Reading and Manipulationg

data in2013-02-15 @HSPH

Kazuki Yoshida, M.D. MPH-CLE student

FREEDOMTO  KNOW

Reading data in

n Usually the first task in real-life data analysis.

Supportedn .RData (native) files: load()

n .csv files: read.csv()

n .xls/.xlsx files: gdata::read.xls() or xlsx::read.xlsx()

n .sas7bdat files: sas7bdat ::read.sas7bdat()

n .dta files: foreign::read.dta()

n and more...http://cran.r-project.org/doc/manuals/R-data.html

foreign::read.dta()

package name(packages add functions) function name

functions are followed by (),in which you specify arguments

Create a folder for this group

Open R Studio

Make sure your working directory

is correct

Download files

n Rosner (ASCII, comma-separated and Stata): http://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20bI&product_isbn_issn=9780538733496

n Hernan (Excel and SAS): http://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/

.csvhttp://www.wondergraphs.com/img/SFO_Landings.csv

For comma-, tab-, or space-separated text

new.dat <- read.csv(“file.csv”)

name of object to create

file name herefunction to read .csv files

assignment operator

Space separated

http://www.biostat.harvard.edu/~fitzmaur/ala2e/tlc.dat

read.table(“file.dat”)or

read.table(“file.dat”, header = T)

http://www.biostat.harvard.edu/~fitzmaur/ala2e/tlc.dat

tab-separated

read.delim(“file.tsv”)http://www.brookscole.com/cgi-wadsworth/

course_products_wp.pl?fid=M20b&flag=student&product_isbn_issn=9780495384

960&disciplinenumber=1038&template=AUS

Excel files

Install xlsx package

Just click box to load

To install/load a package

install.packages(“package”, dep = T)

library(package)

xlsdat <- read.xlsx(“file.xls”, 1)

name of object to create

file name herefunction to read .xlsx files

assignment operator

sheet number

library(sas7bdat)sasdat <- read.sas7bdat(“file.sas7bdat”)

SAS native files

library(foreign)xptdat <- read.xport(“file.xpt”)

ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/nhanes/2009-2010/DEMO_F.xpt

SAS xport files

library(foreign)statadat <- read.dta(“file.dta”)

http://www.biostat.harvard.edu/~fitzmaur/ala2e/headache.dta

Fixed width

fwfdat <- read.fwf(“file.txt”, width = c(3, 5, ...))

Use width = list(c(3,5,..), c(5,7,..)) for multiple rows per subject

Manipulating data in R

n Objects

n Classes

n Various data objects

Objects

n Just about everything named in R is an object

n An object is a container that

n knows its class (eg, I have numbers inside!).

n has contents (eg, Actual numbers).

Examples of objects

n data, which you use for analysis (various classes)

n functions, which perform analysis (function class)

n results, which come out of analysis (various classes)

Classes of data values inside data objects

n Numeric: Continuous variables

n Factor: Categorical variables

n Logical: TRUE/FALSE binary variables

n etc...

Class?

n An object’s class tells R how the object should be handled.

n For example, summarizing data should work differently for numbers and categories!

Data objects

n Vector (contains single class of data values)

n List (contains multiple classes of data values)

Data objects

n Vector (contains single class of data values)

n Array including Matrix

n List (contains multiple classes of data values)

n Data frame

Vector

n Smallest building block of data objects

n Single dimension

n Combination of values of same class

n vec1 <- c(2013, 2, 15, -10) # combine

n vec2 <- 1:16 # integers 1 to 16

Arrayn Vector folded into a multidimensional structure

n 2-dimensional array is a matrix

n vec3 <- 1:16

n dim(vec3) <- c(4, 4) # 4 x 4 structure

n dim(vec3) <- c(2, 2, 4) # 2 x 2 x 4 structure

n arr1 <- array(1:60, dim = c(3,4,5))

List

n Combination of any values or objects

n Can contain objects of multiple classes

n eg, a list of two vectors, a matrix, three arrays

n list1 <- list(first = 1:17, second = matrix(letters, 13,2))

n list2 <- list(alpha = c(1,4,5,7), beta = c("h","s","p","h"))

Data frame

n Special case of a list

n List of same-length vectors vertically aligned

n df1 <- data.frame(list2)

n list3 <- list(small = letters, large = LETTERS, number = 1:26)

n df2 <- data.frame(list3)

Access by indexes

n letters[3] # 1-dimensional object

n arr1[1,2,3] # 3-dimensional object

n arr1[1, ,3] # implies 1,(all),3

n df1[ ,3] # implies (all),3

n list1[[1]] # list needs [[ ]]

Access named elements

n list3

n list3$small

n list3[["small"]]

n df1$large

n df1[, "large"]