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alexander-danielalexander-daniel
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added showcase for js libby
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‎hard-coded/showcase/showcase-subsection-placeholder/config.json

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@@ -1,5 +1,5 @@
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{
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"has_thumbnail": false,
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"name": "Showcase",
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"relative_url": "javascript-graphing-library/showcase/"
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"relative_url": "showcase"
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}

‎reports/test-report.txt

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@@ -162,6 +162,7 @@ Complete examples:
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shiny-ggplot2
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shiny-r
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show-legend
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showcase-example-placeholder
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simple-3d-scatter
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simple-annotation
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simple-contour

‎test-published/api-docs/chart-types/bar/gg-basic-bar/ggplot2/code.txt

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@@ -3,6 +3,8 @@ library(plotly)
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# Learn about API authentication here: {{BASE_URL}}/r/getting-started
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# Find your api_key here: {{BASE_URL}}/settings/api
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py <- plotly()
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# Consider the following data frame
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researchers <- data.frame(
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country = c("Canada", "Canada", "Germany", "USA"),
@@ -13,5 +15,6 @@ researchers <- data.frame(
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# Let us plot the number of papers (y) per name (x)
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plt <- ggplot(researchers, aes(x = name, y = papers)) + geom_bar(stat = "identity")
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out <- py$ggplotly(plt)
18+
out <- py$ggplotly(plt, kwargs=list(filename="gg-basic-bar", fileopt="overwrite"))
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plotly_url <- out$response$url

‎test-published/api-docs/chart-types/facets/facet_wrap/ggplot2/code.txt

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@@ -4,9 +4,11 @@ require(lattice)
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# Learn about API authentication here: {{BASE_URL}}/r/getting-started
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# Find your api_key here: {{BASE_URL}}/settings/api
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py <- plotly()
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w <- ggplot(data=barley, aes(x=yield, y=variety, color=year)) +
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geom_point() +
9-
facet_wrap(~site)
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geom_point() +
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facet_wrap(~site)
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out <- py$ggplotly(w)
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out <- py$ggplotly(w, kwargs=list(filename="facet_wrap", fileopt="overwrite"))
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plotly_url <- out$response$url

‎test-published/api-docs/chart-types/functions/x-squared/ggplot2/code.txt

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@@ -3,12 +3,14 @@ library(plotly)
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# Learn about API authentication here: {{BASE_URL}}/r/getting-started
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# Find your api_key here: {{BASE_URL}}/settings/api
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py <- plotly()
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eq <- function(x) {x*x}
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tmp <- data.frame(x=1:50, y=eq(1:50))
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p <- qplot(x, y, data=tmp, xlab="X-axis", ylab="Y-axis")
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c <- stat_function(fun=eq)
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p <- qplot(x, y, data=tmp, xlab="X-axis", ylab="Y-axis") +
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stat_function(fun=eq)
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out <- py$ggplotly(p + c)
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out <- py$ggplotly(p, kwargs=list(filename="x-squared", fileopt="overwrite"))
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plotly_url <- out$response$url

‎test-published/api-docs/chart-types/geom_polygon/geom_polygon_1/ggplot2/code.txt

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@@ -3,6 +3,8 @@ library(plotly)
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# Learn about API authentication here: {{BASE_URL}}/r/getting-started
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# Find your api_key here: {{BASE_URL}}/settings/api
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py <- plotly()
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ids <- factor(c("1.1", "2.1", "1.2", "2.2", "1.3", "2.3"))
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values <- data.frame(
@@ -20,7 +22,8 @@ positions <- data.frame(
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datapoly <- merge(values, positions, by=c("id"))
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(p <- ggplot(datapoly, aes(x=x, y=y)) + geom_polygon(aes(fill=value, group=id)))
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p <- ggplot(datapoly, aes(x=x, y=y)) +
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geom_polygon(aes(fill=value, group=id))
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out <- py$ggplotly(p)
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out <- py$ggplotly(p, kwargs=list(filename="geom_polygon_1", fileopt="overwrite"))
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plotly_url <- out$response$url

