{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![](https://github.com/bigdata-icict/ETL-Dataiku-DSS/raw/master/tutoriais/pcdas_1.5.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Notebook para criação de tabela de indicadores da PNS - módulo P 2013 Tabagismo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bibliotecas Utilizadas" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Lendo pacotes necessários\n", "library(survey)\n", "library(ggplot2)\n", "library(dplyr)\n", "library(foreign)\n", "library(forcats)\n", "library(tidyverse)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Carregando microdados da PNS" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [ { "data": { "text/html": [ "\n", "
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  201. ⋯
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'V0001'\n", "\\item 'V0024'\n", "\\item 'UPA\\_PNS'\n", "\\item 'V0006\\_PNS'\n", "\\item 'UPA'\n", "\\item 'V0015'\n", "\\item 'V0020'\n", "\\item 'V0022'\n", "\\item 'V0026'\n", "\\item 'V0031'\n", "\\item 'V0025'\n", "\\item 'A001'\n", "\\item 'A002'\n", "\\item 'A003'\n", "\\item 'A004'\n", "\\item 'A005'\n", "\\item 'A006'\n", "\\item 'A007'\n", "\\item 'A008'\n", "\\item 'A009'\n", "\\item 'A010'\n", "\\item 'A011'\n", "\\item 'A012'\n", "\\item 'A013'\n", "\\item 'A014'\n", "\\item 'A015'\n", "\\item 'A016'\n", "\\item 'A017'\n", "\\item 'A01801'\n", "\\item 'A01802'\n", "\\item 'A01803'\n", "\\item 'A01804'\n", "\\item 'A01805'\n", "\\item 'A01806'\n", "\\item 'A01807'\n", "\\item 'A01808'\n", "\\item 'A01809'\n", "\\item 'A01810'\n", "\\item 'A01811'\n", "\\item 'A01812'\n", "\\item 'A01813'\n", "\\item 'A01814'\n", "\\item 'A01815'\n", "\\item 'A01816'\n", "\\item 'A01817'\n", "\\item 'A01818'\n", "\\item 'A019'\n", 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'X02203'\n", "\\item 'X02204'\n", "\\item 'X02205'\n", "\\item 'X02206'\n", "\\item 'X024'\n", "\\item 'X02501'\n", "\\item 'X02502'\n", "\\item 'X02503'\n", "\\item 'X02504'\n", "\\item 'X02505'\n", "\\item 'X02506'\n", "\\item 'X02507'\n", "\\item 'X02508'\n", "\\item 'X02509'\n", "\\item 'X02510'\n", "\\item 'W00101'\n", "\\item 'W00102'\n", "\\item 'W00103'\n", "\\item 'W00201'\n", "\\item 'W00202'\n", "\\item 'W00203'\n", "\\item 'W00301'\n", "\\item 'W00302'\n", "\\item 'W00303'\n", "\\item 'W00401'\n", "\\item 'W00402'\n", "\\item 'W00403'\n", "\\item 'W00404'\n", "\\item 'W00405'\n", "\\item 'W00406'\n", "\\item 'W00407'\n", "\\item 'W00408'\n", "\\item 'V0028'\n", "\\item 'V0029'\n", "\\item 'V00281'\n", "\\item 'V00291'\n", "\\item 'V00282'\n", "\\item 'V00292'\n", "\\item 'V00283'\n", "\\item 'V00293'\n", "\\item 'VDC001'\n", "\\item 'VDC002'\n", "\\item 'VDD004'\n", "\\item 'VDD004A'\n", "\\item 'VDE001'\n", "\\item 'VDE002'\n", "\\item 'VDF002'\n", "\\item 'VDF003'\n", "\\item 'VDDATA'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'V0001'\n", "2. 'V0024'\n", "3. 'UPA_PNS'\n", "4. 'V0006_PNS'\n", "5. 'UPA'\n", "6. 'V0015'\n", "7. 'V0020'\n", "8. 'V0022'\n", "9. 'V0026'\n", "10. 'V0031'\n", "11. 'V0025'\n", "12. 'A001'\n", "13. 'A002'\n", "14. 'A003'\n", "15. 'A004'\n", "16. 'A005'\n", "17. 'A006'\n", "18. 'A007'\n", "19. 'A008'\n", "20. 'A009'\n", "21. 'A010'\n", "22. 'A011'\n", "23. 'A012'\n", "24. 'A013'\n", "25. 'A014'\n", "26. 'A015'\n", "27. 'A016'\n", "28. 'A017'\n", "29. 'A01801'\n", "30. 'A01802'\n", "31. 'A01803'\n", "32. 'A01804'\n", "33. 'A01805'\n", "34. 'A01806'\n", "35. 'A01807'\n", "36. 'A01808'\n", "37. 'A01809'\n", "38. 'A01810'\n", "39. 'A01811'\n", "40. 'A01812'\n", "41. 'A01813'\n", "42. 'A01814'\n", "43. 'A01815'\n", "44. 'A01816'\n", "45. 'A01817'\n", "46. 'A01818'\n", "47. 'A019'\n", "48. 'A020'\n", "49. 'A021'\n", "50. 'A022'\n", "51. 'A02301'\n", "52. 'A02302'\n", "53. 'A02303'\n", "54. 'A02304'\n", "55. 'A024'\n", "56. 'B001'\n", "57. 'B002'\n", "58. 'B003'\n", "59. 'B004'\n", "60. 'C001'\n", "61. 'C00301'\n", "62. 'C004'\n", "63. 'C006'\n", "64. 'C00701'\n", "65. 'C00702'\n", "66. 'C00703'\n", "67. 'C008'\n", "68. 'C009'\n", "69. 'C010'\n", "70. 'C011'\n", "71. 'C012'\n", "72. 'D001'\n", "73. 'D002'\n", "74. 'D003'\n", "75. 'D004'\n", "76. 'D005'\n", "77. 'D006'\n", "78. 'D007'\n", "79. 'D008'\n", "80. 'D009'\n", "81. 'D010'\n", "82. 'D011'\n", "83. 'D012'\n", "84. 'D013'\n", "85. 'D014'\n", "86. 'D015'\n", "87. 'E001'\n", "88. 'E002'\n", "89. 'E003'\n", "90. 'E004'\n", "91. 'E005'\n", "92. 'E006'\n", "93. 'E007'\n", "94. 'E008'\n", "95. 'E01001'\n", "96. 'E01002'\n", "97. 'E01003'\n", "98. 'E011'\n", "99. 'E01201'\n", "100. 'E014'\n", "101. 'E01501'\n", "102. 'E01601'\n", "103. 'E01602'\n", "104. 'E01603'\n", "105. 'E01604'\n", "106. 'E01605'\n", "107. 'E017'\n", "108. 'E01801'\n", "109. 'E01802'\n", "110. 'E01803'\n", "111. 'E01804'\n", "112. 'E01805'\n", "113. 'E019'\n", "114. 'E020'\n", "115. 'E021'\n", "116. 'E022'\n", "117. 'E023'\n", "118. 'E024'\n", "119. 'E025'\n", "120. 'E02501'\n", "121. 'E02502'\n", "122. 'E026'\n", "123. 'E027'\n", "124. 'F001'\n", "125. 'F00102'\n", "126. 'F007'\n", "127. 'F00702'\n", "128. 'F008'\n", "129. 'F00802'\n", "130. 'VDF001'\n", "131. 'VDF00102'\n", "132. 'G001'\n", "133. 'G002'\n", "134. 'G00201'\n", "135. 'G003'\n", "136. 'G004'\n", "137. 'G005'\n", "138. 'G006'\n", "139. 'G007'\n", "140. 'G00701'\n", "141. 'G008'\n", "142. 'G009'\n", "143. 'G010'\n", "144. 'G014'\n", "145. 'G015'\n", "146. 'G01501'\n", "147. 'G016'\n", "148. 'G017'\n", "149. 'G018'\n", "150. 'G021'\n", "151. 'G022'\n", "152. 'G02201'\n", "153. 'G023'\n", "154. 'G024'\n", "155. 'G02501'\n", "156. 'G02502'\n", "157. 'G02503'\n", "158. 'G026'\n", "159. 'G027'\n", "160. 'G032'\n", "161. 'I001'\n", "162. 'I002'\n", "163. 'I003'\n", "164. 'I004'\n", "165. 'I005'\n", "166. 