Introduction to R 21/11/2016

Transcription

1 Introduction to R 21/11/2016 C3BI Vincent Guillemot & Anne Biton

2 R: presentation and installation

3 Where?

4 How to install and use it? Follow the steps: you don t need advanced rights to install it! Open the R GUI. Test a command: plot(-10:10, (-10:10)ˆ2). Open an R script and save it in your working directory.

5 Rstudio

6 ...

7 The few commands you must know Command What it does read.table Read a tabulated file. write.table Write a matrix or data frame. plot Command for graphical representation. x <- 1 Assign sthg (here 1) to object x. 1:10 Create a vector containing integers 1 to 10. x[1:10] Extract a subvector from x.

8 ... Command What it does c(2, 5) Create a vector containing 2 and 5. A[, 2:5] Extract columns 2 to 5 of matrix A. DF$variable Extract from data frame DF its column called variable.?rnorm Get help on the function called rnorm.??gaussian Get help on the topic gaussian.

9 A beginner s test If you already know the previous commands move to the back of the room, you can work independently on the handout and the exercises and go home whenever you are finished ; If you are not familiar with these commands, move to the front of the room. In any case, please ask us any R related question during the class!

10 Basic commands

11 Prompt A prompt is a special character that appears in the R console: > means that R is awaiting for an R command ; + means that R is awaiting for the end of the current command; A blank prompt means that R is computing something. E.g., type: 1+1 2* 3 Sys.sleep(10)

12 Brackets Brackets Use () In functions, e.g. sin(2*pi). [] While indexing, e.g. x[1:2]. {} In code blocks, e.g. { x <- rnorm(10) y <- x[1:2] mean(y) }

13 Writing your scripts: survival tips 1. Use spaces: x <- -1 is OK, x<--1 not so much Indent! 3. Save your scripts, it s so easy with RStudio. 4. Comment, comment, comment (use #). You are collaborating with at least one person: your future self! Hadley Wickham

14 What this course is about Write short R programs Read and predict the outcome of simple R functions / programs Make graphical representations Read data and write tables To go beyond (or slower), there s a lot of material available online: Quick-R, TryR, Data Camp, cookbook-r etc.

15 Types of exercises Three levels: 1. Copy & paste some code and see what it does. 2. Read some code and explain what it does. 3. Create your own code to answer a question.

16 Ex Copy and execute the following command: log(exp(2)). 2. What does this code do: log10(10ˆ3)? 3. Find a function to run a t-test.

17 R Objects

18 Types... The type of an object is directly associated to the way it is stored in memory: character : let <- "a" double : nbr <- 2.0 integer : intg <- 1L logical : TRUE or T or FALSE or F Particular values: NA, +Inf, NaN

19 Types... and classes The class of an object describes how different values are structured within the object: vector: v <- c("a", "b", "a") factor: fac <- factor(v) matrix: M <- matrix(1:4, 2, 2) data.frame: D <- data.frame(v, fac) list, etc.

20 Transformations as.integer as.numeric as.character as.factor as.vector...

21 Classes Here are the classes that you need to know of: vectors and factors, matrices, data-frames, lists, functions.

22 FAMuSSS FAMuSSS : The Functional Single Nucleotide Polymorphisms Associated with Human Muscle Size and Strength Study

23 Load an RData file In the RData file famusss.rdata, there is an example of each of the 5 R classes we mentioned: Name Class Content ndrm.diff Vector Difference in strength in the non-dominant arm snp1 Factor SNP rs577x located in the gene ACTN3 M Matrix Matrix containing the Age, height and weight of the individuals D Data-frame Sample data extracted from the FAMuSSS data L List List containing various objects bmi Function Computes the BMI of an individual from their weight (lb) and height (in)

24 Ex Load the objects with the following command load("famusss.rdata") 2. Print all the objects: what type of data do they contain? 3. What is the BMI of a person 70 inch tall person weighting 150 lb? 4. What does L$Dimensions do? What does names(l) do? 5. Extract the element called GenderTable from L?

25 Vectors Create them with the combine function c or with the : operator: x <- c(1, 10, -4, 5.0) i <- 1:10 Access elements from a vector with the square brackets x[1] ## [1] 1 x[3:4] ## [1] -4 5

26 Factors You can create factors in a number of ways, one of them is with function gl: f0 <- gl(n = 3, k = 6, labels = c("crtl", "A", "B"))

27 Ex What does f0 == "A" do? 2. What do rep and seq do? 3. Create a vector called v of length What does v[f0 == "A"] do? 5. Extract from v the values for which f0 is equal to B?

28 Matrices A matrix is a two-dimensional kind of vector: A <- matrix(0, 2, 2) B <- matrix(c("un","deux","trois","quatre"), 2, 2) A[1,] ## [1] 0 0 B[2,2] ## [1] "quatre"

29 Data frames A data frame is a two-dimensional structure that allows different types for its columns: D <- data.frame(a=1:10, b=letters[1:10], cos=cos(1:10)) D[1:2,2:3] ## b cos ## 1 a ## 2 b D$a[3] ## [1] 3 D[[1]] ## [1]

30 Lists In R, data frames are special lists: L <- list(1:10, b=3, f=cos, char=letters[5:7]) names(l) ## [1] "" "b" "f" "char"

31 Block of code A block allows to gather several commands in order to execute all of them at once! { } a <- 1 b <- 2 It is used in functions, loops (for, while... ) Control-flow constructs (?Control).

