1) Objects in R can take various classes like numeric, factor, and logical and know their class through a class attribute. 2) Common data objects in R include vectors (containing a single data type), lists (containing multiple data types), matrices (2D arrays), and data frames (lists of equal-length vectors displayed as a table). 3) The class of an object determines how it can be manipulated and analyzed in R; for example, summarizing numerical versus categorical data requires different functions.

1. Manipulationg
data in
2013-02-22 @HSPH
Kazuki Yoshida, M.D.
MPH-CLE student
FREEDOM
TO
KNOW

2. Manipulating data in R
n What are Objects?
n What is Class attribute?
n Various data objects you will see in R.

3. Objects
n Just about everything named in R is an object
n An object is a container that
n knows its class (label for what’s inside).
n has contents (eg, Actual numbers).

4. Examples of objects
n dataset, which you use for analysis (various
classes)
n functions, which perform analysis (function class)
n results, which come out of analysis (various
classes)
n In effect, you always get a new dataset filled
with results when you analyze data.

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

6. 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!

7. Object
iables
var
ical !
gor ide
C ate ins
Class attribute
http://en.wikipedia.org/wiki/File:3_D-Box.jpg

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

9. 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

10. 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

11. Vector
1-dimensional

12. Array/Matrix
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))

13. Matrix
Folded vector with dimension

14. 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 List_name$Variable_name operation with $ operator
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"))

15. List
Multi-part object
Can contain vectors,
arrays, or lists!

16. 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)

17. Data Frame
Multiple vectors of same length tied together!

18. 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 [[ ]]

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