1. Statistical Programming Using the R Language Lecture 2 Basic Concepts II Darren J. Fitzpatrick, Ph.D April 2016

2. Lecture I - Recap Yesterday: Basic usage of RStudio Some programming concepts Variables, Data Types, Data Structures, et.c Basic R syntax Dealing with data frames – indexing Reading and Writing Files

3. Trinity College Dublin, The University of Dublin Lecture 2 - Overview Loops & Conditionals the WHILE loop the FOR loop the if(){} statemnt Plotting Packages installing, loading

4. Trinity College Dublin, The University of Dublin Loops & Control I Programming often deals with repetitive tasks. We could code these tasks repetitively or encapsulate them in a loop – one piece of code does the same task a predetermined number of times. Loops - constructs that allow the automation of repetitive tasks without repeating the writing of code. Iteration – each pass through a loop. Control – the creation of a condition that determines the termination of a loop.

5. Trinity College Dublin, The University of Dublin Loops & Control II Tedious Solution x <- 0 x <- x + 1. x <- x + 1 While Loop x <- 0 while(x < 10){ x <- x + 1} Create a loop to add 1 to variable x while x < 10 while( condition ){ do something } The WHILE loop

6. Trinity College Dublin, The University of Dublin Loops & Control III For Loop x <- 0 for (i in 1:10){ x <- x + 1 } The FOR loop Tedious Solution x <- 0 x <- x + 1. x <- x + 1 for (i in start:finish ){ do something }

7. Trinity College Dublin, The University of Dublin Conditionals I Similar to the WHILE loop, conditionals allow commands to be executed only when that condition is met. a <- 10 b <- 5 if (a >= b){ c <- a + b } if ( condition ){ do something } What would happen if the condition a >= b were not true, say, a <= b ?

8. Trinity College Dublin, The University of Dublin Conditionals II The conditional if statement can be extended to any number of conditions. The else if() portion of the conditional can be repeated as often as required. In lecture one, we covered logical operators - conditions if ( condition 1 ){ do something }else if ( condition 2 ){ do something }else{ do something }

9. Trinity College Dublin, The University of Dublin Some Examples – but first the preliminaries... Yesterday you saved an RScript (problems.R) and an R session (problems.RData) in your R_Course folder. We need to: Reload the R session (.RData) Open the script (.R) if it does not open automatically Reset the the working directory

10. Trinity College Dublin, The University of Dublin Preliminaries I Load the session from yesterday – problems.RData

11. Trinity College Dublin, The University of Dublin Preliminaries II Open your script (problems.R)

12. Trinity College Dublin, The University of Dublin Preliminaries III To set the wd, follow the above and navigate to the R_Course folder. Set the working directory (wd) to be the R_Course folder.

13. Trinity College Dublin, The University of Dublin Preliminaries IV Yesterday, we read in a file called colon_cancer_data_set.txt and generated two dataframes, affected and unaffected from that data. df <- read.table('colon_cancer_data_set.txt', header=T) affected <- df[which(df$Status=='A'), 1:7464] unaffected <- df[which(df$Status=='U'), 1:7464] These variables should be available in the session problems.RData that you just loaded. Note! You can list the variables in your work space by running the ls() command in the console.

14. Trinity College Dublin, The University of Dublin Problem I Iterate over the columns of the affected data and calculate the mean of each column. for (i in 1:ncol(affected)){ mean_exp <- mean(affected[,i]) print(mean_exp) } Printing the values illustrates the point but it doesn't allow you to store them in memory.

15. Trinity College Dublin, The University of Dublin Problem II Iterate over the columns of the affected data, calculate the mean of each column and store the results as a variable. mean_holder <- c() for (i in 1:ncol(affected)){ mean_exp <- mean(affected[,i]) mean_holder <- c(mean_holder, mean_exp) }

16. Trinity College Dublin, The University of Dublin FOR loops & apply() mean_holder <- c() for (i in 1:ncol(affected)){ mean_exp <- mean(affected[,i]) mean_holder <- c(mean_holder, mean_exp) } mean_a <- apply(affected, 2, mean) } The output from the FOR loop is equivalent to the apply() function. In R, loops are sometimes necessary but R has tricks to avoid them. This can have enormous implications for compute time on large data sets. R loops are inefficient!

17. Trinity College Dublin, The University of Dublin R is suitable for making publication quality graphics. R can generally create simple plots using a single function. We will look at the following plots: histograms ( hist() ) boxplots ( boxplot() ) scatterplots ( plot(), scatterplot() ) Basic Plotting

18. Trinity College Dublin, The University of Dublin Random Data To illustrate the plotting functions, I am just going to use some random data. var1 <- rnorm(1000) var2 <- rnorm(1000) Randomly generate 1000 data points pulled from a normal distribution. Note, random data is very useful if you want to figure out how a function works.

