R is software for statistical computing. It differs from many commerical software programs like Stata or SAS in that it is free and open-source, and that nearly all functionality requires command-line programming. This brief R tutorial covers the basics of R functionality to be used in the NME course labs.

## Other Resources

This tutorial will not cover everything to know about R. For a more complete background to learning the language, we recommend the following:

When learning a new programming language, the best method is regular practice and integration into one’s daily life. Try using R on a regular basis for data management, analysis, and modeling tasks.

When stumped, the built-in documentation usually provides a solution. If not, http://stackoverflow.com/ is a helpful resource; one can limit search results with an [r] tag in the search box.

## Basics

To get started, we will introduce some of the core functionality and data structures in R. At its core, R is a fancy calculator with memory.

3*2     
[1] 6

But it is also a programming language with defined syntax and native constants like pi.

# Comments are embedded in code with a #
4*pi
[1] 12.56637

Separate commands need to be put on separate lines, unless a ; is used.

letters 
 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q"
[18] "r" "s" "t" "u" "v" "w" "x" "y" "z"
LETTERS
 [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q"
[18] "R" "S" "T" "U" "V" "W" "X" "Y" "Z"

R contains many arithmetical functions.

round(4.2)
[1] 4
ceiling(4.2)
[1] 5
floor(4.2)
[1] 4

### Packages

R is built on a foundation of functions installed with the base R program. A strength of R is the extension of this software through easily accessible built in packages contributed by users. EpiModel, ergm, and network are examples of packages. Most add-on packages are available through CRAN, the central repository for R. You can install any package from CRAN by simply typing:

install.packages("packagename")

This will typically install not only the package but all the related packages needed. Once a package is installed, it may be loaded for use by typing:

library("packagename")

### Help and Documentation

Help files are accessed through the console in either of the following ways.

?mean
help("mean")

A help index is available for all contributed packages like EpiModel by typing:

help(package = "EpiModel")

Many packages have built-in documentation (called vignettes) that are accessible for the network package, for example, by typing:

browseVignettes("network")

### Objects

Nearly all R programming is “object-oriented”. To assign a value to an object, use <-.

a <- 3                      

To evaluate an object, type its name in the console window.

a
[1] 3

Once an object is stored, it may be called for further operations.

a*2
[1] 6
b <- a/2
b
[1] 1.5

To test the value of an object, use ==.

a == 3
[1] TRUE
a^2 == 8
[1] FALSE

To test inequality, use !=.

a != 3
[1] FALSE
a != 4
[1] TRUE

To see the list of objects in the current environment:

ls()
[1] "a" "b"

## Vectors

To create a vector of one element repeated many times, use rep (for repeat)

rep(4, 10)
 [1] 4 4 4 4 4 4 4 4 4 4

To create a vector with different elements, use c (for combine).

d <- c(4, 3, 1, 7)
d
[1] 4 3 1 7

These can be combined arbitrarily to create a chained vector.

c(rep(9, 5), 6, 1, rep(5, 9))                       
 [1] 9 9 9 9 9 6 1 5 5 5 5 5 5 5 5 5

Colon notation gives you a vector of sequential integers.

1:10
 [1]  1  2  3  4  5  6  7  8  9 10

Square brackets pull out elements from a vector.

d[2]                                
[1] 3
d[2:3]
[1] 3 1

Negative in a bracket means “eveything but”.

d[-2]
[1] 4 1 7

### Empty Vectors

If you want to make a vector but leave it empty for the moment (useful to time-series simulations), there are several options. The following is simplest when you do not know the size of the vector in advance.

f <- vector()
f
logical(0)

If you do know the vector size, it is preferable to create a non-empty vector filled with an arbitrary value that will never be used for a true value. Here, we create a vector with a length of 10, which for the moment are filled with a missing data value NA. Note that when we put an assignment operation in parentheses, R will print the outcome of that assignment to the console. Spaces around any operations are acceptable, but it is recommended that you use a clear and consistent coding type (for example: http://adv-r.had.co.nz/Style.html) so that others can easily read your work.

