Is it possible to make the range slider work with a scatter plot? The slider works if I change the scatter to a line plot but that does not work for me as I can not use the box select tool with line plots. The snippet below is simplified to demonstrate the issue. I suspect the embedded JavaScript is the issue but I may be wrong as it works just fine with a line plot. Thanks.
from bokeh.io import show
from bokeh.models import CustomJS, RangeSlider, Column, Row
from bokeh.plotting import figure
x = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
y = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
scatter_plot = figure(width=250, plot_height=600)
scatter_plot.scatter(x, y)
callback = CustomJS(args=dict(y_range=scatter_plot.y_range), code="""
var start = cb_obj.value
y_range.start = start[0]
y_range.end = start[1]
""")
depth_slider = RangeSlider(width=250, show_value=False, start=-20, end=120, value=(20, 80), step=20,
title="Y Scale")
depth_slider.js_on_change('value', callback)
layout = Column(Row(depth_slider), scatter_plot, )
show(layout)
There seems to be some race condition going on or a discrepancy between how Python and JS version of Bokeh work.
By default, all ranges are instances of DataRange1d class which recompute start and end when needed. In this case, it for some reason recomputes the values after you set them manually.
To fix it, specify the range manually from the get go:
scatter_plot = figure(..., y_range=(20, 80))
I want to make a plot from raster data using levelplot. How to add a header which contains logo.png and title, like showed on this picture?
Here is my data: SST
Here is my basic code to produce this map:
r<-crop(raster(flname, varname="sst"), extent(90, 144, -20, 25))
png('SST.png', height = 2000, width = 2500, res = 300)
print(levelplot(r, col.regions = sst, at=seq(20, 34, 0.1),
yscale.components=yscale.raster.subticks,
xscale.components=xscale.raster.subticks,
margin=FALSE, ylab='Latitude', xlab='Longitude',
main=paste0(flname,' (deg-C)')))
dev.off()
You can use package magick to compose images:
library(rasterVis)
library(magick)
header <- image_read("~/Desktop/headerWithLogo.png")
fig <- image_graph(width = 600, height = 600, res=96)
f <- system.file("external/test.grd", package="raster")
r <- raster(f)
levelplot(r, margin=F, main ="Test \n")
dev.off()
out <- image_composite(fig, header, offset = "+50-5")
print(out)
I have an example data set which represents my bigger data set I'm dealing with that looks like this:
dat <- data.frame(groupid = c(rep("2ppm", 5), rep("20ppm", 5)),
var1 = c(222, 212, 245, 233, 213, 444, 454, 464, 434, 424),
var2 = c(111, 112, 145, 133, 113, 744, 754, 764, 734, 724));
I want to plot the variables side-by-side grouped by their groupid, hence I did the following:
mDat <- melt(dat, by = "groupid");
With this data set, I can easily plot the boxplots needed using ggplot2:
bp <- ggplot(mDat, aes(x = variable, y = value, fill = groupid)) +
geom_boxplot();
So far so good, however, I want to add an additional boxplot to the end of the plot, where all values in both variables are plotted to see the overall spread in a boxplot; I couldn't figure out how to modify the melted data set to get this result, e.g. add another group to groupid called all.
Thanks in advance for your help!
As the title says, a single ColorRamp palette when used in two different scetterplots built by plotly, gives two slightly differently looking (note the middle part) colorbars even though the upper and the lower bounds of corresponding data sets are manually set to be identical in both plots.
I'd like to make the plots visually comparable, and for that I'd obviously have to have identical colorbars. Is there a way to do that?
