Advanced plots with ggplot

Input data

proc<-sample(c("EMR","RFA","Biopsies"), 100, replace = TRUE)
#Sample dates
dat<-sample(seq(as.Date('2013/01/01'), as.Date('2017/05/01'), by="day"), 100)
#Generate 20 hospital numbers in no particular order:
HospNum_Id<-sample(c("P433224","P633443","K522332","G244224","S553322","D0739033","U873352","P223333","Y763634","I927282","P223311","P029834","U22415","U234252","S141141","O349253","T622722","J322909","F630230","T432452"), 100, replace = TRUE)
rndm<-sample(seq(0,40),100,replace=T)
df<-data.frame(proc,dat,HospNum_Id,rndm)
df$proc<-as.character(df$proc)

Lets just get the data ready first. We want to see how many procedures are done per year.

Tots<-df %>%
  mutate(year = format(dat, "%Y")) %>%
  group_by(year)%>%
  summarise(n = n(),avg=mean(rndm))

kable(Tots)

A basic ggplot

so now we can plot this out. Firstly we specify the dataset (Tots) and then the x and y axis from this dataset (year and n). The next layer then specifies how the values are plotted. In this case I have used points (with size parameter and colour parameters) and a line.

ggplot(Tots) + geom_point(aes(x=year,y=n),size=16,colour="red")+
  geom_point(aes(x=year,y=avg),size=16,colour="black")+
  labs(title="My plot") +
  scale_color_manual("",labels = c("SVStart", "SVEnd"), values = c("blue", "red")) +
  xlab("The year") + 
  ylab("The frequency") +
  theme(axis.text.x=element_text(angle=-90)) +
  theme(legend.position="top")

Grouping with ggplot- stacked barcharts

What about trying to create a stacked barchart? This involves understanding that the data should firstly be in a tidy format and let ggplot do the grouping. For this we’ll use a slightly different dataframe:

Tots2<-df %>%
  mutate(year = format(dat, "%Y"))

  
  ggplot(Tots2, aes(x=year, y=rndm,fill=proc)) + geom_bar(stat="identity")+
    xlab("The year") + 
  ylab("The frequency") +
  theme(axis.text.x=element_text(angle=-90)) +
  theme(legend.position="top")

#Or perhaps you just want a histogram (this can also be done with geom_bar(stat="count") and just specifying the x-axis similar to below)
  ggplot(Tots2, aes(x=year,fill=proc))+geom_histogram(stat="count")+
      xlab("The year") + 
  ylab("The frequency") +
  theme(axis.text.x=element_text(angle=-90)) +
  theme(legend.position="top")

Reorder axis in ggplot

You may also want to change the order of the x-axis in this situation. I’ve always found this tricky, but one way to do it is:

  ggplot(Tots2, aes(x=proc))+geom_bar(stat="count")+ theme(axis.text.x=element_text(angle=-90,size=16)) 

#I want to change the order of the bars so I have to change them into factors
Tots2$proc <- factor(Tots2$proc, levels=unique(Tots2$proc))
#or
Tots2$proc <- factor(Tots2$proc, levels=c("EMR", "Biopsies", "RFA"))
 ggplot(Tots2, aes(x=proc))+geom_bar(stat="count")+ theme(axis.text.x=element_text(angle=-90,size=16)) 

For a great cheat-sheet that clearly explains all of the layers of a ggplot, try here

Other visualisation tools

1. ggedit

Because there are many parameters to master in ggplot, ggedit was invented to make this easier. It in a shiny gadget that can be run frmo within RStudio as an add-in

2. ggpubr. This is supposed to create publication ready plots. The link to the site can be found here

Interactive graphs

I thought I would also add some plots which are simply easy to produce and great to look at. These are done with the in-built mtcars dataset.

library(dygraphs)
dygraph(nhtemp, main = "New Haven Temperatures") %>% 
  dyRangeSelector(dateWindow = c("1920-01-01", "1960-01-01"))

library(magrittr)
library(highcharter)
highchart() %>% 
  hc_title(text = "Scatter chart with size and color") %>% 
   hc_add_series(mtcars, "scatter", hcaes(x = wt, y = mpg, size  = drat, color = hp))

library(networkD3)
data(MisLinks, MisNodes)
forceNetwork(Links = MisLinks, Nodes = MisNodes, Source = "source",
             Target = "target", Value = "value", NodeID = "name",
             Group = "group", opacity = 0.4)