Introduction to R: Data Types

Transcription

1 Introduction to R: Data Types Florian Geier September 26, 2018

2 Recapitulation

3 Possible workspaces Install R & RStudio on your laptop Use an RStudio server: Unibas members: if you plan to work with data stored on scicore, get a scicore account and start working on the scicore RStudio server FMI members: use the FMI RStudio server If none of these options are applicable, sign-up for a password and use a [temporary cloud service]

4 RStudio server for Unibas members: Connect to the university network via VPN Go to and login with your scicore username and password RStudio server for FMI members: Connect to the eduroam WLAN Login with where FMIuser is your FMIuser name; use the FMI password Start VPN (PulseSecure client, ask FMI-IT in case you don t have it) Go to and login with your FMIuser and password Temporary cloud service: Sign-up for a user name on the sheet Go to and login with your user name

5 Recommended work style RStudio preferences: General -> Save workspace to.rdata on exit: Never or Ask (Optional) General -> Restore previously open source documents at start-up: uncheck Directory settings: Create a course folder introductiontor For each lecture create a folder within the course folder, e.g. introductiontor/lecture2 Within this folder create an R script which harbors the code you write during the lecture Comment your code!

6 File system

7 Ask questions (online)! In between lectures you can ask questions under: Anyone can ask or answer questions related to the course topics

8 Data Types

9 integer logical array list numeric complex data.frame matrix vector character

10 What task do you want to solve? # add numbers 3/4 > pi/4 # compare numbers "hello world!" # print words

11 Logical Two possible values (TRUE or FALSE) Result of a logical expression x <- "foo" x==1 x!=1 ## [1] TRUE Take a look at R s logical operators with help(" ")

12 Function typeof() returns the data type, while is.logical() directly test if a data type is logical typeof(x == 3) ## [1] "logical" is.logical(na) ## [1] TRUE NA (not available, missing value) is a special logical value Don t use the short-cuts T and F for TRUE and FALSE

13 Numbers R offers different number types, among them: integer (natural numbers) numeric (real numbers; also called double in R) complex 12L ## [1] 12 typeof(12l) ## [1] "integer" 12 ## [1] 12 is.integer(12) ## [1] FALSE is.double(12) ## [1] TRUE

14 There are many arithmetic functions defined. Check the help pages to understand their functionallity, e.g. help.search("arithmetic Operators") or help.search("trigonometric"),?log2,?sd etc. sum(2,3,4) ## [1] 9 mean(2,3,4) # is it what you expect? ## [1] 2 Generally, comparison between numbers is tricky because of limited precision log(5/3) == log(5) - log(3) ## [1] FALSE

15 Special values: Inf, NaN, NULL NaN: if the result of a calculation is not a number sqrt(-1) ## Warning in sqrt(-1): NaNs produced ## [1] NaN Inf: infinity log(0) ## [1] -Inf NULL: often returned by expressions and functions whose value is undefined

16 Character A character (string) is either single ( ) or double (" ") quoted "Hello world!" ## [1] "Hello world!" Functions paste() or sprintf() can be used for combining/formatting numbers and characters and will be discussed later in the course

17 Coercion It is possible to transform one data type into another using the as.<data_type>() set of functions as.logical(0) # also try with any integer > 0 ## [1] FALSE as.numeric(true) ## [1] 1 as.integer("12.3") ## [1] 12 If needed atomic data types are automatically coerced to a more flexible type following the order: logical integer double character 1L/3L ## [1] sum(true,false,true) ## [1] 2

18 Vectors: R s basic data structure All basic data structures in R are implemented as vectors is.vector(3) ## [1] TRUE Vectors can be created in several ways, among are: combining elements with c() numerical sequences using : c("rice","milk","butter") ## [1] "rice" "milk" "butter" All elements of an (atomic) vector must be of the same type c("rice","milk",3) # automatic coercion ## [1] "rice" "milk" "3"

19 All arithmetic operations are applied element-wise x <- 1:8 x ## [1] length(x) ## [1] x/4 ## [1] y <- 1:3 x * y # warning: shorter vector is recycled! ## Warning in x * y: longer object length is not a multiple ## length ## [1]

20 Matrix A matrix is 2-dimensional: it has row and columns # matrix is filled column-wise by default: A <- matrix(1:12, nrow=3, ncol=4) A ## [,1] [,2] [,3] [,4] ## [1,] ## [2,] ## [3,] Can be converted to vectors using as.vector() as.vector(a) ## [1]

21 Two other helpful matrix constructors are rbind() and cbind() # row-wise binding: B <- rbind(c("cow","pig"), c("goat", "chicken")) B ## [,1] [,2] ## [1,] "cow" "pig" ## [2,] "goat" "chicken" # column-wise binding: C <- cbind(c("cow","pig"), c("goat", "chicken")) C ## [,1] [,2] ## [1,] "cow" "goat" ## [2,] "pig" "chicken"