‎test-published/api-docs/chart-types/gg-themes/few/ggplot2/code.txt

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@@ -4,10 +4,12 @@ library("ggthemes")
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# Learn about API authentication here: {{BASE_URL}}/r/getting-started
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# Find your api_key here: {{BASE_URL}}/settings/api
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py <- plotly()
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dsamp <- diamonds[sample(nrow(diamonds), 1000), ]
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few <- (qplot(carat, price, data = dsamp, colour = cut) +
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theme_few() +
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scale_colour_few())
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out <- py$ggplotly(few)
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out <- py$ggplotly(few, kwargs=list(filename="few", fileopt="overwrite"))
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plotly_url <- out$response$url

‎test-published/api-docs/chart-types/gg-themes/igray/ggplot2/code.txt

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# Learn about API authentication here: {{BASE_URL}}/r/getting-started
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# Find your api_key here: {{BASE_URL}}/settings/api
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py <- plotly()
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dsamp <- diamonds[sample(nrow(diamonds), 1000), ]
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gray <- (qplot(carat, price, data = dsamp, colour = cut) +
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theme_igray())
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out <- py$ggplotly(gray)
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out <- py$ggplotly(gray, kwargs=list(filename="igray", fileopt="overwrite"))
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plotly_url <- out$response$url

‎test-published/api-docs/chart-types/gg-themes/tableau/ggplot2/code.txt

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# Learn about API authentication here: {{BASE_URL}}/r/getting-started
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# Find your api_key here: {{BASE_URL}}/settings/api
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py <- plotly()
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dsamp <- diamonds[sample(nrow(diamonds), 1000), ]
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tableau <- (qplot(carat, price, data = dsamp, colour = cut) +
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theme_igray() +
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scale_colour_tableau())
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out <- py$ggplotly(tableau)
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out <- py$ggplotly(tableau, kwargs=list(filename="tableau", fileopt="overwrite"))
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plotly_url <- out$response$url

‎test-published/api-docs/chart-types/histogram/geom_histogram/ggplot2/code.txt

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# Learn about API authentication here: {{BASE_URL}}/r/getting-started
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# Find your api_key here: {{BASE_URL}}/settings/api
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m <- ggplot(movies, aes(x=rating))
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out <- py$ggplotly(m + geom_histogram(aes(weight = votes)))
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py <- plotly()
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m <- ggplot(movies, aes(x=rating)) +
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geom_histogram(aes(weight = votes))
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out <- py$ggplotly(m, kwargs=list(filename="geom_histogram", fileopt="overwrite"))
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plotly_url <- out$response$url

‎test-published/api-docs/chart-types/line_and_scatter/gg-alpha/ggplot2/code.txt

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# Learn about API authentication here: {{BASE_URL}}/r/getting-started
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# Find your api_key here: {{BASE_URL}}/settings/api
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py <- plotly()
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prettyPlot <- ggplot(data=diamonds, aes(x=carat, y=price, colour=clarity))
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prettyPlot <- prettyPlot + geom_point(alpha = 1/10)
8-
out <- py$ggplotly(prettyPlot)
10+
out <- py$ggplotly(prettyPlot, kwargs=list(filename="gg-alpha", fileopt="overwrite"))
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plotly_url <- out$response$url

‎test-published/api-docs/chart-types/line_and_scatter/gg-basic-line/ggplot2/code.txt

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# Learn about API authentication here: {{BASE_URL}}/r/getting-started
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# Find your api_key here: {{BASE_URL}}/settings/api
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py <- plotly()
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orange <- qplot(
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age,
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circumference,
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data = Orange,
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colour = Tree,
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geom = "line")
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out <- py$ggplotly(orange)
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out <- py$ggplotly(orange, kwargs=list(filename="gg-basic-line", fileopt="overwrite"))
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plotly_url <- out$response$url

‎test-published/api-docs/chart-types/line_and_scatter/gg-basic-scatter/ggplot2/code.txt