19. Trinity College Dublin, The University of Dublin Histograms I To produce histograms, we use the hist() function. var1 <- rnorm(1000) var2 <- rnorm(1000) hist(var1)

20. Trinity College Dublin, The University of Dublin Histograms II hist(var1, main='Distribution of Random Data', xlab='Variable 1', col='darkgrey' ) abline(v=mean(var1), col='red')

21. Trinity College Dublin, The University of Dublin Histograms III Using the par() function, it is possible to partition the plotting window into multiple squares to as to view multiple plots simultaneously. par(mfrow=c(1, 2)) # 1 rows, 2 columns hist(var1, xlab='Variable 1', col='darkgrey') abline(v=mean(var1), col='red') hist(var2, xlab='Variable 2', col='brown') abline(v=mean(var2), col='red')

22. Trinity College Dublin, The University of Dublin Histograms IV Using the par() function, it is possible to partition the plotting window into multiple squares in order to view multiple plots simultaneously.

23. Trinity College Dublin, The University of Dublin Colours R has an extensive repertoire of colour options for plots. http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf Plot colours are typically indicated by the col argument, e.g., col = 'darkred' col = 'gold' col = 'darksalmon'

24. Trinity College Dublin, The University of Dublin Annotating Plots with Text It is possible to add text to plots using the text() function. hist(var1, xlab='Variable 1', col='darkgrey') abline(v=mean(var1), col='red') text(0.5, 187, as.character(round(mean(var1), 2))) In my experience, the text() function is more hassle than it's worth and such changes are best made manually using something like photoshop.

25. Trinity College Dublin, The University of Dublin Setting the limits on the x- and y-axes hist(var1, xlab='Variable 1', col='darkgrey', xlim=c(-6, 6), ylim=c(0, 200)) abline(v=mean(var1), col='red') text(0.7, 200, as.character(round(mean(var1), 2)))

26. Trinity College Dublin, The University of Dublin Boxplots I Boxplots (or box and whisker plots) are also a useful way of visualising the distribution of data. Boxplots show the median, the quartiles and the outliers. Boxplots also clearly demarcate outliers. Boxplots are compact – you can visualise many of them together to get an overview of multiple distributions

27. Trinity College Dublin, The University of Dublin Boxplots II boxplot(var1, var2, names=c('Variable 1', 'Variable 2'), col=c('darkgrey', 'lightgrey')) Notice the use of vectors, c(), to specify multiple values.

28. Trinity College Dublin, The University of Dublin Boxplots III Different ways of looking at the same data. Do they capture the same information?

29. Trinity College Dublin, The University of Dublin Scatterplots I plot(var1, var2, main='Scatterplot', xlab='Variable 1', ylab='Variable 2') plot(var1, var2, main='Scatterplot', xlab='Variable 1', ylab='Variable 2', col='red', pch=20, # point type cex=0.2)# point size

30. Trinity College Dublin, The University of Dublin Scatterplots II For plots that position points, the arguments pch and cex determine the point type and size, respectively. A selection of point types that can be set using pch argument.

31. Trinity College Dublin, The University of Dublin Additional Plotting Functions We have looked at the hist(), boxplot() and plot() functions. R has other 'base package' functions for plotting that work similarly to the above, e.g. barplot()scatterplot() pie()pairs() stripchart()dotchart()

32. Trinity College Dublin, The University of Dublin Packages The base package in R consists of a repertoire of functions that come automatically with R. R has thousands of additional packages created by developers free of charge. We will install a third party plotting package called ggplot2. install.packages('ggplot2') # To install package R will prompt you a couple of times to install ggplot2 as a local library – type y (yes) for each prompt. library(ggplot2) # Load package for use

33. Trinity College Dublin, The University of Dublin Slightly More Advanced Plotting ggplot2 is perhaps the most elegant way of creating graphs in R. ggplot2 is a course in itself – I will give some examples of how it works. To read further: http://ggplot2.orghttp://ggplot2.org The quick way to using ggplot2 is the use of qplot() function which is part of the ggplot2 package. qplot(x, y, data=, color=, shape=, size=, alpha=, geom=, method=, formula=, facets=, xlim=, ylim= xlab=, ylab=, main=, sub=) The qplot() function

34. Trinity College Dublin, The University of Dublin Slightly More Advanced Plotting – qplot() example var1 <- rnorm(1000) var2 <- rnorm(1000) lab1 <- rep('Variable_1', 1000) lab2 <- rep('Variable_2', 1000) var_df <- data.frame(vars= c(var1, var2), labs= c(lab1, lab2)) Make some data. qplot(labs, vars, data=var_df, geom="boxplot", fill=labs, main='qplot() example', xlab='', ylab='Random Variables')

35. Trinity College Dublin, The University of Dublin Slightly More Advanced Plotting – qplot() example qplot(labs, vars, data=var_df, geom="boxplot", fill=labs, main='qplot() example', xlab='', ylab='Random Variables') ggplot2 is subject in itself. Below as a good starting point: http://www.statmethods.net/adv graphs/ggplot2.html

36. Lecture 2 – problem sheet A problem sheet entitled lecture_2_problems.pdf is located on the course website (http://bioinf.gen.tcd.ie/workshops/R). Some of the code required for the problem sheet has been covered in this lecture. Consult the help pages if unsure how to use a function. Please attempt the problems for the next 30-45 mins. We will be on hand to help out. Solutions will be posted this afternoon.

37. Thank You