'I006'\n", "167. 'I007'\n", "168. 'I00701'\n", "169. 'I008'\n", "170. 'I009'\n", "171. 'I010'\n", "172. 'I011'\n", "173. 'J001'\n", "174. 'J002'\n", "175. 'J003'\n", "176. 'J004'\n", "177. 'J005'\n", "178. 'J006'\n", "179. 'J007'\n", "180. 'J008'\n", "181. 'J009'\n", "182. 'J010'\n", "183. 'J011'\n", "184. 'J012'\n", "185. 'J013'\n", "186. 'J014'\n", "187. 'J015'\n", "188. 'J016'\n", "189. 'J017'\n", "190. 'J018'\n", "191. 'J019'\n", "192. 'J020'\n", "193. 'J021'\n", "194. 'J022'\n", "195. 'J023'\n", "196. 'J024'\n", "197. 'J025'\n", "198. 'J026'\n", "199. 'J027'\n", "200. 'J029'\n", "201. ⋯\n", "202. 'R032'\n", "203. 'R033'\n", "204. 'R034'\n", "205. 'R035'\n", "206. 'R03601'\n", "207. 'R03602'\n", "208. 'R03603'\n", "209. 'R03604'\n", "210. 'R03605'\n", "211. 'R03606'\n", "212. 'R03607'\n", "213. 'R03608'\n", "214. 'R03609'\n", "215. 'R03610'\n", "216. 'R03611'\n", "217. 'R037'\n", "218. 'R038'\n", "219. 'R039'\n", "220. 'R040'\n", "221. 'R041'\n", "222. 'R04101'\n", "223. 'R042'\n", "224. 'R04201'\n", "225. 'R043'\n", "226. 'R044'\n", "227. 'R045'\n", "228. 'R046'\n", "229. 'R047'\n", "230. 'R048'\n", "231. 'R04901'\n", "232. 'R04902'\n", "233. 'R04903'\n", "234. 'S001'\n", "235. 'S002'\n", "236. 'S003'\n", "237. 'S004'\n", "238. 'S005'\n", "239. 'S006'\n", "240. 'S007'\n", "241. 'S008'\n", "242. 'S009'\n", "243. 'S01001'\n", "244. 'S01002'\n", "245. 'S01003'\n", "246. 'S01004'\n", "247. 'S01005'\n", "248. 'S01101'\n", "249. 'S01102'\n", "250. 'S01103'\n", "251. 'S012'\n", "252. 'S013'\n", "253. 'S01401'\n", "254. 'S01402'\n", "255. 'S01403'\n", "256. 'S01404'\n", "257. 'S01405'\n", "258. 'S015'\n", "259. 'S016'\n", "260. 'S017'\n", "261. 'S018'\n", "262. 'S019'\n", "263. 'S020'\n", "264. 'S021'\n", "265. 'S022'\n", "266. 'S023'\n", "267. 'S024'\n", "268. 'S025'\n", "269. 'S026'\n", "270. 'S027'\n", "271. 'S028'\n", "272. 'S029'\n", "273. 'S030'\n", "274. 'S031'\n", "275. 'S032'\n", "276. 'S033'\n", "277. 'S034'\n", "278. 'S035'\n", "279. 'S036'\n", "280. 'S037'\n", "281. 'S038'\n", "282. 'S039'\n", "283. 'S040'\n", "284. 'S041'\n", "285. 'S042'\n", "286. 'S043'\n", "287. 'S044'\n", "288. 'S045'\n", "289. 'S046'\n", "290. 'S047'\n", "291. 'S048'\n", "292. 'S049'\n", "293. 'S050'\n", "294. 'S051'\n", "295. 'S052'\n", "296. 'S053'\n", "297. 'S054'\n", "298. 'S055'\n", "299. 'S056'\n", "300. 'S057'\n", "301. 'S058'\n", "302. 'U001'\n", "303. 'U00201'\n", "304. 'U00202'\n", "305. 'U00203'\n", "306. 'U004'\n", "307. 'U005'\n", "308. 'U006'\n", "309. 'U009'\n", "310. 'U010'\n", "311. 'U011'\n", "312. 'U014'\n", "313. 'U015'\n", "314. 'U01701'\n", "315. 'U01702'\n", "316. 'U01801'\n", "317. 'U01802'\n", "318. 'U019'\n", "319. 'U020'\n", "320. 'U021'\n", "321. 'U022'\n", "322. 'U023'\n", "323. 'U02301'\n", "324. 'U024'\n", "325. 'U02401'\n", "326. 'U025'\n", "327. 'X001'\n", "328. 'X002'\n", "329. 'X003'\n", "330. 'X004'\n", "331. 'X005'\n", "332. 'X006'\n", "333. 'X007'\n", "334. 'X008'\n", "335. 'X011'\n", "336. 'X012'\n", "337. 'X01401'\n", "338. 'X01402'\n", "339. 'X01501'\n", "340. 'X01502'\n", "341. 'X016'\n", "342. 'X017'\n", "343. 'X018'\n", "344. 'X019'\n", "345. 'X02001'\n", "346. 'X02002'\n", "347. 'X02003'\n", "348. 'X02004'\n", "349. 'X02005'\n", "350. 'X02006'\n", "351. 'X02201'\n", "352. 'X02202'\n", "353. 'X02203'\n", "354. 'X02204'\n", "355. 'X02205'\n", "356. 'X02206'\n", "357. 'X024'\n", "358. 'X02501'\n", "359. 'X02502'\n", "360. 'X02503'\n", "361. 'X02504'\n", "362. 'X02505'\n", "363. 'X02506'\n", "364. 'X02507'\n", "365. 'X02508'\n", "366. 'X02509'\n", "367. 'X02510'\n", "368. 'W00101'\n", "369. 'W00102'\n", "370. 'W00103'\n", "371. 'W00201'\n", "372. 'W00202'\n", "373. 'W00203'\n", "374. 'W00301'\n", "375. 'W00302'\n", "376. 'W00303'\n", "377. 'W00401'\n", "378. 'W00402'\n", "379. 'W00403'\n", "380. 'W00404'\n", "381. 'W00405'\n", "382. 'W00406'\n", "383. 'W00407'\n", "384. 'W00408'\n", "385. 'V0028'\n", "386. 'V0029'\n", "387. 'V00281'\n", "388. 'V00291'\n", "389. 'V00282'\n", "390. 'V00292'\n", "391. 'V00283'\n", "392. 'V00293'\n", "393. 'VDC001'\n", "394. 'VDC002'\n", "395. 'VDD004'\n", "396. 'VDD004A'\n", "397. 'VDE001'\n", "398. 'VDE002'\n", "399. 'VDF002'\n", "400. 'VDF003'\n", "401. 'VDDATA'\n", "\n", "\n" ], "text/plain": [ " [1] \"V0001\" \"V0024\" \"UPA_PNS\" \"V0006_PNS\" \"UPA\" \"V0015\" \n", " [7] \"V0020\" \"V0022\" \"V0026\" \"V0031\" \"V0025\" \"A001\" \n", " [13] \"A002\" \"A003\" \"A004\" \"A005\" \"A006\" \"A007\" \n", " [19] \"A008\" \"A009\" \"A010\" \"A011\" \"A012\" \"A013\" \n", " [25] \"A014\" \"A015\" \"A016\" \"A017\" \"A01801\" \"A01802\" \n", " [31] \"A01803\" \"A01804\" \"A01805\" \"A01806\" \"A01807\" \"A01808\" \n", " [37] \"A01809\" \"A01810\" \"A01811\" \"A01812\" \"A01813\" \"A01814\" \n", " [43] \"A01815\" \"A01816\" \"A01817\" \"A01818\" \"A019\" \"A020\" \n", " [49] \"A021\" \"A022\" \"A02301\" \"A02302\" \"A02303\" \"A02304\" \n", " [55] \"A024\" \"B001\" \"B002\" \"B003\" \"B004\" \"C001\" \n", " [61] \"C00301\" \"C004\" \"C006\" \"C00701\" \"C00702\" \"C00703\" \n", " [67] \"C008\" \"C009\" \"C010\" \"C011\" \"C012\" \"D001\" \n", " [73] \"D002\" \"D003\" \"D004\" \"D005\" \"D006\" \"D007\" \n", " [79] \"D008\" \"D009\" \"D010\" 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\"I006\" \"I007\" \"I00701\" \n", " [169] \"I008\" \"I009\" \"I010\" \"I011\" \"J001\" \"J002\" \n", " [175] \"J003\" \"J004\" \"J005\" \"J006\" \"J007\" \"J008\" \n", " [181] \"J009\" \"J010\" \"J011\" \"J012\" \"J013\" \"J014\" \n", " [187] \"J015\" \"J016\" \"J017\" \"J018\" \"J019\" \"J020\" \n", " [193] \"J021\" \"J022\" \"J023\" \"J024\" \"J025\" \"J026\" \n", " [199] \"J027\" \"J029\" \"J030\" \"J031\" \"J032\" \"J033\" \n", " [205] \"J034\" \"J035\" \"J036\" \"J037\" \"J038\" \"J039\" \n", " [211] \"J04001\" \"J04002\" \"J041\" \"J042\" \"J043\" \"J044\" \n", " [217] \"J045\" \"J046\" \"J047\" \"J048\" \"J049\" \"J050\" \n", " [223] \"J051\" \"J052\" \"J053\" \"J054\" \"J055\" \"J056\" \n", " [229] \"J057\" \"J058\" \"J059\" \"J060\" \"K001\" \"K002\" \n", " [235] \"K003\" \"K004\" \"K005\" \"K006\" \"K007\" \"K008\" \n", " [241] \"K009\" \"K010\" \"K011\" \"K012\" \"K013\" \"K014\" \n", " [247] \"K015\" \"K016\" \"K017\" \"K018\" \"K019\" \"K020\" \n", " [253] \"K021\" 