An Introduction to R for Epidemiologists using RStudio ...sjm2186/SER2014/intro.pdfFirst steps: Use...

An Introduction to R for Epidemiologists using RStudiothe basics

Steve Mooney (much borrowed from C. DiMaggio)

Department of EpidemiologyColumbia UniversityNew York, NY 10032

sjm2186@columbia.edu

An Introduction to R for Epidemiologists using RStudioIntroduction to R Concepts and Object Types

SER Summer 2014

Outline

1 getting our hands dirtycalculating, assigning, combiningfrom calculations to programming

2 how R thinks (vs. SAS and SPSS)

3 data

4 packages

5 help

6 objectsabout objectsvectormatrix & arraylistdataframe

S. Mooney (Columbia University) R intro 2014 2 / 56

getting our hands dirty calculating, assigning, combining

First steps: Use R as a calculator

math operators and functions

arithmetic + , - , * , /

power ^

convert 68 degrees Fahrenheit to Celsius (C 0 = 59(F 0 − 32))

5/9*(68-32)

First type it directly in the console. Then type it into the editor and sendit to the console.(Remember how to do that?)

assignment operator‘memory’ key

y <- 5/9*(68-32) #assignment (no display)

(y <- 5/9*(68-32)) #assignment (display)

functions

FunctionName(parameter1, parameter2, ...)

math operators and functions

mathematical functions - sqrt, log, exp, sin, cos, tan

simple functions - max, min, length, sum, mean, var, sort

abs(-23) #absolute value

exp(8) # exponentiation

log(exp(8)) # natural logarithm

sqrt(64) # square root

concatenation functioncombine or ”vectorize”

x <- c(100,90,80,70,60)

y <- c("a", "b", "c", "d")

Put it together: Vectorized computations

The calcuation you tried with 68 (a scalar) can also work with the vectoryou just created:

5/9*(x-32)

z<-5/9*(x-32)

getting our hands dirty from calculations to programming

write your own functionR is a programming language

my.function<-function(x){

5/9*(x-32)

my.function(68)

[1] 20

a<-c(134,156,222)

my.function(a)

[1] 56.66667 68.88889 105.55556

We’ll revisit creating functions if we have time...

how R thinks (vs. SAS and SPSS)

Outline

3 data

4 packages

5 help

A Quick Warning...

We’re going to shift gears into abstract territory for a little while.

I hope the material that follows will orient you as we learn more concretepieces of R.

But I want to acknowledge that it gets away from the concrete; pleasedon’t worry if you feel you’re not grasping all the details fully right now.

Programming and AnalyzingHow to work with R

In my experience, data analysis is usually an iterative process:

1 Massage data (merge datasets, select the items you want to analyze,ensure measures are created properly, etc)

2 Call some procedure to do analytic step (e.g. look at 2x2 table)

3 Interpret output & generate new questions (back to step 1 or 2)

In SAS (& SPSS(?)), data massage mostly happens in DATA statementsand analysis mostly happens in PROC steps.

In R, there’s no formal separation between massage and analysis: we usesimilar functions for both.

Programming and AnalyzingGetting abstract for a moment...

Most data massage and analysis procedures actually have similar components:

1 Three classes of thing you tell the statistical program:1 What type of operation to do2 How specifically to do it3 A dataset to do it on

2 Two types of thing happen when you run the code:1 Changes get made to the data2 Output or results are returned

Let’s look at how this plays out in SAS, SPSS, and R...

Programming and AnalyzingFunction input (SAS example)

In SAS:

1 The type of thing to do is the DATA or PROC XYZ statement.2 The dataset to use is specified with data=XYZ for a PROC step and set

XYZ; for a data step.3 And everything else is how specifically to do it.

For example, consider the following SAS statement:

PROC FREQ DATA=XYZ; table X*Y/missing; RUN;

FREQ is the type of thing.XYZ is the dataset (and the X*Y specifies the columns)./missing is how specifically to do the FREQ

Programming and AnalyzingFunction input (SPSS example)

In SPSS:

1 The type of thing to do is the statement type.2 The dataset is implicit based on a previous DATA statement.3 And everything else is how specifically to do it.

For example, consider the following SPSS statement:

crosstabs

/tables X by Y

/missing=report

crosstabs is the type of thing.the current dataset is the dataset (and X by Y specifies the columns)./missing= report indicates how specifically to do the crosstab

Programming and AnalyzingFunction input in R

1 The type of thing to do is the function name type.2 The dataset and how specifically to do it are both parameters to the

function.

For example, consider the following R statement:

table(XYZ$X, XYZ$Y, na.rm=TRUE)

table is the type of thing.XYZ$X and XYZ$Y are the data.na.rm=TRUE is how specifically to handle missing data

Programming and AnalyzingFunction output in R

I claimed that analytic steps have up to two kinds of effects:

1 Changes made to the data2 Output or results returned

In SAS and SPSS:

1 There is an output window that displays the results of an analyticprocedure.

2 Some procedures change data and others do not. The programmer knowswhich procedures modify the data

1 An analytic function typically returns an object whose default display isthe result of interest.

2 If the programmer wants data modified by the procedure, she or he usuallyworks with the return value of the function in the next programming step.

