Out of the box, R comes with many functions that make data analysis easy. In this post, I’ll review some of the functions that I use frequently, in whatever order rolls off the top of my head.

To get the ball rolling, we need a vector to work with. Let’s define a vector x that contains some random(ish) numbers:

> x = c(1, 4, -2, 10, 15, -5, -1, 2, 8, 0)

(Remember that the c function is how we combine numbers into vectors.)

Now on to the functions

length

Having filled a vector with some data, often the first thing I want to know is how much data I have. R has a function for that called length. It tells you how many elements in a vector:

> length(x)
[1] 10

Nice! It looks like I put 10 numbers into x.

sort

When I see numbers that are in no particular order, I have an urge to sort them. R has a sort function for that:

> sort(x)
 [1] -5 -2 -1  0  1  2  4  8 10 15

It just feels better seeing things in order!

mean

Now let’s get to some summary statistics. The mean is by far the most popular. R has a function for that call (drum roll) mean:

> mean(x)
[1] 3.2

median

Let’s not leave out our friendly median function, which returns the midpoint of the data:

> median(x)
[1] 1.5

Hmm … the median is not the same as the mean. That means the data (that I made up) has a skew. More on skewness sometime in the future.

standard deviation

The standard deviation is a workhouse stat that mathematicians love and the general public often misunderstands. Calculate it in R with sd:

> sd(x)
[1] 6.124632

max/min

Want to know the high and/or low values in your data. Use the max and min functions

> max(x)
[1] 15

> min(x)
[1] -5

summary

If you want to see all the summary statistics in one go, R has a function for that called summary:

> summary(x)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
  -5.00   -0.75    1.50    3.20    7.00   15.00

From left to right, you get the minimum value, the 1st quartile, the median, the mean, the third quartile, and the max. Nifty!

quantiles

Speaking of quartiles, R has a nice function for calculating ‘quantiles’. It’s called quantiles. If that language is confusing, you can think of the function as returning percentiles.

For example, here’s how I’d get the 30th percentile in x:

> quantile(x, 0.3)
 30%
-0.3

R gives me the percentile I’m calculating, followed by its value.

Here’s the 90th percentile:

> quantile(x, 0.9)
 90%
10.5

head and tail

The head and tail functions return the start and end of a vector.

Here’s the first 4 values of x:

> head(x, 4)
[1]  1  4 -2 10

And here are the last 3 values of x:

> tail(x, 3)
[1] 2 8 0

N largest/smallest values

To get the single largest/smallest value, we’ve got the max and min functions. But what about if I want to know the 3 largest/smallest values? How would I do that?

The answer is that we combine the max/min functions with head/tail functions.

Suppose we want the 3 largest values in x. First we’d sort x:

> sort(x)
 [1] -5 -2 -1  0  1  2  4  8 10 15

You can see that the 3 largest values live in the last 3 elements. So we’ll take the tail of the sorted values:

x_sort = sort(x)
tail(x_sort, 3)
[1]  8 10 15

Or suppose we want the 2 smallest values. Now we take the head of x_sort:

x_sort = sort(x)
head(x_sort, 2)
[1] -5 -2

That pretty much covers it

I’d say that the functions above cover 90% of the calculating that I do in my own research.

The hard part isn’t using these functions. (As you can see, they’re super easy.) The hard part is usually getting the data into a suitable form to apply these functions.

More on that in the future.