R Built-in Functions

Several functions are built into the core R distribution.

Mathematical Functions

Most built-in functions operate on numeric vectors by applying the function element-wise. These include:

Trigonometric Functions

See here.

There is also an atan2 function; atan2(y, x) calculates the angle (in radians) between the x-axis and the vector [x y].

Statistical Functions

These functions take a (or, at least one) vector that represents observations and return a computed statistic.

NA values cause most of these functions to also return NA. Pass the na.rm = TRUE option to ignore these values instead.

The sample function is used as:

> samp.without.replacement <- sample(a, size=5)
> samp.with.replacement <- sample(a, size=10, replace=TRUE)
> weighted.samp <- sample(a, size=5, prob=b) # b must be same length as a

If the size option is not passed, then the result is the same size as the input, effectively just performing a randomized sort.

Also note that if the input to the sample function is a scalar positive integer n, then the sample is actually run on the sequence 1:n.

These functions take a (or at least one) matrix where the columns represent vectors of observations.

Distribution Functions

Some of these functions take an output size argument (i.e., n). Others take an input that can be a scalar or a vector. The return value matches this input.

For the uniform distribution from min to max (defaulting to 0 and 1 respectively):

For the normal distribution (defaulting to the standard normal distribution):

For the binomial distribution with a given size and event probability prob:

R does not support functions relating to the Bernoulli distribution. Instead use the above binomial distribution functions with a size of 1.

rbinom(10, size = 1, prob = 0.7)

For the Poisson distribution with a given lambda parameter:

For the Chi-squared distribution with df degrees of freedom:

Set the random number generator's seed to ensure that random values are reproducible.

set.seed(123)

String Functions

Most built-in functions operate on character vectors by applying the function element-wise. These include:

Functions that notably do not treat vectors in that manner:

• cat concatenates and prints the vector

• print prints the vector

• If the collapse=" " option is passed on paste, then it takes the vectorized output and concatenates into a single string

The sprintf function provides C-style string formatting. The format string must be a scalar. The substitution variables can be scalars or vectors, in which case the scalars are recycled as necessary. If multiple substitution variables are vectors, they must be the same length.

The glue library adds a vectorized function of the same name that supports Python f-string-style interpolation.

library(glue)

glue("file_{1:3}.txt") # "file_1.txt" "file_2.txt" "file_3.txt"

It also adds a function intended for composing human-readable messages:

glue_collapse(c("foo", "bar", "baz"), sep = ", ", last = ", and ") # "foo, bar, and baz"

Plotting functions

The plot function plots points.

x <- seq(0, 2*pi, length.out=100)
y <- sin(x)
plot(x,y)

Options include:

• type="p" to draw points. Alternatively...

  • "l" for lines connecting points

  • "h" for 'histogram like vertical lines'; this draws a line from the x-axis up to the plotted point

  • "s" for 'stair steps'; this draws lines connecting points, but as a horizontal line and a vertical line rather than a single direct line

• main="Main title"

• sub="Subtitle"

• xlab="x-axis"

• ylab="x-axis"

• pch=1 to draw points as empty circles. Alternatively...

  • 0 as empty squares

  • 5 as empty diamonds

  • 2 as empty upward triangles

  • 6 as empty downward triangles

  • 16 as filled circles

  • 15 as filled squares

  • 18 as filled diamond

  • 17 as filled upward triangles

  • "*" as asterisks

    • can be any single character

• col="black" to draw points in black. Alternatively...

  • "red" in red, "blue" in blue, and so on, any color name that is recognized by R

    • run colors() to see the list of recognized color names

  • "#000000" in black, or any hex code

• lty=1 to draw a solid line. Alternatively...

  • "solid"

  • 2 or "dashed" for a dashed line

  • 3 or "dotted" for a dotted line

  • 4 or "dotdash" for a dash-dotted line

Many of these option specifications are reused for other graphing functions.

To overlay plots, try:

plot(x, y1, pch=1, col="red")
plot(x, y2, pch=0, col="blue")
legend("topright", pch=c(1,0), col=c("red", "blue"), legend=c("y1", "y2"), text.col=c("red", "blue"))

The legend function first takes one or two positioning arguments. These can be coordinates (in the same units as the plot itself). It can also be a descriptive word: one of "topleft", "topright", "bottomleft", or "bottomright".

Beyond that, the legend function takes these options:

• pch=... for how to draw the points of the legend. If a scalar is provided, it is used for all points.

• col=... for the color to draw the points of the legend. If a scalar is provided, it is used for all points.

• legend=... for the text that labels the points of the legend.

• text.col=... for the color to write the text labels. If a scalar is provided, it is used for all points.

To add a regression line to a plot, try:

plot(x, y)
abline(lm(y ~ x))

The abline function takes some of the same options as above; in particular col and lty.

To create a histogram from data, use the hist function.

To create a density plot from data, chain the plot and density functions.

plot(density(x, na.rm=TRUE), main="Distribution of x")

To create a boxplot from data, try:

boxplot(x)
boxplot(x ~ y) # split by levels of y

To create a bar chart from statistics, try:

x <- c(3,2,1)
names(x) <- c("foo", "bar", "baz")
barplot(x)

To set two plots side-by-side, try:

par(mfrow=c(1,2))
plot(x, y)
plot(x,z)

The par function can be used this way to create a panel of plots in any arrangement.

Vector Functions

These functions operate on a vector and return a scalar:

These functions operate on a vector and have some side effect, like printing something to the screen, rather than returning a value:

These functions are useful for working with logical vectors:

To recast other data types, especially lists (which can feature nesting), into vectors, use the unlist function.

Equality Testing

Testing for equality of two vectors is tricky. The all.equal function does evaluate to TRUE in the case that two vectors are equal. If they are not equal however, it evaluates to a text description of the differences, and inconveniently this is a truthy value.

Always use the identical function to test for equality.

Sequence Generation

The seq function has a number of overloaded parameterizations.

seq(start,finish)  #equivalent to seq(start,finish,1)
seq(finish,start)  #equivalent to seq(finish,start,-1)
seq(finish)        #equivalent to seq(1,finish,1)

seq(start, finish, by=0.1)          #custom step size
seq(start, finish, 0.1)             #shorthand, same as above
seq(start, finish, length.out=100)  #automatically calculates step size

The rep function also has a number of parameterizations.

rep(1:3, times=2)               #1 2 3 1 2 3
rep(1:3, length.out=7)          #1 2 3 1 2 3 1
rep(1:3, each=2)                #1 1 2 2 3 3
rep(1:3, each=2, length.out=7)  #1 1 2 2 3 3 1

Sorting

The sort function has a few options.

• To sort descending instead of ascending, pass decreasing = TRUE.

• To retain NA values and sort them to the end, pass na.last = TRUE. Or to retain them and sort them to the beginning, set as FALSE. The default is for NA values to be removed.

Matrix functions

The replicate function collects the results of a repeated command into a matrix. For example, to generate 10 samples of 100 observations each from the standard normal distribution, with each sample becoming a column, try:

replicate(10, rnorm(100))

The apply function applies a function to a rows and/or columns of a matrix.

row_sums <- apply(x, MARGIN=1, FUN=sum)
col_sums <- apply(x, MARGIN=2, FUN=sum)
all_sums <- apply(x, MARGIN=c(1,2), FUN=sum)

See also

R documentation on functions in the base package

R documentation on functions in the stats package

R documentation on functions in the graphics package

CategoryRicottone