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When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
summary(cars)
## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00
Plotly
library(plotly)
## Loading required package: ggplot2
## Warning: replacing previous import 'lifecycle::last_warnings' by
## 'rlang::last_warnings' when loading 'tibble'
## Warning: replacing previous import 'lifecycle::last_warnings' by
## 'rlang::last_warnings' when loading 'pillar'
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
fig <- plot_ly(y = ~rnorm(50), type = "box")
fig <- fig %>% add_trace(y = ~rnorm(50, 1))
fig
library(plotly)
y1 <- c(0.75, 5.25, 5.5, 6, 6.2, 6.6, 6.80, 7.0, 7.2, 7.5, 7.5, 7.75, 8.15,
8.15, 8.65, 8.93, 9.2, 9.5, 10, 10.25, 11.5, 12, 16, 20.90, 22.3, 23.25)
y2 <- c(0.75, 5.25, 5.5, 6, 6.2, 6.6, 6.80, 7.0, 7.2, 7.5, 7.5, 7.75, 8.15,
8.15, 8.65, 8.93, 9.2, 9.5, 10, 10.25, 11.5, 12, 16, 20.90, 22.3, 23.25)
y3 <- c(0.75, 5.25, 5.5, 6, 6.2, 6.6, 6.80, 7.0, 7.2, 7.5, 7.5, 7.75, 8.15,
8.15, 8.65, 8.93, 9.2, 9.5, 10, 10.25, 11.5, 12, 16, 20.90, 22.3, 23.25)
y4 <- c(0.75, 5.25, 5.5, 6, 6.2, 6.6, 6.80, 7.0, 7.2, 7.5, 7.5, 7.75, 8.15,
8.15, 8.65, 8.93, 9.2, 9.5, 10, 10.25, 11.5, 12, 16, 20.90, 22.3, 23.25)
fig <- plot_ly(type = 'box')
fig <- fig %>% add_boxplot(y = y1, jitter = 0.3, pointpos = -1.8, boxpoints = 'all',
marker = list(color = 'rgb(7,40,89)'),
line = list(color = 'rgb(7,40,89)'),
name = "All Points")
fig <- fig %>% add_boxplot(y = y2, name = "Only Whiskers", boxpoints = FALSE,
marker = list(color = 'rgb(9,56,125)'),
line = list(color = 'rgb(9,56,125)'))
fig <- fig %>% add_boxplot(y = y3, name = "Suspected Outlier", boxpoints = 'suspectedoutliers',
marker = list(color = 'rgb(8,81,156)',
outliercolor = 'rgba(219, 64, 82, 0.6)',
line = list(outliercolor = 'rgba(219, 64, 82, 1.0)',
outlierwidth = 2)),
line = list(color = 'rgb(8,81,156)'))
fig <- fig %>% add_boxplot(y = y4, name = "Whiskers and Outliers", boxpoints = 'outliers',
marker = list(color = 'rgb(107,174,214)'),
line = list(color = 'rgb(107,174,214)'))
fig <- fig %>% layout(title = "Box Plot Styling Outliers")
fig
# Need to install plotly from Github to get funnel plots
# devtools::install_github("ropensci/plotly")
library(plotly)
fig <- plot_ly()
fig <- fig %>%
add_trace(
type = "funnel",
y = c("Website visit", "Downloads", "Potential customers", "Requested price", "invoice sent"),
x = c(39, 27.4, 20.6, 11, 2))
fig <- fig %>%
layout(yaxis = list(categoryarray = c("Website visit", "Downloads", "Potential customers", "Requested price", "invoice sent")))
fig
library(plotly)
df <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/violin_data.csv")
fig <- df %>%
plot_ly(
x = ~day,
y = ~total_bill,
split = ~day,
type = 'violin',
box = list(
visible = T
),
meanline = list(
visible = T
)
)
fig <- fig %>%
layout(
xaxis = list(
title = "Day"
),
yaxis = list(
title = "Total Bill",
zeroline = F
)
)
fig
library(plotly)
trace_0 <- rnorm(100, mean = 5)
trace_1 <- rnorm(100, mean = 0)
trace_2 <- rnorm(100, mean = -5)
x <- c(1:100)
data <- data.frame(x, trace_0, trace_1, trace_2)
fig <- plot_ly(data, x = ~x, y = ~trace_0, name = 'trace 0', type = 'scatter', mode = 'lines')
fig <- fig %>% add_trace(y = ~trace_1, name = 'trace 1', mode = 'lines+markers')
fig <- fig %>% add_trace(y = ~trace_2, name = 'trace 2', mode = 'markers')
fig
Bandas de flotación
library(plotly)
