├── r ├── DTM.RData ├── Info.RData ├── 20131031_retorica.R ├── Autor_Matrix.RData ├── 20131031_rotulos1.txt └── ExpAgendVMVA.R ├── img ├── null.jpg ├── favicon.png └── thumb │ ├── 00937763aaef8d55fe457cdfda27f41e6ace362b.jpg │ ├── 00d386ff3837804ba4014b42b11a7ccb8f0bc97b.jpg │ ├── 0129ff3e33f4d10f48d9f90a0af9b56847e0e7b4.jpg │ ├── 01d7d86eea770ce386d7396d2156a3d888bd262a.jpg │ ├── 0201f111b3d4a0d34b6603d3436096d9b05ecb09.jpg │ ├── 0266197ac5eec522149d0e3366db952f7f5c4454.jpg │ ├── 03866ce6855aa6698dff8b41c277c0abc943aff3.jpg │ ├── 04038e8d3ad876ca01033a08a5b8fbd40e3c9d06.jpg │ ├── 04f0629e183a8beb0cc56133711accbda05b2653.jpg │ ├── 050ca924357683b613f2b8178809bf1dcdda02dc.jpg │ ├── 0541dc040165dd0c91b337657728e28d6b874880.jpg │ ├── 056b990df5d8779be3f3561e89237b39e6b6c1d5.jpg │ ├── 06323c10060d268d4408f7d8b6e926f72100b9cb.jpg │ ├── 065e1113cca802b43766a6d37c5456a18beb70f4.jpg │ ├── 0723058e52f6c412949498452e6f931f0a45af5c.jpg │ ├── 07d1a45ad0fe01dad8b8e6026bcae3049e257145.jpg │ ├── 08563fa8c8eee69a221bbf55134201564979f83b.jpg │ ├── 09298ad5fd5337c867cf696a5a80b920d85e3d63.jpg │ ├── 0a3767662aeee29922e79f79416b5a4a807a7d69.jpg │ ├── 0c7467e68ea6c9bf4110283e6a00b847247404f3.jpg │ ├── 0cfb87e8187874f694ead2fa4ac6e17416121798.jpg │ ├── 0d953ba030738d6ddc7749a8231ed5a67ef51870.jpg │ ├── 0de500807b10b3bd4b1f579d422562ee51d82ce7.jpg │ ├── 0e8665ef03fda45f5c52fb5ea6f946dd69c9b658.jpg │ ├── 0ed79e5bb237f58d3c084b3aa8eb5b7be66c0ed7.jpg │ ├── 0ef28595a3fa3b23ea130f58cc1d9fa46552cbcd.jpg │ ├── 0f2bd32138a4b59ff497daf9a33b1009f9251a22.jpg │ ├── 107a1eb5301c0a7fe09744c01e7dd6760e1190c1.jpg │ ├── 10b8e50cc7ed4d3a59558cf16d19f70e0202af24.jpg │ ├── 10e42af826706a9b4e1c63161fa69536d01a1501.jpg │ ├── 114c65d57b79d07294e5d9770b64c606a22a002e.jpg │ ├── 11537445852d578faaf3fdb148974a1dea13c8a7.jpg │ ├── 1240414eba487212ba3517e8c8c30461baed10eb.jpg │ ├── 1250055dc9e3a92beaa0f12d73241b3d5209ad72.jpg │ ├── 149a5e27afbe9fbd3c03fbe25da240cf6fb47cea.jpg │ ├── 1620dd47a3bab27670f44eb743da5f345fbf86dd.jpg │ ├── 16e29e535d360de1bce04312317a9139f9f88454.jpg │ ├── 1724d78a66a89c4eed0aca363a4b769f3a2e89f1.jpg │ ├── 182f9fa25f7b6c0464f856631f2f4d728245f94c.jpg │ ├── 1883361ddfec1eb3198550b23ab1e432dffcc723.jpg │ ├── 189e8086ab8f7e4997b31c8af807c2019c2a58e9.jpg │ ├── 191b24584c0511afa1c93ab006f304b998433e7c.jpg │ ├── 194df9c2bb77bb749d8abd81d8cd6781db1329c5.jpg │ ├── 1979067b46fab0fffed458866234e7eaac1f95ec.jpg │ ├── 1989b4f480dfb0371816ef332f217f18b0b0d4d3.jpg │ ├── 19c9e4c1f0dab2e606697fa15fc076df321b9ad3.jpg │ ├── 1a19b29c9fb78d2aea8539b598846c6f3b273174.jpg │ ├── 1a3c43c4f532cab546eb4ffeb2c1a9664a245e27.jpg │ ├── 1a874afc01f95876e56197781b882f732128031e.jpg │ ├── 1ae5a0501d134aa8833b7afb87645b39d690d126.jpg │ ├── 1b134159a6dbcfb6cbee99ba52d9630fb090da02.jpg │ ├── 1bc5a498b82a22fe4c34359ed083033ff9442aaf.jpg │ ├── 1c55194e8da256756b8d789a2f197b4d3956900a.jpg │ ├── 1cbe28f273780c2990066b94b473a0a2b916d7ed.jpg │ ├── 1ce5fcd14ddf4d889015354b4e45fdf6308981b5.jpg │ ├── 1d41ca5464f784be0a8c0ac0d48c4a88f3ac3e1b.jpg │ ├── 1dce24a146021e5fa7d239131dd2f7f4a75213f8.jpg │ ├── 1dec2ca51ce1c34f64d1b080da87e4abb41ab02f.jpg │ ├── 1df46035fac520ff88ba94673e4eab78a5d7190b.jpg │ ├── 1e2aaced5830799ed57493d0c89e89f96925b8c8.jpg │ ├── 1f37a371eb2c30acc9eef322a92cea8712b3d0e9.jpg │ ├── 1fb51a178b436ca96924123f4a0d7b2983296723.jpg │ ├── 208eb55419be8335678163ed55cd32fb025765bb.jpg │ ├── 20ca3311032df098b29a3c699f719c80af8f6b55.jpg │ ├── 2194890e7ef0f04bda5aabc18dfc92cc949c85d4.jpg │ ├── 228fd27978139982d5f4d0421b3102c41ebef3b0.jpg │ ├── 22bda8d4175a9480787748bda7137f9bda842757.jpg │ ├── 22c1bc2d47536c1333e2f4e1cd43fcbd8c909714.jpg │ ├── 22d7f245aad99f7d5e019d5618365ec3e5aa76e4.jpg │ ├── 24cd5fe8672dc19e4d73c7997faf3e307b21b741.jpg │ ├── 24d01050bf8bfed7851c353ac738ba3ba1543fb8.jpg │ ├── 250edf9b5bebb9f6bd0e989902af4fadf047cb04.jpg │ ├── 25296f3582bbfcbab380640896df42d0638ee80c.jpg │ ├── 25892abd8374ba8bf01b1e317342b3b170c00241.jpg │ ├── 262cc4b3f149f32ab71d4615bfab9c6ffb23be12.jpg │ ├── 27a2a920fae39f703adac56cd6ceb5a7c0b37b08.jpg │ ├── 284ff534fe8d9534fde5c0cb3897c098b998b2a0.jpg │ ├── 2855c8b7bd94f632aeb05d89796d19aad65dc0a3.jpg │ ├── 28a65d7270db262c7085daace12a2a1f49e4f2a1.jpg │ ├── 28da7531b7e8c4469f8a14a7943ffb2708d6d634.jpg │ ├── 295793f18edea82599ace07f59b5bb5938af2300.jpg │ ├── 2968af0b6317ab3217ca3484d83a4f34418f158c.jpg │ ├── 29ef320b8478b0f8f39f06f9b1fc58652c7e9f32.jpg │ ├── 29f3e6c437fa5e9b0aed8b1880b516546099f29e.jpg │ ├── 29fffab35973bd5f4d3ba327c0c0650f165a771d.jpg │ ├── 2a0a15f92f425029f872e1df7bc32ce8b05a4aa8.jpg │ ├── 2a5d669ebd59e2ecbc9428cd4bbd2a8717d570dd.jpg │ ├── 2af7fe78ce3107c6025742d013285e10fb370f03.jpg │ ├── 2b15e857c4655696213c706430ffbf4f9c74e555.jpg │ ├── 2c28fa8ecc6d9dd6d43a56372a0b35bb154cf150.jpg │ ├── 2c78bc44d5990d6cc48c4f8b7c1bd0c136493d26.jpg │ ├── 2c8044b8ab82820366d0060e718ff872726e9459.jpg │ ├── 2cd8a5f12f8d3ff6eeea6bc7e5e246c1b5de7db2.jpg │ ├── 2d196aef5799168a34fc60410bd56f00a98a840a.jpg │ ├── 2d6221b1661cdda50f165d2d8e0ccb46cd7ed4eb.jpg │ ├── 2d84c4dbc1e16f44582dbc71208f6f52d9a9118d.jpg │ ├── 2dad607187e045278ab9cc80cf5dd1ae55909c7d.jpg │ ├── 2dd69c892e302af6100777fc8716c0113b798044.jpg │ ├── 2e07e3364241164059ebe0d8bbf09feace44795e.jpg │ ├── 2eaa4ed4c1fce54c62053c481980abbae494f8ef.jpg │ ├── 2eb49dc48236b18f7045c704a5951911ff092eba.jpg │ ├── 2ece0cab171fb6dc114c1602bb04f0a86b4a89bd.jpg │ ├── 2f2c9647a9bdf929dbee50f00e23efc428255aa8.jpg │ ├── 2f3a728891d48a4304dfa683ecd6b245acfbb20e.jpg │ ├── 2f95c7a9fa8aaf804147b8870539aae3126d9436.jpg │ ├── 300aca47df5a7472fd47f9077ae625abd44f519f.jpg │ ├── 301db4b2594c4badb0c1b37525baa529ac02fbdf.jpg │ ├── 307f84454ab77c0a4498fdbbe5d7578cf1310b7e.jpg │ ├── 31a220e78823b0ac23775609a28fbda9698a7ef8.jpg │ ├── 339f91f0e688e55c2c3f59b8384212cde759d1be.jpg │ ├── 33a94ca9b93a1438ac36e8a1e77154be25da5d7a.jpg │ ├── 33ab9eb5262c437e0b09bdb7d43a79cbcc0c6cd1.jpg │ ├── 34146fb6783f946f5407647d8021db083aba4edf.jpg │ ├── 347255032b48b2711a98573ecbdedfe2ffdf233c.jpg │ ├── 35f67aa99654534a8f57fc9264249a9a5af682c5.jpg │ ├── 368db07e0d3c1681a9ae1b7489681e88e8e0d398.jpg │ ├── 36ab58d5087d28c3cd3b14ec749bdc71109ad138.jpg │ ├── 37ddb4e255dc79239684489f12fc9541c5d73246.jpg │ ├── 37f459fe3205f27960111deecadb5917424084fa.jpg │ ├── 3854244a52c29fed2ece7b5720438ffbd979cd71.jpg │ ├── 386cac0ec401f28426d9650b1e9d4b05449e3284.jpg │ ├── 38a900a4384865883ab55077a73844289dc13ddd.jpg │ ├── 39f5427518dda31d33042dbaa6dbf831288462c7.jpg │ ├── 3b90c71e0f9350db6c5638ed3b84b0aa2bcfefb9.jpg │ ├── 3c0c682614c615cbd5ea3487adaa43a21aed4b74.jpg │ ├── 3ce301e7f751a16667ef33984a04ba9611a9c612.jpg │ ├── 3d813bb35807cea1a561273f6308ce6a8bff0658.jpg │ ├── 3da00f79b7ed65e973b915475b594c74d2b73d94.jpg │ ├── 3f1002b058e81a87e423cea7733a6b6eaf030578.jpg │ ├── 3f89a4a082458faa7f42316322d7adfb1b0cc424.jpg │ ├── 417147190a797d96853fd55ef9c3e1b191902996.jpg │ ├── 41ba407f59809c1f09860815c63456029af929be.jpg │ ├── 429b2a54d2ce16f27bc3f94f227324f3d0dd5029.jpg │ ├── 4379cd62c7e7f0f0d7ee7b5a9dc0802e81666140.jpg │ ├── 43bc25a021e28d8cf71f6015941d5274f0da9429.jpg │ ├── 4446302e24620020482e49caab3f5041977189e5.jpg │ ├── 456c7ac4ed5e996ce2e12e357797f3612a262ef8.jpg │ ├── 4725c2054f3239295201553bbcf38ba46e97a98a.jpg │ ├── 47340ba7519b9689531f573326267f372fe6dd3e.jpg │ ├── 47bc59606ef2cb75d376f94148550b3dc304f82e.jpg │ ├── 47e8018826c30c3820ef8eb9e61f5a3cb064abc8.jpg │ ├── 481f9412fb6fd9aeb3899be40514ab8e5fd8f6bf.jpg │ ├── 49251a3516567e657726b47048394f0ad3ae7221.jpg │ ├── 496a28d035cdc39e91f7e7995661e4e9c179a6de.jpg │ ├── 49cb386c2bc74de031c17d24d1618ec5a4339b4f.jpg │ ├── 4a2ec80bece2f2667fd4f7c208641923fd4578e7.jpg │ ├── 4bf7f49c763796a9ea08059849122c5e87f17add.jpg │ ├── 4ce5da888b836b98896e742dadd6d411004f478f.jpg │ ├── 502c64fc00f14bafb75836819190e4383422b3b2.jpg │ ├── 520433878e924f777af626274242aaee7c8ed06e.jpg │ ├── 533450bc9f6fe2f277fb22eceecebb0d65f974d7.jpg │ ├── 5433a1a3ae86af0c03a0609e186240edbc746500.jpg │ ├── 55e58ae8c01955e4e196d5fdf8743ed5b6753624.jpg │ ├── 561b78b8502ecbb447b255e668e1283e2b25f11f.jpg │ ├── 56aa5e7e813b627a9de34b4cbb022b781c3d0403.jpg │ ├── 56f82fe9331f0108ff2094b6d9afe9ecd98e1360.jpg │ ├── 577170c13535f435aecfa996965567525b4fde35.jpg │ ├── 57ffeb2340fc4d197b23f74ba9a159fe9515affc.jpg │ ├── 58b15cf9345e3d4ab3ce62f623d32ee9302fdf60.jpg │ ├── 5902d6f593e7dc6e01e15015c9bdfede2e6cdf8e.jpg │ ├── 590720014cb25f07f1ebfb0991a00695269c0418.jpg │ ├── 59810c33d6a4bb205086b6ddc274f4816d0a0bfd.jpg │ ├── 59a478ada7d6d63b35715cd49cac575ed6ee6107.jpg │ ├── 5a1d46e70d8fba83262b638248c04ab6e4085cbf.jpg │ ├── 5a7535e873bd77c721ed8500f3042305a9361dae.jpg │ ├── 5a82979773245ae0a41c04ffbe6f1a851394f91c.jpg │ ├── 5b18747906b59df53bee452aacf63adf3aa15c9f.jpg │ ├── 5b786274f7650e7932d45b27f67d8646a0b4ef84.jpg │ ├── 5bad754dcdb0c600a49607397a181c36c545d366.jpg │ ├── 5c1e953e5a672d70c191f8398f0dfc84eda94232.jpg │ ├── 5cce30cecde793b13c5ef7c83a159c0e6920d7a9.jpg │ ├── 5d39b908d115d9a1c8f2a5615cfd094cb4cd4280.jpg │ ├── 5d86f821c67de563d058ee00be04e10026f2f7d7.jpg │ ├── 5dd2196dc4010a2bbeb81be12ae838cbd40897b8.jpg │ ├── 5e5b68377877f7def25c334277ce10edb9df02e2.jpg │ ├── 5ebb706b0f467e0327aa028c8495229090b7f97f.jpg │ ├── 5f253e62e4520b8f5cf8798f7e8cc86ef55beb52.jpg │ ├── 5f7281200e8f492eef359297e9dce005602b5273.jpg │ ├── 5fcf800129de7bd6624c5b8f929b4c39a086752f.jpg │ ├── 5fe20a1117907ebf0d2a19195a34c642d18adbbe.jpg │ ├── 615ed51c7b2edb04682c99369cda69fff50a164e.jpg │ ├── 61e6c9b79ddcfd0571792ba665498799cf4bb949.jpg │ ├── 62228af4ecd686822f3e3331ea9bf816a5a383f5.jpg │ ├── 6262453494e76ea083d574cb54e68483de9739c9.jpg │ ├── 628e79c086721104da0ef7a1fb211bf87674bd08.jpg │ ├── 633ae2e570633416d0c9a7f5f646f5451154e83a.jpg │ ├── 643f6fed47e91b1233f89c5c17a0d44044011197.jpg │ ├── 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6ceac19d56369bcc9f588baf4424b88395a76e29.jpg │ ├── 6d1ab00f7ee13c493063a091d22b12bbb3ed9725.jpg │ ├── 6d443dcd7c252cefbbf0f65ff959667c5fb84ad5.jpg │ ├── 6d7b99bf591c1c4b9c7f057a375df93ca89a545a.jpg │ ├── 6e2959f4bf603282206e8ea0f597bcc71df6762b.jpg │ ├── 6e6114d8506f9526989ba3d3918eb74ce1d26a7d.jpg │ ├── 6eaf17594e6697e2f7f2c262fc23e28f5fae5a4b.jpg │ ├── 6f175f96cd8a6aab94680c854545f117e5b6a7f5.jpg │ ├── 6f9a34a655beeb113bc1f54cb02e4303acbcd8e9.jpg │ ├── 6ffdbe94339f4cfc732c9952c0117e4fd57bb4c6.jpg │ ├── 70ef0390f6b6ee42387b0b753f778dd8e471a124.jpg │ ├── 714537474636d28b536052040775be1d580a95ee.jpg │ ├── 714b5423474209ce6e0a096628e12ee4cf887e7e.jpg │ ├── 725e19e3256f7576990edd3acb0cb146a3293cc7.jpg │ ├── 7371504159b8c18bb9c025c33cfcb2aa82f4bee5.jpg │ ├── 7396b2f7a76da0667bfec4d29cfa6c97ff809031.jpg │ ├── 74315a3c7fdc74a061e6eaf7a40e6568603d6c14.jpg │ ├── 7458f22feacd8631f267a0d522837b328596de1e.jpg │ ├── 75075b940db1f86e6744ee181203a1bb5bd5ca75.jpg │ ├── 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ABELARDO CAMARINHA 13 |
PSB / SP
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ABELARDO CAMARINHA 17 |
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Retórica

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Visualização da ênfase temática dos deputados brasileiros durante seus discursos.

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Dos mais de 14 mil discursos proferidos por deputados no Pequeno Expediente levantamos os 70 principais temas e quais os deputados mais relacionados com cada um deles.

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BELARDO CAMARINHA PT/SP

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Análise estatística e temática dos discursos feita por Davi Moreira e Manoel Galdino. Visualização dos dados por Luis Carli.

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Esse projeto faz uso do software R e das bibliotecas d3.js e lodash.js

