# Beginner's guide to R: Painless data visualization

### Part 4 of our hands-on guide covers simple graphics, bar graphs and more complex charts.

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`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)` What happens to your bar chart when you don't instruct R not to plot continuous variables.

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.

Related:
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