( g <- rep(NA, 10) )
 [1] NA NA NA NA NA NA NA NA NA NA

Next we fill in the vector with values 0 and 1. The elements 1, 2, 3, 4, 6, and 9 are given the value of 1. Next, we use the is.na function to find which elements in the vector are equal to NA, and then assign the value of 0 to those elements.

g[c(1:4, 6, 9)] <- 1
g[is.na(g)] <- 0
g
 [1] 1 1 1 1 0 1 0 0 1 0

### Evaluating Vectors

Evaluation can be done for vectors too.

g == 1
 [1]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE

To get global summaries of vector evaluations, use the any and all functions.

any(g == 1)
[1] TRUE
all(g == 1)
[1] FALSE

To return the positions of the elements that fulfill a criterion, use which.

which(g == 1)                           
[1] 1 2 3 4 6 9

To see the length of a vector, use the length function.

length(g)
[1] 10

Basic arithmetic functions for vectors are possible.

mean(g)
[1] 0.6

### Missing Data

For missing data: use NA.

i <- c(3, NA, 1)  

Operations on NA will return NA.

i*2                                 
[1]  6 NA  2
i == 1
[1] FALSE    NA  TRUE

Many built-in functions will not simply ignore NA values, but base their default output on them. The mean or sum of any vector with an NA will always be NA.

mean(i)
[1] NA
sum(i)
[1] NA

However, there is often an option to override this default, thereby calculating the statistics by dropping those missing values.

mean(i, na.rm = TRUE)
[1] 2
sum(i, na.rm = TRUE)
[1] 4

### Set Theory

R includes several set theory functions to evaluate vectors.

a <- 1:10
b <- 5:15
union(a,b)
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
intersect(a,b)
[1]  5  6  7  8  9 10
setdiff(a,b)
[1] 1 2 3 4
setdiff(b,a)
[1] 11 12 13 14 15

The %in% function is a general way to evaluate vectors of arbitrary length to see if there are any matches between the vectors.

a <- 1:3
b <- 2:3
a %in% b
[1] FALSE  TRUE  TRUE

It may be used to check whether an object matches at least one element of a vector.

type <- "SIR"
type %in% c("SIR", "SIS")
[1] TRUE

To test an inequality of a match, the ! must be on the outside of the entire evaluation:

!(type %in% c("SIR", "SIS"))
[1] FALSE

## Matrices and Arrays

Vectors may be combined into matrixes or arrays of arbitrary complexity. Note the use of the paste function to make character vectors to create row and column names.

a <- 1:25
m <- matrix(a, nrow = 5, ncol = 5)
rownames(m) <- paste("row", 1:5, sep="")
colnames(m) <- paste("col", 1:5, sep="")
m
     col1 col2 col3 col4 col5
row1    1    6   11   16   21
row2    2    7   12   17   22
row3    3    8   13   18   23
row4    4    9   14   19   24
row5    5   10   15   20   25

Here is a three dimensional array. We create a second vector by multipying each element of m by 2.

n <- m*2
( a <- array(c(m, n), dim=c(5, 5, 2)) )
, , 1

[,1] [,2] [,3] [,4] [,5]
[1,]    1    6   11   16   21
[2,]    2    7   12   17   22
[3,]    3    8   13   18   23
[4,]    4    9   14   19   24
[5,]    5   10   15   20   25

, , 2

[,1] [,2] [,3] [,4] [,5]
[1,]    2   12   22   32   42
[2,]    4   14   24   34   44
[3,]    6   16   26   36   46
[4,]    8   18   28   38   48
[5,]   10   20   30   40   50

Note R includes complex matrix albegra functions that we will not cover here (for example see help("%*%") for matrix multiplication).

