R code

Introduction to R and RStudio

Installation

If you haven’t already, please install both R and RStudio following the instructions in this video

Change default setting

This setting will cause you issues in the future if you don’t change it now. Set R/RStudio to not automatically an RData file:

Brief introduction

For an extremely detailed introduction, please see

help.start()

In this documentation, the above command will be executed at the command prompt, see below.

From help.start():

R is a free software environment for statistical computing and graphics.

and from https://www.rstudio.com/products/RStudio/:

RStudio is an integrated development environment (IDE) for R.

R interface

In contrast to many other statistical software packages that use a point-and-click interface, e.g. SPSS, JMP, Stata, etc, R has a command-line interface. The command line has a command prompt, e.g. >, see below.

>

This means, that you will be entering commands on this command line and hitting enter to execute them, e.g. 

help()

Use the up arrow to recover past commands.

hepl()
help() # Use up arrow and fix

R GUI (or RStudio)

Most likely, you are using a graphical user interface (GUI) and therefore, in addition, to the command line, you also have a windowed version of R with some point-and-click options, e.g. File, Edit, and Help.

In particular, there is an editor to create a new R script. So rather than entering commands on the command line, you will write commands in a script and then send those commands to the command line using Ctrl-R (PC) or Command-Enter (Mac).

a = 1 
b = 2
a + b
## [1] 3

Multiple lines can be run in sequence by selecting them and then using Ctrl-R (PC) or Command-Enter (Mac).

Intro Activity

One of the most effective ways to use this documentation is to cut-and-paste the commands into a script and then execute them.

Cut-and-paste the following commands into a new script and then run those commands directly from the script using Ctrl-R (PC) or Command-Enter (Mac).

x <- 1:10
y <- rep(c(1,2), each=5)
m <- lm(y~x)
s <- summary(m)

Now, look at the result of each line

x
y
m
s
s$r.squared
Click for solution 
x
##  [1]  1  2  3  4  5  6  7  8  9 10
y
##  [1] 1 1 1 1 1 2 2 2 2 2
m
## 
## Call:
## lm(formula = y ~ x)
## 
## Coefficients:
## (Intercept)            x  
##      0.6667       0.1515
s
## 
## Call:
## lm(formula = y ~ x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.4242 -0.1667  0.0000  0.1667  0.4242 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.6667     0.1880   3.546  0.00756 **
## x             0.1515     0.0303   5.000  0.00105 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2752 on 8 degrees of freedom
## Multiple R-squared:  0.7576, Adjusted R-squared:  0.7273 
## F-statistic:    25 on 1 and 8 DF,  p-value: 0.001053
s$r.squared
## [1] 0.7575758

Using R as a calculator

Basic calculator operations

All basic calculator operations can be performed in R.

1+2
## [1] 3
1-2
## [1] -1
1/2
## [1] 0.5
1*2
## [1] 2
2^3 # same as 2**3
## [1] 8

For now, you can ignore the [1] at the beginning of the line, we’ll learn about that when we get to vectors.

Advanced calculator operations

Many advanced calculator operations are also available.

(1+3)*2 + 100^2  # standard order of operations (PEMDAS)
## [1] 10008
sin(2*pi)        # the result is in scientific notation, i.e. -2.449294 x 10^-16 
## [1] -2.449294e-16
sqrt(4)
## [1] 2
log(10)          # the default is base e
## [1] 2.302585
log(10, base = 10)
## [1] 1

Using variables

A real advantage to using R rather than a calculator (or calculator app) is the ability to store quantities using variables.

a = 1
b = 2
a + b
## [1] 3
a - b
## [1] -1
a / b
## [1] 0.5
a * b
## [1] 2
b ^ 3
## [1] 8

Assignment operators =, <-, and ->

When assigning variables values, you can also use arrows <- and -> and you will often see this in code, e.g. 

a <- 1 # recommended
2 -> b # uncommon, but sometimes useful
c = 3  # similar to other languages

Now print them.

a
## [1] 1
b
## [1] 2
c
## [1] 3

Using informative variable names

While using variables alone is useful, it is much more useful to use informative variables names.

# Rectangle
length <- 4
width  <- 3

area <- length * width
area
## [1] 12
perimeter <- 2 * (length + width)


# Circle
radius <- 2

area   <- pi*radius^2 # this overwrites the previous `area` variable
circumference <- 2*pi*radius

area
## [1] 12.56637
circumference
## [1] 12.56637
# (Right) Triangle
opposite     <- 1
angleDegrees <- 30
angleRadians <- angleDegrees * pi/180

(adjacent     <- opposite / tan(angleRadians)) # = sqrt(3)
## [1] 1.732051
(hypotenuse   <- opposite / sin(angleRadians)) # = 2
## [1] 2

Calculator Activity

Bayes’ Rule

Suppose an individual tests positive for a disease, what is the probability the individual has the disease? Let

  • \(D\) indicates the individual has the disease
  • \(N\) means the individual does not have the disease
  • \(+\) indicates a positive test result
  • \(-\) indicates a negative test

The above probability can be calculated using Bayes’ Rule:

\[ P(D|+) = \frac{P(+|D)P(D)}{P(+|D)P(D)+P(+|N)P(N)} = \frac{P(+|D)P(D)}{P(+|D)P(D)+(1-P(-|N))\times(1-P(D))} \]

where

Calculate the probability the individual has the disease if the test is positive when

  • the specificity of the test is 0.95,
  • the sensitivity of the test is 0.99, and
  • the prevalence of the disease is 0.001.
Click for solution 
specificity <- 0.95
sensitivity <- 0.99
prevalence <- 0.001
probability <- (sensitivity*prevalence) / (sensitivity*prevalence + (1-specificity)*(1-prevalence))
probability
## [1] 0.01943463

Data types

Objects in R can be broadly classified according to their dimensions:

  • scalar
  • vector
  • matrix
  • array (higher dimensional matrix)

and according to the type of variable they contain:

  • logical
  • integer
  • numeric
  • character (string)

Scalars

Scalars have a single value assigned to the object in R.

a <- 3.14159265 
b <- "STAT 587 (Eng)" 
c <- TRUE

Print the objects

a
## [1] 3.141593
b
## [1] "STAT 587 (Eng)"
c
## [1] TRUE

Vectors

The c() function creates a vector in R

a <- c(1, 2, -5, 3.6)
b <- c("STAT", "587", "(Eng)")
c <- c(TRUE, FALSE, TRUE, TRUE)

To determine the length of a vector in R use length()

length(a)
## [1] 4
length(b)
## [1] 3
length(c)
## [1] 4

To determine the type of a vector in R use class()

class(a)
## [1] "numeric"
class(b)
## [1] "character"
class(c)
## [1] "logical"

Vector construction

Create a numeric vector that is a sequence using : or seq().

1:10
##  [1]  1  2  3  4  5  6  7  8  9 10
5:-2
## [1]  5  4  3  2  1  0 -1 -2
seq(from = 2, to = 5, by = .05)
##  [1] 2.00 2.05 2.10 2.15 2.20 2.25 2.30 2.35 2.40 2.45 2.50 2.55 2.60 2.65 2.70
## [16] 2.75 2.80 2.85 2.90 2.95 3.00 3.05 3.10 3.15 3.20 3.25 3.30 3.35 3.40 3.45
## [31] 3.50 3.55 3.60 3.65 3.70 3.75 3.80 3.85 3.90 3.95 4.00 4.05 4.10 4.15 4.20
## [46] 4.25 4.30 4.35 4.40 4.45 4.50 4.55 4.60 4.65 4.70 4.75 4.80 4.85 4.90 4.95
## [61] 5.00

Another useful function to create vectors is rep()

rep(1:4, times = 2)
## [1] 1 2 3 4 1 2 3 4
rep(1:4, each  = 2)
## [1] 1 1 2 2 3 3 4 4
rep(1:4, each  = 2, times = 2)
##  [1] 1 1 2 2 3 3 4 4 1 1 2 2 3 3 4 4

Arguments to functions in R can be referenced either by position or by name or both. The safest and easiest to read approach is to name all your arguments. I will often name all but the first argument.

Accessing vector elements

Elements of a vector can be accessed using brackets, e.g. [index].

a <- c("one","two","three","four","five")
a[1]
## [1] "one"
a[2:4]
## [1] "two"   "three" "four"
a[c(3,5)]
## [1] "three" "five"
a[rep(3,4)]
## [1] "three" "three" "three" "three"

Alternatively we can access elements using a logical vector where only TRUE elements are accessed.

a[c(TRUE, TRUE, FALSE, FALSE, FALSE)]
## [1] "one" "two"

You can also see all elements except some using a negative sign -.

a[-1]
## [1] "two"   "three" "four"  "five"
a[-(2:3)]
## [1] "one"  "four" "five"

Modifying elements of a vector

You can assign new values to elements in a vector using = or <-.

a[2] <- "twenty-two"
a
## [1] "one"        "twenty-two" "three"      "four"       "five"
a[3:4] <- "three-four" # assigns "three-four" to both the 3rd and 4th elements
a
## [1] "one"        "twenty-two" "three-four" "three-four" "five"
a[c(3,5)] <- c("thirty-three","fifty-five")
a
## [1] "one"          "twenty-two"   "thirty-three" "three-four"   "fifty-five"