Here's the code:
myxaxis <- list(range = c(16, 44), dtick=2, gridwidth = 1, title = "Length of carbon chain") #setting the visible area of x axis
myyaxis <- list(range = c(0, 8), gridwidth = 1, title = "No. of double bonds") #setting the visible area of y axis
mycolors <- colorRampPalette(c('green', 'red', 'black'))(n = 100) #creating an RColorBrewer palette
ch_new1 <- cbind.data.frame(c('PA', 'PA', 'PA', 'PA', 'PA', 'PA', 'PA', 'PA', 'PA', 'upper bound', 'lower bound'), c(4.571087, 6.522441, 6.522441, 5.081869, 4.471815, 5.744834, 7.329796, 5.472866, 5.744834, 1, 1), c(10.52337, 16.75454, 16.0976, 16.47356, 18.94973, 17.46351, 10.97607, 18.11186, 11.64033, 0.2085327, 71.18021), c(32, 34, 34, 36, 36, 36, 38, 38, 38, 100, 100), c(1, 1, 2, 2, 3, 4, 4, 5, 6, 100, 100), c(0.4128963, 16.68394, 26.52718, 23.50851, 16.02339, 3.971546, 6.854153, 3.24342, 2.774968, 1, 1)) #the first dataset
colnames(ch_new1) <- c('Species', 'log_inversed_pval','fold_difference', 'N_of_carbons','N_of_double_bonds', 'rel_abund')
d <- plot_ly(ch_new1, x=~N_of_carbons, y=~N_of_double_bonds, text = ~paste('Percent of total', Species, '=', round(rel_abund, 0)), size=~rel_abund, color=~fold_difference, colors = mycolors)%>% #producing the scatter plot
layout(
xaxis = myxaxis,
yaxis = myyaxis,
title = paste('PA', '2b')
)%>%
colorbar(title="Fold difference", ypad=20)
export(d)
ch_new2 <- cbind.data.frame(c('LPC', 'LPC', 'LPC', 'lower limit', 'upper limit'), c(7.329796, 7.329796, 5.081869, 1, 1), c(2.952345, 5.042931, 3.700331, 0.2085327, 71.18021), c(18, 20, 22, 100, 100), c(0, 3, 5, 100, 100), c(82.87528, 13.56943, 3.555281, 1, 1)) #the second dataset
colnames(ch_new2) <- c('Species', 'log_inversed_pval','fold_difference', 'N_of_carbons','N_of_double_bonds', 'rel_abund')
d <- plot_ly(ch_new2, x=~N_of_carbons, y=~N_of_double_bonds, text = ~paste('Percent of total', Species, '=', round(rel_abund, 0)), size=~rel_abund, color=~fold_difference, colors = mycolors)%>% #creating the second scatterplot
layout(
xaxis = myxaxis,
yaxis = myyaxis,
title = paste(unique(ch$Species)[i], fraction)
)%>%
colorbar(title="Fold difference", ypad=20)
export(d)
chart #1 with bright red middle
chart #2 with dim red middle
I've solved the problem on my own.
Turns out that by adding one or several "anchoring" dummy points placed beyond the margins of the plot (so they are not shown) helps to make the plot colorbars almost identical.
The initial dataset
ch_new1 <- cbind.data.frame(c(...)) #the first dataset
should be appended with anchoring dummy points:
ch_new1 <- cbind.data.frame(c(...)) #the first dataset
ch_new1 <- rbind(ch_new, list('middle anchor point', 1, 50, 100, 100, 1))
ch_new1 <- rbind(ch_new, list('quarter anchor point', 1, 25, 100, 100, 1))
tl;dr anchor the variable responsible for colorbar to multiple reference points (10, 20, 30, 40, 50, ...)
I am interested in making custom icons while using Leaflet in R.
The documentation has a nice example, but when experimenting with it, I realized that there is no way to allow the custom marker to resize when the map changes size.
Since size is one of the parameters and is fixed (iconWidth = 38, iconHeight = 95,), obviously the marker will stay consistently sized as zoom changes for map beneath it.
greenLeafIcon <- makeIcon(
iconUrl = "http://leafletjs.com/docs/images/leaf-green.png",
iconWidth = 38, iconHeight = 95,
iconAnchorX = 22, iconAnchorY = 94,
shadowUrl = "http://leafletjs.com/docs/images/leaf-shadow.png",
shadowWidth = 50, shadowHeight = 64,
shadowAnchorX = 4, shadowAnchorY = 62
)
Are there any hacks / ideas / protocols for having custom icons resize along with zoom?