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\"Q023\" \"Q024\" \"Q026\" \"Q027\" \n", " [595] \"Q028\" \"Q029\" \"Q030\" \"Q031\" \"Q032\" \"Q033\" \n", " [601] \"Q03401\" \"Q03402\" \"Q035\" \"Q036\" \"Q037\" \"Q038\" \n", " [607] \"Q039\" \"Q040\" \"Q041\" \"Q042\" \"Q043\" \"Q044\" \n", " [613] \"Q045\" \"Q04601\" \"Q04602\" \"Q04603\" \"Q04604\" \"Q04605\" \n", " [619] \"Q04606\" \"Q04607\" \"Q04608\" \"Q04609\" \"Q04701\" \"Q04702\" \n", " [625] \"Q04703\" \"Q04704\" \"Q04705\" \"Q048\" \"Q049\" \"Q050\" \n", " [631] \"Q051\" \"Q052\" \"Q053\" \"Q054\" \"Q05501\" \"Q05502\" \n", " [637] \"Q05503\" \"Q05504\" \"Q05505\" \"Q05506\" \"Q05507\" \"Q05508\" \n", " [643] \"Q05509\" \"Q056\" \"Q057\" \"Q058\" \"Q059\" \"Q060\" \n", " [649] \"Q061\" \"Q06201\" \"Q06202\" \"Q06203\" \"Q06204\" \"Q06205\" \n", " [655] \"Q06206\" \"Q063\" \"Q06301\" \"Q06302\" \"Q06303\" \"Q06304\" \n", " [661] \"Q064\" \"Q06501\" \"Q06502\" \"Q06503\" \"Q06504\" \"Q066\" \n", " [667] \"Q067\" \"Q068\" \"Q069\" \"Q070\" \"Q071\" \"Q07201\" \n", " [673] \"Q07202\" \"Q07203\" \"Q07204\" \"Q07205\" \"Q07206\" \"Q073\" \n", " [679] \"Q074\" \"Q075\" \"Q076\" \"Q07701\" \"Q07702\" \"Q078\" \n", " [685] \"Q079\" \"Q080\" \"Q08101\" \"Q08102\" \"Q08103\" \"Q08104\" \n", " [691] \"Q08105\" \"Q082\" \"Q083\" \"Q084\" \"Q085\" \"Q08601\" \n", " [697] \"Q08603\" \"Q08604\" \"Q08605\" \"Q087\" \"Q088\" \"Q089\" \n", " [703] \"Q09001\" \"Q09003\" \"Q09004\" \"Q09005\" \"Q091\" \"Q092\" \n", " [709] \"Q093\" \"Q094\" \"Q095\" \"Q09601\" \"Q09602\" \"Q09603\" \n", " [715] \"Q097\" \"Q098\" \"Q100\" \"Q101\" \"Q102\" \"Q103\" \n", " [721] \"Q104\" \"Q105\" \"Q106\" \"Q107\" \"Q108\" \"Q109\" \n", " [727] \"Q110\" \"Q11001\" \"Q11002\" \"Q11003\" \"Q11004\" \"Q111\" \n", " [733] \"Q112\" \"Q113\" \"Q11401\" \"Q11402\" \"Q11403\" \"Q115\" \n", " [739] \"Q116\" \"Q11601\" \"Q11602\" \"Q11603\" \"Q117\" \"Q11801\" \n", " [745] \"Q11802\" \"Q11803\" \"Q11804\" \"Q119\" \"Q120\" \"Q121\" \n", " [751] \"Q122\" \"Q123\" \"Q124\" \"Q125\" \"Q12601\" \"Q12602\" \n", " [757] \"Q12603\" \"Q12604\" \"Q12605\" \"Q127\" \"Q128\" \"Q130\" \n", " [763] \"Q131\" \"Q132\" \"Q133\" \"Q134\" \"Q135\" \"Q136\" \n", " [769] \"Q137\" \"R001\" \"R002\" \"R003\" \"R004\" \"R005\" \n", " [775] \"R006\" \"R007\" \"R008\" \"R009\" \"R010\" \"R011\" \n", " [781] \"R012\" \"R013\" \"R014\" \"R015\" \"R016\" \"R017\" \n", " [787] \"R018\" \"R019\" \"R020\" \"R021\" \"R022\" \"R023\" \n", " [793] \"R024\" \"R025\" \"R026\" \"R027\" \"R028\" \"R029\" \n", " [799] \"R030\" \"R031\" \"R032\" \"R033\" \"R034\" \"R035\" \n", " [805] \"R03601\" \"R03602\" \"R03603\" \"R03604\" \"R03605\" \"R03606\" \n", " [811] \"R03607\" \"R03608\" \"R03609\" \"R03610\" \"R03611\" \"R037\" \n", " [817] \"R038\" \"R039\" \"R040\" \"R041\" \"R04101\" \"R042\" \n", " [823] \"R04201\" \"R043\" \"R044\" \"R045\" \"R046\" \"R047\" \n", " [829] \"R048\" \"R04901\" \"R04902\" \"R04903\" \"S001\" \"S002\" \n", " [835] \"S003\" \"S004\" \"S005\" \"S006\" \"S007\" \"S008\" \n", " [841] \"S009\" \"S01001\" \"S01002\" \"S01003\" \"S01004\" \"S01005\" \n", " [847] \"S01101\" \"S01102\" \"S01103\" \"S012\" \"S013\" \"S01401\" \n", " [853] \"S01402\" \"S01403\" \"S01404\" \"S01405\" \"S015\" \"S016\" \n", " [859] \"S017\" \"S018\" \"S019\" \"S020\" \"S021\" \"S022\" \n", " [865] \"S023\" \"S024\" \"S025\" \"S026\" \"S027\" \"S028\" \n", " [871] \"S029\" \"S030\" \"S031\" \"S032\" \"S033\" \"S034\" \n", " [877] \"S035\" \"S036\" \"S037\" \"S038\" \"S039\" \"S040\" \n", " [883] \"S041\" \"S042\" \"S043\" \"S044\" \"S045\" \"S046\" \n", " [889] \"S047\" \"S048\" \"S049\" \"S050\" \"S051\" \"S052\" \n", " [895] \"S053\" \"S054\" \"S055\" \"S056\" \"S057\" \"S058\" \n", " [901] \"U001\" \"U00201\" \"U00202\" \"U00203\" \"U004\" \"U005\" \n", " [907] \"U006\" \"U009\" \"U010\" \"U011\" \"U014\" \"U015\" \n", " [913] \"U01701\" \"U01702\" \"U01801\" \"U01802\" \"U019\" \"U020\" \n", " [919] \"U021\" \"U022\" \"U023\" \"U02301\" \"U024\" \"U02401\" \n", " [925] \"U025\" \"X001\" \"X002\" \"X003\" \"X004\" \"X005\" \n", " [931] \"X006\" \"X007\" \"X008\" \"X011\" \"X012\" \"X01401\" \n", " [937] \"X01402\" \"X01501\" \"X01502\" \"X016\" \"X017\" \"X018\" \n", " [943] \"X019\" \"X02001\" \"X02002\" \"X02003\" \"X02004\" \"X02005\" \n", " [949] \"X02006\" \"X02201\" \"X02202\" \"X02203\" \"X02204\" \"X02205\" \n", " [955] \"X02206\" \"X024\" \"X02501\" \"X02502\" \"X02503\" \"X02504\" \n", " [961] \"X02505\" \"X02506\" \"X02507\" \"X02508\" \"X02509\" \"X02510\" \n", " [967] \"W00101\" \"W00102\" \"W00103\" \"W00201\" \"W00202\" \"W00203\" \n", " [973] \"W00301\" \"W00302\" \"W00303\" \"W00401\" \"W00402\" \"W00403\" \n", " [979] \"W00404\" \"W00405\" \"W00406\" \"W00407\" \"W00408\" \"V0028\" \n", " [985] \"V0029\" \"V00281\" \"V00291\" \"V00282\" \"V00292\" \"V00283\" \n", " [991] \"V00293\" \"VDC001\" \"VDC002\" \"VDD004\" \"VDD004A\" \"VDE001\" \n", " [997] \"VDE002\" \"VDF002\" \"VDF003\" \"VDDATA\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Carregando banco de dados para R versão 3.5.0 ou superior\n", "load(\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Definição de peso e filtragem de respondentes do questionário" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " Min. 1st Qu. Median Mean 3rd Qu. Max. \n", " 0.004156 0.243959 0.521557 1.000000 1.147413 31.179597 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Selecionando registros válidos para o módulo P e calculando peso amostral - summary de verificação\n", "pns2013.1<-pns2013 %>% filter(M001==1)\n", "pns2013.1<-pns2013.1 %>% mutate(peso_morador_selec=((V00291*(60202/145572211))))\n", "pns2013.1<-pns2013.1 %>% filter(!is.na(peso_morador_selec))\n", "summary(pns2013.1$peso_morador_selec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Criação de variáveis dos indicadores" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Sim
