Lecture 7 : Introduction to R Pat Browne

1. Spatial DatabasesFirst law of geography [Tobler]: Everything is related to everything, but nearby things are more related than distant things. Lecture 7 : Introduction to R Pat Browne

2. Introduction to programming in R • R is a computer language and environment that allows users to program algorithms and use pre-written packages. R is a free software environment for statistical computing and graphics (including mapping). • There are special R-packages for handling and analyzing spatial data. For example, The sp package provides classes and methods for points, lines, polygons, and grids. • R can extract spatial data from PostgreSQL. Also, R can be combined with SQL using PL/R.

3. Installing R Studio • R for Windows can be downloaded from • See Lab for installation details.

4. Starting R • We will look at the main features of R, see lab1.doc for more details. This lecture also presents an introduction to programming. • The basic components of current languages are: • Data types e.g. Integers, String, Polygon. • Variables to refer to data types e.g. a <- 2 • Operations on those data types e.g. area(polygon) • Control structures e.g. sequence, iteration, and conditions. • Logic is an important part of programming, but it is often implicit and external to the language. Some languages like SQL are quite close to logic.

5. Starting R: Programs consists of Data, Operations etc. • The basic components of current languages are: • Concrete data types e.g. Integer, String, Polygon. • Variables to refer to data types e.g. a <- 2 • Operations on those data types e.g. area(polygon) • Control structures e.g. sequence, iteration, and conditions. • Logic is an important part of programming, but it is often implicit and external to the language. Some languages like SQL are quite close to logic.

6. Starting R: Variables • Variables provide a means of accessing the data stored in computer memory. R provides a number of specialized data structures or objects (also called data types). These objects are referenced in your programs using variables. Store: a <- 2 Access: a Store: b <-”Pat” Access: b • Assigns the variable a the number 2 and the variable b the string “Pat”.

7. Starting R: Data types • A data type represents a constraint placed upon the interpretation of data in a type system, describing representation, interpretation, legal operations and structure of values. • Data types are a way to limit the kind of data that can be used by a particular program or stored in a database table. Types restrict the data to a certain set of values (e.g. 1,2,3,..for Integers). • Data types also are restricted to certain operations on the type (e.g. addition for Integers). R comes with a range of standard data types that can be used to represent strings, integers, real numbers, and dates, but R also has types that are especially suited to statistics such as vectors and tables.

8. Starting R: Data types The c() function combines its argument into a vector. In R the term modes is used to describe data types. There are 4 basic types or modes: numeric, character, complex , and logical. These can be combined to form collections or what are called objects in R.

9. Starting R: Data types (Objects)

10. Starting R: Data types (Objects)

11. Starting R: Data types (Objects)

12. Starting R: Finding data types

13. Starting R: Data types • Numbers: 1, 1.4. • Strings: “ABC” or “abc” • Vector: • Arrays: are vectors plus dimension vector (dim) • Factors: for nominal & ordered categorical data • Data Frames: matrix-like for data of different types • Tables • One Way Tables • Two Way Tables

14. Starting R: Data types- Numbers a <- 3 b <- sqrt(a*a+3) • List of the defined variables/objects: > ls() • We can add 1 to every element of a list > a <- c(1,2,3,4,5) > a+1 • We can get the mean, variance, and standard deviation from a list of numbers > mean(a) > var(a) > sd(a)

15. Starting R: Data types- Strings > a <- "hello" > a [1] "hello" > b <- c("hello","there") > b [1] > b [2]

16. Starting R: Data types-Vector • R operates on named data structures. The simplest such structure is the numeric vector, which is a single entity consisting of an ordered collection of numbers. To set up a vector named x use the R command > x <- c(10.4, 5.6, 3.1, 6.4, 21.7) > x[2] • Variable assignment can be written as <- in R. The above assignment uses the function c() which can take an arbitrary number of vector arguments and whose value is a vector got by concatenating its arguments end to end. • A number occurring by itself in an expression is taken as a vector of length one.