Matrices

Matrices can be constructed using cbind(), rbind(), and matrix():

m1 <- cbind(c(1,2), c(3,4))       # Column bind
m2 <- rbind(c(1,3), c(2,4))       # Row bind

m1
##      [,1] [,2]
## [1,]    1    3
## [2,]    2    4
all.equal(m1, m2)
## [1] TRUE
m3 <- matrix(1:4, nrow = 2, ncol = 2)
all.equal(m1, m3)
## [1] TRUE
m4 <- matrix(1:4, nrow = 2, ncol = 2, byrow = TRUE)
all.equal(m3, m4)
## [1] "Mean relative difference: 0.4"
m3
##      [,1] [,2]
## [1,]    1    3
## [2,]    2    4
m4
##      [,1] [,2]
## [1,]    1    2
## [2,]    3    4

Accessing matrix elements

Elements of a matrix can be accessed using brackets separated by a comma, e.g. [row index, column index].

m <- matrix(1:12, nrow=3, ncol=4)
m
##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    2    5    8   11
## [3,]    3    6    9   12
m[2,3]
## [1] 8

Multiple elements can be accessed at once

m[1:2,3:4]
##      [,1] [,2]
## [1,]    7   10
## [2,]    8   11

If no row (column) index is provided, then the whole row (column) is accessed.

m[1:2,]
##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    2    5    8   11

Like vectors, you can eliminate rows (or columns)

m[-c(3,4),]
##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    2    5    8   11

Be careful not to forget the comma, e.g. 

m[1:4]
## [1] 1 2 3 4

Packages

When you install R, you actually install several R packages. When you initially start R, you will load the following packages

  • [stats]
  • [graphics]
  • [grDevices]
  • [utils]
  • [datasets]
  • [methods]
  • [base]

You can find this information by running the following code and looking at the attached base packages.

sessionInfo()
## R version 4.2.2 (2022-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.2
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## loaded via a namespace (and not attached):
##  [1] digest_0.6.29   R6_2.5.1        jsonlite_1.8.0  magrittr_2.0.3 
##  [5] evaluate_0.15   stringi_1.7.6   rlang_1.0.2     cli_3.3.0      
##  [9] rstudioapi_0.13 jquerylib_0.1.4 bslib_0.3.1     rmarkdown_2.14 
## [13] tools_4.2.2     stringr_1.4.0   xfun_0.31       yaml_2.3.5     
## [17] fastmap_1.1.0   compiler_4.2.2  htmltools_0.5.2 knitr_1.39     
## [21] sass_0.4.1

These packages provide a lot of functionality and have been in existence (almost as they currently are) from the early days of R.

While a lot of functionality exist in these packages, much more functionality exists in user contributed packages. On the comprehensive R archive network (CRAN), there are (as of 2023/01/29) 19,122 packages available for download. On Bioconductor, there are an additional 2,183. There are also additional packages that exist outside of these repositories.

Install packages

To install packages from CRAN, use the install.packages function. For example,

install.packages("tidyverse")

or, to install all the packages needed for this class,

install.packages(c("tidyverse",
                   "gridExtra",
                   "rmarkdown",
                   "knitr"))

R packages almost always depend on other packages. When you use the install.packages() function, R will automatically install these dependencies.

You may run into problems during this installation process. Sometimes the dependencies will fail. If this occurs, try to install just that dependency using install.packages().

Sometimes you will be asked whether you want to install a newer version of a package from source. My general advice (for those new to R) is to say no and instead install the older version of the package. If you want to install from source, you will need Rtools (Windows) or Xcode (Mac). Alternatively, you can wait a couple of days for the newer version to be pre-compiled.

Load packages

The installation only needs to be done once. But we will need to load the packages in every R session where we want to use them. To load the packages, use

library("dplyr")
library("tidyr")
library("ggplot2")

alternatively, you can load the entire (not very big) tidyverse.

library("tidyverse")
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.7     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

Getting help for R

Learning R

To learn R, you may want to try the swirl package. To install, use

install.packages("swirl")

After installation, use the following to get started

library("swirl")
swirl()

Also, the R 4 Data Science book is extremely helpful.

General help

As you work with R, there will be many times when you need to get help.

My basic approach is

  1. Use the help contained within R.
  2. Perform an internet search for an answer.
  3. Find somebody else who knows.

In all cases, knowing the R keywords, e.g. a function name, will be extremely helpful.

Help within R I

If you know the function name, then you can use ?<function>, e.g.

?mean

The structure of help is

  • Description: quick description of what the function does
  • Usage: the arguments, their order, and default values (if any)
  • Arguments: more thorough description about the arguments
  • Value: what the funtion returns
  • See Also: similar functions
  • Examples: examples of how to use the function

Help within R II

If you cannot remember the function name, then you can use help.search("<something>"), e.g.

help.search("mean")

Depending on how many packages you have installed, you will find a lot or a little here.

Getting help on ggplot2

Although the general R help can still be used, e.g. 

?ggplot
?geom_point

It is much more helpful to google for an answer

geom_point 
ggplot2 line colors

The top hits will all have the code along with what the code produces.

Helpful sites

These sites all provide code. The first two also provide the plots that are produced.