As part of my continuing fun and games getting to grips with ggplot2’s vast multitude of functions, here I give a basic intro to plotting bar graphs. Bit by bit, I’m slowly creating my own library of code to call on when needed!
Let’s begin by making up some data. This is for a pretend experiment with four participants (labelled “ppt1”, “ppt2”, and so on). They take part at two different time points (Time 1 and Time 2 – cool name or what). Some kind of stimulus is presented to them in different regions in the display. We’ll call these two “Region A” and “Region B”. Their performance is indicated by mean_score. Here is the code to generate some fake data – note the use of randomisation here with rnorm, meaning your means will be different to those I illustrate here:
ppt <- rep(c("ppt1", "ppt2", "ppt3", "ppt4"), 8 ) time<- c(rep("Time 1",8), rep("Time 2",8)) mean_score<-c(rnorm(8, mean=500, sd=20), rnorm(8, mean=250, sd=80)) region <- c(rep("Region A",4), rep("Region B",4),rep("Region A",4), rep("Region B",4)) bar_data<-data.frame(ppt,time, region, mean_score)
This leaves us with a shiny new dataframe called bar_data.
The Bar Graphs / Bar Charts
Now for the code. What we want to do is take a look at how each participant scored, with their mean_score for each Time and each Region.
bars<-ggplot(bar_data)+ aes(x=region, y=mean_score)+ geom_bar()+ facet_grid(facets=ppt~time) ggsave(bars, file="bars.png")
We set up the x and y axes using the aes command. geom_bar() draws up the bar chart. Next, all we need is facet_grid which creates a grid-like arrangement for various combinations of the different factor levels in the columns. We specify facets = ppt~time which tells ggplot to draw one bar chart for each level of ppt and time . Finally, ggsave is a handy way to output a high-dpi image straight out to your current working directory. This gives publication-quality plots in an instant. At last, we can rejoice as we find it gives us the following graph: