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To convert ggplot2 graph into a plotly graph in R, we can follow the below steps −First of all, create a data frame.Then, create a ggplot2 graph and save it in an object.After that, load plotly package and create the ggplot2 graph using ggplotly function.Create the data frameLet's create a data frame as shown below − Live Demox
To display NA group values in scatterplot created with ggplot2 using color brewer in R, we can follow the below steps −First of all, create a data frame.Then, create the scatterplot with default colors.After that, use scale_color_brewer function to create the scatterplot with color of points (including NA) based on color palette given in scale_color_brewer.Create the data frameLet's create a data frame as shown below − Live Demox
Macro substitution is a mechanism that provides a string substitution. It can be achieved through "#deifne".It is used to replace the first part with the second part of the macro definition, before the execution of the program.The first object may be a function type or an object.SyntaxThe syntax for macros is as follows −#define first_part second_partProgramIn the program for every occurrence of first_part is replaced with the second_part throughout the code. Live Demo#include #define square(a) a*a int main(){ int b, c; printf("enter b element:"); scanf("%d", &b); c=square(b);//replaces c=b*b before execution of program printf("%d", c); return 0; }OutputYou will see the following ... Read More
To combine multiple columns into one in R data frame without using column names, we can follow the below steps −First of all, create a data frame.Then, convert the data frame into a single column data frame.Again, convert the data frame into a single column without column names displayed in rows using row.names function.Create the data frameLet's create a data frame as shown below −Example Live Demox
To enable ffmpeg for matplotlib.animation, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Set the ffmpeg directory.Create a new figure or activate an existing figure, using figure() method.Add an 'ax1' to the figure as part of a subplot arrangement.Plot the divider based on the pre-existing axes.Create random data to be plotted, to display the data as an image, i.e., on a 2D regular raster.Create a colorbar for a ScalarMappable instance, cb.Set the title as the current frame.Make a list of colormaps.Make an animation by repeatedly calling a function, animate. The ... Read More
To create a graph in base R with multiple shades of a particular color, we can follow the below steps −First of all, create color shades using colorRampPalette then plot a graph.Use the color shades to create the graph.Example 1Create the color shadesUsing colorRampPalette function to create the color shades between red and darkred color then creating the plot − Live DemoColor
To make multipartite graph in networkx, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a list of subset sizes and colors.Define a method for multilayered graph that could return a multilayered graph object.Set the color of the nodes.Position the nodes in layers of straight lines.Draw the graph G with Matplotlib.Set equal axis properties.To display the figure, use show() method.Exampleimport itertools import matplotlib.pyplot as plt import networkx as nx plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True subset_sizes = [5, 5, 4, 3, 2, 4, 4, 3] subset_color = ... Read More
To plot the difference of two distributions in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a and b datasets using Numpy.Get kdea and kdeb, i.e., representation of a kernel-density estimate using Gaussian kernels.Create a grid using Numpy.Plot the gird with kdea(grid), kdeb(grid) and kdea(grid)-kdeb(grid), using plot() method.Place the legend at the upper-left corner.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt import scipy.stats plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True a = np.random.gumbel(50, 28, 100) b = np.random.gumbel(60, 37, 100) ... Read More
The Axes class contains most of the figure elements − Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system.stepsSet the figure size and adjust the padding between and around the subplots.Set the axes linewidth using rcParams.Add an axes to the current figure and make it the current axes.Set the axes spines color.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True plt.rcParams['axes.linewidth'] = 5 ax = plt.axes() ax.spines['bottom'].set_color('yellow') ax.spines['top'].set_color('red') ax.spines['right'].set_color('black') ax.spines['left'].set_color('blue') plt.show()OutputRead More
To visualize 95% confidence interval in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x and y data sets.Get the confidence interval dataset.Plot the x and y data points using plot() method.Fill the area within the confidence interval range.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.arange(0, 10, 0.05) y = np.sin(x) # Define the confidence interval ci = 0.1 * np.std(y) / np.mean(y) plt.plot(x, y, color='black', ... Read More