Programming and AnalyzingFunction output in R

Consider the R statement

table(XYZ$X, XYZ$Y, na.rm=TRUE)

This is a function that returns an object (a 2x2 matrix, in this case) whosedefault visualization looks like a 2x2 table.

If you want a chi-square test on that 2x2 table, you can use the output fromthe table function as the input to the chisq.test function as follows:

chisq.test(table(XYZ$X, XYZ$Y, na.rm=TRUE))

Using return values rather than side effects is characteristic of a functionalprogramming model of language design.

Don’t worry

This may seem complicated or abstract, but it will become more clear afterusing R more.

Outline

3 data

4 packages

5 help

the cbind() functionsCombining vectors into matrices

weight <- c(134, 156, 222)

height <- c(60, 63, 72)

bmi <- (weight*703)/height^2

cbind(weight, height, bmi)

weight height bmi

[1,] 134 60 26.16722

[2,] 156 63 27.63114

[3,] 222 72 30.10532

getting your own data into R”there’s a function for that”

read.table() (/read.csv/read.fwf) is how you get data into base R

but RStudio’s Import Dataset can generate the code for you...

cars<-read.table(

"http://www.columbia.edu/~sjm2186/SER2014/cars.txt",

header=T, stringsAsFactors=F)

str(cars)

We will revisit this...

packages

Outline

3 data

4 packages

5 help

packages

Packages contain code that enable extra functionality in RAnalogous to a SAS file containing several macros

install.packages("epitools")

library(epitools)

epitab(c(10, 20, 30, 40))

We will revisit these as well...

Outline

3 data

4 packages

5 help

getting help

R has a lot of built-in help mechanisms...

help() opens help page

apropos() displays all objects matching topic

library(help=packageName) help on a specific package

vignette(package=”packageName”);

help(sample) ; ?sample ; ??sample

apropos("sam")

library(help=epitools)

vignette(package="utils")

vignette("Sweave")

getting help

..but web resources can be even more helpful:

tutorial: http://www.ats.ucla.edu/stat/r/

search: http://www.r-project.org/search.html

books: Venebles, Aragon, etc.

Two major online communities:

R mailing list archive: http://r.789695.n4.nabble.com/

Stack Overflow: http://stackoverflow.com/questions/tagged/r

objects

Outline

3 data

4 packages

5 help

objects

5 important objects

objects are ”specialized data structures”

1 vector - collection of like elements (numbers, characters...)

2 matrix - 2-dimensional vector

3 array - >2-dimensional vector

4 list - collection of groups of like elements any kind

5 dataframe - tabular data set, each row a record, each column a (like)element or variable

objects

objects for epidemiologists

matrix for contingency, e.g. 2x2, tables

arrays for stratified tables

dataframe for observations and variables

factors for categorical variables

numeric representation of charactersread.table converts characters to factors

objects

examples of R objects

x <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)

y <- matrix(x, nrow = 2)

z <- array(x, dim = c(2, 3, 2))

mylist <- list(x, y, z)

names <- c("alice", "bob", "charlie")

gender <- c("girl", "boy", "boy")

age <- c(28, 22, 34)

race <- factor(c("Asian", "Asian", "Black"),

levels=c("Asian", "Black", "White"))

data<- data.frame(names, gender, age, race)

objects about objects

Outline

3 data

4 packages

5 help

modesatomic vs recursive objects

mode - type of data: numeric, character, logical, factor

age <- c(34, 20); mode(age)

lt25 <- age<25

mode(lt25)

atomic - only one modecharacter, numeric, factor, or logical, i.e. vectors, matrices, arrays

logical (1 for TRUE 0 for FALSE)categorical - appear numeric but stored as factors

recursive - more than one mode

lists, data frames, functions

atomic objectsall elements the same

vector: one dimension

y <- c("Tom", "Dick", "Harry") ; y #character

x <- c(1, 2, 3, 4, 5) ; x #numeric

z <- x<3 ; z #logical

matrix: two-dimensional vector

x <- c("a", "b", "c", "d")

y <- matrix(x, 2, 2) ; y

array: n-dimensional vector

x <- 1:8

y <- array(x, dim=c(2, 2, 2)) ; y

recursive objectsdiffer

list: collections of data

x <- c(1, 2, 3)

y <- c("Male", "Female", "Male")

z <- matrix(1:4, 2, 2)

xyz <- list(x, y, z)

dataframe: tabular (2-dimensional) list

subjno <- c(1, 2, 3, 4) ; age <- c(34, 56, 45, 23)

sex <- c("Male", "Male", "Female", "Male")

case <- c("Yes", "No", "No", "Yes")

mydat <- data.frame(subjno, age, sex, case) ; mydat

coercionchanging an object’s mode

this is importantR will automatically coerce all the elements in an atomic object to a singlemode (character >numeric >logical)

c("hello", 4.56, FALSE)

c(4.56, FALSE)

coercing objectsdo it yourself

is.xxx / as.xxx - to assess / coerce objectsxxx = vector, matrix, array, list, data.frame, function, character,numeric, factor, na etc...

