ggpcp {monoClust} | R Documentation |
Parallel Coordinates Plot with Circular Variables
Description
Making a parallel coordinates plot with the circular variables are plotted as ellipses. The function currently works well with data with one circular variable.
Usage
ggpcp(
data,
circ.var = NULL,
is.degree = TRUE,
rotate = 0,
north = 0,
cw = FALSE,
order.appear = NULL,
linetype = 1,
size = 0.5,
alpha = 0.5,
clustering,
medoids = NULL,
cluster.col = NULL,
show.medoids = FALSE,
labelsize = 4,
xlab = "Variables",
ylab = NULL,
legend.cluster = "groups"
)
Arguments
data |
Data set. |
circ.var |
Circular variable(s) in the data set, indicated by names or index in the data set. |
is.degree |
Whether the unit of the circular variables is degree or not
(radian). Default is |
rotate |
The rotate (offset, shift) of the circular variable, in radians. Default is 0 (no rotation). |
north |
What value of the circular variable is labeled North. Default is 0 radian. |
cw |
Which direction of the circular variable is considered increasing
in value, clockwise ( |
order.appear |
The order of appearance of the variables, listed by a vector of names or index. If set, length has to be equal to the number of variables in the data set. |
linetype |
Line type. Default is solid line. See details in
|
size |
Size of a line is its width in mm. Default is 0.5. See details in
|
alpha |
The transparency of the lines. Default is 0.1. |
clustering |
Cluster membership. |
medoids |
Vector of medoid observations of cluster. Only required when
|
cluster.col |
Color of clusters, indicating by a vector. If set, the
length of this vector must be equal to the number of clusters in
|
show.medoids |
Whether to highlight the median lines or not. Default is
|
labelsize |
The size of labels on the plot. Default is 4. |
xlab |
Labels for x-axis. |
ylab |
Labels for y-axis. |
legend.cluster |
Labels for group membership. Implemented by setting
label for ggplot |
Value
A ggplot2 object.
Examples
# Set color constant
COLOR4 <- c("#e41a1c", "#377eb8", "#4daf4a", "#984ea3")
# Reduce the size of the data for for sake of example speed
set.seed(12345)
wind_reduced <- wind_sensit_2007[sample.int(nrow(wind_sensit_2007), 50), ]
sol42007 <- MonoClust(wind_reduced, cir.var = 3, nclusters = 4)
library(ggplot2)
ggpcp(data = wind_reduced,
circ.var = "WDIR",
# To improve aesthetics
rotate = pi*3/4-0.3,
order.appear = c("WDIR", "has.sensit", "WS"),
alpha = 0.5,
clustering = sol42007$membership,
medoids = sol42007$medoids,
cluster.col = COLOR4,
show.medoids = TRUE) +
theme(panel.background = element_rect(color = "white"),
panel.border = element_rect(color = "white", fill = NA),
panel.grid.major = element_line(color = "#f0f0f0"),
panel.grid.minor = element_blank(),
axis.line = element_line(color = "black"),
legend.key = element_rect(color = NA),
legend.position = "bottom",
legend.direction = "horizontal",
legend.title = element_text(face = "italic"),
legend.justification = "center")