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38 | 39 | 40 | -------------------------------------------------------------------------------- /style.css: -------------------------------------------------------------------------------- 1 | /* 2 | #"<--- Copyright 2013 de Retórica/Davi Moreira, Luis Carli e Manoel Galdino 3 | # Este arquivo é parte do programa Retorica. 4 | # O Retorica é um software livre; você pode redistribuí-lo e/ou modificá-lo dentro 5 | # dos termos da [GNU General Public License OU GNU Affero General Public License] 6 | # como publicada pela Fundação do Software Livre (FSF); na versão 3 da Licença. 7 | # Este programa é distribuído na esperança que possa ser útil, mas SEM NENHUMA GARANTIA; 8 | # sem uma garantia implícita de ADEQUAÇÃO a qualquer MERCADO ou APLICAÇÃO EM PARTICULAR. 9 | # Veja a licença para maiores detalhes. Você deve ter recebido uma cópia da 10 | # [GNU General Public License OU GNU Affero General Public License], sob o título "LICENCA.txt" 11 | */ 12 | 13 | html, 14 | body { 15 | font-family: 'helvetica'; 16 | font-size: 12px; 17 | font-family: "Whitney SSm A", "Whitney SSm B"; 18 | font-weight: 500; 19 | margin: 0; 20 | } 21 | a { 22 | color: #595959; 23 | } 24 | .container { 25 | width: 1200px; 26 | margin: auto; 27 | } 28 | .title { 29 | font-family: "Whitney A", "Whitney B"; 30 | font-size: 30px; 31 | font-weight: 400px; 32 | position: absolute; 33 | margin-left: 40px; 34 | margin-top: 55px; 35 | } 36 | .description2 { 37 | opacity: 0; 38 | } 39 | .description, 40 | .description2 { 41 | position: absolute; 42 | margin-left: 40px; 43 | top: 105px; 44 | width: 300px; 45 | line-height: 20px; 46 | } 47 | .description .headline, 48 | .description2 .headline { 49 | font-size: 16px; 50 | line-height: 22px; 51 | } 52 | .description .detail, 53 | .description2 .detail, 54 | .description .site, 55 | .description2 .site, 56 | .description .email, 57 | .description2 .email { 58 | font-size: 13px; 59 | margin-top: 20px; 60 | margin-bottom: 0px; 61 | } 62 | .description .site, 63 | .description2 .site { 64 | margin-top: 5px; 65 | } 66 | .description span.nome, 67 | .description2 span.nome { 68 | font-weight: 600px; 69 | } 70 | .description span.partido, 71 | .description2 span.partido { 72 | margin-left: 16px; 73 | color: #262626; 74 | } 75 | .inputContainer { 76 | display: none; 77 | position: absolute; 78 | margin-left: 850px; 79 | top: 60px; 80 | width: 200px; 81 | } 82 | .foot { 83 | position: absolute; 84 | top: 850px; 85 | margin-left: 40px; 86 | margin-bottom: 80px; 87 | width: 300px; 88 | } 89 | .topicCircle { 90 | fill: steelblue; 91 | stroke: steelblue; 92 | fill-opacity: 1; 93 | stroke-width: 0; 94 | } 95 | .depCircle { 96 | fill: #000000; 97 | stroke: black; 98 | stroke-width: 0; 99 | fill-opacity: .2; 100 | } 101 | text { 102 | pointer-events: none; 103 | } 104 | .depImage { 105 | opacity: 0; 106 | display: none; 107 | } 108 | .popover, 109 | .popover2 { 110 | opacity: 0; 111 | pointer-events: none; 112 | font-size: 10px; 113 | color: white; 114 | background: black; 115 | position: absolute; 116 | padding: 9px; 117 | left: 20px; 118 | top: 20px; 119 | } 120 | .popover .nome, 121 | .popover2 .nome { 122 | text-decoration: lowercase; 123 | font-weight: 600; 124 | } 125 | .popover .partido, 126 | .popover2 .partido { 127 | margin-top: 4px; 128 | text-align: center; 129 | color: #d9d9d9; 130 | } 131 | .popover .tick, 132 | .popover2 .tick { 133 | position: absolute; 134 | left: 90px; 135 | top: -7px; 136 | width: 0; 137 | height: 0; 138 | border-left: 7px solid transparent; 139 | border-right: 7px solid transparent; 140 | border-bottom: 7px solid #000000; 141 | } 142 | .typeahead, 143 | .tt-query, 144 | .tt-hint { 145 | width: 260px; 146 | height: 18px; 147 | padding: 8px 12px; 148 | font-size: 13px; 149 | line-height: 20px; 150 | border: 2px solid #ccc; 151 | -webkit-border-radius: 8px; 152 | -moz-border-radius: 8px; 153 | border-radius: 8px; 154 | outline: none; 155 | } 156 | .typeahead { 157 | background-color: #fff; 158 | } 159 | .typeahead:focus { 160 | border: 2px solid #0097cf; 161 | } 162 | .tt-query { 163 | -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); 164 | -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); 165 | box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); 166 | } 167 | .tt-hint { 168 | color: #999999; 169 | } 170 | .tt-dropdown-menu { 171 | width: 287px; 172 | margin-top: 6px; 173 | padding: 8px 0; 174 | background-color: #fff; 175 | border: 1px solid #ccc; 176 | border: 1px solid rgba(0, 0, 0, 0.2); 177 | -webkit-border-radius: 8px; 178 | -moz-border-radius: 8px; 179 | border-radius: 8px; 180 | -webkit-box-shadow: 0 2px 2px rgba(0, 0, 0, 0.2); 181 | -moz-box-shadow: 0 2px 2px rgba(0, 0, 0, 0.2); 182 | box-shadow: 0 2px 2px rgba(0, 0, 0, 0.2); 183 | } 184 | .tt-suggestion { 185 | padding: 3px 20px; 186 | font-size: 13px; 187 | line-height: 18px; 188 | } 189 | .tt-suggestion.tt-is-under-cursor { 190 | color: #fff; 191 | background-color: #0097cf; 192 | } 193 | .tt-suggestion p { 194 | margin: 0; 195 | } 196 | .gist { 197 | font-size: 14px; 198 | } 199 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Documentação do Projeto Retórica 2 | 3 | O projeto é fruto das pesquisas realizadas por Davi Moreira no Departamento de Ciência Política da Universidade de São Paulo, por Luis Carli na Faculdade de Arquitetura e Urbanismo da Universidade de São Paulo e por Manoel Galdino na Hyperativa Consultoria e Internet Ltda. 4 | 5 | Retórica é uma aplicação web que permite a todos os cidadãos conhecerem os temas mais enfatizados pelos Deputados Federais em seus discursos. 6 | 7 | A análise utiliza os mais de 14.000 discursos proferidos no Pequeno Expediente pelos Deputados federais da atual legislatura (2011-2014). Foram considerados os deputados com mais de um discurso proferido entre 02 de fevereiro de 2011 e 01 de outubro de 2013 (inclusive), totalizando 447 parlamentares. 8 | 9 | ## Motivação 10 | 11 | O processo legislativo brasileiro e a atividade parlamentar não consistem apenas em votos e na tramitação de matérias. A política brasileira, sob o sistema democrático de representação, também é desenvolvida a partir do diálogo, do debate, da exposição de ideias e de preferências. Por esta razão a Constituição de 1988 e o Regimento Interno da Câmara dos Deputados garantem aos parlamentares o direito à palavra através de espaços institucionais para seu pronunciamento como, por exemplo, o Grande e o Pequeno expedientes. 