In mbjones/flowers: Flower Plots

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

flowers

Flowers is a package for generating flower plots. It was derived from code from Jim Regetz at NCEAS, and then rewritten and extended by the OHI project. This package formalizes the approach into an easily re-usable function for generating custom flower plots for multiple scenarios.

Quick Start

This is a basic example which shows you how to create a flower plot from an appropriately structured data set:

library(flowers)
data(ohi)
plot_flower(ohi, "OHI Example Flower")

Currently plot_flower() expects particular column names and semantics, but this could be made more flexible. See the structure of OHI for an example. In particular, it uses columns score, weight, label, and category to create the plot.

By default the flower petals are colored proportionally to the score values as show in the OHI example above. One can provide a color palette (colors) to the plot_flower() function to control the gradient used.

The weight variable controls the relative widths of the petals, and should range from 0 to 1. The petal labels are taken from the label variable, and the grouping category labels are taken from the category variable. Other columns in the data frame are ignored.

str(ohi)

Alternatively, by setting fixed_colors = TRUE you can also color the petals with discrete colors determined by the label values, in which case you will likely want to provide a colors palette with at least as many colors as you have petals in the plot. Here's an example with four fixed petals, in which we also provide only missing values to category so that no grouping labels are used:

    library(dplyr)
    df <- data.frame(order = c(1, 4, 3, 2),
                        score = c(90, 80, 70, 60),
                        weight = c(1, 1, 1, 1),
                        goal = c("F", "A", "I", "R"),
                        label = c("Findable", "Accessible", "Interoperable", "Reusable"),
                        category = c(NA, NA, NA, NA),
                        stringsAsFactors = FALSE) %>% arrange(order)
    d1_colors <- c( "#c70a61", "#ff582d", "#1a6379", "#60c5e4")
    plot_flower(df, title = "FAIR Metrics", fixed_colors=TRUE, colors = d1_colors)

Installation

You can install the development version of flowers from GitHub with:

devtools::install_github("mbjones/flowers")

You can install the released version of flowers from CRAN with:

install.packages("flowers")

mbjones/flowers documentation built on Dec. 23, 2019, 10:24 p.m.