is.matrix(1:3) # false

as.matrix(1:3)

is.matrix(as.matrix(1:3)) # true

# coercing factor to character

sex <- factor(c("M", "M", "M", "M", "F", "F", "F", "F"))

unclass(sex) #does not coerce into character

as.character(sex) #works

Reviewbasic characteristics of R objects

Objects - vector, matrix, array, list, dataframe

mode() - ”type” of object: numeric, character, factor, logical

vectors and matrices - atomic, one mode onlylists and data frames - recursive, can be of >1 mode

class() - for simple vectors, same as mode

more complex objects, array and data frames have their own classaffects how printed, plotted and otherwise handled

objects vector

Outline

3 data

4 packages

5 help

objects vector

vectors are 1-dimensional strings of like elements

the basic building block of data in R

use them for quick data entry

objects vector

fun with vectors

y<-1:5 #create a vector of consecutive integers

y+2 #scalar addition

2*y #scalar multiplication

x<-c(1,3,2,10,5)

cumsum(x)

objects vector

more fun with vectorsvectorized arithmetic

c(1,2,3,4)/2

c(1,2,3,4)/c(4,3,2,1)

log(c(0.1,1,10,100), 10)

c(1,2,3,4) + c(4,3)

c(1,2,3,4) + c(4,3,2)

objects vector

creating numerical vectorssequences

the sequence operator :

seq() greater flexibility

> seq(1, 5, by = 0.5) # specify interval

> seq(1, 5, length = 8) #specify length

objects vector

operations on vectors

x <- rnorm(100)

sum(x)

x <- rep(2, 10)

cumsum(x)

mean(x)

sum(x)/length(x)

var(x) #sample variance

sqrt(var(x)) #sample standard deviation

x <- rnorm(100)

y <- rnorm(100)

var(x, y) # covariance

objects vector

logical vectorsthe special vector...

series of TRUEs and FALSEs (Ts and Fs)

created with relational operators:

<, >, <=, >=, ==, !=

used to index, select and subset data

objects vector

about logical vectors

logical operators are the key to indexing, and indexing is the keyto manipulating data

= <= > >= == !

x<-1:26

temp<- x > 13 #logical vector temp

#same length as vector x

#TRUE= 1, when condition met

#FALSE = 0, when not met

sum(temp)

We will revist logical vectors when we discuss indexing (coming soon...)

objects matrix & array

Outline

3 data

4 packages

5 help

a matrix is a 2-dimensional vector2x2 and contingency tables

Option 1: define the matrix from raw data:

myMatrix<-matrix(c("a","b","c","d"),2,2)

myMatrix

myMatrix2<-matrix(c("a","b","c","d"),2,2, byrow=T)

myMatrix2

colnames(myMatrix2)<-c("case", "control")

rownames(myMatrix2)<-c("exposed", "unexposed")

myMatrix2

cbind and rbind

Option 2: define the data by binding vectors together:

names<-c("Alice", "Bob", "Charlie")

ages<-c(6,7,8)

cbind(names, ages)

rbind(names, ages)

caution: recycling

cbind and rbind - will recycle data

when performing vector or mixed vector and array arithmetic, shortvectors are extended by recycling till they match size of otheroperands

R may return an error message, but still complete the operation

creating a matrix from dataThe more common scenario...

table() - from characters

titanic<-read.csv(

"http://www.columbia.edu/~sjm2186/SER2014/titanic.csv",

stringsAsFactors=F) #load titanic data

str(titanic) # Check the structure

table(titanic$sex,titanic$survived) # Make the matrix

an array is an n-dimensional vectorstratified epi tables

stratified titanic survival table:

sex vs. survival vs. passenger class

table(titanic$sex,titanic$survived, titanic$pclass)

objects list

Outline

3 data

4 packages

5 help

objects list

a list is a recursive collection of unlike elementslike epi ”variables” and ”observations”

often used to ”store” function results

str() is your friend

also, see stackoverflow discussion

x <- 1:5 ; y <- matrix(c("a","c","b","d"), 2,2)

z <- c("Peter", "Paul", "Mary")

mm <- list(x, y, z)

str(mm)

objects dataframe

Outline

3 data

4 packages

5 help

objects dataframe

dataframestabular epi data sets

2-dimensional tabular lists with equal-length fieldseach row is a record or observationeach column is a field or variable (usually numeric vector or factors)

data(infert)

str(infert)

head(infert)

”a list that behaves like a matrix”

objects dataframe

creating data frames

1 data.frame()

x <- data.frame(id=1:2, sex=c("M","F"))

2 read.table(), read.csv(), read.delim(), read.fwf()

titanic<-read.csv(

"http://www.columbia.edu/~sjm2186/SER2014/titanic.csv",

stringsAsFactors=F) #load titanic data

str(titanic)

(caution: default char → factor, numeric → integer)

objects dataframe

Exercises

You should now be able to complete exercises 1 and 2 inhttp://www.columbia.edu/~sjm2186/SER2014/Exercises.pdf

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