Introduction to R. Adrienn Szabó. DMS Group, MTA SZTAKI. Aug 30, /62

Transcription

1 Introduction to R Adrienn Szabó DMS Group, MTA SZTAKI Aug 30, /62

2 1 What is R? What is R for? Who is R for? 2 Basics Data Structures Control Structures 3 ExtRa stuff R packages Unit testing in R 2/62

3 What is R? R is a dialect of the S language.... but seriously... R is a... ˆ Programming language (free, open source) ˆ Computing environment (like Matlab) ˆ Community (quite an active one) ˆ Ecosystem (rapid conversion from data-science knowledge to productivity) (S was developed at Bell Labs in the 1970s as an internal statistical analysis environment) 3/62

4 What is R? Some say that it s Not Really A Programming Language but rather R is an interactive environment for doing statistics * python-displacing-r-as-the-programming-language-for-data-science 4/62

5 What for? ˆ statistical computing (and statistical tests) ˆ dataset exploration ˆ analysis (time series analysis, classification, clustering, etc.) ˆ linear and nonlinear modelling ˆ visualization ˆ recently the favourite tool of data scientists 5/62

6 What for? 6/62

7 R is not only for programmers! A couple of titles from the latest user! conference (more than 150 talks): ˆ Teaching R to high school students (and teachers) ˆ Visualizing Lack of Fit in Complex Regression Mode ˆ A real time, responsive Quantitative trading analysis Mobile App using R ˆ eegr: an R package to analyze electrophysiological (EEG) signals (MTA TTK) 7/62

8 More titles BD Because everyone has to deal with Big Data nowadays... ˆ PivotalR: A Package for Machine Learning on Big Data ˆ Massive Predictive Modeling (Oracle) ˆ Domino: A Platform-as-a-Service for Industrialized Data Analysis ˆ Plyrmr: a data manipulation DSL for big data 8/62

9 More titles ML Machine Learning is cool... ˆ 10 R packages to win Kaggle competitions ˆ The Arborist: a Scalable Decision Tree Implementation ˆ Representing Model Ensembles in PMML ˆ Distributed Matrix Exponentiation in R 9/62

10 More titles Networks & Twitter & maps Who doesn t want to study Twitter or social data? ˆ Simulating Influenza Transmission with Real Network Data ˆ Spatial Tweetstistics with R: Geographical Distribution of English Loan Words in Spanish Tweets ˆ Opportunities through the use of Open-Street-Map data in social sciences 10/62

11 More titles RR These folks seem to care about Reproducible Research as well... ˆ R and Reproducibility: a Proposal ˆ rctrack: An R Package that Automatically Collects and Archives Details for Reproducible Computing ˆ Fostering the next generation of open science with R ˆ Teaching data analysis in R through the lens of reproducibility (poster) 11/62

12 Why? 12/62

13 Features of R ˆ Free software ˆ Runs on almost any standard computing platform/os ˆ Active development, about yearly releases + bugfixes ˆ Sophisticated graphics capabilities ˆ Useful for interactive work, but contains a powerful programming language for developing new tools (user -> programmer) 13/62

14 R vs. Python ˆ Python is more general-purpose, easier to write programs in it 14/62

15 R vs. Python ˆ R has more "stats + data analytics" libraries ready-to-use 15/62

16 1 What is R? What is R for? Who is R for? 2 Basics Data Structures Control Structures 3 ExtRa stuff R packages Unit testing in R 16/62

17 Getting help Code: built-in man pages >?library library package:base R Documentation Loading and Listing of Packages Description: Usage: library and require load add-on packages. library(package, help, pos = 2, lib.loc =... 17/62

18 Data structures ˆ ˆ ˆ ˆ ˆ Vector Matrix Array List Data frame 18/62

19 Numbers, assignment To assign a single number: the <- operator: Note: the = operator works the same way in almost all cases, but its usage is not advised. Code: numbers > a <- 4 > b <- 5 > a + b [1] 9 > a - b [1] -1 > a ^ b [1] /62

20 Vector Vectors (similar to Lists in Java) can be crated with the c() function (short name for concatenate ). Vectors can hold any kinds of things, but the items of one vector have to be of the same type. Code: vector examples > v1 <- c(1, 2, 3, 4, 5, 6) > v2 <- c(0.8, 0.1) > v1 + v2 [1] Magic! 20/62

21 Vector You can concatenate items and vectors as you please. Code: vector examples 2 > v1 <- c(1, 2, 3) > v2 <- c(0.8, 0.1) > c(22, v1, -3.9, v2) [1] > c(v1, "Sponge", "Bob") [1] "1" "2" "3" "Sponge" "Bob" Warning: the result vector s elements can be turned into the more general type! 21/62

