20130215 Reading data into R

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20130215 Reading data into R

  1. 1. Reading andManipulationg data in 2013-02-15 @HSPH Kazuki Yoshida, M.D. MPH-CLE student FREEDOM TO  KNOW
  2. 2. Reading data inn Usually the first task in real-life data analysis.
  3. 3. 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
  4. 4. package name(packages add functions) function name foreign::read.dta() functions are followed by (), in which you specify arguments
  5. 5. Create a folder for this group
  6. 6. OpenR Studio
  7. 7. Make sure yourworking directory is correct
  8. 8. Download filesn Rosner (ASCII, comma-separated and Stata): http://www.cengage.com/cgi-wadsworth/ course_products_wp.pl? fid=M20bI&product_isbn_issn=9780538733496n Hernan (Excel and SAS): http:// www.hsph.harvard.edu/miguel-hernan/causal- inference-book/
  9. 9. .csvhttp://www.wondergraphs.com/img/SFO_Landings.csv
  10. 10. For comma-, tab-, orspace-separated text
  11. 11. name of object to create assignment operatornew.dat <- read.csv(“file.csv”) function to read .csv files file name here
  12. 12. Space separatedhttp://www.biostat.harvard.edu/~fitzmaur/ala2e/tlc.dat
  13. 13. read.table(“file.dat”) or read.table(“file.dat”, header = T)http://www.biostat.harvard.edu/~fitzmaur/ala2e/tlc.dat
  14. 14. tab-separated
  15. 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. 16. Excel files
  17. 17. Install xlsx package
  18. 18. Just clickbox to load
  19. 19. To install/load a packageinstall.packages(“package”, dep = T) library(package)
  20. 20. name of object to create assignment operatorxlsdat <- read.xlsx(“file.xls”, 1) function to read .xlsx files file name here sheet number
  21. 21. SAS native files library(sas7bdat)sasdat <- read.sas7bdat(“file.sas7bdat”)
  22. 22. SAS xport files library(foreign) xptdat <- read.xport(“file.xpt”)ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/nhanes/ 2009-2010/DEMO_F.xpt
  23. 23. library(foreign) statadat <- read.dta(“file.dta”)http://www.biostat.harvard.edu/~fitzmaur/ala2e/ headache.dta
  24. 24. Fixed width
  25. 25. fwfdat <- read.fwf(“file.txt”, width = c(3, 5, ...)) Use width = list(c(3,5,..), c(5,7,..)) for multiple rows per subject
  26. 26. Manipulating data in Rn Objectsn Classesn Various data objects
  27. 27. Objectsn Just about everything named in R is an objectn An object is a container that n knows its class (eg, I have numbers inside!). n has contents (eg, Actual numbers).
  28. 28. Examples of objectsn data, which you use for analysis (various classes)n functions, which perform analysis (function class)n results, which come out of analysis (various classes)
  29. 29. Classes of data values inside data objectsn Numeric: Continuous variablesn Factor: Categorical variablesn Logical: TRUE/FALSE binary variablesn etc...
  30. 30. 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!
  31. 31. Data objectsn Vector (contains single class of data values)n List (contains multiple classes of data values)
  32. 32. Data objectsn Vector (contains single class of data values) n Array including Matrixn List (contains multiple classes of data values) n Data frame
  33. 33. Vectorn Smallest building block of data objectsn Single dimensionn Combination of values of same classn vec1 <- c(2013, 2, 15, -10) # combinen vec2 <- 1:16 # integers 1 to 16
  34. 34. Arrayn Vector folded into a multidimensional structuren 2-dimensional array is a matrixn vec3 <- 1:16n dim(vec3) <- c(4, 4) # 4 x 4 structuren dim(vec3) <- c(2, 2, 4) # 2 x 2 x 4 structuren arr1 <- array(1:60, dim = c(3,4,5))
  35. 35. Listn Combination of any values or objectsn Can contain objects of multiple classesn eg, a list of two vectors, a matrix, three arraysn 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"))
  36. 36. Data framen Special case of a listn List of same-length vectors vertically alignedn df1 <- data.frame(list2)n list3 <- list(small = letters, large = LETTERS, number = 1:26)n df2 <- data.frame(list3)
  37. 37. Access by indexesn letters[3] # 1-dimensional objectn arr1[1,2,3] # 3-dimensional objectn arr1[1, ,3] # implies 1,(all),3n df1[ ,3] # implies (all),3n list1[[1]] # list needs [[ ]]
  38. 38. Access named elementsn list3n list3$smalln list3[["small"]]n df1$largen df1[, "large"]

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