The document discusses reading and manipulating data in R. It describes how to read different file types like CSV, Excel, SAS, Stata and text files into R objects using various packages and functions. It also explains different data objects in R like vectors, arrays, lists and data frames, and how to access elements within these objects using indexes or names.

1. Reading and
Manipulationg
data in
2013-02-15 @HSPH
Kazuki Yoshida, M.D.
MPH-CLE student
FREEDOM
TO
KNOW

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

3. Supported
n .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

4. package name
(packages add functions) function name
foreign::read.dta()
functions are followed by (),
in which you specify arguments

5. Create a folder for
this group

6. Open
R Studio

7. Make sure your
working directory
is correct

8. 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/

9. .csv
http://www.wondergraphs.com/img/SFO_Landings.csv

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

11. name of object to create
assignment operator
new.dat <- read.csv(“file.csv”)
function to read .csv files
file name here

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

13. read.table(“file.dat”)
or
read.table(“file.dat”, header = T)
http://www.biostat.harvard.edu/~fitzmaur/ala2e/tlc.dat

14. tab-separated

15. 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

16. Excel files

17. Install xlsx package

18. Just click
box to load

19. To install/load a package
install.packages(“package”, dep = T)
library(package)

20. name of object to create
assignment operator
xlsdat <- read.xlsx(“file.xls”, 1)
function to read .xlsx files
file name here
sheet number

22. SAS native files
library(sas7bdat)
sasdat <- read.sas7bdat(“file.sas7bdat”)

23. SAS xport files
library(foreign)
xptdat <- read.xport(“file.xpt”)
ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/nhanes/
2009-2010/DEMO_F.xpt

25. library(foreign)
statadat <- read.dta(“file.dta”)
http://www.biostat.harvard.edu/~fitzmaur/ala2e/
headache.dta

26. Fixed width

27. fwfdat <- read.fwf(“file.txt”, width = c(3, 5, ...))
Use width = list(c(3,5,..), c(5,7,..))
for multiple rows per subject

28. Manipulating data in R
n Objects
n Classes
n Various data objects

29. 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).

30. 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)

31. Classes of data values
inside data objects
n Numeric: Continuous variables
n Factor: Categorical variables
n Logical: TRUE/FALSE binary variables
n etc...

32. 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!

33. Data objects
n Vector (contains single class of data values)
n List (contains multiple classes of data values)

34. 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

35. 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

36. Array
n 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))

37. 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"))

38. 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)

39. 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 [[ ]]

40. Access named elements
n list3
n list3$small
n list3[["small"]]
n df1$large
n df1[, "large"]