Faceting is a very useful feature in ggplot which allows us to split our data by one or more variables, and generate a plot on each subset of the data. For e.g, the code below will generate a scatter plot depic
Multiple plot types cater to different needs and datasets. From simple line and bar charts to more complex scatter plots and histograms, each type serves a unique purpose.Line charts, for example, are excellent for displaying data trends over time, whilescatter plotsare optimal for examining the ...
Notice that creating the ggplot graphics object itself is very fast. The time-consuming work comes from the actually plotting of the image. Here is a timing of a plot with geom_point_img where we save (rather than plot) the graphics object: system.time({ p <- ggplot(posters_sim) + ge...
The default for multi-panel plots inggplot2is to use equivalent scales in each panel. But sometimes you want to allow a panel’s own data to determine the scale. This is not often a good idea since it may give your user the wrong impression about the data but to do this you can se...
Fortify a trackeRdata object for plotting with ggplot2.
Filed under Data Visualization, R programming, Tutorials Tagged with BC Cancer Agency, bcca, BCCDC, British Columbia Cancer Agency, British Columbia Centre for Disease Control, Data Visualization, ggplot2, graphs, Hadley Wickham, Joanna Zhao, Naomi Robbins, plot, plots, plotting, R, R programming...
ggperiodic is an attempt to solve the issue of plotting periodic data in ggplot2. It automatically augments your data to wrap it around to any arbitrary domain. Installation You can install the latest version from CRAN with install.packages("ggperiodic") ...
Mapping movement data with ggmap and ggplot. I hope that you found this post helpful or at least interesting. Please let me know if you have an R question that you would like explained on here. And thanks for following along with my R journey. ShareTweet To leave a comment for the ...
Python >>> import matplotlib.pyplot as plt While we’re at it, let’s also import NumPy, which we’ll use for generating data later on, and call np.random.seed() to make examples with (pseudo)random data reproducible: Python >>> import numpy as np >>> np.random.seed(444) The...
Interactive comparison of Python plotting libraries for exploratory data analysis. Examples of using Pandas plotting, plotnine, Seaborn, and Matplotlib. Includes comparison with ggplot2 for R.