22 By default * multiplies element-wise (with re-cycling) Use %*% for matrix-multiplication (or scalar product for vectors) # element-wise multiplication with re-cycling in column-ord x <- 1:4 A * x ## [,1] [,2] [,3] [,4] ## [1,] ## [2,] ## [3,] # matrix multiplication: A %*% x ## [,1] ## [1,] 70 ## [2,] 80 ## [3,] 90

23 Factor Factors are used to store categorical data The factor levels (categories) define the set of allowed values Factors are extensively used in the analysis of linear models

24 x <- c("head","tail","tail","head","tail") fx <- factor(x) fx ## [1] head tail tail head tail ## Levels: head tail levels(fx) ## [1] "head" "tail" typeof(fx) # internally represented as integer ## [1] "integer" as.integer(fx) ## [1]

25 Data frame R s excel sheet (only better!) and the most common way to store data Like a matrix its 2-dimensional, but each column can be of a different type D <- data.frame(person=c("mike", "Peter", "Jane"), degree=c("phd", "Msc", "Phd"), trained=c(t, F, T), Salary=c(76e3, 65e3, 77e3)) D ## Person degree trained Salary ## 1 Mike Phd TRUE ## 2 Peter Msc FALSE ## 3 Jane Phd TRUE 77000

26 A quick summary of the data types in a data frame is given by str() str(d) ## 'data.frame': 3 obs. of 4 variables: ## $ Person : Factor w/ 3 levels "Jane","Mike",..: ## $ degree : Factor w/ 2 levels "Msc","Phd": ## $ trained: logi TRUE FALSE TRUE ## $ Salary : num

27 On creation you can control the data type of each column explicitly: D <- data.frame(person=c("mike", "Peter", "Jane"), degree=factor(c("phd", "Msc", "Phd"), levels=c('bc','msc','phd')), trained=c(true, FALSE, TRUE), Salary=as.integer(c(76e3, 65e3, 77e3)), stringsasfactors=false) str(d) ## 'data.frame': 3 obs. of 4 variables: ## $ Person : chr "Mike" "Peter" "Jane" ## $ degree : Factor w/ 3 levels "Bc","Msc","Phd": ## $ trained: logi TRUE FALSE TRUE ## $ Salary : int Note the option stringsasfactors=false which turns-off the automatic conversion of character strings to factors.

28 How about accessing elements/rows/columns of a data.frame? This is the topic of the following lecture(s) As an outlook try D[1,] ## Person degree trained Salary ## 1 Mike Phd TRUE D[,'Person'] ## [1] "Mike" "Peter" "Jane" D[2,'trained'] ## [1] FALSE

29 List List is used as a general container in R Each element in the list can be of a different data type Lists are created with function list() BloodPressure <- rbind(systolic=c(134, 120, 129), diastolic=c(96, 80, 88)) data <- list(bloodpressure=bloodpressure, Patients=D)

30 Check the list structure with str() str(data) ## List of 2 ## $ BloodPressure: num [1:2, 1:3] ##..- attr(*, "dimnames")=list of 2 ##....$ : chr [1:2] "systolic" "diastolic" ##....$ : NULL ## $ Patients :'data.frame': 3 obs. of 4 variables: ##..$ Person : chr [1:3] "Mike" "Peter" "Jane" ##..$ degree : Factor w/ 3 levels "Bc","Msc","Phd": 3 2 ##..$ trained: logi [1:3] TRUE FALSE TRUE ##..$ Salary : int [1:3]

31 Summary Type Dimension Element Type Vector - all elements are of Matrix 2 the same type e.g. numeric, Array 2 character or logical Factor - elements come from a limited set of categories Data frame 2 R s excel sheet; each column is of one type List - container; every element can be of different type

32 Exercises Create a vector containing numbers 42 to 89. Create a vector containing the sequence 4.5 to 11.5 in steps of 0.5. Create a vector where the first 50 elements are TRUE the remaining 30 are FALSE. Create a vector of length 100 where every second element is FALSE. Hint: check the %% operator. Given the vector c(40,27,33,1,34,82), count how many values are larger then the average value. Hint: use type coercion and sum(). What is the result of 3/0? Compare with (3/0) * 0. Calculate all values for x {0, π/2, π} of the following expression: 16x(π x) y = 5π 2 4x(π x) Hint: pi is a pre-defined constant in R.

33 By flipping a coin you observe the following sequence of heads (H) and tails (T): H,H,H,T,T,H,T,T,H. What is a good data type in R to represent this data? Set up the following data frame: Symbol Chromosome Position Strand Etv3 chr Nfia chr Nipal3 chr Strada chr What type is each column? How can you control the data type when setting up the data frame?

34 R comes with many inbuild data sets. Among them are: iris - Edgar Anderson s Iris Data swiss - Swiss Fertility and Socioeconomic Indicators (1888) Data UScitiesD - Distances Between US Cities Check for each dataset what data types it contains! Also look at their help page e.g.?swiss.