scientist$salary_in_usd) mean.data.rs
Product.Data.Analyst <- subset(data.visdat, job_title == “Product Data Analyst”) mean.data.pda <- mean(Product.Data.Analyst$salary_in_usd) mean.data.pda
Principal.Data.Analyst <- subset(data.visdat, job_title == “Principal Data Analyst”) mean.data.pda2 <- mean(Principal.Data.Analyst$salary_in_usd) mean.data.pda2
Infrastructure.Engineer <- subset(data.visdat, job_title == “Machine Learning Infrastructure Engineer”) mean.data.ie <- mean(Infrastructure.Engineer$salary_in_usd) mean.data.ie
Lead.Data.Analyst <- subset(data.visdat, job_title == “Lead Data Analyst”) mean.data.lda <- mean(Lead.Data.Analyst$salary_in_usd) mean.data.lda
Head.of.Data <- subset(data.visdat, job_title == “Head of Data”) mean.data.hod <- mean(Head.of.Data$salary_in_usd) mean.data.hod
ETL.Developer<- subset(data.visdat, job_title == “ETL Developer”) mean.data.etl <- mean(ETL.Developer$salary_in_usd) mean.data.etl
Data.Science.Engineer<- subset(data.visdat, job_title == “Data Science Engineer”) mean.data.dse <- mean(Data.Science.Engineer$salary_in_usd) mean.data.dse
Data.Engineer <- subset(data.visdat, job_title == “Data Engineer”) mean.data.er <- mean(Data.Engineer$salary_in_usd) mean.data.er
Software.Engineer<- subset(data.visdat, job_title == “Computer Vision Software Engineer”) mean.data.se <- mean(Software.Engineer$salary_in_usd) mean.data.se
Business.Data.Analyst<- subset(data.visdat, job_title == “Business Data Analyst”) mean.data.bda <- mean(Business.Data.Analyst$salary_in_usd) mean.data.bda
BI.Data.Analyst <- subset(data.visdat, job_title == “BI Data Analyst”) mean.data.bi <- mean(BI.Data.Analyst$salary_in_usd) mean.data.bi
Analytics.Engineer <- subset(data.visdat, job_title == “Analytics Engineer”) mean.data.ae <- mean(Analytics.Engineer$salary_in_usd) mean.data.ae
Principal.Data.Scientist <- subset(data.visdat, job_title == “Principal Data Scientist”) mean.data.pds <- mean(Principal.Data.Scientist$salary_in_usd) mean.data.pds
Machine.Learning.Scientist <- subset(data.visdat, job_title == “Machine Learning Scientist”) mean.data.mls <- mean(Machine.Learning.Scientist$salary_in_usd) mean.data.mls
Machine.Learning.Engineer <- subset(data.visdat, job_title == “Machine Learning Engineer”) mean.data.mle <- mean(Machine.Learning.Engineer$salary_in_usd) mean.data.mle
Lead.Data.Scientist <- subset(data.visdat, job_title == “Lead Data Scientist”) mean.data.lds <- mean(Lead.Data.Scientist$salary_in_usd) mean.data.lds
Financial.Data.Analyst <- subset(data.visdat, job_title == “Financial Data Analyst”) mean.data.fda <- mean(Financial.Data.Analyst$salary_in_usd) mean.data.fda
Director.of.Data.Science <- subset(data.visdat, job_title == “Director of Data Science”) mean.data.dods <- mean(Director.of.Data.Science$salary_in_usd) mean.data.dods
data.scientist <- subset(data.visdat, job_title == “Data Scientist”) mean.data.s <- mean(data.scientist$salary_in_usd) mean.data.s
Data.Science.Consultant <- subset(data.visdat, job_title == “Data Science Consultant”) mean.data.dsc <- mean(Data.Science.Consultant$salary_in_usd) mean.data.dsc
Data.Architect <- subset(data.visdat, job_title == “Data Architect”) mean.data.da <- mean(Data.Architect$salary_in_usd) mean.data.da
Data.Analytics.Engineer <- subset(data.visdat, job_title == “Data Analytics Engineer”) mean.data.dae <- mean(Data.Analytics.Engineer$salary_in_usd) mean.data.dae
Computer.Vision.Engineer <- subset(data.visdat, job_title == “Computer Vision Engineer”) mean.data.vse <- mean(Computer.Vision.Engineer$salary_in_usd) mean.data.vse
Big.Data.Engineer <- subset(data.visdat, job_title == “Big Data Engineer”) mean.data.bde <- mean(Big.Data.Engineer$salary_in_usd) mean.data.bde