12 | 13 | Após inúmeras pesquisas entre estudiosos de ciência política, sabe-se que as votações nominais realizadas ao longo dos processos legislativos são partidariamente organizadas, reduzindo a expressão individual de cada parlamentar. Logo, é justamente nos espaços reservados para seu pronunciamento que o deputado pode entrar em contato com seus eleitores e assim divulgar suas ideias e pensamentos de forma mais livre. Com acesso fácil e ágil aos temas proferidos por cada deputado, os cidadãos brasileiros podem encontrar afinidades políticas e de ideias com seus representantes. 14 | 15 | ## Metodologia 16 | 17 | Para a identificação de temas presentes nos pronunciamentos de cada parlamentar, o projeto se baseia na metodologia desenvolvida pelo Professor Justin Grimmer da Universidade de Stanford, conhecida como expressed agenda model. Para tanto, foram necessárias quatro etapas: 18 | 19 | ### 1) Obtenção dos dados: 20 | 21 | Infelizmente os dados dos pronunciamentos ainda não estão à disposição no portal de dados abertos (http://www2.camara.leg.br/transparencia/dados-abertos). Por esta razão, desenvolvemos um web-crawler para obter os dados dos pronunciamentos presentes na página da Câmara dos Deputados (www.camara.gov.br). Foram coletados todos os pronunciamentos do pequeno expediente proferidos entre 02/02/2011 e 01/11/2013. 22 | 23 | ### 2) Limpeza e organização dos dados 24 | 25 | Em seguida, cada pronunciamento passou por uma "limpeza" com o objetivo de se obter uma “sacola de palavras” (bag of words) de cada um. Esta “sacola de palavras” permite identificar a frequência com que cada palavra ocorre num pronunciamento, de forma que se possa agrupar os pronunciamentos em temas comuns. 26 | Para que se possa aplicar o algoritmo, é necessário garantir que palavras que variam apenas na flexão, número, conjugação etc. (p. ex., protestaram e protestou, ou árvore e árvores) sejam consideradas as mesmas palavras. Assim, a “limpeza” dos pronunciamentos consiste nos seguintes passos: 27 | 28 | Em primeiro lugar, todos os deputados com apenas um pronunciamento (ou sem pronunciamentos) foram removidos da Análise. Assim, os discursos desses deputados não foram considerados. Em segundo lugar, todas as palavras foram colocadas em caixa baixa e todos os acentos e caracteres de pontuação foram removidos Também foram removidas palavras muito utilizadas e palavras pouco utilizadas. Por fim, através da adaptação do algoritmo de Porter (1980) para o português já desenvolvido por diferentes projetos (Snowball e NILC-USP), foram obtidas as raízes (stems) das palavras restantes. 29 | 30 | 31 | Ao final, foi construída uma matriz (Document Term Matrix), cujas linhas representam cada pronunciamento e cujas palavras representam as colunas. É esta matriz que foi utilizada na estimação dos tópicos das falas dos deputados. Nessa matriz, restaram 447 deputados e 14.500 documentos. 32 | 33 | 34 | ### 3) Estimação de tópicos 35 | 36 | 37 | Com base na metodologia de modelagem de tópicos (topic model) do expressed agenda model. Foram estimados os tópicos presentes em cada pronunciamento feito pelos parlamentares. 38 | 39 | 40 | De forma simplificada, esta metodologia permite agrupar discursos similares em temas comuns de acordo com seus autores. Quanto mais similares forem diferentes discursos (em termos da frequência com que cada palavra ocorre em cada um), maior a chance deles serem agrupados sob o mesmo tema. Se houver pouca similaridade nos temas dos discursos, o algoritmo agrupará os discursos em temas diferentes. 41 | 42 | 43 | Essa técnica está presente no campo de aprendizagem computacional não supervisionada (unsupervised learning), de modo que ela dispensa a definição de temas a priori por um analista e, portanto, torna o processo de classificação inteiramente automatizado. Os temas sob os quais os discursos serão agrupados serão definidos a partir dos dados obtidos nos próprios discursos dos parlamentares. 44 | 45 | Sendo um modelo estatístico hierárquico Bayesiano de modelagem de tópicos, o expressed agenda model permite descobrir os temas mais enfatizados pelos diferentes parlamentares. Leva-se em consideração o fato de que as palavras utilizadas por um Deputado Federal em cada discurso não são aleatórias, mas obedecem à estrutura linguística discursiva de cada deputado. Assim, conseguimos captar a ênfase temática dos discursos de cada um dos 447 deputados analisados. Este algoritmo foi implementado em R, conforme descrito em artigo de Grimmer (Grimmer, Justin. "A Bayesian hierarchical topic model for political texts: Measuring expressed agendas in Senate press releases." Political Analysis18.1 (2010): 1-35). 46 | 47 | ## Visualização 48 | 49 | No endereço http://www.retoricaparlamentar.com, é possível encontrar uma visualização interativa dos temas enfatizados pelos Deputados no Pequeno Expediente. O usuário tem a sua disposição um bubble chart (Gráfico de bolhas), em que cada bolha representa a relevância (medido pela frequência) de cada tema dentre todos os 447 deputados analisados. Dentro de cada bolha temos os deputados que enfatizam aquele tema nos seus discursos. Na visualização, um deputado está associado a um único tema, que é o tema mais enfatizado por cada deputado nos discursos por eles proferidos. Clicando num deputado, é possível visualizar mais informações sobre ele (e-mail, link pra url do deputado etc.). 50 | 51 | -------------------------------------------------------------------------------- /r/ExpAgendVMVA.R: -------------------------------------------------------------------------------- 1 | ##this file implements the expressed agenda model from 2 | ##Grimmer (2010). 3 | 4 | # The code ExpAgendVMVA.R was kindly provided by Professor Justin Grimmer, Stanford University, 5 | # on september 2013. 6 | # If you use it, please, cite his paper. 7 | # +----------------------------------------------------------------+ 8 | # | Citations: | 9 | # | Grimmer, Justin. "A Bayesian hierarchical topic model for | 10 | # | political texts: Measuring expressed agendas in Senate | 11 | # | press releases." Political Analysis 18.1 (2010): 1-35. | 12 | # +----------------------------------------------------------------+ 13 | 14 | ##Suppose that there are D total documents and w words. 