library(dplyr)\nlibrary(plotly)<\/code><\/pre>\n\n\n\nGr\u00e1ficos b\u00e1sicos<\/h2>\n\n\n\nScatter plot<\/h3>\n\n\n\n
Exemplo<\/strong><\/p>\n\n\n\nO banco utilizado \u00e9 o iris<\/em>. Ele cont\u00e9m tr\u00eas esp\u00e9cies de flores (setosa, virginica, versicolor) e outras 4 vari\u00e1veis relacionadas as suas medidas:<\/p>\n\n\n\n- Comprimento da s\u00e9pala<\/li>
- Largura da s\u00e9pala<\/li>
- Comprimento da p\u00e9tala<\/li>
- Largura da p\u00e9tala<\/li><\/ol>\n\n\n\n
![\"\"](\"https:\/\/statplace.com.br\/wp-content\/uploads\/2020\/09\/image.jpeg\")
<\/figure><\/div>\n\n\n\nhead(iris)<\/code><\/pre>\n\n\n\n## Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n## 1 5.1 3.5 1.4 0.2 setosa\n## 2 4.9 3.0 1.4 0.2 setosa\n## 3 4.7 3.2 1.3 0.2 setosa\n## 4 4.6 3.1 1.5 0.2 setosa\n## 5 5.0 3.6 1.4 0.2 setosa\n## 6 5.4 3.9 1.7 0.4 setosa<\/code><\/pre>\n\n\n\nScatter <- plot_ly(data = iris, \n x = ~Sepal.Length, \n y = ~Petal.Length, \n color = ~Species,\n type = 'scatter', \n mode = 'markers') %>%\n layout(xaxis = list(title = \"Comprimento da s\u00e9pala\"),\n yaxis = list(title = \"Comprimento da p\u00e9tala\")) \n \n\nScatter<\/code><\/pre>\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/operdata.com.br\/wp-content\/uploads\/2020\/10\/Captura-de-Tela-2020-10-09-a\u0300s-09.57.49.png?fit=1030%2C752&ssl=1\")
<\/figure><\/div>\n\n\n\nLine plot<\/h3>\n\n\n\n
Exemplo<\/strong><\/p>\n\n\n\nO banco de dados cont\u00e9m informa\u00e7\u00f5es das temperaturas m\u00e9dias (mais baixa e mais alta por dia) de Nova York por m\u00eas em 3 anos (2000, 2007, 2014). Sendo assim as vari\u00e1veis do banco s\u00e3o:<\/p>\n\n\n\n
- Ano<\/li>
- M\u00eas<\/li>
- M\u00e9dias das temperaturas altas<\/li>
- M\u00e9dias das temperaturas baixas<\/li><\/ol>\n\n\n\n
![\"\"](\"https:\/\/i2.wp.com\/operdata.com.br\/wp-content\/uploads\/2020\/09\/image-2.jpeg?fit=1030%2C541&ssl=1\")
<\/figure><\/div>\n\n\n\nhead(Temp.New.york)<\/code><\/pre>\n\n\n\n## month high_2000 low_2000 high_2007 low_2007 high_2014 low_2014\n## 1 January 32.5 13.8 36.5 23.6 28.8 12.7\n## 2 February 37.6 22.3 26.6 14.0 28.5 14.3\n## 3 March 49.9 32.5 43.6 27.0 37.0 18.6\n## 4 April 53.0 37.2 52.3 36.8 56.8 35.5\n## 5 May 69.1 49.9 71.5 47.6 69.7 49.9\n## 6 June 75.4 56.1 81.4 57.7 79.7 58.0<\/code><\/pre>\n\n\n\nline <- plot_ly(Temp.New.york, \n x = ~month, \n y = ~high_2014, \n name = 'High 2014', \n type = 'scatter', \n mode = 'lines',\n line = list(color = 'rgb(205, 12, 24)', \n width = 4)) %>% \n add_trace(y = ~low_2014, \n name = 'Low 2014', \n line = list(color = 'rgb(22, 96, 167)', \n width = 4)) %>% \n add_trace(y = ~high_2007, \n name = 'High 2007', \n line = list(color = 'rgb(205, 12, 24)', \n width = 4, \n dash = 'dash')) %>% \n add_trace(y = ~low_2007, \n name = 'Low 2007', \n line = list(color = 'rgb(22, 96, 167)', \n width = 4, \n dash = 'dash')) %>% \n add_trace(y = ~high_2000, \n name = 'High 2000', \n line = list(color = 'rgb(205, 12, 24)', \n width = 4, \n dash = 'dot')) %>% \n add_trace(y = ~low_2000, \n name = 'Low 2000', \n line = list(color = 'rgb(22, 96, 167)', \n width = 4, \n dash = 'dot')) %>% \n layout(title = \"Temperaturas m\u00e9dias alta e baixa em New York\",\n xaxis = list(title = \"M\u00eas\"),\n yaxis = list (title = \"Temperatura (\u00baF)\"))\n\nline<\/code><\/pre>\n\n\n\n![\"\"](\"https:\/\/i1.wp.com\/operdata.com.br\/wp-content\/uploads\/2020\/10\/Captura-de-Tela-2020-10-09-a\u0300s-09.58.32.png?fit=1030%2C767&ssl=1\")
<\/figure><\/div>\n\n\n\nDensity plot<\/h3>\n\n\n\n
