In the example below the automatic X axis type would be linear (because there are not more than twice as many unique strings as unique numbers) but we force it to be category. It is possible to force the axis type by setting explicitly autotypenumber.
This tutorial explain how to set the properties of 2-dimensional Cartesian axes, namely X-axis and Y-axis.
We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.
Plotly is a free and open-source graphing library for R.
provenance is free software released under the GPL-2 licence and will be further expanded based on user feedback.How to adjust axes properties in R - axes titles, styling and coloring axes and grid lines, ticks, tick labels and more All these tools can be accessed through an intuitive query-based user interface, which does not require knowledge of the R programming language. provenance comprises functions to: (a) calculate the sample size required to achieve a given detection limit (b) plot distributional data such as detrital zircon U–Pb age spectra as Cumulative Age Distributions (CADs) or adaptive Kernel Density Estimates (KDEs) (c) plot compositional data as pie charts or ternary diagrams (d) correct the effects of hydraulic sorting on sandstone petrography and heavy mineral composition (e) assess the settling equivalence of detrital minerals and grain-size dependence of sediment composition (f) quantify the dissimilarity between distributional data using the Kolmogorov–Smirnov and Sircombe–Hazelton distances, or between compositional data using the Aitchison and Bray–Curtis distances (e) interpret multi-sample datasets by means of (classical and nonmetric) Multidimensional Scaling (MDS) and Principal Component Analysis (PCA) and (f) simplify the interpretation of multi-method datasets by means of Generalised Procrustes Analysis (GPA) and 3-way MDS. This paper introduces provenance, a software package within the statistical programming environment R, which aims to facilitate the visualisation and interpretation of large amounts of sedimentary provenance data, including mineralogical, petrographic, chemical and isotopic provenance proxies, or any combination of these.