8906
Não
51296
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Sim] 8906\n", "\\item[Não] 51296\n", "\\end{description*}\n" ], "text/markdown": [ "Sim\n", ": 8906Não\n", ": 51296\n", "\n" ], "text/plain": [ " Sim Não \n", " 8906 51296 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Sim
8729
Não
51473
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Sim] 8729\n", "\\item[Não] 51473\n", "\\end{description*}\n" ], "text/markdown": [ "Sim\n", ": 8729Não\n", ": 51473\n", "\n" ], "text/plain": [ " Sim Não \n", " 8729 51473 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Sim
10258
Não
49944
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Sim] 10258\n", "\\item[Não] 49944\n", "\\end{description*}\n" ], "text/markdown": [ "Sim\n", ": 10258Não\n", ": 49944\n", "\n" ], "text/plain": [ " Sim Não \n", "10258 49944 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Sim
4672
Não
55530
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Sim] 4672\n", "\\item[Não] 55530\n", "\\end{description*}\n" ], "text/markdown": [ "Sim\n", ": 4672Não\n", ": 55530\n", "\n" ], "text/plain": [ " Sim Não \n", " 4672 55530 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Sim
4602
Não
55600
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Sim] 4602\n", "\\item[Não] 55600\n", "\\end{description*}\n" ], "text/markdown": [ "Sim\n", ": 4602Não\n", ": 55600\n", "\n" ], "text/plain": [ " Sim Não \n", " 4602 55600 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Sim
8539
Não
51663
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Sim] 8539\n", "\\item[Não] 51663\n", "\\end{description*}\n" ], "text/markdown": [ "Sim\n", ": 8539Não\n", ": 51663\n", "\n" ], "text/plain": [ " Sim Não \n", " 8539 51663 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Desfechos - Indicadores\n", "\n", "#P019P - Uso atual de produtos derivados do tabaco \n", "pns2013.1 <- pns2013.1 %>% mutate(P019P = ifelse(P050 %in% 1:2 | P067 %in% 1:2, 1, 2))\n", "pns2013.1$P019P<-factor(pns2013.1$P019P, levels=c(1,2), labels=c(\"Sim\",\"Não\"))\n", "summary(pns2013.1$P019P)\n", "\n", "#P020P - Fumo atual do tabaco \n", "pns2013.1 <- pns2013.1 %>% mutate(P020P = ifelse(P050 %in% 1:2, 1, 2))\n", "pns2013.1$P020P<-factor(pns2013.1$P020P, levels=c(1,2), labels=c(\"Sim\",\"Não\"))\n", "summary(pns2013.1$P020P)\n", "\n", "#P021P - Ex-fumantes de tabaco\n", "pns2013.1 <- pns2013.1 %>% mutate(P021P = ifelse(P050 == 3 & P052 %in% 1:2,1,2))\n", "pns2013.1$P021P<-factor(pns2013.1$P021P, levels=c(1,2), labels=c(\"Sim\",\"Não\"))\n", "summary(pns2013.1$P021P)\n", "\n", "#P022P - Tentativa de parar de fumar nos últimos 12 meses entre os fumantes atuais de tabaco\n", "pns2013.1 <- pns2013.1 %>% mutate(P022P = if_else(P060==1 | P05901==0,1,2,missing=2)) \n", "pns2013.1$P022P<-factor(pns2013.1$P022P, levels=c(1,2), labels=c(\"Sim\",\"Não\"))\n", "summary(pns2013.1$P022P)\n", "\n", "\n", "#P023P - Fumo passivo no domícilio entre os não fumantes\n", "pns2013.1 <- pns2013.1 %>% mutate(P023P = if_else(P050==3 & P068<=3,1,2,missing=2)) \n", "pns2013.1$P023P<-factor(pns2013.1$P023P, levels=c(1,2), labels=c(\"Sim\",\"Não\"))\n", "summary(pns2013.1$P023P)\n", "\n", "#P024P - Fumo atual de cigarro\n", "pns2013.1 <- pns2013.1 %>% mutate(P024P = ifelse((P050%in%1:2) & (P05401<=4 | P05404<=4 | P05407<=4),1,2)) \n", "pns2013.1$P024P<-factor(pns2013.1$P024P, levels=c(1,2), labels=c(\"Sim\",\"Não\"))\n", "summary(pns2013.1$P024P)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Definições de abrangências" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Situação urbana ou rural" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
urbano
49245
rural
10957
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[urbano] 49245\n", "\\item[rural] 10957\n", "\\end{description*}\n" ], "text/markdown": [ "urbano\n", ": 49245rural\n", ": 10957\n", "\n" ], "text/plain": [ "urbano rural \n", " 49245 10957 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Situação Urbano ou Rural\n", "pns2013.1 <- pns2013.1 %>% rename(Sit_Urbano_Rural=V0026)\n", "pns2013.1$Sit_Urbano_Rural<-factor(pns2013.1$Sit_Urbano_Rural, levels=c(1,2), labels=c(\"urbano\", \"rural\"))\n", "summary(pns2013.1$Sit_Urbano_Rural)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sexo" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Masculino
25920
Feminino
34282
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Masculino] 25920\n", "\\item[Feminino] 34282\n", "\\end{description*}\n" ], "text/markdown": [ "Masculino\n", ": 25920Feminino\n", ": 34282\n", "\n" ], "text/plain": [ "Masculino Feminino \n", " 25920 34282 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Sexo\n", "pns2013.1 <- pns2013.1 %>% rename(Sexo=C006)\n", "pns2013.1$Sexo<-factor(pns2013.1$Sexo, levels=c(1,2), labels=c(\"Masculino\", \"Feminino\"))\n", "summary(pns2013.1$Sexo)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### UF" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Rondônia
1694
Acre
1814
Amazonas
2586
Roraima
1591
Pará
2004