Exemplo<\/strong><\/p>\n\n\n\nO banco utilizado \u00e9 o diamonds<\/em> do pacote ggplot2. Ele cont\u00e9m pre\u00e7os de mais de 50.000 cortes de diamantes. As vari\u00e1veis s\u00e3o:<\/p>\n\n\n\n- Pre\u00e7o em dol\u00e1r<\/li>
- Peso<\/li>
- Corte<\/li>
- Cor<\/li>
- Pureza<\/li>
- x – comprimento em mm<\/li>
- y – comprimento em mm<\/li>
- z – comprimento em mm<\/li>
- Profundidade<\/li>
- Largura do topo do diamante<\/li><\/ol>\n\n\n\n
![\"\"](\"https:\/\/i0.wp.com\/operdata.com.br\/wp-content\/uploads\/2020\/09\/image-3.jpeg?fit=1030%2C579&ssl=1\")
<\/figure><\/div>\n\n\n\nlibrary(ggplot2)\n\ndens <- with(diamonds, \n tapply(price, \n INDEX = cut, \n density))\n\ndata.dens <- data.frame(x = unlist(lapply(dens, \"[[\", \"x\")),\n y = unlist(lapply(dens, \"[[\", \"y\")),\n cut = rep(names(dens), \n each = length(dens[[1]]$x)))\n\nhead(data.dens)<\/code><\/pre>\n\n\n\n## x y cut\n## Fair1 -1114.8694 6.744087e-08 Fair\n## Fair2 -1073.4981 9.022150e-08 Fair\n## Fair3 -1032.1268 1.196403e-07 Fair\n## Fair4 -990.7555 1.572893e-07 Fair\n## Fair5 -949.3842 2.064376e-07 Fair\n## Fair6 -908.0129 2.708506e-07 Fair<\/code><\/pre>\n\n\n\ndensity <- plot_ly(data.dens, \n x = ~x, \n y = ~y, \n color = ~cut) %>% \n add_lines() %>%\n layout(xaxis = list(title = \"Pre\u00e7o\"),\n yaxis = list(title = \"\")) \n\ndensity<\/code><\/pre>\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/operdata.com.br\/wp-content\/uploads\/2020\/10\/Captura-de-Tela-2020-10-09-a\u0300s-09.59.03.png?fit=1030%2C767&ssl=1\")
<\/figure>\n\n\n\nBar chart<\/h3>\n\n\n\n
Exemplo<\/strong><\/p>\n\n\n\nTamb\u00e9m usa o banco de dados diamonds<\/em>.<\/p>\n\n\n\ndata.bar <- ggplot2::diamonds %>% \n count(cut,\n clarity)\n\nhead(data.bar)<\/code><\/pre>\n\n\n\n## # A tibble: 6 x 3\n## cut clarity n\n## <ord> <ord> <int>\n## 1 Fair I1 210\n## 2 Fair SI2 466\n## 3 Fair SI1 408\n## 4 Fair VS2 261\n## 5 Fair VS1 170\n## 6 Fair VVS2 69<\/code><\/pre>\n\n\n\nbar <- data.bar %>% \n plot_ly(x = ~cut, \n y = ~n, \n color = ~clarity) %>%\n layout(xaxis = list(title = \"Corte\"),\n yaxis = list(title = \"Frequ\u00eancia absoluta (N)\")) \n\nbar<\/code><\/pre>\n\n\n\n![\"\"](\"https:\/\/i1.wp.com\/operdata.com.br\/wp-content\/uploads\/2020\/10\/Captura-de-Tela-2020-10-09-a\u0300s-09.59.29.png?fit=1030%2C723&ssl=1\")
<\/figure>\n\n\n\nBar chart with error bars<\/h3>\n\n\n\n
Exemplo<\/strong><\/p>\n\n\n\nO banco de dados utilizado \u00e9 o ToothGrowth<\/em>. A resposta \u00e9 o comprimento dos odontoblastos (c\u00e9lulas respons\u00e1veis pelo crescimento dos dentes) em 60 cobaias. Cada porquinho-da-\u00edndia recebeu um dos tr\u00eas n\u00edveis de dose de vitamina C (0,5, 1 e 2 mg\/dia) por um dos dois m\u00e9todos de entrega, suco de laranja ou \u00e1cido asc\u00f3rbico. As vari\u00e1veis s\u00e3o:<\/p>\n\n\n\n- Comprimento do dente<\/li>
- Tipo de suplemento<\/li>
- Dose<\/li><\/ol>\n\n\n\n
![\"\"](\"https:\/\/statplace.com.br\/wp-content\/uploads\/2020\/09\/image-1.jpeg\")
<\/figure><\/div>\n\n\n\nlibrary(plyr)\n\ndata_mean <- ddply(ToothGrowth, \n c(\"supp\", \n \"dose\"), \n summarise, \n length = mean(len))\n\ndata_sd <- ddply(ToothGrowth, \n c(\"supp\", \n \"dose\"), \n summarise, \n length = sd(len))\n\nvitamin <- data.frame(data_mean, \n data_sd$length)\n\nvitamin <- rename(vitamin, \n c(\"data_sd.length\" = \"sd\"))\n\nvitamin$dose <- as.factor(vitamin$dose)\n\nhead(vitamin)<\/code><\/pre>\n\n\n\n## supp dose length sd\n## 1 OJ 0.5 13.23 4.459709\n## 2 OJ 1 22.70 3.910953\n## 3 OJ 2 26.06 