nroColorize {Numero} | R Documentation |
Assign colors to map districts based on the respective district values.
nroColorize(values, ranges = NULL, amplitudes = 1, palette = "rhodo")
values |
A vector of K values or a K x N data frame, where K is the number of map districts and N is the number of variables. |
ranges |
A data frame with N rows and 2 columns. |
amplitudes |
Single value or a vector of N elements or a data frame of N rows that contains the column AMPLITUDE. |
palette |
One of pre-defined colormap names (see details) or a sorted vector of hexadecimal color codes as strings. |
The argument ranges
sets the minimum and maximum district values
irrespective of the contents of values
. It can be used as a fixed
reference across different colorings to ensure that the same value produces
the same color across function calls.
The argument amplitudes
controls the proportion of the color range
that is available for the district value range. For proportions below 1,
the minimum district value is assigned to a color that is between the
first and middle element in the color palette, and the maximum is assigned
to a color that is between the middle and the last element. If
amplitude
is greater than 1, extreme low and high values are
clipped to the first and last color in the palette, respectively.
Available color palettes include "gray", "fire", "jungle", "miami", "rhodo" or "tan". Any other word will revert to a rainbow colormap.
A data frame or a matrix of hexadecimal color codes as strings. If the output is a single column, it is converted to a vector.
If amplitudes = NULL
, a data frame that contains the
value ranges that would be used for colors is returned.
Gao S, Mutter S, Casey AE, Mäkinen V-P (2018) Numero: a statistical framework to define multivariable subgroups in complex population-based datasets, Int J Epidemiology, https://doi.org/10.1093/ije/dyy113
# Import data. fname <- system.file("extdata", "finndiane.txt", package = "Numero") dataset <- read.delim(file = fname) # Prepare training data. trvars <- c("CHOL", "HDL2C", "TG", "CREAT", "uALB") trdata <- scale.default(dataset[,trvars]) # K-means clustering. km <- nroKmeans(data = trdata) # Self-organizing map. sm <- nroKohonen(seeds = km) sm <- nroTrain(som = sm, data = trdata) # Assign data points into districts. matches <- nroMatch(centroids = sm, data = trdata) # District averages for all variables. planes <- nroAggregate(topology = sm, districts = matches, data = dataset) # District colors for cholesterol. chol <- nroColorize(values = planes$CHOL) print(head(chol)) # District colors for all variables. colrs <- nroColorize(values = planes) print(head(colrs))