20130222 Data structures and manipulation in R
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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.
![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 [[ ]]](https://image.slidesharecdn.com/20130222datastructuresinr-130221171710-phpapp02/85/20130222-Data-structures-and-manipulation-in-R-18-320.jpg)
![Access named elements
n list3
n list3$small
n list3[["small"]]
n df1$large
n df1[, "large"]](https://image.slidesharecdn.com/20130222datastructuresinr-130221171710-phpapp02/85/20130222-Data-structures-and-manipulation-in-R-19-320.jpg)
20130222 Data structures and manipulation in R
- 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
- 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"]