2.655058\n## 4 VC 0.5 7.98 2.746634\n## 5 VC 1 16.77 2.515309\n## 6 VC 2 26.14 4.797731<\/code><\/pre>\n\n\n\nerror <- plot_ly(data = vitamin[which(vitamin$supp == 'OJ'),], \n x = ~dose, \n y = ~length, \n type = 'bar', \n name = 'OJ',\n error_y = ~list(array = sd,\n color = '#000000')) %>% \n add_trace(data = vitamin[which(vitamin$supp == 'VC'),], \n name = 'VC') %>%\n layout(xaxis = list(title = \"Dose\"),\n yaxis = list(title = \"Comprimento\")) \n\nerror<\/code><\/pre>\n\n\n\n![\"\"](\"https:\/\/i2.wp.com\/operdata.com.br\/wp-content\/uploads\/2020\/10\/Captura-de-Tela-2020-10-09-a\u0300s-09.59.52.png?fit=1030%2C718&ssl=1\")
<\/figure><\/div>\n\n\n\nHistogram<\/h3>\n\n\n\n
Exemplo<\/strong><\/p>\n\n\n\nDuas vari\u00e1veis aleat\u00f3rias que seguem distribui\u00e7\u00e3o normal.<\/p>\n\n\n\n
![\"\"](\"https:\/\/statplace.com.br\/wp-content\/uploads\/2020\/09\/image-2.png\")
<\/figure><\/div>\n\n\n\nplot_ly(alpha = 0.6) %>% \n add_histogram(x = ~rnorm(500)) %>% \n add_histogram(x = ~rnorm(500) + 1) %>% \n layout(barmode = \"overlay\")<\/code><\/pre>\n\n\n\n![\"\"](\"https:\/\/i1.wp.com\/operdata.com.br\/wp-content\/uploads\/2020\/10\/Captura-de-Tela-2020-10-09-a\u0300s-10.00.37-1.png?fit=1030%2C756&ssl=1\")
<\/figure><\/div>\n\n\n\nBoxplots<\/h3>\n\n\n\n
Exemplo<\/strong><\/p>\n\n\n\nTamb\u00e9m usa o pacote diamonds<\/em>.<\/p>\n\n\n\nhead(diamonds)<\/code><\/pre>\n\n\n\n## # A tibble: 6 x 10\n## carat cut color clarity depth table price x y z\n## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>\n## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43\n## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31\n## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31\n## 4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63\n## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75\n## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48<\/code><\/pre>\n\n\n\nplot_ly(diamonds, \n y = ~price, \n color = ~cut, \n type = \"box\")<\/code><\/pre>\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/operdata.com.br\/wp-content\/uploads\/2020\/10\/Captura-de-Tela-2020-10-09-a\u0300s-10.00.59.png?fit=1030%2C697&ssl=1\")
<\/figure><\/div>\n\n\n\nPie chart<\/h3>\n\n\n\n
Exemplo<\/strong><\/p>\n\n\n\nO banco de dados \u00e9 o USPersonalExpenditure<\/em>. Este conjunto de dados consiste em despesas pessoais dos Estados Unidos (em bilh\u00f5es de d\u00f3lares) para os anos de 1940, 1945, 1950, 1955 e 1960 nas categorias:<\/p>\n\n\n\n- comida e tabaco,<\/li>
- opera\u00e7\u00e3o dom\u00e9stica,<\/li>
- medicina e sa\u00fade,<\/li>
- cuidados pessoais e<\/li>
- educa\u00e7\u00e3o privada.<\/li><\/ol>\n\n\n\n
USPersonalExpenditure <- data.frame(\"Categorie\" = rownames(USPersonalExpenditure), \n USPersonalExpenditure)\n\ndata.pie <- USPersonalExpenditure[, c('Categorie', \n 'X1960')]\n\nhead(data.pie)<\/code><\/pre>\n\n\n\n## Categorie X1960\n## Food and Tobacco Food and Tobacco 86.80\n## Household Operation Household Operation 46.20\n## Medical and Health Medical and Health 21.10\n## Personal Care Personal Care 5.40\n## Private Education Private Education 3.64<\/code><\/pre>\n\n\n\ncolors <- c('rgb(211,94,96)', \n 'rgb(128,133,133)', \n 'rgb(144,103,167)', \n 'rgb(171,104,87)', \n 'rgb(114,147,203)')\n\npie <- plot_ly(data.pie, \n labels = ~Categorie, \n values = ~X1960, \n type = 'pie',\n textposition = 'inside',\n textinfo = 'label+percent',\n insidetextfont = list(color = '#FFFFFF'),\n hoverinfo = 'text',\n text = ~paste('$', \n X1960, \n ' billions'),\n