### Reference Values

Rows, columns, and cells for matrices may be accessed using the bracket notation. Pull out rows by position:

m[1:2, ]
     col1 col2 col3 col4 col5
row1    1    6   11   16   21
row2    2    7   12   17   22

Pull out columns by position:

m[, 1:2]  
     col1 col2
row1    1    6
row2    2    7
row3    3    8
row4    4    9
row5    5   10

Pull out a specific cell:

m[3,3]
[1] 13

Change the value of that cell:

m[3,3] <- 100
m
     col1 col2 col3 col4 col5
row1    1    6   11   16   21
row2    2    7   12   17   22
row3    3    8  100   18   23
row4    4    9   14   19   24
row5    5   10   15   20   25

Note that when pulling single rows or columns, the matrix will be converted to a vector.

m[, 1]
row1 row2 row3 row4 row5
1    2    3    4    5 

Often this is necessary to retain the matrix organization. This is possible by:

m[ , 1, drop = FALSE]
     col1
row1    1
row2    2
row3    3
row4    4
row5    5

Arrays can be refernced by position through more commas for each added dimension. This will output the first level of the third dimension.

a[, , 1]
     [,1] [,2] [,3] [,4] [,5]
[1,]    1    6   11   16   21
[2,]    2    7   12   17   22
[3,]    3    8   13   18   23
[4,]    4    9   14   19   24
[5,]    5   10   15   20   25

This will output the second column across both levels of the third dimension.

a[, 2, ]
     [,1] [,2]
[1,]    6   12
[2,]    7   14
[3,]    8   16
[4,]    9   18
[5,]   10   20

Again, as above, to keep that in a three-dimensional array class, use the same syntax as above.

a[, 2, , drop = FALSE]
, , 1

[,1]
[1,]    6
[2,]    7
[3,]    8
[4,]    9
[5,]   10

, , 2

[,1]
[1,]   12
[2,]   14
[3,]   16
[4,]   18
[5,]   20

Use rbind to add rows to a matrix and cbind to add columns. First, we use the seq function to generate a vector of length 5, that starts with the value of 6.

m <- rbind(m, seq(6, 26, 5))
m
     col1 col2 col3 col4 col5
row1    1    6   11   16   21
row2    2    7   12   17   22
row3    3    8  100   18   23
row4    4    9   14   19   24
row5    5   10   15   20   25
6   11   16   21   26

Next, we use the max function to find the maximum value of m, a generate a vector of sequential integers that starts with one higher than that max and ends with 6 higher than that max.

m <- cbind(m, (max(m) + 1):(max(m) + 6))
m
     col1 col2 col3 col4 col5
row1    1    6   11   16   21 101
row2    2    7   12   17   22 102
row3    3    8  100   18   23 103
row4    4    9   14   19   24 104
row5    5   10   15   20   25 105
6   11   16   21   26 106

## Data Frames

Matrices can only store elements of the same type (e.g., only numeric or only character), but as this is a limitation for storing datasets, the data.frame object class may be used. The main restriction with data frames is that each column (variable) must have the same length (same number of observations).

id <- 1:10
sex <- c(rep("Female", 5),rep("Male", 5))
( h <- data.frame(id, sex) )
   id    sex
1   1 Female
2   2 Female
3   3 Female
4   4 Female
5   5 Female
6   6   Male
7   7   Male
8   8   Male
9   9   Male
10 10   Male

Data frames may be accessed using the same bracket notation as above if you want to operate on them by position. But they may also be referenced by column name:

h$sex  [1] Female Female Female Female Female Male Male Male Male Male Levels: Female Male Subset the data frame by variable values. Inside the bracket is a logical evaluation statement to test when each row satisfied two conditions jointly. h[h$sex == "Female" & h$id < 5, ]  id sex 1 1 Female 2 2 Female 3 3 Female 4 4 Female Obtain the dimensions of a data frame (rows first, columns second). dim(h) [1] 10 2 The outputs of functions are themselves objects, which can be evaluated and operated on in turn. nrows.h <- nrow(h) ncols.h <- ncol(h) c(nrows.h, ncols.h) == dim(h) [1] TRUE TRUE To obtain general “meta-data” on a data frame (or any R object), use: str(h) 'data.frame': 10 obs. of 2 variables:$ id : int  1 2 3 4 5 6 7 8 9 10
\$ sex: Factor w/ 2 levels "Female","Male": 1 1 1 1 1 2 2 2 2 2

## Conditional Logic

Conditional logic is heavily used in R to evaluate vectors and calculate object values based on those conditions.