Applied.Machine.Learning.Scientist<- subset(data.visdat, job_title == “Applied Machine Learning Scientist”) mean.data.amls <- mean(Applied.Machine.Learning.Scientist$salary_in_usd) mean.data.amls
AI.Scientist <- subset(data.visdat, job_title == “AI Scientist”) mean.data.ais <- mean(AI.Scientist$salary_in_usd) mean.data.ais
Research.Scientist <- subset(data.visdat, job_title == “Research Scientist”) mean.data.rs <- mean(Research.Scientist$salary_in_usd) mean.data.rs
Principal.Data.Engineer <- subset(data.visdat, job_title == “Principal Data Engineer”) mean.data.pde <- mean(Principal.Data.Engineer$salary_in_usd) mean.data.pde
ML.Engineer <- subset(data.visdat, job_title == “ML Engineer”) mean.data.mle <- mean(ML.Engineer$salary_in_usd) mean.data.mle
Machine.Learning.Developer <- subset(data.visdat, job_title == “Machine Learning Developer”) mean.data.mld <- mean(Machine.Learning.Developer$salary_in_usd) mean.data.mld
Lead.Data.Engineer <- subset(data.visdat, job_title == “Lead Data Engineer”) mean.data.lde <- mean(Lead.Data.Engineer$salary_in_usd) mean.data.lde
Head.of.Data.Science <- subset(data.visdat, job_title == “Head of Data Science”) mean.data.hods<- mean(Head.of.Data.Science$salary_in_usd) mean.data.hods
Director.of.Data.Engineering <- subset(data.visdat, job_title == “Director of Data Engineering”) mean.data.dode<- mean(Director.of.Data.Engineering$salary_in_usd) mean.data.dode
Data.Science.Manager <- subset(data.visdat, job_title == “Data Science Manager”) mean.data.dsm<- mean(Data.Science.Manager$salary_in_usd) mean.data.dsm
Data.Engineering.Manager <- subset(data.visdat, job_title == “Data Engineering Manager”) mean.data.dem<- mean(Data.Engineering.Manager$salary_in_usd) mean.data.dem
Data.Analytics.Manager<- subset(data.visdat, job_title == “Data Analytics Manager”) mean.data.dam<- mean( Data.Analytics.Manager$salary_in_usd) mean.data.dam
Data.Analyst<- subset(data.visdat, job_title == “Data Analyst”) mean.data.da <- mean(Data.Analyst$salary_in_usd) mean.data.da
Cloud.Data.Engineer<- subset(data.visdat, job_title == “Cloud Data Engineer”) mean.data.cde <- mean(Cloud.Data.Engineer$salary_in_usd) mean.data.cde
Applied.Data.Scientist<- subset(data.visdat, job_title == “Applied Data Scientist”) mean.data.ads <- mean(Applied.Data.Scientist$salary_in_usd) mean.data.ads
{r} library(dplyr)
{r} data <- data.frame(nama = c(“Research.Scientist”,“Product.Data.Analyst”,“Principal.Data.Analyst”,“Machine.Learning.Infrastructure.Engineer”, “Lead.Data.Analyst”,“Head.of.Data”,“ETL.Developer”,“Data.Science.Engineer”,“Data.Engineer”,“Computer.Vision.Software.Engineer”,“Business.Data.Analyst”,“BI.Data.Analyst”,“Analytics.Engineer”,“Principal.Data.Scientist”,“Machine.Learning.Scientist”,“Machine.Learning.Engineer”,“Lead.Data.Scientist”,“Financial.Data.Analyst”,“Director.of.Data.Science”,“Data.Scientist”,“Data.Science.Consultant”,“Data.Architect”,“Data.Analytics.ngineer”,“Computer.Vision.Engineer”,“Big.Data.Engineer”,“Applied.Machine.Learning.Scientist”,“AI.Scientist”,“Research.Scientist”,“Principal.Data.Engineer”,“ML.Engineer”,“Machine.Learning.Developer”,“Lead.Data.Engineer”,“Head.of.Data.Science”,“Director.of.Data.Engineering”,“Data.Science.Manager”,“Data.Engineering.Manager”,“Data.Analytics.Manager”,“Data.Analyst”,“Cloud.Data.Engineer”,“Applied.Data.Scientist”), salary = c(109019.5, 13036, 122500, 101145, 92203, 160162.6, 54957, 75803.33, 112725, 105248.7, 76691.2, 74755.17, 175000, 215242.4, 158412.5, 104880.1, 115190, 275000, 195074, 108187.8, 69420.71, 177873.9, 64799.25, 44419.33, 51974, 142068.8, 66135.57, 109019.5, 328333.3, 117504, 85860.67, 139724.5, 146718.8, 156738, 158328.5, 123227.2, 127134.3, 92893.06, 124647, 175655))
print(“Data Awal:”) print(data)
{r}
data[order(data$salary), ]