15 | ##the program requires a D \times w matrix (term-document matrix) 16 | 17 | 18 | 19 | ##Further, suppose that there are N total actors whose attention to issues 20 | ##you would like to measure. You should be sure that the documents 21 | ##are sorted by their author. Then you also should include an N \times 2 matrix 22 | ##specifying the first document in the term-document matrix that was authored 23 | ##by a given author, where the second column specifies the last document 24 | ##authored. place this in the "author" argument 25 | 26 | ##n.cats sets the number of components (topics) in the mixture model 27 | 28 | ##this requires MCMCpack. to install execute 29 | ##install.packages('MCMCpack') 30 | 31 | exp.agenda.vonmon<- function(term.doc, authors, n.cats, verbose=T, kappa=400){ 32 | ##a better length command 33 | len<- length 34 | 35 | ##the likelihood for the dirichlet component of the model 36 | dir.lik<- function(par, pis, prior){ 37 | alphas = par[1:ncol(pis)] 38 | alphas<- exp(alphas) 39 | ester<- log(ddirichlet(pis,alpha=alphas)) 40 | ## ester[which(ester==-Inf)]<- log(1e-323) 41 | priors<- dgamma(alphas, rate=prior, shape=1, log=T) 42 | out<- sum(ester) +sum(priors) 43 | return(out) 44 | } 45 | 46 | n.cats<- n.cats 47 | require(MCMCpack) 48 | N<- nrow(authors) 49 | 50 | ##component labels 51 | taus<- matrix(NA, nrow=nrow(term.doc), ncol=n.cats) 52 | 53 | mus<- matrix(NA, nrow=ncol(term.doc), ncol=n.cats) 54 | for(j in 1:ncol(mus)){ 55 | mus[,j]<- rgamma(ncol(term.doc), shape=10) 56 | } 57 | for(j in 1:ncol(mus)){ 58 | mus[,j]<- mus[,j]/sqrt(mus[,j]%*%mus[,j]) 59 | } 60 | 61 | ##actually alpha in the paper 62 | ##will fix in output 63 | thetas<-matrix(NA, nrow=1, ncol=n.cats) 64 | for(j in 1:nrow(thetas)){ 65 | thetas[j,]<- 20 + rnorm(n.cats, 0,0.025) 66 | } 67 | 68 | pis<- matrix(NA, nrow=N, ncol=n.cats) 69 | for(j in 1:N){ 70 | pis[j,]<- thetas + (authors[j,2] - authors[j, 1])/n.cats 71 | } 72 | 73 | 74 | kappa<- kappa 75 | k<-0 76 | prior<- 1 77 | prior<- c(rep(prior, n.cats)) 78 | prior<- prior*-1 79 | Bayes<- 1 80 | v<- 0 81 | z<- 0 82 | 83 | while(z==0){ 84 | thetas.old<- thetas 85 | pi.gam<- digamma(pis) - digamma(apply(pis, 1, sum)) 86 | exp.pi<- exp(pi.gam) 87 | 88 | ##step one computes the variational 89 | ##posterior, which is going to be very close 90 | ##the original posterior from the ECM algorithm 91 | ##once again, differences in the 'pi.gam' stage 92 | 93 | part1<- kappa*term.doc%*%mus 94 | 95 | 96 | for(j in 1:N){ 97 | for(k in authors[j,1]:authors[j,2]){ 98 | temp<- part1[k,] - max(part1[k,]) 99 | num<- exp.pi[j,]*exp(temp) 100 | taus[k,]<- num/sum(num) 101 | } 102 | } 103 | 104 | 105 | 106 | ## 107 | ## 108 | mus<- matrix(1/sqrt(ncol(term.doc)), nrow=nrow(mus), ncol=n.cats) 109 | m<- 0 110 | 111 | 112 | for(j in 1:N){ 113 | for(k in authors[j,1]:authors[j,2]){ 114 | fills<- term.doc[k,]%o%taus[k,] 115 | mus<- mus + fills 116 | } 117 | } 118 | 119 | for(j in 1:ncol(mus)){ 120 | mus[,j]<- mus[,j]/sqrt(mus[,j]%*%mus[,j]) 121 | } 122 | 123 | 124 | ##the M part for top level Dirichlet 125 | ##is the maximization of alpha parameters 126 | 127 | ##this is the Newton-Raphson algorithm from 128 | ##Blei, Ng, and Jordan (2003) 129 | alpha<- thetas[1,] 130 | ##and we know that the log 131 | N1<- N 132 | te<- log(exp.pi) 133 | suff.stats<- apply(te, 2, sum)/N1 134 | k<-0 135 | a<- 0 136 | while(k==0){ 137 | sum.alpha<- digamma(sum(alpha)) 138 | di.alph<- digamma(alpha) 139 | grads<- Bayes*prior + N1*(sum.alpha - di.alph + suff.stats) 140 | qs<- - N1*trigamma(alpha) 141 | c <- N1*trigamma(sum(alpha)) 142 | b1<- sum(grads/qs) 143 | b2<- 1/c + sum(1/qs) 144 | b<- b1/b2 145 | g<- (grads-b)/qs 146 | alpha2<- alpha - g 147 | ester<- which(alpha2<0) 148 | if(len(ester)>0){ 149 | alpha2[ester]<- pi + rpois(len(ester), rep(3, len(ester))) 150 | } 151 | alpha<- alpha2 152 | #print(alpha) 153 | g<- max(g) 154 | if(abs(g)<1e-8){ 155 | k<-1 156 | } 157 | a<- a + 1 158 | if(a>15000){ 159 | print('Switching to Optim') 160 | temp.pi<- exp.pi/apply(exp.pi,1, sum) 161 | temp<- optim(log(thetas[1,]), dir.lik, control=list(trace=100, fnscale=-1), method='BFGS', 162 | pis=temp.pi, prior=abs(prior)) 163 | alpha<- exp(temp$par) 164 | k<- 1 165 | } 166 | } 167 | 168 | thetas[1,]<- alpha 169 | 170 | 171 | sen.list<- list() 172 | for(j in 1:N){ 173 | ints<- authors[j,1]:authors[j,2] 174 | sen.list[[j]]<- taus[ints,] 175 | } 176 | 177 | 178 | 179 | 180 | for(i in 1:N){ 181 | pre.mle<- apply(sen.list[[i]], 2, sum) 182 | theta.ch<- thetas 183 | #for(j in 1:n.coals){ 184 | #theta.ch[j,]<- theta.ch[j,] + pre.mle 185 | #theta.ch[j,]<- theta.ch[j,]*coals[i,j] 186 | #} 187 | theta.ch<- theta.ch + pre.mle 188 | pis[i,]<- theta.ch 189 | } 190 | 191 | 192 | afr<- abs(thetas.old-thetas) 193 | if(max(afr)<1e-5){ 194 | z<-1 195 | } 196 | cat('\n') 197 | #plot(mus[,1]~mus[,2]) 198 | #print(thetas[1,]) 199 | #print(max(thetas)) 200 | if(verbose==T){ 201 | cat(max(afr), '\n') 202 | cat('next it', '\n') 203 | } 204 | } 205 | 206 | out<- list(pis, mus, taus, thetas) 207 | names(out)<- c('thetas', 'mus', 'rs', 'alpha') 208 | return(out) 209 | 210 | } 211 | 212 | -------------------------------------------------------------------------------- /main.min.js: -------------------------------------------------------------------------------- 1 | /* 2 | #"<--- Copyright 2013 de Retórica/Davi Moreira, Luis Carli e Manoel Galdino 3 | # Este arquivo é parte do programa Retorica. 4 | # O Retorica é um software livre; você pode redistribuí-lo e/ou modificá-lo dentro 5 | # dos termos da [GNU General Public License OU GNU Affero General Public License] 6 | # como publicada pela Fundação do Software Livre (FSF); na versão 3 da Licença. 7 | # Este programa é distribuído na esperança que possa ser útil, mas SEM NENHUMA GARANTIA; 8 | # sem uma garantia implícita de ADEQUAÇÃO a qualquer MERCADO ou APLICAÇÃO EM PARTICULAR. 9 | # Veja a licença para maiores detalhes. Você deve ter recebido uma cópia da 10 | # [GNU General Public License OU GNU Affero General Public License], sob o título "LICENCA.txt" 11 | */ 12 | 13 | d3.custom.forceLayout=function(authors){d3.select(".inputContainer").style({display:"block"});var clipData=[];var inputData=[];_.each(authors,function(d,i){_.each(d.children,function(d,i){if(d.id!