month <- c('January', 'February', 'March', 'April', 'May', 'June', 'July',
'August', 'September', 'October', 'November', 'December')
high_2014 <- c(28.8, 28.5, 37.0, 56.8, 69.7, 79.7, 78.5, 77.8, 74.1, 62.6, 45.3, 39.9)
low_2014 <- c(12.7, 14.3, 18.6, 35.5, 49.9, 58.0, 60.0, 58.6, 51.7, 45.2, 32.2, 29.1)
data <- data.frame(month, high_2014, low_2014)
data$average_2014 <- rowMeans(data[,c("high_2014", "low_2014")])
#The default order will be alphabetized unless specified as below:
data$month <- factor(data$month, levels = data[["month"]])
fig <- plot_ly(data, x = ~month, y = ~high_2014, type = 'scatter', mode = 'lines',
line = list(color = 'transparent'),
showlegend = FALSE, name = 'High 2014')
fig <- fig %>% add_trace(y = ~low_2014, type = 'scatter', mode = 'lines',
fill = 'tonexty', fillcolor='rgba(0,100,80,0.2)', line = list(color = 'transparent'),
showlegend = FALSE, name = 'Low 2014')
fig <- fig %>% add_trace(x = ~month, y = ~average_2014, type = 'scatter', mode = 'lines',
line = list(color='rgb(0,100,80)'),
name = 'Average')
fig <- fig %>% layout(title = "Average, High and Low Temperatures in New York",
paper_bgcolor='rgb(255,255,255)', plot_bgcolor='rgb(229,229,229)',
xaxis = list(title = "Months",
gridcolor = 'rgb(255,255,255)',
showgrid = TRUE,
showline = FALSE,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
zeroline = FALSE),
yaxis = list(title = "Temperature (degrees F)",
gridcolor = 'rgb(255,255,255)',
showgrid = TRUE,
showline = FALSE,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
zeroline = FALSE))
fig
Fuentes y Usos de Fondos
library(plotly)
x = c(375, 128, 78, 27, 0, -327, -12, -78, -12, 0, 32, 89, 0, -45, 0)
y = c("Certificaciones", "Proveedores", "Material Propio", "Alquiler Equipos", "Ingreso Neto", "Compras", "Pago de Deudas",
"Jornales", "Combustibles", "Ganancia", "Inversion", "Intereses Finanzas",
"Ganancias antes impuestos", "Impuestos (15%)", "Ganancias dd Imp.")
measure = c("relative", "relative", "relative", "relative", "total", "relative", "relative", "relative",
"relative", "total", "relative", "relative", "total", "relative", "total")
data = data.frame(x,y=factor(y,levels = y), measure)
fig <- plot_ly(data, x = ~x, y = ~y, measure = ~measure, type = "waterfall", name = "Polo Logístico",
orientation = "h", connector = list(mode = "between", line = list(width = 4, color = "rgb(0, 0, 0)", dash = 0)))
fig <- fig %>%
layout(title = "Perdidas y Ganancias 1er Cuatrimestre<br>waterfall chart displaying positive and negative",
xaxis = list(title = "", tickfont = "16", ticks = "outside"),
yaxis = list(title = "", type = "category", autorange = "reversed"),
xaxis = list(title ="", type = "linear"),
margin = c(l = 150),
showlegend = TRUE)
fig
Índice de daño en infaestructura crítica originada en sismos durante los últimos 5 años.
Nivel | Daño |
---|---|
1 | Destrucción total |
0.9 | |
0.8 | Severo |
0.7 | |
0.6 | |
0.5 | Recuperable con vidas perdidas |
0.4 | Sin vidas perdidas ni daños personales |
0.3 | |
0.2 | Daño en mampostería |
0.1 | Sin daños |
library(plotly)
quakes = read.csv('https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k.csv')
fig <- quakes
fig <- fig %>%
plot_ly(
type = 'densitymapbox',
lat = ~Latitude,
lon = ~Longitude,
coloraxis = 'coloraxis',
radius = 10)
fig <- fig %>%
layout(
mapbox = list(
style="stamen-terrain",
center= list(lon=180)), coloraxis = list(colorscale = "Viridis"))
fig
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