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‎auto-docs/chart-types/area/matlab-basic-area/matlab/code.txt

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% Learn about API authentication here: https://plot.ly/matlab/getting-started
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% Find your api_key here: https://plot.ly/settings/api
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% Learn about API authentication here: {BASE_URL}/matlab/getting-started
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% Find your api_key here: {BASE_URL}/settings/api
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y = [1, 5, 3;
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3, 2, 7;

‎auto-docs/chart-types/area/mpl-basic-area/matplotlib/code.txt

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import matplotlib.pyplot as plt
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import plotly.plotly as py
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# Learn about API authentication here: https://plot.ly/python/getting-started
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# Find your api_key here: https://plot.ly/settings/api
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# Learn about API authentication here: {BASE_URL}/python/getting-started
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fig, ax = plt.subplots()
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ax.plot([2,1,3,1,2])

‎auto-docs/chart-types/area/mpl-multi-fill/matplotlib/code.txt

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import numpy as np
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import matplotlib.pyplot as plt
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import plotly.plotly as py
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# Learn about API authentication here: https://plot.ly/python/getting-started
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# Find your api_key here: https://plot.ly/settings/api
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# Learn about API authentication here: {BASE_URL}/python/getting-started
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# Find your api_key here: {BASE_URL}/settings/api
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x = np.linspace(0, 2*np.pi, 100)
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fig, ax = plt.subplots()

‎auto-docs/chart-types/area/mpl-stacked-line/matplotlib/code.txt

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import numpy as np
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import matplotlib.pyplot as plt
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import plotly.plotly as py
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# Learn about API authentication here: https://plot.ly/python/getting-started
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# Find your api_key here: https://plot.ly/settings/api
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# Learn about API authentication here: {BASE_URL}/python/getting-started
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# Find your api_key here: {BASE_URL}/settings/api
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# create our stacked data manually
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y0 = np.random.rand(100)

‎auto-docs/chart-types/bar/gg-basic-bar/ggplot2/code.txt

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library(plotly)
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# Find your api_key here: https://plot.ly/settings/api
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# Learn about API authentication here: {BASE_URL}/r/getting-started
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# Consider the following data frame
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researchers <- data.frame(

‎auto-docs/chart-types/bar/matlab-basic-bar/matlab/code.txt

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% Learn about API authentication here: https://plot.ly/matlab/getting-started
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% Find your api_key here: https://plot.ly/settings/api
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% Learn about API authentication here: {BASE_URL}/matlab/getting-started
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% Find your api_key here: {BASE_URL}/settings/api
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x = 1900:10:2000;
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y = [75.995,91.972,105.711,123.203,131.669,...

‎auto-docs/chart-types/bar/matlab-grouped-bar/matlab/code.txt

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% Learn about API authentication here: https://plot.ly/matlab/getting-started
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% Find your api_key here: https://plot.ly/settings/api
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% Learn about API authentication here: {BASE_URL}/matlab/getting-started
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% Find your api_key here: {BASE_URL}/settings/api
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Y = [1.0 0.5 0.7
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2.0 1.5 2.0

‎auto-docs/chart-types/bar/matlab-overlaid-bar/matlab/code.txt

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% Learn about API authentication here: https://plot.ly/matlab/getting-started
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% Find your api_key here: https://plot.ly/settings/api
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% Learn about API authentication here: {BASE_URL}/matlab/getting-started
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% Find your api_key here: {BASE_URL}/settings/api
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%bins
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x = 1900:10:2000;

‎auto-docs/chart-types/bar/matlab-stacked-bar/matlab/code.txt

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% Learn about API authentication here: https://plot.ly/matlab/getting-started
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% Learn about API authentication here: {BASE_URL}/matlab/getting-started
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% Find your api_key here: {BASE_URL}/settings/api
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% Create data for childhood disease cases
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measles = [38556 24472 14556 18060 19549 8122 28541 7880 3283 4135 7953 1884]';

‎auto-docs/chart-types/bar/matlab-style-bar/matlab/code.txt

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% Learn about API authentication here: https://plot.ly/matlab/getting-started
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% Find your api_key here: https://plot.ly/settings/api
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% Learn about API authentication here: {BASE_URL}/matlab/getting-started
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% Find your api_key here: {BASE_URL}/settings/api
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%bins
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x = 1900:10:2000;

‎auto-docs/chart-types/bar/mpl-basic-bar/matplotlib/code.txt

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import matplotlib.pyplot as plt
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import plotly.plotly as py
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# Learn about API authentication here: https://plot.ly/python/getting-started
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# Find your api_key here: https://plot.ly/settings/api
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# Learn about API authentication here: {BASE_URL}/python/getting-started
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y = [3, 10, 7, 5, 3, 4.5, 6, 8.1]
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N = len(y)

‎auto-docs/chart-types/box/matlab-basic-box-plot/matlab/code.txt

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% Learn about API authentication here: https://plot.ly/matlab/getting-started
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% Find your api_key here: https://plot.ly/settings/api
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% Learn about API authentication here: {BASE_URL}/matlab/getting-started
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% Find your api_key here: {BASE_URL}/settings/api
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% some random points
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data1 = normrnd(5,1,100,1);

‎auto-docs/chart-types/bubble/matlab-bubble-chart/matlab/code.txt

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% Learn about API authentication here: https://plot.ly/matlab/getting-started
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% Find your api_key here: https://plot.ly/settings/api
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% Learn about API authentication here: {BASE_URL}/matlab/getting-started
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% Find your api_key here: {BASE_URL}/settings/api
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x = 80 * randn(1, 30);
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y = 80 * randn(size(x));

‎auto-docs/chart-types/bubble/mpl-7d-bubble/matplotlib/code.txt

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import matplotlib.pyplot as plt
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import numpy as np
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import plotly.plotly as py
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# Learn about API authentication here: https://plot.ly/python/getting-started
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# Find your api_key here: https://plot.ly/settings/api
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# Learn about API authentication here: {BASE_URL}/python/getting-started
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# Find your api_key here: {BASE_URL}/settings/api
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n = 50
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x, y, z, s, ew = np.random.rand(5, n)

‎auto-docs/chart-types/bubble/mpl-bubble-colormap/matplotlib/code.txt

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import numpy as np
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import plotly.plotly as py
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# Learn about API authentication here: {BASE_URL}/python/getting-started
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# decay and frequency for position and signal
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w1 = 4.

‎auto-docs/chart-types/contour/matlab-contour-subplots/matlab/code.txt

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% Learn about API authentication here: https://plot.ly/matlab/getting-started
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% Find your api_key here: https://plot.ly/settings/api
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% Learn about API authentication here: {BASE_URL}/matlab/getting-started
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% Find your api_key here: {BASE_URL}/settings/api
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%
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[X,Y,Z] = peaks;

‎auto-docs/chart-types/contour/matlab-meshgrid-contour/matlab/code.txt

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% Learn about API authentication here: https://plot.ly/matlab/getting-started
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% Learn about API authentication here: {BASE_URL}/matlab/getting-started
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% Find your api_key here: {BASE_URL}/settings/api
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x = linspace(-2*pi,2*pi);
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y = linspace(0,4*pi);

‎auto-docs/chart-types/contour/matlab-simple-contour/matlab/code.txt

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% Learn about API authentication here: {BASE_URL}/matlab/getting-started
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% Find your api_key here: {BASE_URL}/settings/api
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[X,Y,Z] = peaks;
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contour(X,Y,Z,20)

‎auto-docs/chart-types/error-bar/matlab-symmetric-error-bars/matlab/code.txt

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% Learn about API authentication here: https://plot.ly/matlab/getting-started
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% Learn about API authentication here: {BASE_URL}/matlab/getting-started
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% Find your api_key here: {BASE_URL}/settings/api
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x = 0:pi/10:pi;
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y = sin(x);

‎auto-docs/chart-types/facets/facet_wrap/ggplot2/code.txt

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library(plotly)
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require(lattice)
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# Learn about API authentication here: {BASE_URL}/r/getting-started
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w <- ggplot(data=barley, aes(x=yield, y=variety, color=year)) +
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geom_point() +

‎auto-docs/chart-types/functions/x-squared/ggplot2/code.txt

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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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eq <- function(x) {x*x}
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tmp <- data.frame(x=1:50, y=eq(1:50))

‎auto-docs/chart-types/geom_polygon/geom_polygon_1/ggplot2/code.txt

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library(plotly)
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# Learn about API authentication here: {BASE_URL}/r/getting-started
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ids <- factor(c("1.1", "2.1", "1.2", "2.2", "1.3", "2.3"))
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‎auto-docs/chart-types/gg-themes/few/ggplot2/code.txt

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library(plotly)
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library("ggthemes")
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# Learn about API authentication here: {BASE_URL}/r/getting-started
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dsamp <- diamonds[sample(nrow(diamonds), 1000), ]
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few <- (qplot(carat, price, data = dsamp, colour = cut) +

‎auto-docs/chart-types/gg-themes/igray/ggplot2/code.txt

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library(plotly)
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library("ggthemes")
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# Find your api_key here: https://plot.ly/settings/api
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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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dsamp <- diamonds[sample(nrow(diamonds), 1000), ]
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gray <- (qplot(carat, price, data = dsamp, colour = cut) +

‎auto-docs/chart-types/gg-themes/tableau/ggplot2/code.txt

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library(plotly)
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library("ggthemes")
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# Find your api_key here: https://plot.ly/settings/api
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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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dsamp <- diamonds[sample(nrow(diamonds), 1000), ]
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tableau <- (qplot(carat, price, data = dsamp, colour = cut) +

‎auto-docs/chart-types/heatmap/matlab-basic-heatmap/matlab/code.txt

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% Learn about API authentication here: https://plot.ly/matlab/getting-started
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% Find your api_key here: https://plot.ly/settings/api
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% Learn about API authentication here: {BASE_URL}/matlab/getting-started
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% Find your api_key here: {BASE_URL}/settings/api
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size = 50;
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z = zeros(size, size);