17. Starting R: Data types-Arrays • Arrays are vectors plus the dim attribute (dimension vector), matrices are arrays with a dim attribute of length 2. Arrays are ordered column major order

18. Starting R: Data types-Matrices • Arrays are vectors plus the dim attribute (dimension vector), matrices are arrays with a dim attribute of length 2. Arrays are ordered column major order

19. Starting R: Data types-Tables x=c("Yes","No","No","Yes","Yes") > table(x) x No Yes 2 3

20. Types of Categoricaldata • Nominal: Mutually exclusive categories: male/female, dead/alive, smoker/non-smoker, bus/car/train. Tends to be unordered or have no logical hierarchy • Ordinal: Can be ranked in a meaningful order. Distance between values is not relevant as there is no distance information: race positions (1st, 2nd, 3rd), grouped amounts (1-5, 6-10, 11-15 per day). Unlike nominal data, ordinal data can be compared against each other.

21. Starting R: Data types- Factor • When looking at the impact of carbon dioxide (CO2) on the growth rate of a tree you might try to observe how different trees grow when exposed to different preset concentrations of CO2. The different levels are often called categories or factors. CO2 is measured in parts per million by volume (ppmv). Levels could be L1: 0-3, L2:3-6, L3:6-9, L4:9-12 ppmv (ignoring double inclusion of boundaries).

22. Starting R: Data types- Factor • Categorical data is often used to classify data into various levels or factors. For example, smoking data could be a factor in a broader survey on health issues. R has a special class for working with factors, R will adapt itself when it knows it has a factor. >x=c("Yes","No","No","Yes","Yes") > factor(x) [1] Yes No No Yes Yes Levels: No Yes

23. Starting R: Data types- Factor • We will assume that your data files are stored in C:\My-R-Dir\ • Load in the file tree91.csv. • tree <- read.csv(file="C:\\My-R-Dir\\trees91.csv",header=TRUE,sep=","); • The summary operation prints out the possible values and the frequency that they occur. Find summary of the chamber identification label (CHBR) • summary(tree$CHBR)

24. Starting R: Data types- Factor • summary(tree$CHBR) • Note the output of the summary operation produces quartiles. A quartile is one of three points (including the median), that divide a data set into four equal groups, each representing a fourth of the distributed sampled population.

25. Starting R: Data types- Factor • A nominal value is represented as a factor in R. The factor stores the nominal values as a vector of integers in the range [ 1... k ] • where k is the number of unique values in the nominal variable e.g. male=1,female=2, • and an internal vector of character strings (the original values) mapped to these integers.

26. Starting R: Data types- Factor • Consider variable gender with 20 male entries and 30 female • gender <- c(rep("male",20), rep("female", 30)) • gender <- factor(gender) • Stores gender as 20 1s and 30 2s, where 1=female, 2=male internally (alphabetically) • R now treats gender as a nominal variable • summary(gender) • What does rep() do? How would you find out? • Type ? rep() into R and see.

27. Starting R: Data types- Factor • An ordered factor is used to represent an ordinal variable. Consider a variable rating coded as large, medium, small rating <- c(rep("large",10), rep("medium", 10),rep("small", 10) ) rating <- ordered(rating) • R codes rating to 1,2,3 and associates: 1=large, 2=medium, 3=small internally • R uses factor for nominal variables and ordered for ordinal variables in statistical procedures and graphical analyses. • Try the command plot(rating)

28. Starting R: Data types- Factor • A factor is a vector object used to specify a discrete classification (grouping) of the components of other vectors of the same length. R provides both ordered and unordered factors. The application of factors is with model formulae. A sample of 30 tax accountants from all the states of Australia by a character vectors as • state <- c("tas", "sa", "qld", "nsw", "nsw", "nt", "wa", "wa", "qld", "vic", "nsw", "vic", "qld", "qld", "sa", "tas", "sa", "nt", "wa", "vic", "qld", "nsw", "nsw", "wa", "sa", "act", "nsw", "vic", "vic", "act") • A factor is created using the factor() function: • statef <- factor(state) • summary(statef) • To find out the levels of a factor the function levels() can be used. levels(statef) [1] "act" "nsw" "nt" "qld" "sa" "tas" "vic" "wa"

29. Starting R: Data types- Matrix • A matrix is a collection of data elements arranged in a two-dimensional rectangular layout. The following is an example of a matrix with 2 rows and 3 columns.