Amapá
1332
Tocantins
1515
Maranhão
1774
Piauí
1804
Ceará
2560
Rio Grande do Norte
1691
Paraíba
1943
Pernambuco
2591
Alagoas
1748
Sergipe
1553
Bahia
2641
Minas Gerais
3779
Espírito Santo
1724
Rio de Janeiro
3486
São Paulo
5305
Paraná
3012
Santa Catarina
1623
Rio Grande do Sul
2913
Mato Grosso do Sul
1809
Mato Grosso
1476
Goiás
2423
Distrito Federal
1811
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Rondônia] 1694\n", "\\item[Acre] 1814\n", "\\item[Amazonas] 2586\n", "\\item[Roraima] 1591\n", "\\item[Pará] 2004\n", "\\item[Amapá] 1332\n", "\\item[Tocantins] 1515\n", "\\item[Maranhão] 1774\n", "\\item[Piauí] 1804\n", "\\item[Ceará] 2560\n", "\\item[Rio Grande do Norte] 1691\n", "\\item[Paraíba] 1943\n", "\\item[Pernambuco] 2591\n", "\\item[Alagoas] 1748\n", "\\item[Sergipe] 1553\n", "\\item[Bahia] 2641\n", "\\item[Minas Gerais] 3779\n", "\\item[Espírito Santo] 1724\n", "\\item[Rio de Janeiro] 3486\n", "\\item[São Paulo] 5305\n", "\\item[Paraná] 3012\n", "\\item[Santa Catarina] 1623\n", "\\item[Rio Grande do Sul] 2913\n", "\\item[Mato Grosso do Sul] 1809\n", "\\item[Mato Grosso] 1476\n", "\\item[Goiás] 2423\n", "\\item[Distrito Federal] 1811\n", "\\end{description*}\n" ], "text/markdown": [ "Rondônia\n", ": 1694Acre\n", ": 1814Amazonas\n", ": 2586Roraima\n", ": 1591Pará\n", ": 2004Amapá\n", ": 1332Tocantins\n", ": 1515Maranhão\n", ": 1774Piauí\n", ": 1804Ceará\n", ": 2560Rio Grande do Norte\n", ": 1691Paraíba\n", ": 1943Pernambuco\n", ": 2591Alagoas\n", ": 1748Sergipe\n", ": 1553Bahia\n", ": 2641Minas Gerais\n", ": 3779Espírito Santo\n", ": 1724Rio de Janeiro\n", ": 3486São Paulo\n", ": 5305Paraná\n", ": 3012Santa Catarina\n", ": 1623Rio Grande do Sul\n", ": 2913Mato Grosso do Sul\n", ": 1809Mato Grosso\n", ": 1476Goiás\n", ": 2423Distrito Federal\n", ": 1811\n", "\n" ], "text/plain": [ " Rondônia Acre Amazonas Roraima \n", " 1694 1814 2586 1591 \n", " Pará Amapá Tocantins Maranhão \n", " 2004 1332 1515 1774 \n", " Piauí Ceará Rio Grande do Norte Paraíba \n", " 1804 2560 1691 1943 \n", " Pernambuco Alagoas Sergipe Bahia \n", " 2591 1748 1553 2641 \n", " Minas Gerais Espírito Santo Rio de Janeiro São Paulo \n", " 3779 1724 3486 5305 \n", " Paraná Santa Catarina Rio Grande do Sul Mato Grosso do Sul \n", " 3012 1623 2913 1809 \n", " Mato Grosso Goiás Distrito Federal \n", " 1476 2423 1811 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Estados - UFs\n", "pns2013.1 <- pns2013.1 %>% rename(Unidades_da_Federacao=V0001)\n", "pns2013.1$Unidades_da_Federacao<-factor(pns2013.1$Unidades_da_Federacao, levels=c(11,12,13,14,15,16,17,21,22,23,24,25,26,27,28,29,31,32,33,35,41,42,43,50,51,52,53),\n", " label=c(\"Rondônia\",\"Acre\",\"Amazonas\",\"Roraima\",\"Pará\",\"Amapá\",\"Tocantins\",\"Maranhão\",\"Piauí\",\"Ceará\",\n", " \"Rio Grande do Norte\",\"Paraíba\",\"Pernambuco\",\"Alagoas\",\"Sergipe\",\"Bahia\",\n", " \"Minas Gerais\",\"Espírito Santo\",\"Rio de Janeiro\",\"São Paulo\",\n", " \"Paraná\",\"Santa Catarina\",\"Rio Grande do Sul\", \n", " \"Mato Grosso do Sul\",\"Mato Grosso\",\"Goiás\",\"Distrito Federal\"))\n", "summary(pns2013.1$Unidades_da_Federacao)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Grandes Regiões" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Norte
12536
Nordeste
18305
Sudeste
14294
Sul
7548
Centro-Oeste
7519
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Norte] 12536\n", "\\item[Nordeste] 18305\n", "\\item[Sudeste] 14294\n", "\\item[Sul] 7548\n", "\\item[Centro-Oeste] 7519\n", "\\end{description*}\n" ], "text/markdown": [ "Norte\n", ": 12536Nordeste\n", ": 18305Sudeste\n", ": 14294Sul\n", ": 7548Centro-Oeste\n", ": 7519\n", "\n" ], "text/plain": [ " Norte Nordeste Sudeste Sul Centro-Oeste \n", " 12536 18305 14294 7548 7519 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Grandes Regiões\n", "\n", "pns2013.1 <- pns2013.1 %>% \n", " mutate(GrandesRegioes = fct_collapse(Unidades_da_Federacao, \n", " `Norte` = c(\"Rondônia\",\"Acre\",\"Amazonas\",\"Roraima\",\"Pará\", \"Amapá\",\"Tocantins\"),\n", " `Nordeste` = c(\"Maranhão\", \"Piauí\", \"Ceará\", \"Rio Grande do Norte\", \"Paraíba\",\"Pernambuco\", \"Alagoas\",\"Sergipe\",\"Bahia\"),\n", " `Sudeste` = c(\"Minas Gerais\", \"Espírito Santo\",\"Rio de Janeiro\", \"São Paulo\"), \n", " `Sul` = c(\"Paraná\", \"Santa Catarina\", \"Rio Grande do Sul\"),\n", " `Centro-Oeste`= c(\"Mato Grosso do Sul\",\"Mato Grosso\", \"Goiás\", \"Distrito Federal\")))\n", "summary(pns2013.1$GrandesRegioes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Capital" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Porto Velho
1694
Rio Branco
1814
Manaus
2586
Boa Vista
1591
Belém
2004
Macapá
1332
Palmas
1515
São Luís
1774
Teresina
1804
Fortaleza
2560
Natal
1691
João Pessoa
1943
Recife
2591
Maceió
1748
Aracaju
1553
Salvador
2641
Belo Horizonte
3779
Vitória
1724
Rio de Janeiro
3486
São Paulo
5305
Curitiba
3012
Florianópolis
1623
Porto Alegre
2913
Campo Grande
1809
Cuiabá
1476
Goiânia
2423
Brasília
1811