32 Functions Syntax : f <- function(arg1=,...) {Commands}. f ends with a return. What can f return? Whatever you like (e.g. in a list). Indent!

33 Ex. 4 Create a matrix filled with random numbers (rnorm). Compute the sum of each column (colsums). Which elements are > 0? Create a second matrix filled with 1s. It should have the same dimensions as the first matrix. Combine it with the first matrix (rbind or cbind). Write a function returning the square and the square root of a positive real number.

34 for loops Repeat a block, depending on an iterator i, n times. for (i in 1:10) { j <- i^2 + i + 1 print(j) } In general, we want to save the result: s <- rep(na, 10) for (i in 1:10) { s[i] <- i^2 + i + 1 } s

35 Ex. 5 What does this loop do? library(tm) library(stringr) aveu <- removepunctuation(scan("phedre.txt",what = "")) nba <- 0 ; nbe <- 0 ; nbi <- 0 ; nbo <- 0 ; nbu <- 0 for (mot in aveu) { nba <- nba + str_count(mot, "a") nbe <- nbe + str_count(mot, "e") nbi <- nbi + str_count(mot, "i") nbo <- nbo + str_count(mot, "o") nbu <- nbu + str_count(mot, "u") } c(a=nba, e=nbe, i=nbi, o=nbo, u=nbu)

36 if, else The random p-value generator: r <- runif(1) if (r < 0.05) { print("youpi!") } else if (r < 0.1) { print("i still trust my result!") } else { print(" :'( ") }

37 Read and write data

38 Many available commands Command Read Save data Yes No load Yes No save No Yes read.table Yes Yes write.table No Yes read.* Yes Yes write.* No Yes

39 Correspondance Figure 1: diagrammer

40 data Example: data(cars). Before and after: ls(). Class of the loaded object: class(cars). Quick object exploration: str(cars). Only the beginning of the table: head(cars).

41 Working directory You may (will) want to change the working directory in which your commands will look for data and save your outputs. You can do this: with the commands setwd and getwd, in a much simpler way with RStudio : Session Set working directory...

42 Tabulated data Column names, lines separated with and EOL (end of line), column separator (tab, ;, etc.), the same number of columns per line.

43 long and wide formats: a wide table ## ctrl trt1 trt2 ## 1: ## 2: ## 3: ## 4: ## 5: ## 6: ## 7: ## 8: ## 9: ## 10:

44 long and wide formats: a long table ## values ind ## 1: 4.17 ctrl ## 2: 5.58 ctrl ## 3: 5.18 ctrl ## 4: 6.11 ctrl ## 5: 4.50 ctrl ## --- ## 26: 5.29 trt2 ## 27: 4.92 trt2 ## 28: 6.15 trt2 ## 29: 5.80 trt2 ## 30: 5.26 trt2

45 read.table 5 important parameters: file where the file is, header whether the first line contains the names of the columns, sep column separator, dec decimal point (3, 1419 or ?), skip how many lines should be skipped.

46 write.table 4 important parameter: x matrix or data.frame to save, file where the file should be stored, sep column separator, dec decimal point (3, 1419 or ?),

47 save and load save can write any R object into an RData file. load reads RData files. Example : x <- 1:10 ; a <- "toto" ; objetaunomtreslong <- pi save(x, a, objetaunomtreslong, file="sauvegarde.rdata") rm(list=ls()) load("sauvegarde.rdata")

48 Plots

49 plot Syntax : plot(objet,...)! Parameter Role main Main title xlab & ylab Axis title xlim & ylim Axis limits type Type of graph : points, lines etc... col Color, e.g. black, red, green...

50 Ex. 6 Apply plot to any function, e.g. choose one among the already built-in functions: sin, cos, exp, log, sqrt...

51 Ex. 7 With plot and grid, reproduce this plot:

52 Add points, and lines or a function You can draw a graph on an existing plot with the following commands: points to add points, lines to add lines, plot(f, add=true,...) to add a function.

53 Ex Generate two variables, x and y, linearly linked to one another. (do not forget to add some noise) 2. Represent the scatter-plot of the two variables with plot. 3. Add to the plot the underlying linear model with lines or plot.

54 Colors, dashes, symbols and width 4 important parameters : pch : to choose the type of point (circle, triangle, etc.), lty : (line type) to choose the line type, col : (color) to choose the color, lwd : (line width) to set the width.

55 legend Argument Meaning x, y Legend position... legend Legend text. bty Type of box = "o" (with) or "n" (without).

56 Ex. 9 Add a legend to this graph plot(1:10, type="b", col="steelblue", lwd=2). 1. Add a legend at the following coordinates: (1, 7). 2. Add a legend without a box around it, in the upper left corner of the graph. 3. Add the legend wherever you want it with locator(1).

57 Combining plots is easy with layout! 1. Create the layout, a matrix indicating the positions and orders of the plots. 2. plot the graphs to populate the layout. Ex: x <- rnorm(100) # Data M <- rbind(1, 2:3) # 3 graphs in the layout layout(m) # Create the layout and put the plot(x) # 1st... hist(x) # 2nd... boxplot(x) # and 3rd graphs

58 Here is the layout we used: 1 2 3

59 The resulting plot x Index Histogram of x Frequency x