30. Starting R: Data types- Matrix > A = matrix( +   c(2, 4, 3, 1, 5, 7), # the data elements +   nrow=2,              # number of rows +   ncol=3,              # number of columns +   byrow = TRUE)        # fill matrix by rows  > A   # print the matrix      [,1] [,2] [,3] [1,]    2    4    3 [2,]    1    5    7 An element at the mth row, nth column of A can be accessed by the expression A[m, n]. > A[2, 3]      # element at 2nd row, 3rd column [1] 7 The entire mth row A can be extracted as A[m, ]. > A[2, ]       # the 2nd row [1] 1 5 7 Similarly, the entire nth column A can be extracted as A[ ,n]. > A[ ,3]       # the 3rd column [1] 3 7

31. Starting R: Data types- Dataframe • A dataframe is more general than a matrix, in that different columns can have different modes (numeric, character, factor, etc.). It is a bit like an SQL table. d <- c(1,2,3,4)e <- c("red", "white", "red", NA)f <- c(TRUE,TRUE,TRUE,FALSE)mydata <- data.frame(d,e,f)names(mydata) <- c("ID","Color","Passed") • There are a variety of ways to identify the elements of a dataframe . mydata[2:3] # columns 2,3 of dataframe mydata[c("ID",“Color")] # columns ID,Color myframe$ID # name in dataframe

32. Starting R: Data types- data.frame • Here we create a data.frame called d. L3 <- LETTERS[1:3] (d <- data.frame(cbind(x=1, y=1:10), fac=sample(L3, 10, replace=TRUE))) • To view four rows: df[1:4,] • To view a column: d$y, d$y, d$fac • Alternative way to view a column: d[,3]

33. Starting R: Data types- Table • One way tables are created with table command, its arguments are a vector of factors, and it calculates the frequency that each factor occurs.

34. Starting R: Data types- one-way Table > a <- factor(c("A","A","B","A","B","B","C","A","C")) > results <- table(a) > results >a A B C 4 3 2 > attributes(results) >attributes(results) $dimnames$a >attributes(results) $dim >attributes(results) $ class > summary(results)

35. Starting R: Data types- two-way Table • Say we want to put the results of two questions into a table: • First question responses are Never, Sometimes, Always, • Second question responses are Yes, No, and Maybe. Two vectors a and b contain the response for each measurement. • In the vectors, responses are represented by position. The third item in a is how the third person responded to the first question, and the third item in b is how the third person responded to the second question. • In the following we can see that two people who said "Maybe" to the first question also said "Sometimes" to the second question.

36. Starting R: Data types- two-way Table ROW COLUMN a <- c("Sometimes","Sometimes","Never","Always","Always","Sometimes","Sometimes","Never") b <- c("Maybe","Maybe","Yes","Maybe","Maybe","No","Yes","No") results <- table(a,b) > results b a Maybe No Yes Always 2 0 0 Never 0 1 1 Sometimes 2 1 1 The table shows that two people who said Maybe to the first question also said Sometimes to the second question. The elements are accessed like a matrix (result(,1). ) How many people responded? The third item in a is how the third person responded to the first question, and the third item in b is how the third person responded to the second question.

37. Useful functions length(object) # number of elements or componentsstr(object)    # structure of an object class(object)  # class or type of an objectnames(object)  # namesc(object,object,...)#combine objects into a vectorcbind(object, object, ...) # combine objects as columnsrbind(object, object, ...) # combine objects as rows

38. Useful functions object()      # prints the objectls(),objects()   # list current objectsrm(object) # delete an objectnewobject<-edit(object) #edit,copy,save fix(object)    # edit in place data.entry(result) # GUI edit in place mode(object) # type of the object.