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Porto Velho] 1694\n", "\\item[Rio Branco ] 1814\n", "\\item[Manaus] 2586\n", "\\item[Boa Vista] 1591\n", "\\item[Belém] 2004\n", "\\item[Macapá] 1332\n", "\\item[Palmas] 1515\n", "\\item[São Luís] 1774\n", "\\item[Teresina] 1804\n", "\\item[Fortaleza] 2560\n", "\\item[Natal] 1691\n", "\\item[João Pessoa] 1943\n", "\\item[Recife] 2591\n", "\\item[Maceió] 1748\n", "\\item[Aracaju] 1553\n", "\\item[Salvador] 2641\n", "\\item[Belo Horizonte] 3779\n", "\\item[Vitória] 1724\n", "\\item[Rio de Janeiro] 3486\n", "\\item[São Paulo] 5305\n", "\\item[Curitiba] 3012\n", "\\item[Florianópolis] 1623\n", "\\item[Porto Alegre] 2913\n", "\\item[Campo Grande] 1809\n", "\\item[Cuiabá] 1476\n", "\\item[Goiânia] 2423\n", "\\item[Brasília] 1811\n", "\\end{description*}\n" ], "text/markdown": [ "Porto Velho\n", ": 1694Rio Branco \n", ": 1814Manaus\n", ": 2586Boa Vista\n", ": 1591Belém\n", ": 2004Macapá\n", ": 1332Palmas\n", ": 1515São Luís\n", ": 1774Teresina\n", ": 1804Fortaleza\n", ": 2560Natal\n", ": 1691João Pessoa\n", ": 1943Recife\n", ": 2591Maceió\n", ": 1748Aracaju\n", ": 1553Salvador\n", ": 2641Belo Horizonte\n", ": 3779Vitória\n", ": 1724Rio de Janeiro\n", ": 3486São Paulo\n", ": 5305Curitiba\n", ": 3012Florianópolis\n", ": 1623Porto Alegre\n", ": 2913Campo Grande\n", ": 1809Cuiabá\n", ": 1476Goiânia\n", ": 2423Brasília\n", ": 1811\n", "\n" ], "text/plain": [ " Porto Velho Rio Branco Manaus Boa Vista Belém \n", " 1694 1814 2586 1591 2004 \n", " Macapá Palmas São Luís Teresina Fortaleza \n", " 1332 1515 1774 1804 2560 \n", " Natal João Pessoa Recife Maceió Aracaju \n", " 1691 1943 2591 1748 1553 \n", " Salvador Belo Horizonte Vitória Rio de Janeiro São Paulo \n", " 2641 3779 1724 3486 5305 \n", " Curitiba Florianópolis Porto Alegre Campo Grande Cuiabá \n", " 3012 1623 2913 1809 1476 \n", " Goiânia Brasília \n", " 2423 1811 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Capital\n", "pns2013.1<- pns2013.1 %>% mutate(Capital= fct_collapse(Unidades_da_Federacao,\n", " `Porto Velho`= \"Rondônia\", \n", " `Boa Vista`= \"Roraima\", \n", " `Rio Branco `= \"Acre\", \n", " `Manaus` = \"Amazonas\",\n", " `Belém` = \"Pará\" ,\n", " `Macapá`= \"Amapá\",\n", " `Palmas` = \"Tocantins\",\n", " `São Luís` = \"Maranhão\",\n", " `Teresina`= \"Piauí\" ,\n", " `Fortaleza`= \"Ceará\",\n", " `Natal`= \"Rio Grande do Norte\",\n", " `João Pessoa`= \"Paraíba\",\n", " `Recife`= \"Pernambuco\",\n", " `Maceió`= \"Alagoas\",\n", " `Aracaju`= \"Sergipe\",\n", " `Salvador`= \"Bahia\",\n", " `Belo Horizonte`= \"Minas Gerais\",\n", " `Vitória`= \"Espírito Santo\",\n", " `Rio de Janeiro`= \"Rio de Janeiro\",\n", " `São Paulo`= \"São Paulo\",\n", " `Curitiba`= \"Paraná\",\n", " `Florianópolis`= \"Santa Catarina\",\n", " `Porto Alegre`= \"Rio Grande do Sul\",\n", " `Campo Grande`= \"Mato Grosso do Sul\",\n", " `Cuiabá`= \"Mato Grosso\",\n", " `Goiânia` = \"Goiás\",\n", " `Brasília`= \"Distrito Federal\"))\n", "summary(pns2013.1$Capital)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Faixa Etária" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
18 a 29 anos
14321
30 a 44 anos
20242
45 a 59 anos
14462
60 a 74 anos
8290
75 anos ou mais
2887
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[18 a 29 anos] 14321\n", "\\item[30 a 44 anos] 20242\n", "\\item[45 a 59 anos] 14462\n", "\\item[60 a 74 anos] 8290\n", "\\item[75 anos ou mais] 2887\n", "\\end{description*}\n" ], "text/markdown": [ "18 a 29 anos\n", ": 1432130 a 44 anos\n", ": 2024245 a 59 anos\n", ": 1446260 a 74 anos\n", ": 829075 anos ou mais\n", ": 2887\n", "\n" ], "text/plain": [ " 18 a 29 anos 30 a 44 anos 45 a 59 anos 60 a 74 anos 75 anos ou mais \n", " 14321 20242 14462 8290 2887 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Faixas Etárias\n", "pns2013.1 <- pns2013.1 %>% mutate(faixa_idade=cut(C008,\n", " breaks = c(18,30, 45, 60, 75,Inf),\n", " labels = c(\"18 a 29 anos\",\"30 a 44 anos\",\"45 a 59 anos\",\"60 a 74 anos\",\"75 anos ou mais\"), \n", " ordered_result = TRUE, right = FALSE))\n", "summary(pns2013.1$faixa_idade) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Raça" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Branca
24106
Preta
5631
Parda
29512
NA's
953
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Branca] 24106\n", "\\item[Preta] 5631\n", "\\item[Parda] 29512\n", "\\item[NA's] 953\n", "\\end{description*}\n" ], "text/markdown": [ "Branca\n", ": 24106Preta\n", ": 5631Parda\n", ": 29512NA's\n", ": 953\n", "\n" ], "text/plain": [ "Branca Preta Parda NA's \n", " 24106 5631 29512 953 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Raça\n", "pns2013.1 <- pns2013.1 %>% mutate(Raca= ifelse(C009==1, 1, \n", " ifelse(C009==2, 2, \n", " ifelse(C009==4, 3, 9))))\n", "\n", "pns2013.1$Raca<-factor(pns2013.1$Raca, levels=c(1,2,3),labels=c(\"Branca\", \"Preta\", \"Parda\"))\n", "\n", "summary(pns2013.1$Raca)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Renda per capita" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Até 1/2 SM
14256
1/2 até 1 SM
17504
1 até 2 SM
15493
2 até 3 SM
5335
Mais de 3 SM
7603
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Até 1/2 SM] 14256\n", "\\item[1/2 até 1 SM] 17504\n", "\\item[1 até 2 SM] 15493\n", "\\item[2 até 3 SM] 5335\n", "\\item[Mais de 3 SM] 7603\n", "\\end{description*}\n" ], "text/markdown": [ "Até 1/2 SM\n", ": 142561/2 até 1 SM\n", ": 175041 até 2 SM\n", ": 154932 até 3 SM\n", ": 5335Mais de 3 SM\n", ": 7603\n", "\n" ], "text/plain": [ " Até 1/2 SM 1/2 até 1 SM 1 até 2 SM 2 até 3 SM Mais de 3 SM \n", " 14256 17504 15493 5335 7603 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Rendimento domiciliar per capita\n", "\n", "pns2013.1 <- pns2013.1 %>% drop_na(VDF003) %>% mutate(rend_per_capita=cut(VDF003,\n", " breaks = c(-Inf,339, 678, 1356, 2034,Inf),\n", " labels=c(\"Até 1/2 SM\",\"1/2 até 1 SM\",\"1 até 2 SM\",\"2 até 3 SM\",\"Mais de 3 SM\"), \n", " ordered_result = TRUE, right = TRUE, na.exclude= TRUE))\n", "\n", "summary(pns2013.1$rend_per_capita)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Escolaridade" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Sem instrução e fundamental incompleto
24080
Fundamental completo e médio incompleto
9212
Médio completo e superior incompleto
19145
Superior completo
7754
