# The Yhat Blog

machine learning, data science, engineering

# Base R Plots

#### by Greg | February 23, 2015

There's a lot of talk about ggplot these days (we even wrote a Python version of it) and for good reason: it's a great plotting package that's easy to use. Despite this, I sometimes find myself wanting something even quicker than ggplot. When that's the case, I turn to base R plots. They're not as pretty and the syntax is a little unpleasant but they're very fast, work on just about anything, and are often used by the pros. In those regards, it's actually really similar to UNIX tools such as grep, sed, and awk.

So sit back, relax, and get ready to have some fun with R base plots!

### The Data

We're using the iris dataset. It's a tried and true classic and while it's not the most exciting data in the world, it's built into R (so you don't need to download anything) and easy to understand.

head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa


The other dataset we'll be using is the USAccDeaths dataset which contains numbers on the accidental deaths in the U.S. from 1973 to 1978. It's also built into R and is a good example of a time series dataset. This will let us show off some of R's handy built-in features for working with time series data.

USAccDeaths
Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec
1973  9007  8106  8928  9137 10017 10826 11317 10744  9713  9938  9161  8927
1974  7750  6981  8038  8422  8714  9512 10120  9823  8743  9129  8710  8680
1975  8162  7306  8124  7870  9387  9556 10093  9620  8285  8466  8160  8034
1976  7717  7461  7767  7925  8623  8945 10078  9179  8037  8488  7874  8647
1977  7792  6957  7726  8106  8890  9299 10625  9302  8314  8850  8265  8796
1978  7836  6892  7791  8192  9115  9434 10484  9827  9110  9070  8633  9240


### Just plot it

Ok first things first: the command to make plots is, you guessed it, plot. More good news: just about every data structure in R is plotable. That's not to say it'll look pretty or even make sense, but you can always try and find out.

plot(iris)


You can add colors to your points by passing a value to the col parameter.

plot(iris, col=iris$Species)  ### Less $

If you get tired of calling the iris data frame with the $ every time, you can "attach" data which will imply that everything from there forward is referencing the dataset you attach. Just don't forget to detach when you're done. So as an example, let's say we want to plot specific values on the x and y axis. Instead of having to prefix our variables with iris$, we'll use attach.

attach(iris)
plot(x=Sepal.Width, y=Sepal.Length)
detach(iris)


### Time Series

Time series plotting is really easy with R. Since R natively has a time series type, plots work right out of the box. In the example below, I'm going to pass the plot function the USAccDeaths dataset.

You can see that we can also assign labels to our x and y axis by using xlab and ylab.

plot(USAccDeaths, xlab="Year", ylab="Accident Deaths in U.S.")


Adding points is also super easy. There are functions called points and lines which, you guessed it again, layer points and lines on your existing plots.

plot(USAccDeaths, xlab="Year", ylab="Accident Deaths in U.S.", main="Traffic Accident Deaths")
points(USAccDeaths, pch=10)


You might have noticed there's a really weird circle with a cross in the middle of it on the points of our graph. You can assign different styles of points using the pch argument. Point styles can even be assigned to different categories (or "levels" in R) of a variable.

plot(x=iris$Petal.Width, y=iris$Petal.Length, pch=as.numeric(iris$Species), col=as.numeric(iris$Species))


### Histograms

One of my very favorite things about R: histograms! When I made the switch from Excel to R, I had heard tales of mad sorcery where I could replace catalogs of frequency tables with one line of R code.

Histograms are great. They're a super easy way to get a quick feel for what your dataset looks like. So while it's one of the first things I learned in R, it's also one of the things I use the most.

hist(iris$Sepal.Width)  ### Density Plots and Legends To display distributions of different variables on the same plot, I recommend using density plots. density creates an estimate of the pdf (probability density function) of your variable. This basically gives you a nice, continuous line representing the distribution of your data. We'll use the lines function to add individual distributions with different colors to our plot. virginica <- subset(iris, Species=="virginica") versicolor <- subset(iris, Species=="versicolor") setosa <- subset(iris, Species=="setosa") # plot distributions for each species plot(density(virginica$Sepal.Width), col="blue")
lines(density(versicolor$Sepal.Width), col="red") lines(density(setosa$Sepal.Width), col="green")
legend(2, 1.2, c("virginica", "versicolor", "setosa"), c("blue", "red", "green"))


### Final Thoughts

So there you have it: the basics about base plots in R. That's all I'll cover today, but if you're interested in learning more here are some other resources:

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