39. Starting R : Input-Output IO • There are many ways to data into R. We focus on just three: • Assignment • Reading a CSV File (writing later) • Loading data from PostgreSQL (later)

40. Starting R : IO-Assignment • Assignment (RHS <- LHS) allows an expression on the RHS to be stored in a name object on the LHS. In R > a <- c(3,5,7,9) > • The above assignment uses the combine command. (c means combine). This makes a vector called a. No output is produced yet. Now we can retrieve the contents of a just by typing it in. • > a • > a[3] • The command gives all of a the second command gives the third element of a . [3] is called the index. The zero entry hold the data type of the avector. Try: • b <- c("one","two","three")

41. Starting R : IO-Assignment cells <- c(1,26,24,68) rnames <- c("R1", "R2") cnames <- c("C1", "C2") mymatrix <- matrix(cells, nrow=2, ncol=2, byrow=TRUE, dimnames= list(rnames, cnames)) Type >attributes(mymatrix) Type >help(array) to find more details on Arrays

42. Starting R : Input: File • Place the file simple.csv in a directory (folder). • Load the file into R using: h <- read.csv(file=“C:\\My-R-Dir\\simple.csv”,head=TRUE,sep=”,”) • View the contents of h: • Now the contents of the file are stored in R as the object named h. • Type >names(h)

43. Starting R: Data types-Matrices • All columns in a matrix must have the same data type (numeric, character, etc.) and the same length. The general format is: mymatrix <- matrix(vector, nrow=r, ncol=c, byrow=FALSE, dimnames=list(char_vector_rownames, char_vector_colnames)) • byrow=TRUE indicates that the matrix should be filled by rows. byrow=FALSE indicates that the matrix should be filled by columns (the default). dimnames provides optional labels for the columns and rows.

44. Review - vectors, lists, matrices, data frames • To make vectors x, y, year, names x <- c(2,3,7,9) y <- c(9,7,3,2) year <- 1990:1993 names <- c("payal", "shraddha", "kritika", "itida") Accessing last element y[length(y)] • To make a list person person <- list(name="payal", x=2, y=9, year=1990) Accessing person$name, person$x , person[1] names(person)

45. Review - vectors, lists, matrices, data frames • To make a matrix, pasting together the columns year , x, y using column bind. m <- cbind(year, x, y) • To make a data frame, which is a list of vectors of the same length D <- data.frame(names, year, x, y) nrow(D) • Accessing one of these vectors D$names Accessing the last element of this vector D$names[nrow(D)] D$names[length(D$names)]

46. Finding the type and class > g <- c(1,3,2) > class(g) [1] "numeric" > typeof(g) [1] "double“ > is(g) [1] "numeric" "vector"

47. Sorting • The variable i is a vector of integers, then the data frame D[i,] picks up rows from D based on the values found in `i'. The order() function makes an integer vector which is a correct ordering for the purpose of sorting. • D <- data.frame(x=c(1,2,3,1), y=c(7,19,2,2)) • Sort on x • indexes <- order(D$x) • D[indexes,] • Print out sorted dataset, sorted in reverse by y D[rev(order(D$y)),]

48. Logical constants & variables • TRUE and FALSE are logical constants • T and F are logical variables • T and F are quite not synonyms for TRUE and FALSE but variables that have the expected values by default • TRUE == TRUE • T == T • Normally give the expected result.

49. Missing Values : NA • Not Available or Missing Values are represented as NA, which is a logical constant (either T or F) which contains a missing value indicator. • Examples is.na(c(1, NA)) #FALSE TRUE is.na(c(NA, NA)) #TRUE TRUE is.na(paste(c(1, NA))) # FALSE FALSE xx <- c(0:4) is.na(xx) <- c(2, 4) xx # 0 NA 2 NA 4

50. Writing your own functions. • R comes with a built-in median function. • Usage: median(x, na.rm = FALSE) • Where x an object for which a method has been defined, or a numeric vector containing the values whose median is to be computed. • na.rm a logical value indicating whether NA values should be removed before the computation proceeds.