22 Vector You can select ranges of vectors to get a shorter one. Indexing begins with 1! Negative indices: leave it out! Code: vector subsetting > v1 <- c(1, 2, 3, 4, 5, 6) > v1[2] [1] 2 > v1[2:4] [1] > v1[c(-2,-5)] [1] /62

23 (Nice random image 1) 23/62

24 Matrix A matrix is a vector represented and accessible in two dimensions. It has a fixed type of elements and fixed number of rows and columns. Code: matrix examples > matrix(1:6, byrow=true, nrow=2) [,1] [,2] [,3] [1,] [2,] > matrix(c(1,2,13,9,8,17,3,4,5), ncol = 3) [,1] [,2] [,3] [1,] [2,] [3,] /62

25 Matrix You can give names to columns and/or rows. Code: matrix naming > matrix(c(1:9),nrow=3,byrow=true, + dimnames=list(c("r1","r2","r3"),c("a","b","c"))) a b c r r r /62

26 Matrix Subsetting works here as well... Code: matrix subsetting > m1 <- matrix(c(1:9),nrow=3,byrow=true, + dimnames=list(c("r1","r2","r3"),c("a","b","c"))) > m1[2,] a b c > m1[,-2] a c r1 1 3 r2 4 6 r /62

27 Matrix Matrix operations are quite similar to vector operations. For example, inequality will return another logical matrix of equal size. Code: matrix example > m1 > 5 a b c r1 FALSE FALSE FALSE r2 FALSE FALSE TRUE r3 TRUE TRUE TRUE > m1[m1>5] [1] /62

28 (Nice random image 2) 28/62

29 Array An array is an extension to matrix in its number of dimensions. It is a vector that is represented and accessible in a given number of dimensions. Let s arrange 20 integers from 0 to 19 in three dimensions: 2 x 5 x 2 Code: array example > a1 <- array(c(0:20), dim = c(2, 5, 2)) 29/62

30 Array Code: array example > array(c(0:20), dim = c(2, 5, 2)),, 1 [,1] [,2] [,3] [,4] [,5] [1,] [2,] ,, 2 [,1] [,2] [,3] [,4] [,5] [1,] [2,] /62

31 Array Subsetting works similarly to matrices, but we can specify the selected row/col indices for each dimension. Code: array example > a1[-1, 1:4, ] [,1] [,2] [1,] 1 11 [2,] 3 13 [3,] 5 15 [4,] 7 17 > a1[-1, 1:4, 2] [1] /62

32 (Nice random image 3) 32/62

33 List A list is a generic vector that is allowed to include different types of objects, even other lists. Code: list example > list(1, c(true,false), c("a","b","c")) [[1]] [1] 1 [[2]] [1] TRUE FALSE [[3]] [1] "a" "b" "c" 33/62

34 List We can assign names to each entry by using named arguments. Code: list example > myl <- list(x=1,y=c(true,false),z=c("a","b","c")) > myl $x [1] 1 $y [1] TRUE FALSE $z [1] "a" "b" "c" 34/62

35 List To access the members of a list by name, use dollar-sign: Code: list example > myl$x [1] 1 > myl $ z [1] "a" "b" "c" > myl$alma NULL 35/62

36 List To access the N-th member of a list, use double square brackets: Code: list example > myl[[1]] [1] 1 > myl [[ 3 ]] [1] "a" "b" "c" > myl[[4]] Error in myl[[4]] : subscript out of bounds 36/62

37 List Even names can be used inside double brackets: Code: list example > myl[["x"]] [1] 1 > elemname <- "z" > myl[[elemname]] [1] "a" "b" "c" 37/62

38 Subsetting a list Use single-square-bracket notation to extract multiple members from a list and construct a new list: Code: subsetting a list > myl["x"] $x [1] 1 > mxl[c("x","y")] $x [1] 1 $y [1] TRUE FALSE 38/62

39 Subsetting a list Code: more examples of subsetting a list > myl[1] $x [1] 1 > myl[c(true, FALSE, TRUE)] $x [1] 1 $z [1] "a" "b" "c" 39/62

40 Setting values of a list Code: setting and adding list members > myl$x <- 0.6 # overwrite element > myl$m <- 4 # add a new named element > myl$y <- NULL # delete by name > myl $x [1] 0.6 $z [1] "a" "b" "c" $m [1] 4 40/62

41 Setting values of a list Code: setting and adding list members > myl[[2]] <- NULL # delete by index ("z") > myl[[1]] <- 0.8 # overwrite element > myl[[5]] <- 5 # add a new element # what do we get? 41/62

42 Code: setting and adding list members Setting values of a list > myl $x [1] 0.8 $m [1] 4 [[3]] NULL [[4]] NULL [[5]] [1] 5 42/62