marker = list(colors = colors,\n line = list(color = '#FFFFFF', \n width = 1)),\n showlegend = FALSE) %>% \n layout(title = 'United States Personal Expenditures by Categories in 1960',\n xaxis = list(showgrid = FALSE, \n zeroline = FALSE, \n showticklabels = FALSE),\n yaxis = list(showgrid = FALSE, \n zeroline = FALSE, \n showticklabels = FALSE))\n\npie<\/code><\/pre>\n\n\n\n![\"\"](\"https:\/\/i2.wp.com\/operdata.com.br\/wp-content\/uploads\/2020\/10\/Captura-de-Tela-2020-10-09-a\u0300s-10.01.28-1.png?fit=1030%2C853&ssl=1\")
<\/figure><\/div>\n\n\n\nBubble chart<\/h3>\n\n\n\n
Exemplo<\/strong><\/p>\n\n\n\nO banco de dados \u00e9 do github e ele cont\u00e9m 4 vari\u00e1veis:<\/p>\n\n\n\n
- Universidades americanas<\/li>
- Sal\u00e1rio de homens<\/li>
- Sal\u00e1rio de mulheres<\/li>
- Diferen\u00e7a entre o sal\u00e1rio de homens e mulheres<\/li><\/ol>\n\n\n\n
![\"\"](\"https:\/\/i0.wp.com\/operdata.com.br\/wp-content\/uploads\/2020\/09\/image-3.png?fit=1030%2C1030&ssl=1\")
<\/figure><\/div>\n\n\n\ndata.bubble <- read.csv(\"https:\/\/raw.githubusercontent.com\/plotly\/datasets\/master\/school_earnings.csv\")\n\nhead(data.bubble)<\/code><\/pre>\n\n\n\n## School Women Men Gap\n## 1 MIT 94 152 58\n## 2 Stanford 96 151 55\n## 3 Harvard 112 165 53\n## 4 U.Penn 92 141 49\n## 5 Princeton 90 137 47\n## 6 Chicago 78 118 40<\/code><\/pre>\n\n\n\nbubble <- plot_ly(data.bubble, \n x = ~Women, \n y = ~Men, \n text = ~School, \n type = 'scatter', \n mode = 'markers',\n marker = list(size = ~Gap, \n opacity = 0.5)) %>% \n layout(title = 'Gender Gap in Earnings per University',\n xaxis = list(showgrid = FALSE),\n yaxis = list(showgrid = FALSE))\n\nbubble<\/code><\/pre>\n\n\n\n![\"\"](\"https:\/\/i2.wp.com\/operdata.com.br\/wp-content\/uploads\/2020\/10\/Captura-de-Tela-2020-10-09-a\u0300s-10.01.54.png?fit=1030%2C793&ssl=1\")
<\/figure><\/div>\n\n\n\nGr\u00e1ficos diferentes<\/h2>\n\n\n\nSunburst chart<\/h3>\n\n\n\n
Exemplo<\/strong><\/p>\n\n\n\nOs bancos de dados s\u00e3o do github e ambos s\u00e3o a respeito de sabores de caf\u00e9, sendo o segundo banco mais completo que o primeiro. As vari\u00e1veis s\u00e3o:<\/p>\n\n\n\n
- Ids<\/li>
- Labels<\/li>
- Parents<\/li><\/ol>\n\n\n\n
![\"\"](\"https:\/\/statplace.com.br\/wp-content\/uploads\/2020\/09\/image-5.png\")
<\/figure><\/div>\n\n\n\nd1 <- read.csv('https:\/\/raw.githubusercontent.com\/plotly\/datasets\/master\/coffee-flavors.csv')\n\nhead(d1)<\/code><\/pre>\n\n\n\n## ids labels parents\n## 1 Enzymatic-Flowery Flowery \n## 2 Enzymatic-Fruity Fruity \n## 3 Enzymatic-Herby Herby \n## 4 Sugar Browning-Nutty Nutty \n## 5 Sugar Browning-Carmelly Carmelly \n## 6 Sugar Browning-Chocolatey Chocolatey<\/code><\/pre>\n\n\n\nd2 <- read.csv('https:\/\/raw.githubusercontent.com\/plotly\/datasets\/718417069ead87650b90472464c7565dc8c2cb1c\/sunburst-coffee-flavors-complete.csv')\n\nhead(d2)<\/code><\/pre>\n\n\n\n## ids labels parents\n## 1 Aromas Aromas \n## 2 Tastes Tastes \n## 3 Aromas-Enzymatic Enzymatic Aromas\n## 4 Aromas-Sugar Browning Sugar Browning Aromas\n## 5 Aromas-Dry Distillation Dry Distillation Aromas\n## 6 Tastes-Bitter Bitter Tastes<\/code><\/pre>\n\n\n\nsunburst <- plot_ly() %>%\n add_trace(ids = d1$ids,\n labels = d1$labels,\n parents = d1$parents,\n type = 'sunburst',\n maxdepth = 2,\n domain = list(column = 0)) %>%\n add_trace(ids = d2$ids,\n labels = d2$labels,\n parents = d2$parents,\n type = 'sunburst',\n maxdepth = 3,\n domain = list(column = 1)) %>%\n layout(grid = list(columns = 2, \n rows = 1),\n margin = list(l = 0, \n r = 0,\n b = 0, \n t = 0),\n sunburstcolorway = c(\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#19d3f3\",\n \"#e763fa\", \"#FECB52\",\"#FFA15A\",\"#FF6692\",\"#B6E880\"),\n extendsunburstcolors = TRUE)\nsunburst<\/code><\/pre>\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/operdata.com.br\/wp-content\/uploads\/2020\/10\/Captura-de-Tela-2020-10-09-a\u0300s-10.02.28.png?fit=1030%2C504&ssl=1\")
