Add main="Graph of demand" if you want a main headline on your graph:
barplot(BOD$demand, main="Graph of demand")
To label the bars on the x axis, use the names.arg argument and set it to the column you want to use for labels:
barplot(BOD$demand, main="Graph of demand", names.arg = BOD$Time)
Sometimes you'd like to graph the counts of a particular variable but you've got just raw data, not a table of frequencies. R's table() function is a quick way to generate counts for each factor in your data.
The R Graphics Cookbook uses an example of a bar graph for the number of 4-, 6- and 8-cylinder vehicles in the mtcars data set. Cylinders are listed in the cyl column, which you can access in R using mtcars$cyl.
Here's code to get the count of how many entries there are by cylinder with the table() function; it stores results in a variable called cylcount:
cylcount <- table(mtcars$cyl)
That creates a table called cylcount containing:
4 6 8
11 7 14
Now you can create a bar graph of the cylinder count:
barplot(cylcount)
ggplot2's qplot() quick plotting function can also create bar graphs:
qplot(mtcars$cyl)
However, this defaults to an assumption that 4, 6 and 8 are part of a variable set that could run from 4 through 8, so it shows blank entries for 5 and 7.
To treat cylinders as distinct groups -- that is, you've got a group with 4 cylinders, a group with 6 and a group with 8, not the possibility of entries anywhere between 4 and 8 -- you want cylinders to be treated as a statistical factor:
qplot(factor(mtcars$cyl))
To create a bar graph with the more robust ggplot() function, you can use syntax such as:
ggplot(mtcars, aes(factor(cyl))) + geom_bar()
Histograms
Histograms work pretty much the same, except you want to specify how many buckets or bins you want your data to be separated into. For base R graphics, use:
hist(mydata$columnName, breaks = n)
where columnName is the name of your column in a mydata dataframe that you want to visualize, and n is the number of bins you want.