一个滑块控制 R 中的多个子图
One slider controlling multiple subplots in R
我想用一个滑块来控制用plotly创建的多个子图。我在 Python 中找到了如下两个答案:
- Plot.ly. Using slider control with multiple plots
- https://community.plot.ly/t/using-one-slider-to-control-multiple-subplots-not-multiple-traces/13955/4
示例(第二个 link):
import plotly.graph_objs as go
from plotly.tools import make_subplots
fig = make_subplots(1, 2)
fig.add_scatter(y=[1, 3, 2], row=1, col=1, visible=True)
fig.add_scatter(y=[3, 1, 1.5], row=1, col=1, visible='legendonly')
fig.add_scatter(y=[2, 2, 1], row=1, col=1, visible='legendonly')
fig.add_scatter(y=[1, 3, 2], row=1, col=2, visible=True)
fig.add_scatter(y=[1.5, 2, 2.5], row=1, col=2, visible='legendonly')
fig.add_scatter(y=[2.5, 1.2, 2.9], row=1, col=2, visible='legendonly')
steps = []
for i in range(3):
step = dict(
method = 'restyle',
args = ['visible', ['legendonly'] * len(fig.data)],
)
step['args'][1][i] = True
step['args'][1][i+3] = True
steps.append(step)
sliders = [dict(
steps = steps,
)]
fig.layout.sliders = sliders
go.FigureWidget(fig)
但我如何在 R 中实现这一点?
这实际上与 python 中的过程完全相同。这是从 this:
派生的示例
library(plotly)
df <- data.frame(x = 1:5,
y = 1:5)
# create steps for slider
steps <- list(
list(args = list("marker.color", "red"),
label = "Red",
method = "restyle",
value = "1"
),
list(args = list("marker.color", "green"),
label = "Green",
method = "restyle",
value = "2"
),
list(args = list("marker.color", "blue"),
label = "Blue",
method = "restyle",
value = "3"
)
)
p1 <- p2 <- df %>%
plot_ly(x = ~x, y = ~y,
mode = "markers",
marker = list(size = 20,
color = 'green'),
type = "scatter")
p <- subplot(p1, p2) %>%
layout(title = "Basic Slider",
sliders = list(
list(
active = 1,
currentvalue = list(prefix = "Color: "),
pad = list(t = 60),
steps = steps)))
p
我想用一个滑块来控制用plotly创建的多个子图。我在 Python 中找到了如下两个答案:
- Plot.ly. Using slider control with multiple plots
- https://community.plot.ly/t/using-one-slider-to-control-multiple-subplots-not-multiple-traces/13955/4
示例(第二个 link):
import plotly.graph_objs as go
from plotly.tools import make_subplots
fig = make_subplots(1, 2)
fig.add_scatter(y=[1, 3, 2], row=1, col=1, visible=True)
fig.add_scatter(y=[3, 1, 1.5], row=1, col=1, visible='legendonly')
fig.add_scatter(y=[2, 2, 1], row=1, col=1, visible='legendonly')
fig.add_scatter(y=[1, 3, 2], row=1, col=2, visible=True)
fig.add_scatter(y=[1.5, 2, 2.5], row=1, col=2, visible='legendonly')
fig.add_scatter(y=[2.5, 1.2, 2.9], row=1, col=2, visible='legendonly')
steps = []
for i in range(3):
step = dict(
method = 'restyle',
args = ['visible', ['legendonly'] * len(fig.data)],
)
step['args'][1][i] = True
step['args'][1][i+3] = True
steps.append(step)
sliders = [dict(
steps = steps,
)]
fig.layout.sliders = sliders
go.FigureWidget(fig)
但我如何在 R 中实现这一点?
这实际上与 python 中的过程完全相同。这是从 this:
派生的示例library(plotly)
df <- data.frame(x = 1:5,
y = 1:5)
# create steps for slider
steps <- list(
list(args = list("marker.color", "red"),
label = "Red",
method = "restyle",
value = "1"
),
list(args = list("marker.color", "green"),
label = "Green",
method = "restyle",
value = "2"
),
list(args = list("marker.color", "blue"),
label = "Blue",
method = "restyle",
value = "3"
)
)
p1 <- p2 <- df %>%
plot_ly(x = ~x, y = ~y,
mode = "markers",
marker = list(size = 20,
color = 'green'),
type = "scatter")
p <- subplot(p1, p2) %>%
layout(title = "Basic Slider",
sliders = list(
list(
active = 1,
currentvalue = list(prefix = "Color: "),
pad = list(t = 60),
steps = steps)))
p