<\/figure><\/div>\n\n\n\nDumbbell plot<\/h3>\n\n\n\n
Exemplo<\/strong><\/p>\n\n\n\nO banco de dados \u00e9 o mesmo utilizado anteriormente, relacionado \u00e0s universidades.<\/p>\n\n\n\n
Data.dumbbell <- read.csv(\"https:\/\/raw.githubusercontent.com\/plotly\/datasets\/master\/school_earnings.csv\")\n\nData.dumbbell$School <- factor(Data.dumbbell$School, \n levels = Data.dumbbell$School[order(Data.dumbbell$Men)])\n\n\nhead(Data.dumbbell)<\/code><\/pre>\n\n\n\n## School Women Men Gap\n## 1 MIT 94 152 58\n## 2 Stanford 96 151 55\n## 3 Harvard 112 165 53\n## 4 U.Penn 92 141 49\n## 5 Princeton 90 137 47\n## 6 Chicago 78 118 40<\/code><\/pre>\n\n\n\nDumbbell <- plot_ly(Data.dumbbell,\n color = I(\"gray80\")) %>% \n add_segments(x = ~Women, \n xend = ~Men, \n y = ~School, \n yend = ~School, \n showlegend = FALSE) %>% \n add_markers(x = ~Women, \n y = ~School, \n name = \"Women\", \n color = I(\"pink\")) %>% \n add_markers(x = ~Men, \n y = ~School, \n name = \"Men\", \n color = I(\"blue\")) %>% \n layout(title = \"Gender earnings disparity\",\n xaxis = list(title = \"Annual Salary (in thousands)\"),\n margin = list(l = 65))\n\nDumbbell<\/code><\/pre>\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/operdata.com.br\/wp-content\/uploads\/2020\/10\/Captura-de-Tela-2020-10-09-a\u0300s-10.02.46-1.png?fit=1030%2C768&ssl=1\")
<\/figure><\/div>\n\n\n\n3D Scatter plot<\/h3>\n\n\n\n
Exemplo<\/strong><\/p>\n\n\n\nO banco utilizado \u00e9 o mtcars<\/em>. Os dados foram extra\u00eddos da revista Motor Trend US de 1974 e abrangem o consumo de combust\u00edvel e 10 aspectos do design e desempenho do autom\u00f3vel para 32 autom\u00f3veis. As vari\u00e1veis s\u00e3o:<\/p>\n\n\n\n- Milhas\/gal\u00e3o<\/li>
- N\u00famero de cilindros<\/li>
- Deslocamento<\/li>
- Pot\u00eancia bruta<\/li>
- Rela\u00e7\u00e3o do eixo traseiro<\/li>
- Peso<\/li>
- 1\/4 de milha<\/li>
- Tipo de motor<\/li>
- Transmiss\u00e3o<\/li>
- N\u00famero de marchas para frente<\/li>
- Modelo do carro<\/li>
- Montadora<\/li><\/ol>\n\n\n\n
mtcars$am[which(mtcars$am == 0)] <- 'Automatic'\n\nmtcars$am[which(mtcars$am == 1)] <- 'Manual'\n\nmtcars$am <- as.factor(mtcars$am)\n\nhead(mtcars)<\/code><\/pre>\n\n\n\n## mpg cyl disp hp drat wt qsec vs am gear carb\n## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 Manual 4 4\n## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 Manual 4 4\n## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 Manual 4 1\n## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 Automatic 3 1\n## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 Automatic 3 2\n## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 Automatic 3 1<\/code><\/pre>\n\n\n\nScatter3D <- plot_ly(mtcars, \n x = ~wt, \n y = ~hp, \n z = ~qsec, \n color = ~am, \n colors = c('#BF382A', \n '#0C4B8E')) %>% \n add_markers() %>% \n layout(scene = list(xaxis = list(title = 'Weight'),\n yaxis = list(title = 'Gross horsepower'),\n zaxis = list(title = '1\/4 mile time')))\n\nScatter3D<\/code><\/pre>\n\n\n\n![\"\"](\"https:\/\/i2.wp.com\/operdata.com.br\/wp-content\/uploads\/2020\/10\/Captura-de-Tela-2020-10-09-a\u0300s-10.03.28-1.png?fit=1030%2C746&ssl=1\")