43 Other list functions Code: List functions > is.list(myl[1]) # [ ] -> sublist [1] TRUE > is.list(myl[[1]]) # [[ ]] -> element [1] FALSE > l2 <- as.list(c(a=1,b=2,c=3)) # vector to list > unlist(l2) # list to vector a b c > unlist(list(a=1, b=2, c="hello")) a b c "1" "2" "hello" 43/62

44 (Nice random image 4) 44/62

45 Factor The term factor refers to a statistical data type used to store categorical variables. Use the function factor() to get factors from a vector of objects. Code: using factors > sdata <- c("male", "Female", "Female", "X", "Male") > sfactors <- factor(sdata) > sfactors [1] Male Female Female X Male Levels: Female Male X A factor is stored internally as a numeric vector with values 1, 2, 3, k, where k is the number of levels. 45/62

46 Data frame Data frames are similar to tables in a relational database. Generalisation of a matrix and a list different columns may have different modes, but all elements of a column must have the same mode (all numeric or all factor, or all character). Typically you ll load a csv file into a data frame object. 46/62

47 Data frame A data frame has colnames(), and rownames(); the length() of a data frame is the same as ncol(); nrow() gives the number of rows. Code: data frame example > df <- data.frame(x = 1:3, y = c("a", "b", "c")) > str(df) data.frame : 3 obs. of 2 variables: $ x: int $ y: Factor w/ 3 levels "a","b","c": Note: data.frame() by default turns strings into factors. Use stringasfactors = FALSE. 47/62

48 Data frame Code: data frame example > myy <- c("a", "b", "c", "n") > df <- data.frame(x = 4:7, y = myy, + stringsasfactors = FALSE) > df x y 1 4 a 2 5 b 3 6 c 4 7 n > str(df) data.frame : 4 obs. of 2 variables: $ x: int $ y: chr "a" "b" "c" "n" 48/62

49 (Nice random image 5) 49/62

50 Control structures if, else testing a condition for execute a loop a fixed number of times while execute a loop while a condition is true repeat execute an infinite loop break break the execution of a loop next skip an interation of a loop return exit a function 50/62

51 Condition This is a valid if/else structure. Code: if and else if(x > 3) { y <- 10 } else { y <- 0 } So is this one. Code: alternative if and else y <- if(x > 3) { 10 } else { 0 } 51/62

52 Loop We can iterate on lists or vectors. Code: for loops x <- c("a", "b", "c", "d") for(letter in x) { print(letter) } # another example for(i in 1:3) print(x[i]) 52/62

53 Functions Functions are objects in their own right! Three components of a function: ˆ arguments ˆ body ˆ environment Functions can return only a single object. (But they can return a list with any objects. Call-by-value: modifying a function argument does not change the original value (some exceptions exist). 53/62

54 Functions Code: defining functions issumaboveten <- function(x, y) { if (x + y > 10) { return(true) } else { return(false) } } # Shorter: the last object will be returned issumabove10 <- function(x, y) { if (x + y > 10) TRUE else FALSE } 54/62

55 Scripts And now we can write nice little scripts to do whatever we want! How to get the examples $ git clone git@info.ilab.sztaki.hu/tutorial $ ls tutorial/r example1 example2 example3 example4 55/62

56 Twitter example 56/62

57 1 What is R? What is R for? Who is R for? 2 Basics Data Structures Control Structures 3 ExtRa stuff R packages Unit testing in R 57/62

58 Packages in R When you install R then you get a base R system with basic functionality to use R. It does include by default some basic packages (utils, stats, datasets, graphics, methods, tools, parallel, etc.) For more specific purposes you either : ˆ find an existing add-on package on CRAN that helps, or maybe on Bioconductor, ˆ or you can write your own package (just for yourself or you can easily publish it as well) 58/62

59 CRAN CRAN (The Comprehensive R Archive Network) is a network of ftp and web servers around the world that store identical & up-to-date versions of code and documentation for R. (Please use the CRAN mirror nearest to you to minimize network load.) More about packages later... 59/62

60 Unit testing in R There are some options: RUnit is the oldest one svunit has a GUI testthat is actively developed, and smarter (but isn t compatible with either of the pervious 2) 60/62

61 Sources 1 From the Coursera course "R programming" by Roger D. Peng Abstracts.pdf 6 final-10-r-xc introduction-to-r 8 Workshop2/Presentations/functions.pdf 61/62

62 Sources of images 1 supply-chain-optimization-blog/bid/349734/ Combining-Machine-Learning-and-Optimization-in-S /importing-a-google-spreadsheet-into-r/ 3 keep-calm-and-study-data-structures-1/ 4 TPgxWY_QNZI/AAAAAAAAAjc/ORVJjtoDBvg/s1600/ program_language_density_plot.png 5 castor-oil-stem-light-micrograph-of-a-high-res http: //illuminarti.weebly.com/patrick-star.html 7 networks.pdf 62/62