Step 1, Download ggplot2, reshape2, dplyr, plotly, and grid from CRAN

• Use the install.package() function to install the followng R packages from CRAN: ggplot2, plotly, reshape2, dplyr, kintr and grid from CRAN for example:
  install.packages("ggplot2")

Step 2, Load each relevant Package

library(ggplot2) 
library(dplyr, warn.conflicts=FALSE)   # Useful for manipulating the dataframes
library(reshape2) # Reformmating dataframes
library(grid)
library(plotly) # Allows us to make the swimmer plot interactive
library(knitr)

Step 3, Create an “example” data set for demonstrative purposes

• We will create a working data set appropriate for this type of graphical represenataion.

set.seed(35) # This sets the seed of R's random number generator
dat <-  data.frame(Subject = 1:15, 
                 Months = sample(5:20, 15, replace=TRUE), # This generates a random set of months from 5 - 20
                 Treated=sample(0:1, 15, replace=TRUE), # This generates 15 random 0 or 1s which correspond to Tx or no Tx
                 Stage = sample(1:4, 15, replace=TRUE), # This randomly generates staging from 1 - 4 
                 Continued=sample(0:15, 15, replace=TRUE))

dat %>% kable

Add Response Data to Data Set

dat <-  dat %>%
  group_by(Subject) %>%
  mutate(Complete=sample(c(4:(max(Months)-1),NA), 1, 
                         prob=c(rep(1, length(4:(max(Months)-1))),5), replace=TRUE),
         Partial=sample(c(4:(max(Months)-1),NA), 1, 
                        prob=c(rep(1, length(4:(max(Months)-1))),5), replace=TRUE),
         Durable=sample(c(-0.5,NA), 1, replace=TRUE))
# of note, `sample()`takes a sample of the specified size from the elements of x using either with or without replacement
# Let's organize the order of the Subjects by Months
dat$Subject <-  factor(dat$Subject, levels=dat$Subject[order(dat$Months)])

dat %>% kable

Melt part of data frame for adding points to bars

• This will collapse the Columns “Complete”, “Partial” and “Durable” into a new column called “variable” and the values of those orginial columns will become a new vector/column called “value”

dat.m <-  melt(dat %>% select(Subject, Months, Complete, Partial, Durable),
             id.var=c("Subject","Months"), na.rm = TRUE) 
# of note, na.rm = TRUE will eliminate those rows with missing values

dat.m %>% kable

Let’s make a static swimmer plot with ggplot

a<- ggplot(dat, aes(Subject, Months)) +
  geom_bar(stat="identity", aes(fill=factor(Stage)), width=0.8) +
  geom_point(data=dat.m, 
             aes(Subject, value, colour=variable, shape=variable), size=5) +
  geom_segment(data=dat %>% filter(Continued==1), 
             aes(x=Subject, xend=Subject, y=Months + 0.1, yend=Months + 1), 
             pch=15, size=0.8, arrow=arrow(type="closed", length=unit(0.1,"in"))) +
  coord_flip() +
  scale_fill_manual(values=hcl(seq(15,375,length.out=5)[1:4],100,75)) +
  scale_colour_manual(values=c(hcl(seq(15,375,length.out=3)[1:2],100,30),"black")) +
  scale_y_continuous(limits=c(-1,20), breaks=0:20) +
  labs(fill="Disease Stage", colour="", shape="",
       x="Subjects on Treatment Drug A") +
  theme_bw() +
  theme(panel.grid.minor=element_blank(),
        panel.grid.major=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())
a

Now let’s make an Interactive Swimmer plot in Plotly by simply using the ggplotly() function of the static plot as an object

ggplotly(a)