<\/figure><\/div>\n\n\n\n3D Surface Plot<\/h3>\n\n\n\n
Exemplo<\/strong><\/p>\n\n\n\nFazer uma pir\u00e2mide de base triangular.<\/p>\n\n\n\n![\"\"](\"https:\/\/statplace.com.br\/wp-content\/uploads\/2020\/09\/image-5.jpeg\")
<\/figure>\n\n\n\nSurface <- plot_ly(x = c(0, 1, 2, 0),\n y = c(0, 0, 1, 2),\n z = c(0, 2, 0, 1),\n i = c(0, 0, 0, 1),\n j = c(1, 2, 3, 2),\n k = c(2, 3, 1, 3),\n facecolor = toRGB(viridisLite::viridis(4)))\n\nSurface<\/code><\/pre>\n\n\n\n![\"\"](\"https:\/\/statplace.com.br\/wp-content\/uploads\/2020\/10\/Captura-de-Tela-2020-10-09-a\u0300s-10.04.40.png\")
<\/figure><\/div>\n\n\n\nMapas usando plotly<\/h2>\n\n\n\nChoropleth map<\/h3>\n\n\n\n
Exemplo<\/strong><\/p>\n\n\n\nO banco de dados utilizado \u00e9 do Githube e ele cont\u00e9m as vari\u00e1veis de todos os pa\u00edses em 2014:<\/p>\n\n\n\n
- Pa\u00eds<\/li>
- PIB (em bilh\u00f5es de d\u00f3lares)<\/li>
- Sigla do pa\u00eds<\/li><\/ol>\n\n\n\n
![\"\"](\"https:\/\/statplace.com.br\/wp-content\/uploads\/2020\/09\/image-6.jpeg\")
<\/figure><\/div>\n\n\n\nData.choropleth <- read.csv('https:\/\/raw.githubusercontent.com\/plotly\/datasets\/master\/2014_world_gdp_with_codes.csv')\n\nhead(Data.choropleth)<\/code><\/pre>\n\n\n\n## COUNTRY GDP..BILLIONS. CODE\n## 1 Afghanistan 21.71 AFG\n## 2 Albania 13.40 ALB\n## 3 Algeria 227.80 DZA\n## 4 American Samoa 0.75 ASM\n## 5 Andorra 4.80 AND\n## 6 Angola 131.40 AGO<\/code><\/pre>\n\n\n\nl <- list(color = toRGB(\"grey\"), \n width = 0.5)\n\ng <- list(showframe = FALSE,\n showcoastlines = FALSE,\n projection = list(type = 'Mercator'))\n\nChoropleth <- plot_geo(Data.choropleth) %>% \n add_trace(z = ~GDP..BILLIONS., \n color = ~GDP..BILLIONS., \n colors = 'Blues',\n text = ~COUNTRY, \n locations = ~CODE, \n marker = list(line = l)) %>% \n colorbar(title = 'GDP Billions US$', \n tickprefix = '$') %>% \n layout(title = '2014 Global GDP<br>Source:<ahref=\"https:\/\/www.cia.gov\/library\/publications\/the-world-factbook\/fields\/2195.html\">CIA World Factbook<\/a>',\n geo = g)\n\nChoropleth<\/code><\/pre>\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/operdata.com.br\/wp-content\/uploads\/2020\/10\/Captura-de-Tela-2020-10-09-a\u0300s-10.05.16.png?fit=1030%2C499&ssl=1\")
<\/figure>\n\n\n\nBubble Map<\/h3>\n\n\n\n
Exemplo<\/strong><\/p>\n\n\n\nO banco de dados utilizado \u00e9 do Githube e ele cont\u00e9m as vari\u00e1veis de todos as cidades dos EUA em 2014:<\/p>\n\n\n\n
- Nome da cidade<\/li>
- Popula\u00e7\u00e3o<\/li>
- Latitude<\/li>
- Longitude<\/li>
- Quantile<\/li><\/ol>\n\n\n\n
![\"\"](\"https:\/\/i0.wp.com\/operdata.com.br\/wp-content\/uploads\/2020\/09\/image-8.jpeg?fit=1030%2C644&ssl=1\")
<\/figure><\/div>\n\n\n\nData.bmap <- read.csv('https:\/\/raw.githubusercontent.com\/plotly\/datasets\/master\/2014_us_cities.csv')\n\nData.bmap$q <- with(Data.bmap, \n cut(pop, \n quantile(pop)))\n\nlevels(Data.bmap$q) <- paste(c(\"1st\", \n \"2nd\", \n \"3rd\", \n \"4th\", \n \"5th\"), \n \"Quantile\")\nData.bmap$q <- as.ordered(Data.bmap$q)\n\nhead(Data.bmap)<\/code><\/pre>\n\n\n\n## name pop lat lon q\n## 1 New York 8287238 40.73060 -73.98658 4th Quantile\n## 2 Los Angeles 3826423 34.05372 -118.24273 4th Quantile\n## 3 Chicago 2705627 41.87555 -87.62442 4th Quantile\n## 4 Houston 2129784 29.75894 -95.36770 4th Quantile\n## 5 Philadelphia 1539313 39.95233 -75.16379 4th Quantile\n## 6 Phoenix 1465114 33.44677 -112.07567 4th Quantile<\/code><\/pre>\n\n\n\ng <- list(scope = 'usa',\n projection = list(type = 'albers usa'),\n showland = TRUE,\n landcolor = toRGB(\"gray85\"),\n subunitwidth = 1,\n countrywidth = 1,\n subunitcolor = toRGB(\"white\"),\n countrycolor = toRGB(\"white\"))\n\nBubble.map <- plot_geo(Data.bmap, \n locationmode = 'USA-states', \n sizes = c(1, 250)) %>% \n add_markers(x = ~lon, \n y = ~lat, \n size = ~pop, \n color = ~q, \n hoverinfo = \"text\",\n text = ~paste(Data.bmap$name, \"<br \/>\", \n Data.bmap$pop\/1e6, \n \" million\")) %>% \n layout(title = '2014 US city populations<br>(Click legend to toggle)', \n geo = g)\n\nBubble.map<\/code><\/pre>\n\n\n\n## Warning: `line.width` does not currently support multiple values.\n\n## Warning: `line.width` does not currently support multiple values.\n\n## Warning: `line.width` does not currently support multiple values.\n\n## Warning: `line.width` does not currently support multiple values.<\/code><\/pre>\n\n\n\nGr\u00e1ficos animados no plotly<\/h2>\n\n\n\nExemplo: Gr\u00e1fico de bolhas<\/h3>\n\n\n\n
Banco de dados<\/strong><\/p>\n\n\n\nO pacote \u201cgapminder\u201d \u00e9 um trecho dos dados dispon\u00edveis em https:\/\/www.gapminder.org\/<\/a>. Para cada um dos 142 pa\u00edses, o pacote fornece valores para expectativa de vida, PIB per capita e popula\u00e7\u00e3o, a cada cinco anos, de 1952 a 2007.<\/p>\n\n\n\n![\"\"](\"https:\/\/statplace.com.br\/wp-content\/uploads\/2020\/09\/image-6.png\")
<\/figure><\/div>\n\n\n\nlibrary(gapminder)\n\nBanco.de.dados <- gapminder \nhead(Banco.de.dados)<\/code><\/pre>\n\n\n\n## # A tibble: 6 x 6\n## country continent year lifeExp pop gdpPercap\n## <fct> <fct> <int> <dbl> <int> <dbl>\n## 1 Afghanistan Asia 1952 28.8 8425333 779.\n## 2 Afghanistan Asia 1957 30.3 9240934 821.\n## 3 Afghanistan Asia 1962 32.0 10267083 853.\n## 4 Afghanistan Asia 1967 34.0 11537966 836.\n## 5 Afghanistan Asia 1972 36.1 13079460 740.\n## 6 Afghanistan Asia 1977 38.4 14880372 786.<\/code><\/pre>\n\n\n\nObjetivo<\/h3>\n\n\n\n
O objetivo \u00e9 fazer um gr\u00e1fico que contenha todas as inform\u00e7\u00f5es desse banco de dados.<\/p>\n\n\n\n
figura <- Banco.de.dados %>%\n plot_ly(x = ~gdpPercap, \n y = ~lifeExp, \n size = ~pop, \n color = ~continent, \n frame = ~year, \n text = ~country, \n hoverinfo = \"text\",\n type = 'scatter',\n mode = 'markers') %>% \n layout(xaxis = list(type = \"log\",\n title = \"PIB per capita\"),\n yaxis = list(title = \"Expectativa de vida\"))\n\nfigura<\/code><\/pre>\n\n\n\nanimation_opts<\/h3>\n\n\n\n- frame: a quantidade de tempo entre os quadros (em milissegundos). Observe que esse valor deve incluir a transi\u00e7\u00e3o.<\/li>
- transition: a dura\u00e7\u00e3o da transi\u00e7\u00e3o suave entre os quadros (em milissegundos).<\/li>
- easing: O tipo de atenua\u00e7\u00e3o da transi\u00e7\u00e3o. Veja a lista de op\u00e7\u00f5es aqui https:\/\/github.com\/plotly\/plotly.js\/blob\/master\/src\/plots\/animation_attributes.js<\/a><\/li><\/ul>\n\n\n\n
figura2 <- figura %>%\n animation_opts(frame = 1000, \n easing = 'sin')\n\nfigura2<\/code><\/pre>\n\n\n\nanimation_button<\/h3>\n\n\n\n- xanchor: posi\u00e7\u00e3o no eixo x<\/li>
- yanchor: posi\u00e7\u00e3o no eixo y<\/li><\/ul>\n\n\n\n
figura3 <- figura2 %>%\n animation_button(x = 1,\n xanchor = \"right\",\n y = 0,\n yanchor = \"bottom\")\n\nfigura3<\/code><\/pre>\n\n\n\nanimation_slider<\/h3>\n\n\n\n- currentvalue: sinalizar o tempo que est\u00e1 durante a anima\u00e7\u00e3o.<\/li>
- hide: esconder a barra do tempo.<\/li><\/ul>\n\n\n\n