### If Statements

If statments evaluate the value of a single element of a vector (i.e., a scalar); if the condition is true, then the code following the conditional evaluation will be run, or otherwise, skipped. Note the syntax that the evaluation condition must go in parentheses.

a <- 3
if (a > 5) j <- 99
if (a < 5) j <- 77
j
[1] 77

### If/Else Statements

If/Else Statements enforce a counterfactual to the first conditional evaluation. If the conditional is false, then the else alternative must be true, and the code is evaluated. Note the use of brackets here to put the assignment operation on a following line. This is generally the best style choice as it makes the “then” part unambiguous.

if (j == 77) {
k <- j+1
} else {
k <- j-1
}
k
[1] 78

### The ifelse Function for Vectors

As noted, the statements above only work for single elements to be evaluated. R includes the ifelse function to evaluate multiple length vectors such as the following. Note the use of sample to generate a random vector choosing equally among three races with replacement. The ifelse function then evaluates the race vector, and if that condition is true, sets the new variable white.race to be TRUE, otherwise to be FALSE. Finally we store the results in a data frame.

race <- sample(c("Black", "White", "Asian"), size = 10, replace = TRUE)
white.race <- ifelse(race == "White", TRUE, FALSE)
( df <- data.frame(race, white.race) )
    race white.race
1  White       TRUE
2  Asian      FALSE
3  Black      FALSE
4  Asian      FALSE
5  Asian      FALSE
6  White       TRUE
7  Black      FALSE
8  Asian      FALSE
9  Black      FALSE
10 White       TRUE

## Loops

Loops in R are the core method to simulate epidemic models because the values at any time step depend on the stochastic realizations of the previous step: an iterative process is needed.

### For Loops

In the following, we recreate a proportional growth model in which new infections are a function of the prevalence at the prior time step and a fixed growth rate. In the loop, we create an iterator, i, which runs from time 2 to the number of time steps (10) in sequential integers. We initialize the vector prevalence to save the output, and start with its initial value of the initial.infected. Then we update the prevalence vector for each time step i by calling the value of that vector at the prior time step and performing the basic calculation.

initial.infected <- 20
time.steps <- 10
growth.rate <- 0.2

prevalence <- initial.infected
for (i in 2:time.steps) {
prevalence[i] <- prevalence[i-1] + prevalence[i-1]*growth.rate
}
prevalence
 [1]  20.00000  24.00000  28.80000  34.56000  41.47200  49.76640  59.71968
[8]  71.66362  85.99634 103.19561

### While Loops

While loops are a useful, but less common way to update vectors based on conditions. Intead of some interation in time, while loops require that a condition be satisfied to evaluate their contents. Below we track prevalence now without history as a single element vector; the loop will continue to run as long as prevalence is below 50. Note that the final value of prevalence is above 50 because it does not evaluate prevalence until after the previous iteration is run.

prevalence <- initial.infected
while (prevalence < 50) {
prevalence <- prevalence + prevalence*growth.rate
}
prevalence
[1] 59.71968

## Functions

All of R is functions. At their basic, functions take an input, run calculations, and supply an output.

### A Basic Function

Below, we define a function f, which takes as input two arguments, x and y, and returns their product.

f <- function(x, y) x*y
f(3, 5)
[1] 15

Because R is an interpreted rather than compiled language (like C or C++), there is no guarantee that any function will work. Robust functions, like those in R packages, have a set of input checks to prevent errors like this. The problem here, of course, if that * (itself a function!) expects a numeric input but we have supplied a character vector.

f("Male", "Female")

### Error Checking and Return

When building new functions, we could write in an error check to make sure problems like these are identified early and clearly. Below, we set our own error message (that is not much more helpful than the error message supplied by *) early in the function. If either x or y in the function are not numeric, then the function will stop and print the error message. Otherwise, they will be evaluated and returned. Note that an explicit return call must be used in functions if any object assignment is conducted within the function.

