I'm trying to generate a map that looks like this in R:
The boxes represent individual observations, while the colors represent data pertaining to those individual observations. Anyone have any idea how this might be accomplished?
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I am attempting to create a heatmap using a data set that has only one value per coordinate, with that value being a continuous variable. All of the examples I have found using leaflet.extras::addHeatmap() use data that can have multiple values per coordinate, and create the heatmap based on the density of counts in an area. There doesn't seem to be a way to pass a weight instead.
My ultimate goal is to have something that looks like a raster based on these values:
However I don't want to use a raster due to the pixelation along the coasts.
When I pass the data to addHeatmap() and include the argument intensity = ~my_weighted_value, I get something like this:
And at increased zoom levels, it just ends up being a bunch of circles:
What is the proper way to take weighted spatial data and add a heatmap that looks like the raster?
Try to scale my_weighted_value back by *.00001 or something. Your weighted value appears to be exceeding the max.
Suppose i have a plot like the following:
I want to get the portion of the data where the majority (say 90%) of the data lay, for example, i want to isolate the plot into something like:
in which the points lay in the black frame contributes to (90%) of the data.
How can i do this in R?
Edited for comment:
What if i have the following plot:? the majority part probably start from 0.
I have data on excel and I want to represent one persons data graphically in bar charts beside each other. All the values are numerical and I want it to look something like this:
where the w,x,y represent different variables like games played and turnovers and the three colours represent three different people. I have data on 20 people.
I don't know how to single out the individual data or represent multiple data points on the barchart.
Any questions, I'll try to describe as best as I can. Thanks in advance.
I have a very large data set (~250,000 records) that I have used to create a linear model. I have plotted predicted vs. actual
.
I tried to use identify() to select the two cluster of values near the center of the graph and coord() to identify them. There are a few problems here: 1)There are many, many more points in those clusters than I can click on and identify individually, and 2)I need to know ALL of them, select all of them somehow with out selecting any others, and subset my data to just those points.
This model was created using a satellite image paired with ancillary spatial data. Each entry in the table corresponds to a particular point on the map. I need to identify where these two clusters are located on the map. My data frame includes the FID (which I can use to link back to the map), the original predictor, the response, and my predicted values.
I appreciate any help!
I have a realtime and big(> milions point) graph.
Which class should i use: QCPGraph or QCPFinancial?
What are the advantages and disadvantages of each?
They are two different types of graphs. I don't think there is an advantage of one over the other. It depends on what you want to represent in your graph.
QCPFinancial:
A plottable representing a financial stock chart.
This plottable represents time series data binned to certain
intervals, mainly used for stock charts. The two common
representations OHLC (Open-High-Low-Close) bars and Candlesticks can
be set via setChartStyle.
QCPGraph:
A plottable representing a graph in a plot.
Graphs are used to
display single-valued data. Single-valued means that there should only
be one data point per unique key coordinate. In other words, the graph
can't have loops. If you do want to plot non-single-valued curves,
rather use the QCPCurve plottable.
See also this example for a simple QCPGraph.
Or this example for QCPFinancial.