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Sem instrução e fundamental incompleto] 24080\n", "\\item[Fundamental completo e médio incompleto] 9212\n", "\\item[Médio completo e superior incompleto] 19145\n", "\\item[Superior completo] 7754\n", "\\end{description*}\n" ], "text/markdown": [ "Sem instrução e fundamental incompleto\n", ": 24080Fundamental completo e médio incompleto\n", ": 9212Médio completo e superior incompleto\n", ": 19145Superior completo\n", ": 7754\n", "\n" ], "text/plain": [ " Sem instrução e fundamental incompleto Fundamental completo e médio incompleto \n", " 24080 9212 \n", " Médio completo e superior incompleto Superior completo \n", " 19145 7754 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Escolaridade\n", "pns2013.1 <- pns2013.1 %>% mutate(gescol = ifelse(VDD004A %in% 1:2, 1, \n", " ifelse(VDD004A%in% 3:4, 2, \n", " ifelse(VDD004A%in% 5:6, 3,4\n", " ))))\n", "\n", "pns2013.1$gescol<-factor(pns2013.1$gescol, levels=c(1,2,3,4), \n", " labels=c(\"Sem instrução e fundamental incompleto\",\"Fundamental completo e médio incompleto\",\n", " \"Médio completo e superior incompleto\",\"Superior completo\"))\n", "summary(pns2013.1$gescol)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Criando Indicadores" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filtrando base de indicadores" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " V0024 UPA_PNS peso_morador_selec P019P \n", " Min. :1110011 Min. :1100001 Min. : 0.004156 Sim: 8903 \n", " 1st Qu.:2210013 1st Qu.:2200075 1st Qu.: 0.243935 Não:51288 \n", " Median :2951023 Median :2900192 Median : 0.521557 \n", " Mean :3035304 Mean :3007768 Mean : 1.000020 \n", " 3rd Qu.:4110111 3rd Qu.:4100002 3rd Qu.: 1.147380 \n", " Max. :5310220 Max. :5300180 Max. :31.179597 \n", " \n", " P020P P021P P022P P023P P024P C008 \n", " Sim: 8726 Sim:10257 Sim: 4671 Sim: 4602 Sim: 8536 Min. : 18.00 \n", " Não:51465 Não:49934 Não:55520 Não:55589 Não:51655 1st Qu.: 30.00 \n", " Median : 41.00 \n", " Mean : 43.32 \n", " 3rd Qu.: 55.00 \n", " Max. :101.00 \n", " \n", " C009 V0031 P050 P05901 \n", " Min. :1.00 Min. :1.000 Min. :1.000 Min. : 0.00 \n", " 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:3.000 1st Qu.: 5.00 \n", " Median :3.00 Median :2.000 Median :3.000 Median :12.00 \n", " Mean :2.61 Mean :2.308 Mean :2.733 Mean :14.78 \n", " 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:21.00 \n", " Max. :9.00 Max. :4.000 Max. :3.000 Max. :84.00 \n", " NA's :49934 \n", " Sit_Urbano_Rural Sexo Unidades_da_Federacao\n", " urbano:49234 Masculino:25915 São Paulo : 5304 \n", " rural :10957 Feminino :34276 Minas Gerais : 3779 \n", " Rio de Janeiro : 3485 \n", " Paraná : 3009 \n", " Rio Grande do Sul: 2913 \n", " Bahia : 2640 \n", " (Other) :39061 \n", " GrandesRegioes Capital faixa_idade \n", " Norte :12535 São Paulo : 5304 18 a 29 anos :14315 \n", " Nordeste :18302 Belo Horizonte: 3779 30 a 44 anos :20239 \n", " Sudeste :14291 Rio de Janeiro: 3485 45 a 59 anos :14461 \n", " Sul : 7545 Curitiba : 3009 60 a 74 anos : 8289 \n", " Centro-Oeste: 7518 Porto Alegre : 2913 75 anos ou mais: 2887 \n", " Salvador : 2640 \n", " (Other) :39061 \n", " Raca rend_per_capita \n", " Branca:24101 Até 1/2 SM :14256 \n", " Preta : 5631 1/2 até 1 SM:17504 \n", " Parda :29506 1 até 2 SM :15493 \n", " NA's : 953 2 até 3 SM : 5335 \n", " Mais de 3 SM: 7603 \n", " \n", " \n", " gescol \n", " Sem instrução e fundamental incompleto :24080 \n", " Fundamental completo e médio incompleto: 9212 \n", " Médio completo e superior incompleto :19145 \n", " Superior completo : 7754 \n", " \n", " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Selecionando variáveis para cálculo de indicadores no survey_ALTERAR\n", "pns2013Psurvey<- pns2013.1 %>% select(\"V0024\",\"UPA_PNS\",\"peso_morador_selec\", \"P019P\",\"P020P\",\"P021P\",\"P022P\",\"P023P\",\"P024P\",\n", " \"C008\",\"C009\",\"V0031\",\"P050\",\"P05901\", \"Sit_Urbano_Rural\",\"Sexo\",\"Unidades_da_Federacao\", \"GrandesRegioes\",\n", " \"Capital\",\"faixa_idade\", \"Raca\",\"rend_per_capita\",\"gescol\")\n", "summary(pns2013Psurvey)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exporta tabela filtrada" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "#Salvando csv para cálculo de indicadores no survey\n", "path <- \"\"\n", "write.csv(pns2013Psurvey, file.path(path, \"pns2013Psurvey.csv\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cria plano amostral complexo" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "#survey design\n", "desPNSP=svydesign(id=~UPA_PNS, strat=~V0024, weight=~peso_morador_selec, nest=TRUE, data=pns2013Psurvey)\n", "desPNSP18=subset(desPNSP, C008>=18)\n", "desPNSPC=svydesign(id=~UPA_PNS, strat=~V0024, weight=~peso_morador_selec, nest=TRUE, data=pns2013Psurvey)\n", "desPNSPC18=subset(desPNSPC, C008>=18 & V0031==1)\n", "desPNSPR18=subset(desPNSP, C008>=18 & C009!=9)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "#survey design - P022P\n", "desPNSP022P_18=subset(desPNSP, C008>=18 & (P050==1 | P050==2 | P05901==0))\n", "desPNSP022P_R18=subset(desPNSP, C008>=18 & (P050==1 | P050==2 | P05901==0) & C009!=9)\n", "desPNSP022P_C18=subset(desPNSP, C008>=18 & V0031==1 & (P050==1 | P050==2 | P05901==0))" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "#survey design - P023P\n", "desPNSP023P_18=subset(desPNSP, C008>=18 & P050==3)\n", "desPNSP023P_R18=subset(desPNSP, C008>=18 & P050==3 & C009!