f <- function(x, y) {
if (!is.numeric(x) | !is.numeric(y)) {
stop("x and y must be numeric!!")
}
out <- x*y
return(out)
}

### Stochastic Growth

As an extended example, we build on our proportional growth model above, and add elements of stochasticity into the model. If we specify that stochastic=TRUE when we call the function, it will add a random draw from a normal distribution to the growth rate. Here we have incorporated many elements of the tutorial: functions, for loops, conditional logic, and operations on vectors!

pg.model <- function(initial.infected, time.steps,
growth.rate, stochastic) {
prevalence <- initial.infected
for (i in 2:time.steps) {
if (stochastic == TRUE) {
prevalence[i] <- prevalence[i-1] +
prevalence[i-1]*(growth.rate +
rnorm(1, mean = 0.1, sd = 0.01))
} else {
prevalence[i] <- prevalence[i-1] +
prevalence[i-1]*growth.rate
}
}
return(prevalence)
}

We run the model twice with the same parameter values but once as deterministic and once as stochastic. Note that it is not necessary to specify the argument names when calling a function, as long as the order of argument inputs is correct.

pg.model(initial.infected = 20, time.steps = 10,
growth.rate = 0.2, stochastic = FALSE)
 [1]  20.00000  24.00000  28.80000  34.56000  41.47200  49.76640  59.71968
[8]  71.66362  85.99634 103.19561
pg.model(20, 10, 0.2, TRUE)
 [1]  20.00000  25.96300  34.07056  44.37433  58.40073  76.20964  99.26875
[8] 128.93632 166.96713 219.39289

## Statistical Distributions

In stochastic epidemic modeling, it is necessary to draw from statistical distributions to determine, for example, the number of births that will occur in a time step given a population size and a birth rate. For discrete events, we most often use the Poisson and binomial distribution functions in R. The r prefix on each distribution generates one or several random draws from that distribution with the parameters set in the function.

### Normal Distribution

round(rnorm(n = 5, mean = 100, sd = 10), 1)
[1]  99.2  92.8  98.0 114.4 103.6

### Poisson Distribution

popsize <- 1000
birthrate <- 0.01
rpois(n = 5, lambda = popsize*birthrate)
[1]  4  9  9  8 14

### Binomial Distribution

transmission.rate <- 0.5
rbinom(n = 10, size = 1, prob = transmission.rate)
 [1] 0 0 1 0 0 1 1 1 1 0

### Uniform Distribution

runif(n = 10) <= transmission.rate
 [1] FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE

## Plotting

R excels at data visualization, but often some work is involved in tweaking the graphical parameters. Plots can be as arbitrarily complex as needed. Let’s set a three variable plot (perhaps, height by weight by age). We will sample values for x, y, and z from three different distributions.

n <- 25
x <- sort(sample(20:220, n))
y <- 10 + 2*x + rnorm(n, 0, 10)
z <- rpois(n, 10)

The build in plot function defaults will produce a quick output.

plot(x, y, cex = z/4)

But it is possible to make many graphical choices for beautiful output for presentations and publications. To see what’s going on here, see the help documentation for each of these functions.

tcol <- EpiModel::transco("steelblue", 0.5)
Warning: replacing previous import 'statnet.common::colMeans.mcmc.list' by
'ergm::colMeans.mcmc.list' when loading 'tergm'
Warning: replacing previous import 'statnet.common::sweep.mcmc.list' by
'ergm::sweep.mcmc.list' when loading 'tergm'
plot(x, y, cex = z/4, pch = 21, col = "black", bg = tcol,
lwd = 1.2, axes = FALSE, ylim = c(0, 500), xlim = c(0, 250),
yaxs = "r", xaxs = "r")
axis(2, seq(0, 500, 100), col = "white", las = 2,
cex.axis = 0.9, mgp = c(2, 0.5, 0))
axis(1, seq(0, 250, 50), cex.axis = 0.9, mgp = c(2, 0.5, 0))
abline(h = seq(100, 500, 100), col = EpiModel::transco("black", 0.35))

Last updated: 2018-08-16