figura4 <- figura3 %>%\n animation_slider(currentvalue = list(prefix = \"YEAR \", \n font = list(color=\"red\")))\n\nfigura4<\/code><\/pre>\n\n\n\nExemplo: gr\u00e1fico de linhas<\/h2>\n\n\n\nBanco de dados<\/h3>\n\n\n\n
O banco de dados \u00e9 o txhousing<\/em>. Ele cont\u00e9m informa\u00e7\u00f5es sobre o mercado imobili\u00e1rio no Texas fornecidas pelo centro imobili\u00e1rio TAMU, http:\/\/recenter.tamu.edu\/<\/a>. As vari\u00e1veis s\u00e3o:<\/p>\n\n\n\n- Cidade<\/li>
- Data<\/li>
- M\u00eas<\/li>
- Ano<\/li>
- N\u00famero de vendas<\/li>
- Valor total de vendas<\/li>
- Pre\u00e7o m\u00e9dio de venda<\/li>
- Total de listagens ativas<\/li>
- Quantidade de tempo que levaria para vender todas as listagens atuais no ritmo atual de vendas<\/li><\/ol>\n\n\n\n
![\"\"](\"https:\/\/statplace.com.br\/wp-content\/uploads\/2020\/09\/image-7.jpeg\")
<\/figure><\/div>\n\n\n\nlibrary(plotly)\n\naccumulate_by <- function(dat, var) {\n var <- lazyeval::f_eval(var, dat)\n lvls <- plotly:::getLevels(var)\n dats <- lapply(seq_along(lvls), function(x) {\n cbind(dat[var %in% lvls[seq(1, x)], ], frame = lvls[[x]])\n })\n dplyr::bind_rows(dats)\n}\n\nData.txhousing <- txhousing %>%\n filter(year > 2005, \n city %in% c(\"Abilene\", \"Bay Area\")) %>% \n accumulate_by(~date)\n\nhead(Data.txhousing)<\/code><\/pre>\n\n\n\n## city year month sales volume median listings inventory date\n## 1 Abilene 2006 1 107 11890000 90800 559 3.4 2006.000\n## 2 Bay Area 2006 1 367 58297417 133100 3038 5.7 2006.000\n## 3 Abilene 2006 1 107 11890000 90800 559 3.4 2006.000\n## 4 Abilene 2006 2 152 16875000 98200 561 3.4 2006.083\n## 5 Bay Area 2006 1 367 58297417 133100 3038 5.7 2006.000\n## 6 Bay Area 2006 2 471 77665581 138900 3157 5.8 2006.083\n## frame\n## 1 2006.000\n## 2 2006.000\n## 3 2006.083\n## 4 2006.083\n## 5 2006.083\n## 6 2006.083<\/code><\/pre>\n\n\n\nGr\u00e1fico final<\/h3>\n\n\n\nfig <- Data.txhousing %>%\n plot_ly(x = ~date, \n y = ~median,\n split = ~city,\n frame = ~frame, \n type = 'scatter',\n mode = 'lines', \n line = list(simplyfy = F)) %>% \n layout(xaxis = list(title = \"Date\",\n zeroline = F),\n yaxis = list(title = \"Median\",\n zeroline = F)) %>% \n animation_opts(frame = 100,\n transition = 0,\n redraw = FALSE) %>% \n animation_slider(hide = T) %>% \n animation_button(x = 1, \n xanchor = \"right\", \n y = 0, \n yanchor = \"bottom\")\n\nfig<\/code><\/pre>\n\n\n\nPodemos usar o plotly com o ggplot para tornar a visualiza\u00e7\u00e3o de dados interativa. Os gr\u00e1ficos saem do jeito que voc\u00ea j\u00e1 conhece no ggplot com algumas op\u00e7\u00f5es oferecidas pelo plotly. <\/p>\n\n\n\n
Voc\u00ea pode ler mais sobre o pacote no site do plotly<\/a> ou ent\u00e3o j\u00e1 partir para o c\u00f3digo no R usando essa folha de atalhos com os gr\u00e1ficos <\/a>mais usados. <\/p>\n","protected":false},"excerpt":{"rendered":"O pacote Plotly cria gr\u00e1ficos interativos a partir de gr\u00e1ficos do ggplot2 e\/ou a biblioteca JavaScript \u2018plotly.js\u2019 inspirada na gram\u00e1tica dos gr\u00e1ficos.<\/p>\n","protected":false},"author":8,"featured_media":7012,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"","site-content-layout":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","spay_email":"","footnotes":""},"categories":[445],"tags":[73,284,247,285,286],"class_list":["post-6993","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ferramentas-e-tecnologias","tag-grafico","tag-grafico-animado","tag-pacote","tag-plotly","tag-rstats-2"],"yoast_head":"\n
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