="NA"){clipData.push(d);inputData.push(d.author)}})});$("input").typeahead({name:"deputados",local:inputData});var w=1200,h=1100,topicCircleOpacity=1,topicCircleSelOpacity=.2;var docs=_.map(authors,function(d,i){return{topic:d.topic,value:d.value}});var rScale=d3.scale.linear().domain(d3.extent(docs,function(d,i){return d.value})).range([20,120]);var rOpenScale=d3.scale.sqrt().domain(d3.extent(authors,function(d,i){return d.children.length})).range([100,300]);var cScale=d3.scale.linear().domain(rScale.domain()).range(["hsl(163, 47%, 57%)","hsl(163, 99%, 20%)"]).interpolate(d3.interpolateHcl);_.each(docs,function(d,i){d.r=rScale(d.value);d._r=d.r});var pack=d3.layout.pack();var force=d3.layout.force().size([w,h]).gravity(.04).nodes(docs);var drag=force.drag().on("dragstart",dragstart);force.start();var svg=d3.select("svg#main").attr({width:w,height:h}).style({fill:"white"});svg.append("defs").selectAll("clipPath").data(clipData).enter().append("clipPath").attr("id",function(d,i){return"c"+d.id}).append("circle").attr({"class":function(d,i){return"clipPathCircle c"+d.id},x:0,y:0,r:30});svg.append("rect").attr({x:0,y:0,width:w,height:h}).on("click",closeTopics);svg=svg.append("g").attr({transform:"translate(60,-100)"});var cGroups=svg.selectAll(".cGroups").data(docs).enter().append("g").attr({"class":"cGroups"}).style({cursor:"pointer"});cGroups.append("circle").attr({"class":"topicCircle",r:function(d,i){return d.r-2}}).style({fill:function(d,i){return cScale(d.value)},stroke:function(d,i){return d3.hsl(cScale(d.value)).darker(1.5)}});cGroups.append("g").attr({"class":"depG",transform:function(d,i){return"translate("+(-d.r+4)+","+(-d.r+4)+")"}}).each(drawDep);var topicLabel=cGroups.append("g").attr("class","topicLabel").attr({"pointer-events":"none"});topicLabel.append("rect").attr({x:function(d,i){return-d.r+5},y:-10,width:function(d,i){return d.r*2-10},height:20}).style({fill:function(d,i){return cScale(d.value)},opacity:.6});topicLabel.append("text").attr({x:0,y:0,dy:".35em","text-anchor":"middle"}).style({fill:"hsl(0,0%,95%)"}).text(function(d,i){return d.topic});topicLabel.style({display:function(d,i){var rect=d3.select(this).select("text").node().getBoundingClientRect();if(d.r*2nx2||x2ny2||y2' + data, evt, data) 37 | }) 38 | 39 | var w = 1200, 40 | h = 1100, 41 | topicCircleOpacity = 1, 42 | topicCircleSelOpacity = .2 43 | 44 | var docs = _.map(authors, function(d,i){ 45 | return { 46 | topic: d.topic, 47 | value: d.value 48 | } 49 | }) 50 | 51 | var rScale = d3.scale.linear() 52 | .domain(d3.extent(docs, function(d,i){return d.value})) 53 | // .domain([0,d3.max(docs, function(d,i){return d.value})]) 54 | .range([20,120]) 55 | 56 | var rOpenScale = d3.scale.sqrt() 57 | .domain(d3.extent(authors, function(d,i){return d.children.length})) 58 | .range([100,300]) 59 | 60 | var cScale = d3.scale.linear() 61 | .domain(rScale.domain()) 62 | .range(["hsl(163, 47%, 57%)", "hsl(163, 99%, 20%)"]) 63 | .interpolate(d3.interpolateHcl) 64 | 65 | _.each(docs, function(d,i){ 66 | d.r = rScale(d.value) 67 | d._r = d.r 68 | }) 69 | 70 | var pack = d3.layout.pack() 71 | 72 | var force = d3.layout.force() 73 | .size([w, h]) 74 | .gravity(0.04) 75 | .nodes(docs) 76 | 77 | var drag = force.drag() 78 | .on("dragstart", dragstart); 79 | 80 | force.start() 81 | 82 | /////////////// 83 | //DRAW//////// 84 | ///////////// 85 | 86 | var svg = d3.select('svg#main') 87 | // .append('svg') 88 | .attr({ 89 | width: w, height: h 90 | }) 91 | .style({ 92 | fill: 'white' 93 | }) 94 | 95 | //Defs 96 | svg.append('defs') 97 | .selectAll('clipPath').data(clipData) 98 | .enter().append('clipPath') 99 | .attr('id', function(d,i){ 100 | return 'c' + d.id 101 | }) 102 | .append('circle') 103 | .attr({ 104 | 'class': function(d,i){ 105 | return 'clipPathCircle c' + d.id 106 | }, 107 | x:0, y:0, 108 | r:30 109 | }) 110 | 111 | 112 | 113 | //Background 114 | svg.append('rect') 115 | .attr({ 116 | x:0, y:0, width: w, height: h 117 | }) 118 | .on('click', closeTopics) 119 | 120 | svg = svg.append('g') 121 | .attr({ 122 | transform: 'translate(60,-100)' 123 | }) 124 | 125 | //Topics 126 | var cGroups = svg.selectAll('.cGroups').data(docs) 127 | .enter().append('g') 128 | .attr({'class': 'cGroups'}) 129 | .style({ 130 | cursor: 'pointer' 131 | }) 132 | 133 | cGroups.append('circle') 134 | .attr({ 135 | class: 'topicCircle', 136 | r: function(d,i){return d.r-2} 137 | }) 138 | .style({ 139 | fill: function(d,i){return cScale(d.value)}, 140 | stroke: function(d,i){ 141 | return d3.hsl(cScale(d.value)).darker(1.5) 142 | } 143 | }) 144 | 145 | cGroups.append('g') 146 | .attr({ 147 | class: 'depG', 148 | transform: function(d,i){ 149 | return 'translate(' + (-d.r+4) + ',' + (-d.r+4) + ')' 150 | } 151 | }) 152 | .each(drawDep) 153 | 154 | var topicLabel = cGroups.append('g') 155 | .attr('class', 'topicLabel') 156 | .attr({ 157 | 'pointer-events': 'none' 158 | }) 159 | // .style({ 160 | // display: function(d,i){ 161 | // return d.r < 30 ? 'none' : 'block' 162 | // } 163 | // }) 164 | 165 | topicLabel.append('rect') 166 | .attr({ 167 | x: function(d,i){return -d.r+5}, 168 | y: -10, 169 | width: function(d,i){return d.r*2-10}, 170 | height: 20 171 | }) 172 | .style({ 173 | fill: function(d,i){return cScale(d.value)}, 174 | opacity: .6 175 | }) 176 | 177 | topicLabel.append('text') 178 | .attr({ 179 | x:0, y:0, dy: '.35em', 180 | 'text-anchor': 'middle' 181 | }) 182 | .style({ 183 | fill: 'hsl(0,0%,95%)' 184 | }) 185 | .text(function(d,i){return d.topic}) 186 | 187 | topicLabel.style({ 188 | display: function(d,i){ 189 | var rect = d3.select(this).select('text').node().getBoundingClientRect() 190 | if (d.r*2 < rect.width + 10) { 191 | d.label = false 192 | return 'none' 193 | } else { 194 | d.label = true 195 | return 'block' 196 | } 197 | 198 | } 199 | }) 200 | 201 | cGroups 202 | .call(drag) 203 | .on('mouseenter', function(d,i){ 204 | if (d.fixed) return 205 | d3.select(this) 206 | .select('.topicCircle') 207 | .style({ 208 | 'stroke-width': 2 209 | }) 210 | 211 | if (d.label) return 212 | var rect = this.getBoundingClientRect() 213 | 214 | var popover = d3.select('.popover2') 215 | popover.select('.nome') 216 | .text(d.topic) 217 | 218 | var rectPop = d3.select('.popover2').node() 219 | .getBoundingClientRect() 220 | 221 | popover.style({ 222 | opacity: 1, 223 | left: (rect.left - rectPop.width/2 + d.r) + 'px', 224 | top: (rect.top + 10 + d.r*2) + 'px' 225 | }) 226 | 227 | popover.select('.tick') 228 | .style({ 229 | left: (rectPop.width/2 - 7) + 'px' 230 | }) 231 | 232 | }) 233 | .on('mouseleave', function(d,i){ 234 | if (d.fixed) return 235 | d3.select(this) 236 | .select('.topicCircle') 237 | .style({ 238 | 'stroke-width': 0 239 | }) 240 | d3.select('.popover2') 241 | .style({ 242 | opacity: 0 243 | }) 244 | }) 245 | .on('click', function(d,i){ 246 | 247 | d3.select('.popover2') 248 | .style({ 249 | opacity: 0 250 | }) 251 | 252 | if (d.fixed) { 253 | closeTopics(d,i) 254 | return 255 | } 256 | closeTopics(d,i) 257 | 258 | d3.select('.title') 259 | .text(d.topic) 260 | d3.select('.description') 261 | .transition() 262 | .style({ 263 | opacity: 0 264 | }) 265 | 266 | var sel = d3.select(this) 267 | 268 | cGroups.filter(function(d,i){return d.fixed == true}) 269 | .selectAll('.topicCircle') 270 | .transition() 271 | .duration(900) 272 | .attrTween('r', rTweenSmall) 273 | .style({ 274 | 'fill-opacity': topicCircleOpacity, 275 | 'stroke-width': 0 276 | }) 277 | 278 | _.each(docs, function(d,i){ 279 | d.fixed = false 280 | }) 281 | 282 | sel.transition() 283 | .duration(900) 284 | .attrTween('transform', posTween) 285 | .each('end', function(d,i){return d.fixed = true}) 286 | 287 | sel.selectAll('.topicCircle') 288 | .transition() 289 | .duration(900) 290 | .attrTween('r', rTweenBig) 291 | .style({ 292 | 'fill-opacity': topicCircleSelOpacity, 293 | 'stroke-width': 0 294 | }) 295 | 296 | sel.select('.topicLabel') 297 | .transition() 298 | .style({ 299 | opacity: 0 300 | }) 301 | sel.selectAll('.depImage') 302 | .style({ 303 | display: 'block' 304 | }) 305 | .transition(100) 306 | .delay(function(d,i){return 600 + i*10}) 307 | .style({ 308 | opacity: 1 309 | }) 310 | 311 | force.resume() 312 | 313 | }) 314 | 315 | d3.select('svg#main') 316 | .append('path') 317 | .attr({ 318 | d: 'M40 96 h300' 319 | }) 320 | .style({ 321 | stroke: 'black', 322 | 'stroke-width': 2 323 | }) 324 | 325 | function closeTopics (d,i){ 326 | 327 | d3.select('.title') 328 | .text('Retórica') 329 | d3.select('.description') 330 | .transition() 331 | .style({ 332 | opacity: 1 333 | }) 334 | d3.select('.description2') 335 | .transition() 336 | .style({ 337 | opacity: 0 338 | }) 339 | 340 | var sel = cGroups.filter(function(d,i){return d.fixed == true}) 341 | 342 | sel.selectAll('.topicCircle') 343 | .transition() 344 | .duration(900) 345 | .attrTween('r', rTweenSmall) 346 | .style({ 347 | 'fill-opacity': topicCircleOpacity 348 | }) 349 | 350 | sel.select('.topicLabel') 351 | .transition() 352 | .delay(500) 353 | .style({ 354 | opacity: 1 355 | }) 356 | 357 | sel.selectAll('.depImage') 358 | .transition() 359 | .style({ 360 | opacity: 0 361 | }) 362 | .each('end', function(d,i){ 363 | d3.select(this) 364 | .style({ 365 | display: 'none' 366 | }) 367 | }) 368 | 369 | force.resume() 370 | _.each(docs, function(d,i){ 371 | d.fixed = false 372 | }) 373 | } 374 | 375 | function rTweenBig (d,i) { 376 | var sel = d3.select(this.parentNode).select('.depG') 377 | var author = _.find(authors, function(d2,i2){ 378 | return d2.topic == d.topic 379 | }) 380 | 381 | var r = rOpenScale(author.children.length) 382 | var i = d3.interpolate(d.r, r) 383 | return function (t) { 384 | sel.each(drawDep) 385 | var v = i(t) 386 | d.r = v 387 | return v 388 | } 389 | } 390 | function rTweenSmall (d,i) { 391 | var sel = d3.select(this.parentNode).select('.depG') 392 | var i = d3.interpolate(d.r, d._r) 393 | return function (t) { 394 | sel.each(drawDep) 395 | var v = i(t) 396 | d.r = v 397 | return v 398 | } 399 | } 400 | function posTween (d,i) { 401 | var iX = d3.interpolate(d.x, w/2) 402 | var iY = d3.interpolate(d.y, h/2) 403 | return function (t) { 404 | var x = iX(t) 405 | var y = iY(t) 406 | d.x = x 407 | d.y = y 408 | return 'translate(' + d.x + ',' + d.y + ')' 409 | } 410 | } 411 | 412 | ///////////////// 413 | 414 | force.on('tick', function(e){ 415 | var q = d3.geom.quadtree(docs), 416 | i = 0, 417 | n = docs.length 418 | 419 | while (++i < n) { 420 | q.visit(collide(docs[i])); 421 | } 422 | 423 | cGroups 424 | .attr({ 425 | transform: function(d,i){ 426 | return 'translate(' + d.x + ',' + d.y + ')' 427 | } 428 | }) 429 | .select('circle') 430 | .attr({ 431 | // r: function(d,i){return d.r - 2} 432 | }) 433 | }) 434 | 435 | function drawDep (d,i){ 436 | var sel = d3.select(this) 437 | sel.attr({ 438 | transform: function(d,i){ 439 | return 'translate(' + (-d.r+4) + ',' + (-d.r+4) + ')' 440 | } 441 | }) 442 | pack.size([d.r*2-8, d.r*2-8]) 443 | var nodes = pack.nodes(_.find(authors, function(d2,i2){ 444 | return d2.topic == d.topic 445 | })) 446 | 447 | var depCircleG = sel.selectAll('g.depCircleG').data(nodes, function(d,i){return d.id}) 448 | 449 | var depCircleG_enter = depCircleG.enter().append('g') 450 | .attr({ 451 | 'class': 'depCircleG' 452 | }) 453 | .on('mouseenter', function(d2,i){ 454 | if (!d.fixed) {return} 455 | d3.select(this).select('.depCircle') 456 | .style({ 457 | 'stroke-width': 4 458 | }) 459 | 460 | var rect = this.getBoundingClientRect() 461 | 462 | var popover = d3.select('.popover') 463 | popover.select('.nome') 464 | .text(d2.author) 465 | popover.select('.partido') 466 | .text(d2.partido + ' / ' + d2.uf) 467 | 468 | var rectPop = d3.select('.popover').node() 469 | .getBoundingClientRect() 470 | 471 | popover.style({ 472 | opacity: 1, 473 | left: (rect.left - rectPop.width/2 + d2.r) + 'px', 474 | top: (rect.top + 5 + d2.r*2) + 'px' 475 | }) 476 | 477 | popover.select('.tick') 478 | .style({ 479 | left: (rectPop.width/2 - 7) + 'px' 480 | }) 481 | 482 | }) 483 | .on('mouseleave', function(d2,i){ 484 | if (!d.fixed) return 485 | d3.select(this).select('.depCircle') 486 | .style({ 487 | 'stroke-width': 0 488 | }) 489 | 490 | d3.select('.popover') 491 | .style({ 492 | opacity: 0 493 | }) 494 | }) 495 | .on('click', function(d2,i){ 496 | if (!d.fixed) return 497 | d3.event.stopPropagation() 498 | 499 | d3.select('.description2 .nome') 500 | .text(d2.author) 501 | d3.select('.description2 .partido') 502 | .text(d2.partido + '/' + d2.uf) 503 | d3.select('.description2 .email') 504 | .text(function(){ 505 | if (d2.email == 'NA') { 506 | return 'email não disponível' 507 | } else { 508 | return d2.email 509 | } 510 | }) 511 | d3.select('.description2 .site') 512 | .attr({ 513 | href: d2.url 514 | }) 515 | 516 | d3.select('.description2') 517 | .transition() 518 | .style({ 519 | opacity: 1 520 | }) 521 | 522 | }) 523 | depCircleG_enter.each(function(d,i){ 524 | 525 | var sel = d3.select(this) 526 | sel.append('circle') 527 | .attr({ 528 | 'class': 'depCircle' 529 | }) 530 | if (d.foto != 'NA' && d.id != undefined) { 531 | sel.append('image') 532 | .attr({ 533 | 'class': 'depImage', 534 | x:0, y:0, 535 | 'preserveAspectRatio': 'xMinYMin slice', 536 | 'xlink:href': function(){ 537 | return 'img/thumb/'+d.foto 538 | }, 539 | 'clip-path': function(){ 540 | return 'url(#c'+ d.id +')' 541 | } 542 | }) 543 | } else { 544 | sel.append('image') 545 | .attr({ 546 | 'class': 'depImage', 547 | x:0, y:0, 548 | 'preserveAspectRatio': 'xMinYMin slice', 549 | 'xlink:href': function(){ 550 | return 'img/null.jpg' 551 | }, 552 | 'clip-path': function(){ 553 | return 'url(#c'+ d.id +')' 554 | } 555 | }) 556 | // sel.append('rect') 557 | // .attr({ 558 | // 'class': 'depImage', 559 | // x:0, y:0, 560 | // 'preserveAspectRatio': 'xMinYMin slice', 561 | // 'clip-path': function(d,i){ 562 | // return 'url(#c'+ d.id +')' 563 | // } 564 | // }) 565 | // .style({ 566 | // fill: 'white' 567 | // }) 568 | } 569 | }) 570 | 571 | depCircleG.attr({ 572 | transform: function(d,i){ 573 | return 'translate(' + d.x + ',' + d.y + ')' 574 | } 575 | }) 576 | .style({ 577 | display: function(d) { return d.children ? "none" : "block" } 578 | }) 579 | depCircleG.select('.depCircle') 580 | .attr({ 581 | // cx: function(d,i){return d.x}, 582 | // cy: function(d,i){return d.y}, 583 | r: function(d,i){ 584 | // console.log(d) 585 | // return d.parent.children.length > 1 ? d.r - 2 : d.r - 4 586 | return d.r - 2 587 | } 588 | }) 589 | 590 | depCircleG.select('.depImage') 591 | .attr({ 592 | x: function(d,i){return -d.r+2}, 593 | y: function(d,i){return -d.r+2}, 594 | width: function(d,i){return d.r*2-4}, 595 | height: function(d,i){return d.r*2-4}, 596 | 'clip-path': function(d,i){ 597 | return 'url(#c'+ d.id +')' 598 | } 599 | }) 600 | .each(function(d,i){ 601 | var _s = '.c' + d.id 602 | d3.select(_s) 603 | .attr({ 604 | r: function(d,i){return d.r-2} 605 | }) 606 | }) 607 | 608 | 609 | 610 | 611 | depCircleG.exit().remove() 612 | } 613 | 614 | function dragstart () { 615 | 616 | } 617 | 618 | function collide(node) { 619 | var r = node.r + 16, 620 | nx1 = node.x - r, 621 | nx2 = node.x + r, 622 | ny1 = node.y - r, 623 | ny2 = node.y + r; 624 | return function(quad, x1, y1, x2, y2) { 625 | if (quad.point && (quad.point !== node)) { 626 | var x = node.x - quad.point.x, 627 | y = node.y - quad.point.y, 628 | l = Math.sqrt(x * x + y * y), 629 | r = node.r + quad.point.r; 630 | if (l < r) { 631 | l = (l - r) / l * .5; 632 | node.x -= x *= l; 633 | node.y -= y *= l; 634 | quad.point.x += x; 635 | quad.point.y += y; 636 | } 637 | } 638 | return x1 > nx2 639 | || x2 < nx1 640 | || y1 > ny2 641 | || y2 < ny1; 642 | }; 643 | } 644 | 645 | 646 | } 647 | 648 | //////////////////////////////////// 649 | /////////////////////////////////// 650 | 651 | 652 | queue() 653 | // .defer(d3.csv, "data/doc_topic_one.csv") 654 | // .defer(d3.csv, "data/autor_topic_one.csv") 655 | // .defer(d3.csv, "data/autor_topic_one_enfase.csv") 656 | .defer(d3.csv, "data/autorFinal70.csv") 657 | .await(ready); 658 | 659 | 660 | function ready (error, authorsEnf) { 661 | // console.log(autorFinal[0]) 662 | //docs 663 | // var docs = _(docs) 664 | // .map(function(d,i){ 665 | // return { 666 | // i:i, 667 | // topic:d.x 668 | // } 669 | // }) 670 | // .groupBy('topic') 671 | // .map(function(d,i){ 672 | // return { 673 | // topic:i, 674 | // value: d.length 675 | // } 676 | // }) 677 | // .value() 678 | 679 | //authors 680 | // var authors = _(authors) 681 | // .map(function(d,i){ 682 | // return { 683 | // author:i, 684 | // topic:d.x, 685 | // value:1 686 | // } 687 | // }) 688 | // .groupBy('topic') 689 | // .map(function(d,i){ 690 | // return { 691 | // topic:i, 692 | // children: d 693 | // } 694 | // }) 695 | // .value() 696 | 697 | //authorsEnf 698 | var authorsEnf = _(authorsEnf) 699 | // .filter(function(d,i){return d.enfaze>.1}) 700 | .map(function(d,i){ 701 | return { 702 | author: d.autor, 703 | topic: d.rotulo, 704 | value: d.enfase, 705 | uf: d.uf, 706 | partido: d.partido, 707 | url: d.url, 708 | foto: d.foto, 709 | email: d.email, 710 | id: i 711 | } 712 | }) 713 | .groupBy('topic') 714 | .map(function(d,i){ 715 | return { 716 | topic:i, 717 | value: d3.sum(d, function(d,i){return d.value}), 718 | children: d 719 | } 720 | }) 721 | .value() 722 | 723 | //authorsPerc 724 | // var _authorsPerc = [] 725 | // _.each(authorsPerc, function(author,iAuthor){ 726 | // _.each(author, function(topic,iTopic){ 727 | // _authorsPerc.push({ 728 | // author: iAuthor, 729 | // topic: iTopic, 730 | // value: topic*100 731 | // }) 732 | // }) 733 | // }) 734 | // var authorsPerc = _(_authorsPerc) 735 | // .filter(function(d,i){return d.value>30}) 736 | // .groupBy('topic') 737 | // .map(function(d,i){ 738 | // return { 739 | // topic: i, 740 | // children: d 741 | // } 742 | // }) 743 | // .value() 744 | 745 | // console.log(authorsEnf[0]) 746 | 747 | // console.log(authors) 748 | // console.log(_.max(authors, 'topic')) 749 | // console.log(_.min(authors, 'topic')) 750 | 751 | d3.custom.forceLayout(authorsEnf) 752 | 753 | } 754 | --------------------------------------------------------------------------------