‎auto-docs/chart-types/histogram/geom_histogram/ggplot2/code.txt

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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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m <- ggplot(movies, aes(x=rating))
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out <- py$ggplotly(m + geom_histogram(aes(weight = votes)))

‎auto-docs/chart-types/histogram/matlab-basic-histogram/matlab/code.txt

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% Learn about API authentication here: https://plot.ly/matlab/getting-started
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% Learn about API authentication here: {BASE_URL}/matlab/getting-started
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% Find your api_key here: {BASE_URL}/settings/api
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% some random points
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x = normrnd(5,1,100,1);

‎auto-docs/chart-types/histogram/matlab-horizontal-histogram/matlab/code.txt

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% Learn about API authentication here: https://plot.ly/matlab/getting-started
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% Learn about API authentication here: {BASE_URL}/matlab/getting-started
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% Find your api_key here: {BASE_URL}/settings/api
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% horizontal histogram
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sd = 0.1;

‎auto-docs/chart-types/histogram/matlab-overlaid-histogram/matlab/code.txt

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% Learn about API authentication here: {BASE_URL}/matlab/getting-started
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% Find your api_key here: {BASE_URL}/settings/api
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% overlaid histogram
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sd1 = 0.1;

‎auto-docs/chart-types/histogram/matlab-stacked-histogram/matlab/code.txt

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% Learn about API authentication here: https://plot.ly/matlab/getting-started
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% Learn about API authentication here: {BASE_URL}/matlab/getting-started
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% Find your api_key here: {BASE_URL}/settings/api
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% stacked histogram
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sd1 = 0.1;

‎auto-docs/chart-types/histogram/mpl-basic-histogram/matplotlib/code.txt

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import plotly.plotly as py
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# Learn about API authentication here: {BASE_URL}/python/getting-started
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gaussian_numbers = np.random.randn(1000)
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plt.hist(gaussian_numbers)

‎auto-docs/chart-types/line_and_scatter/gg-alpha/ggplot2/code.txt

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library(plotly)
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# Learn about API authentication here: {BASE_URL}/r/getting-started
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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)

‎auto-docs/chart-types/line_and_scatter/gg-basic-line/ggplot2/code.txt

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library(plotly)
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# Learn about API authentication here: {BASE_URL}/r/getting-started
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orange <- qplot(
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age,

‎auto-docs/chart-types/line_and_scatter/gg-basic-scatter/ggplot2/code.txt

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library(plotly)
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# Learn about API authentication here: {BASE_URL}/r/getting-started
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ggiris <- qplot(Petal.Width, Sepal.Length, data = iris, color = Species)
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‎auto-docs/chart-types/line_and_scatter/gg-geom_line/ggplot2/code.txt

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library(plotly)
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library(plyr)
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# Learn about API authentication here: {BASE_URL}/r/getting-started
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mry <- do.call(rbind, by(movies, round(movies$rating), function(df) {
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nums <- tapply(df$length, df$year, length)

‎auto-docs/chart-types/line_and_scatter/gg-geom_path/ggplot2/code.txt

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library(plotly)
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# Learn about API authentication here: {BASE_URL}/r/getting-started
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myear <- ddply(movies, .(year), colwise(mean, .(length, rating)))
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p <- ggplot(myear, aes(length, rating))

‎auto-docs/chart-types/line_and_scatter/gg-geom_point/ggplot2/code.txt

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library(plotly)
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# Learn about API authentication here: {BASE_URL}/r/getting-started
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p <- ggplot(mtcars, aes(wt, mpg))
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out <- py$ggplotly(p + geom_point(aes(colour = factor(cyl))))

‎auto-docs/chart-types/line_and_scatter/gg-geom_segment/ggplot2/code.txt

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library(plotly)
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p <- ggplot(seals, aes(x = long, y = lat))
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‎auto-docs/chart-types/line_and_scatter/gg-line-scatter/ggplot2/code.txt

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library(plotly)
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# Learn about API authentication here: https://plot.ly/r/getting-started
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# Find your api_key here: https://plot.ly/settings/api
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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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model <- lm(mpg ~ wt + factor(cyl), data=mtcars)
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grid <- with(mtcars, expand.grid(

‎auto-docs/chart-types/line_and_scatter/mpl-line-scatter/matplotlib/code.txt

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import matplotlib.pyplot as plt
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import plotly.plotly as py
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# Learn about API authentication here: https://plot.ly/python/getting-started
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# Find your api_key here: https://plot.ly/settings/api
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# Learn about API authentication here: {BASE_URL}/python/getting-started
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# Find your api_key here: {BASE_URL}/settings/api
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x = [1,2,3,4]
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y = [3,4,8,6]

‎auto-docs/chart-types/line_and_scatter/mpl-line-style/matplotlib/code.txt

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import numpy as np
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import plotly.plotly as py
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# Learn about API authentication here: https://plot.ly/python/getting-started
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# Find your api_key here: https://plot.ly/settings/api
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# Learn about API authentication here: {BASE_URL}/python/getting-started
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# Find your api_key here: {BASE_URL}/settings/api
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# evenly sampled time at 200ms intervals
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t = np.arange(0., 5., 0.2)

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