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# Learn about API authentication here: {{BASE_URL}}/r/getting-started
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# Find your api_key here: {{BASE_URL}}/settings/api
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py <- plotly()
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ggiris <- qplot(Petal.Width, Sepal.Length, data = iris, color = Species)
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8-
out <- py$ggplotly(ggiris)
10+
out <- py$ggplotly(ggiris, kwargs=list(filename="gg-basic-scatter", fileopt="overwrite"))
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plotly_url <- out$response$url

‎test-published/api-docs/chart-types/line_and_scatter/gg-geom_line/ggplot2/code.txt

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# Learn about API authentication here: {{BASE_URL}}/r/getting-started
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# Find your api_key here: {{BASE_URL}}/settings/api
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py <- plotly()
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mry <- do.call(rbind, by(movies, round(movies$rating), function(df) {
810
nums <- tapply(df$length, df$year, length)
911
data.frame(rating=round(df$rating[1]), year = as.numeric(names(nums)), number=as.vector(nums))
1012
}))
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12-
p <- ggplot(mry, aes(x=year, y=number, group=rating))
13-
out <- py$ggplotly(p + geom_line(aes(colour = rating)) + scale_colour_gradient(low="red"))
14+
p <- ggplot(mry, aes(x=year, y=number, group=rating)) +
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geom_line(aes(colour = rating)) +
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scale_colour_gradient(low="red")
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out <- py$ggplotly(p, kwargs=list(filename="gg-geom_line", fileopt="overwrite"))
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plotly_url <- out$response$url

‎test-published/api-docs/chart-types/line_and_scatter/gg-geom_path/ggplot2/code.txt

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# Learn about API authentication here: {{BASE_URL}}/r/getting-started
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# Find your api_key here: {{BASE_URL}}/settings/api
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py <- plotly()
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myear <- ddply(movies, .(year), colwise(mean, .(length, rating)))
8-
p <- ggplot(myear, aes(length, rating))
9-
out <- py$ggplotly(p + geom_path(colour='green') + scale_size(range = c(1, 3)))
10+
p <- ggplot(myear, aes(length, rating)) +
11+
geom_path(colour='green') +
12+
scale_size(range = c(1, 3))
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out <- py$ggplotly(p, kwargs=list(filename="gg-geom_path", fileopt="overwrite"))
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plotly_url <- out$response$url

‎test-published/api-docs/chart-types/line_and_scatter/gg-geom_point/ggplot2/code.txt

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@@ -3,7 +3,10 @@ library(plotly)
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# Learn about API authentication here: {{BASE_URL}}/r/getting-started
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# Find your api_key here: {{BASE_URL}}/settings/api
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6-
p <- ggplot(mtcars, aes(wt, mpg))
7-
out <- py$ggplotly(p + geom_point(aes(colour = factor(cyl))))
6+
py <- plotly()
7+
8+
p <- ggplot(mtcars, aes(wt, mpg)) +
9+
geom_point(aes(colour = factor(cyl)))
10+
out <- py$ggplotly(p, kwargs=list(filename="gg-geom_point", fileopt="overwrite"))
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plotly_url <- out$response$url

‎test-published/api-docs/chart-types/line_and_scatter/gg-geom_segment/ggplot2/code.txt

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# Learn about API authentication here: {{BASE_URL}}/r/getting-started
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# Find your api_key here: {{BASE_URL}}/settings/api
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6-
p <- ggplot(seals, aes(x = long, y = lat))
6+
py <- plotly()
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8-
out <- py$ggplotly((p <- p + geom_segment(aes(xend = long + delta_long, yend = lat + delta_lat))))
8+
p <- ggplot(seals, aes(x = long, y = lat)) +
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geom_segment(aes(xend = long + delta_long, yend = lat + delta_lat))
10+
out <- py$ggplotly(p, kwargs=list(filename="gg-geom_segment", fileopt="overwrite"))
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plotly_url <- out$response$url

‎test-published/api-docs/chart-types/line_and_scatter/gg-line-scatter/ggplot2/code.txt