=9)\n", "desPNSP023P_C18=subset(desPNSP, C008>=18 & V0031==1 & P050==3 & !is.na(P023P))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Criação da tabela de indicadores" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Essa tabela é responsável por unir os indicadores no formato do painel de indicadores" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "matrizIndicadores = data.frame()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Definição de variáveis para iteração dos indicadores" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "#Cálculo dos indicadores usando o pacote survey \n", "ListaIndicadores = c(~P019P,~P020P,~P021P,~P022P,~P023P,~P024P)\n", "ListaIndicadoresTexto = c(\"P019P\",\"P020P\",\"P021P\",\"P022P\",\"P023P\",\"P024P\")\n", "ListaDominios = c(~Sexo,~Raca,~rend_per_capita,~faixa_idade,~Sit_Urbano_Rural,~Unidades_da_Federacao,~GrandesRegioes,~gescol,~Capital)\n", "ListaDominiosTexto = c(\"sexo\",\"raça\",\"rend_per_capita\",\"fx_idade_18\",\"urb_rur\",\"uf\",\"região\",\"gescol\",\"capital\")\n", "ListaTotais = c('Brasil','Capital')\n", "Ano <- \"2013\"" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "#Cálculo dos indicadores usando o pacote survey \n", "i <- 0\n", "for( indicador in ListaIndicadores){\n", " i <- i + 1\n", " j <- 1\n", " for (dominio in ListaDominios){\n", " if (ListaDominiosTexto[j]==\"capital\"){\n", " if(ListaIndicadoresTexto[i] == \"P022P\"){\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSP022P_C18 , svymean,vartype= \"ci\")\n", " }else if(ListaIndicadoresTexto[i] == \"P023P\"){\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSP023P_C18 , svymean,vartype= \"ci\")\n", " }else{\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSPC18 , svymean,vartype= \"ci\")\n", " }\n", " \n", " }else if (ListaDominiosTexto[j]==\"raça\"){\n", " if(ListaIndicadoresTexto[i] == \"P022P\"){\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSP022P_R18 , svymean,vartype= \"ci\")\n", " }else if(ListaIndicadoresTexto[i] == \"P023P\"){\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSP023P_R18 , svymean,vartype= \"ci\")\n", " }else{\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSPR18 , svymean,vartype= \"ci\")\n", " }\n", " \n", " }else {\n", " if(ListaIndicadoresTexto[i] == \"P022P\"){\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSP022P_18 , svymean,vartype= \"ci\")\n", " }else if(ListaIndicadoresTexto[i] == \"P023P\"){\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSP023P_18 , svymean,vartype= \"ci\")\n", " }else{\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSP18 , svymean,vartype= \"ci\")\n", " }\n", " }\n", " \n", " dataframe_indicador<-data.frame(dataframe_indicador)\n", " colnames(dataframe_indicador) <- c(\"abr_nome\",\"Sim\",\"Não\",\"LowerS\",\"LowerN\",\"UpperS\",\"UpperN\")\n", " dataframe_indicador$Indicador <- ListaIndicadoresTexto[i]\n", " dataframe_indicador$abr_tipo <- ListaDominiosTexto[j]\n", " dataframe_indicador$Ano <- Ano\n", " dataframe_indicador <- dataframe_indicador %>% select(\"abr_tipo\",\"abr_nome\",\"Ano\",\"Indicador\",\"Sim\",\"Não\",\"LowerS\",\"LowerN\",\"UpperS\",\"UpperN\")\n", " matrizIndicadores <-rbind(matrizIndicadores,dataframe_indicador)\n", " j <- j + 1\n", " \n", " }\n", "}\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Criando a tabela pela abrangência total" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "matriz_totais <- data.frame()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Preenchendo a tabela com as abrangências Brasil e total de capitais" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "i=0\n", "for(indicador in ListaIndicadores){\n", " i <- i+1\n", " for(total in ListaTotais){\n", " dataframe_indicador <- data.frame()\n", " dataframe_indicador_S <- data.frame()\n", " dataframe_indicador_N <- data.frame()\n", " if (total == \"Capital\"){\n", " if(ListaIndicadoresTexto[i] == \"P022P\"){\n", " dataframe_indicador <- svymean(indicador,desPNSP022P_C18)\n", " }else if(ListaIndicadoresTexto[i] == \"P023P\"){\n", " dataframe_indicador <- svymean(indicador,desPNSP023P_C18) \n", " }else{\n", " dataframe_indicador <- svymean(indicador,desPNSPC18)\n", " }\n", " \n", " } else {\n", " if(ListaIndicadoresTexto[i] == \"P022P\"){\n", " dataframe_indicador <- svymean(indicador,desPNSP022P_18)\n", " }else if(ListaIndicadoresTexto[i] == \"P023P\"){\n", " dataframe_indicador <- svymean(indicador,desPNSP023P_18)\n", " }else{\n", " dataframe_indicador <- svymean(indicador,desPNSP18)\n", " }\n", " }\n", " \n", " dataframe_indicador <- cbind(data.frame(dataframe_indicador),data.frame(confint(dataframe_indicador)))\n", " dataframe_indicador <- dataframe_indicador %>% \n", " select('mean','X2.5..','X97.5..') \n", " dataframe_indicador_S <- dataframe_indicador %>% \n", " slice(1)\n", " dataframe_indicador_N <- dataframe_indicador %>% \n", " slice(2)\n", " dataframe_indicador <- cbind(dataframe_indicador_S,dataframe_indicador_N)\n", " colnames(dataframe_indicador) <- c('Sim','LowerS','UpperS','Não','LowerN','UpperN')\n", " dataframe_indicador <- dataframe_indicador %>% \n", " select('Sim','Não','LowerS','LowerN','UpperS','UpperN')\n", " dataframe_indicador$Indicador <- ListaIndicadoresTexto[i]\n", " print(ListaIndicadoresTexto[i])\n", " dataframe_indicador$abr_tipo <- \"total\"\n", " dataframe_indicador$abr_nome <- total\n", " dataframe_indicador$Ano <- Ano \n", " print(colnames(dataframe_indicador))\n", " dataframe_indicador <- dataframe_indicador %>% \n", " select(\"abr_tipo\",\"abr_nome\",\"Ano\",\"Indicador\",\"Sim\",\"Não\",\"LowerS\",\"LowerN\",\"UpperS\",\"UpperN\")\n", " \n", " matriz_totais <-rbind(matriz_totais,dataframe_indicador)\n", " \n", " }\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Unindo tabela de indicadores e de totais" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "matrizIndicadores<-rbind(matrizIndicadores,matriz_totais)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exportando tabela de indicadores" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "write.table(matrizIndicadores,file=\"\",sep = \";\",dec = \",\",row.names = FALSE)" ] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 4 }