data-visualization

问题 I want to fill the colour on a map. However, the plot doesn't come out as aspected. How can I visualize the data with longitude and latitude? install.packages("WDI") install.packages("tidyverse") library(WDI) library(tidyverse) literacy_globe <- WDI(country = "all", indicator = "SE.ADT.LITR.ZS", start = 2015, end = 2018, extra = TRUE) literacy_globe <- na.omit(literacy_globe) ggplot(literacy_globe, aes(x = longitude, y = latitude, group = iso3c)) + geom_point(aes(fill = income), colour =

问题 I want to plot the bar graph based value of dropdown widget. Code import pandas as pd from bokeh.io import output_file, show from bokeh.layouts import widgetbox from bokeh.models.widgets import Dropdown from bokeh.plotting import curdoc from bokeh.charts import Bar, output_file,output_server, show #use output_notebook to visualize it in notebook df=pd.DataFrame({'item':["item1","item2","item2","item1","item1","item2"],'value':[4,8,3,5,7,2]}) menu = [("item1", "item1"), ("item2", "item2")]

问题 We have a requirement to display charts in an ASP.NET MVC app but the people using the app will need to be able to print the charts to paper and also save them as an image for use in presentations. They need to be high quality for the print/present versions and it seems to me that the web based charting tools are just that - optimised for web. The co i work for have historically used fusion charts but getting the quality out of that has eluded them. If we have to build a whole separate "out

问题 We have a requirement to display charts in an ASP.NET MVC app but the people using the app will need to be able to print the charts to paper and also save them as an image for use in presentations. They need to be high quality for the print/present versions and it seems to me that the web based charting tools are just that - optimised for web. The co i work for have historically used fusion charts but getting the quality out of that has eluded them. If we have to build a whole separate "out

问题 Earlier, I had posted a question and was able to load in my data successfully and create a topGO object after some help. I'm trying to visualise GO terms that are significantly associated with the list of differentially expressed genes that I have from mouse RNA-seq data. Now, I'd want to raise a concern about ViSEAGO's tutorial. The tutorial initially specifies loading two files: 'selection.txt' and 'background.txt'. The origin of these files is not clearly stated. However, after a lot of

问题 I was wondering if it was possible to apply hue to only the lower part of a seaborn PairGrid . For example, say I have the following figure: For what I need to present I'd like to keep the density plots on the diagonal, the overall scatter plots on the upper (with printed correlation coefficients above them which I know how to do), but on the lower I want to split the points up by hue just to show my audience what happens if we do subset the data. I thought about just finding out the

问题 I have a dataset with >1000 observations belonging to either group A or group B, and ~150 categorical and continuous variables. Small version below. set.seed(16) mydf <- data.frame(ID = 1:50, group = sample(c("A", "B"), 50, replace = TRUE), length = rnorm(n = 50, mean = 0, sd = 1), weight = runif(50, min=0, max=1), color = sample(c("red", "orange", "yellow", "green", "blue"), 50, replace = TRUE), size = sample(c("big", "small"), 50, replace = TRUE)) I would like to visually compare group A

问题 I want to add a rectangle in my d3.js graph highlighting a specific data region. The problem is I don't want to specify a starting point and then a height and length. Rather I would like to specify two points positioned diametral – on the upper left and lower right corner of the rectangle. The highlight area rectangle needs to go from my lowest X value to the highest X value in my dataset and from a specific lower y bound to a specific higher y bound. 回答1: If you are just passing the x and y

问题 Problem description Since bokeh 0.12.5 it is possible to use interactive legends to hide or mute glyphs via clicking on them in the legend box. This is a great feature especially for more complex plots. However, when used in conjunction with the hover tool, hidden glyphs should not trigger the hover tool because the user is provided with a tooltip for glyphs which are not visible (due to muting via the interactive legend). Here is minimal code example for clarification: import numpy as np

问题 I'm looking for anything in python that I can use to do a nested proportional area chart in circles. Preferably something built with (or on top of) matplotlib. Here's an example of what such plot looks like for reference: 回答1: A nested circle diagram, where the circle area is proportional to the data could look as follows. It would take a sorted list or array of data and optionally the respective labels as input and plot a couple of circles. import numpy as np import matplotlib.pyplot as plt