SubgrPlots (version 0.1.3)

plot_alluvial: Alluvial plot

Description

this function produces an alluvial diagram. This function is a copy of alluvial::alluvial but a 'rotate' option is added to rotate the labels.

Usage

plot_alluvial(
  ...,
  freq,
  col = "gray",
  border = 0,
  layer,
  hide = FALSE,
  alpha = 0.5,
  gap.width = 0.05,
  xw = 0.1,
  cw = 0.1,
  blocks = TRUE,
  ordering = NULL,
  axis_labels = NULL,
  cex = par("cex"),
  cex.axis = par("cex.axis"),
  rotate = 0,
  las = 1,
  bottom.mar = 2
)

Arguments

...

vectors or data frames, all for the same number of observations

freq

numeric, vector of frequencies of the same length as the number of observations

col

vector of colors of the stripes

border

vector of border colors for the stripes

layer

numeric, order of drawing of the stripes

hide

logical, should particular stripe be plotted

alpha

numeric, vector of transparency of the stripes

gap.width

numeric, relative width of inter-category gaps

xw

numeric, the distance from the set axis to the control points of the xspline

cw

numeric, width of the category axis

blocks

logical, whether to use blocks to tie the flows together at each category, versus contiguous ribbons (also admits character value "bookends")

ordering

list of numeric vectors allowing to reorder the alluvia on each axis separately, see Examples

axis_labels

character, labels of the axes, defaults to variable names in the data

cex

numeric, scaling of fonts of category labels and axis labels respectively. See par.

cex.axis

cex value to be passed to the axis. See help(par)

rotate

angle to rotate the labels. This argument is passed as an srt argument

las

direction of the labels of the covariates. 1 is horizontal, 2 is vertical.

bottom.mar

bottom margin to be passed as a first argument to mar()

Examples

# NOT RUN {
library(dplyr)

# Alluvial plot
# Load the data to be used
data(prca)
dat <- prca
dat$trt = dat$rx
dat %>%
  dplyr::select(trt, bm, hx, pf) %>%
  dplyr::group_by(trt, bm, hx, pf) %>%
  dplyr::summarise(Freq = n()) -> alldat
alldat %>%
  ungroup() %>%
  mutate(trt = ifelse(trt == 0 , "Control", "Treatment"),
         bm = ifelse(bm == 0 , "No", "Yes"),
         hx = ifelse(hx == 0 , "No", "Yes"),
         pf = ifelse(pf == 0 , "No", "Yes"))-> alldat

plot_alluvial(alldat[,c(1,3,2,4)], freq = alldat$Freq,
              xw=0.2,cw = 0.12,cex = 1,
              alpha  = 0.8,
              col=ifelse(alldat$trt == "Treatment","#1f78b4", "#a6cee3"),
              layer = alldat$trt  == 1, rotate = 90)


# Alluvial plot using survival rate at 24 months
data(prca)
dat <- prca
dat %>%
  mutate(survival = factor(ifelse(survtime > 24 , "Yes", "No"), levels = c("No", "Yes")),
         trt = rx) -> dat
dat %>%
  dplyr::select(trt, bm, hx, pf, survival) %>%
  dplyr::group_by(trt, bm, hx, pf, survival) %>%
  dplyr::summarise(Freq = n()) -> alldat
alldat %>%
  ungroup() %>%
  mutate(trt = ifelse(trt == 0 , "Control", "Treatment"),
         bm = ifelse(bm == 0 , "No", "Yes"),
         hx = ifelse(hx == 0 , "No", "Yes")) -> alldat

plot_alluvial(alldat[,c(5,1,3,2,4)], freq = alldat$Freq,
              xw=0.2,cw = 0.12,cex = 1,
              alpha  = 0.8,
              col=ifelse(alldat$survival  == "Yes",
                         ifelse(alldat$trt  == "Treatment","#80b1d3","#d5e2eb"),
                         ifelse(alldat$trt  == "Treatment","#faa8d2","#fbe0ee")),
              layer = alldat$trt  == 1, rotate = 90, las = 2, bottom.mar = 5)

# }