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# Learn about API authentication here: {{BASE_URL}}/r/getting-started
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# Find your api_key here: {{BASE_URL}}/settings/api
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py <- plotly()
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model <- lm(mpg ~ wt + factor(cyl), data=mtcars)
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grid <- with(mtcars, expand.grid(
810
wt = seq(min(wt), max(wt), length = 20),
@@ -11,6 +13,7 @@ grid <- with(mtcars, expand.grid(
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grid$mpg <- stats::predict(model, newdata=grid)
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14-
viz2 <- qplot(wt, mpg, data=mtcars, colour=factor(cyl)) + geom_line(data=grid)
15-
out <- py$ggplotly(viz2)
16+
viz2 <- qplot(wt, mpg, data=mtcars, colour=factor(cyl)) +
17+
geom_line(data=grid)
18+
out <- py$ggplotly(viz2, kwargs=list(filename="gg-line-scatter", fileopt="overwrite"))
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plotly_url <- out$response$url

‎test-published/api-docs/chart-types/line_shapes/geom_step/ggplot2/code.txt

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# Learn about API authentication here: {{BASE_URL}}/r/getting-started
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# Find your api_key here: {{BASE_URL}}/settings/api
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py <- plotly()
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df <- data.frame(x=sort(rnorm(47)))
7-
p <- ggplot(df, aes(seq_along(x), x))
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p <- ggplot(df, aes(seq_along(x), x)) +
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geom_step()
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out <- py$ggplotly(p + geom_step())
12+
out <- py$ggplotly(p, kwargs=list(filename="geom_step", fileopt="overwrite"))
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plotly_url <- out$response$url

‎test-published/api-docs/chart-types/maps/canadian-cities/ggplot2/code.txt

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# Learn about API authentication here: {{BASE_URL}}/r/getting-started
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# Find your api_key here: {{BASE_URL}}/settings/api
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py <- plotly()
7+
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data(canada.cities, package="maps")
79
viz <- ggplot(canada.cities, aes(long, lat)) +
8-
borders(regions="canada", name="borders") +
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coord_equal() +
10-
geom_point(aes(text=name, size=pop), colour="red", alpha=1/2, name="cities")
10+
borders(regions="canada", name="borders") +
11+
coord_equal() +
12+
geom_point(aes(text=name, size=pop), colour="red", alpha=1/2, name="cities")
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out <- py$ggplotly(viz)
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out <- py$ggplotly(viz, kwargs=list(filename="canadian-cities", fileopt="overwrite"))
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plotly_url <- out$response$url

‎test-published/api-docs/references/plotly_js.json

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"name": "Layout Options",
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"parent": "hard-coded"
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},
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{
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"branches": [
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{
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"branches": [
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{
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"code": "showcase/showcase-subsection-placeholder/showcase-example-placeholder/plotly_js/code.txt",
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"id": "showcase-example-placeholder",
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"languages": [
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"plotly_js"
1741+
],
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"name": "Showcase Example Placeholder",
1743+
"parent": "showcase-subsection-placeholder",
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"type": "model",
1745+
"url": "https://plot.ly/~TestBot/815"
1746+
}
1747+
],
1748+
"has_thumbnail": false,
1749+
"id": "showcase-subsection-placeholder",
1750+
"name": "Showcase",
1751+
"parent": "showcase",
1752+
"relative_url": "javascript-graphing-library/showcase/"
1753+
}
1754+
],
1755+
"has_thumbnail": false,
1756+
"id": "showcase",
1757+
"name": "Showcase",
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"parent": "hard-coded"
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},
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{
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"branches": [
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{

‎test-published/api-docs/showcase/showcase-subsection-placeholder/showcase-example-placeholder/plotly_js/code.txt

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var trace1 = {
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x: [1, 2, 3, 4],
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y: [0, 2, 3, 5],
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fill: "tozeroy",
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type: "scatter"
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};
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var trace2 = {
8+
x: [1, 2, 3, 4],
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y: [3, 5, 1, 7],
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fill: "tonexty",
11+
type: "scatter"
12+
};
13+
var data = [trace1, trace2];
14+
Plotly.plot(divid, data);

‎test-published/images/showcase-example-placeholder.png

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