R and Rstudio: Getting Started (Day 0 Tutorial)

Transcription

1 R and Rstudio: Getting Started (Day 0 Tutorial) Data Use in the Natural Sciences SCIE4401 Atakelty Hailu February 23, Introduction This unit is taught with R, a software for statistical computing and graphics development. The base R software includes a large number of methods (functions or procedures) for data manipulation, statistical computations and statistical tests and a lot more. Its capabilities can easily be extended by installing add-on packages written by others. You can also add your own methods to implement specialized techniques. You will see that R is commonly described as a programming language and software environment for statistical computing and graphical visualisation. This description better reflects the fact that R is more than a standard application software (such as GenStat or Eviews) and that it combines the strenghts of both tailor-made software and general computer programming languages. R has been around for over two decades and is very well documented. And there is a vast network of servers known as CRAN (Comprehensive R Archive Network) mirror sites that it can be downloaded from. See R is platform independent, meaning that the R scripts you write work in the same way whether you are using a Windows, a Unix/Linux or a Mac/iOS operating system. R can be used on its own. However, it is more productive and less frustrating to work with a user-friendly front end such as Rstudio, which is an integrated programming environment (IDE). An IDE is menu driven and makes the use of R easy for beginners and others, reducing frustration and enhancing productivity. In SCIE4401, we will be using the Rstudio IDE. Below you will find information on R/Rstudio installation, a description of the Rstudio environment as well as exercises that will help you familiarise yourself with that IDE (Section 2), and a set of online resources that will help you learn about R so that you are ready for the second day of tutorials and subsequent sessions (Section 3). It is important that you check, before the tutorials, that you are able to log on to the computers in the lab. In the past, there have always been one or two students who were not able to do that at the start of the semester. If you are in the same situation, contact IT and try to get the problem sorted out as soon as you can. If you 1

2 are not able to log on and are not sure that your access problem will be sorted out for the tutorial, make every effort to bring your own laptop to the lab. The laptop would be useful, especially if it has R and Rstudio already installed (see below for advice on how to do the installations). 1.1 Installing R/Rstudio Both R and Rstudio have been installed in the labs where the tutorials are to be held. To launch Rstudio in those labs, try to access it via the Start Menu\All Programs\Statistical analysis\rstudio menu choice sequence. 1 Rstudio will give you an R console. Therefore, you do not need to launch R directly. Beyond the labs, you might find it useful to have your own installation of R and Rstudio on your personal computer/laptop. To install R, please go the R-project website at click on the CRAN link, choose an Australian mirror site to download the software. Once R is installed on your computer, you can download and install the desktop version of Rstudio from the developer s site at The learning curve With good and sustained effort, it is easy to learn R. Start learning early in the semesteer and build your knowledge gradually. In particular, it is useful for you to learn as much as you can about Rstudio and basic R commands before the tutorials for Day 1. And there are a large number of resources that you can use to climb up the learning curve. These come in the form of the information and exercices provided in the unit, help pages that are part of the installed software as well as tutorials and other information sources on the web (including the R project search engine (Rseek) at The key, therefore, is to learn how to look for answers, i.e. to learn how to learn well. If you are a bit confused or feel you are working too hard at the beginning, try to remember that the benefits of learning R/Rstudio do not end with the semester. You are investing in a software that you can use to produce high quality research outputs in your future work. Previous experience in this unit shows that many of the students who were completely clueless when they started become good users of R/Rstudio by the end of the semester. 2 Using Rstudio When Rstudio is launched, you get a graphical user interface (gui) that has the following four default panes or sections inside: (1) the Source pane: This is the section where R scripts (commands or source code) are displayed. In the figure below, the source pane is displaying lines of code (and comments in between), all highlighted in blue. 1 Note that in previous semesters, this sequence was different: Start Menu \All Programs\Applications - Coursework \Agricultural and Resource Economics\RStudio 2

3 (2) the R console: This pane is exactly what you would have if you were running R on its own, i.e. a command prompt for interacting with R. Any commands written directly into the R console can be executed by hitting the return key. Commands executed from the source pane are sent down to the R console for execution. (3) the Workspace/Histroy pane: R (and Rstudio) keep a history of command execution and this list is of commands is in the History tab of this pane. The Workspace tab provides a list of objects (variables and data objects) that are in the workspace. This is a good place to check if an object (e.g. a data frame or a vector) does indeed exist in the workpace. (4) the Plots/Files... pane: This section has several tabs. The Plots tab is where any plots created are displayed. This tab also allows you to navigate through a list of plots and also to export them into different image formats (for use in reporting) or to just copy the plot into the clipboard and then paste it directly into your a Word file. The Files tab shows a list of the files and directories in the current working directory. Note that the working directory is what R/Rstudio uses as the default directory to look for files or to write files, and this directory is shown in blue above the list of files/directories. A common problem among beginners is not knowing what this working directory is and wether the file to be read is where R is looking for it. The working directory can be set through the Set Working Directory command under the Session menu in Rstudio. If a new project is being set up, the working directory can be specified as part of the set up. It can also be reset by using the setwd() command directly in the R Console. The are other tabs in the pane explore to find out more. Figure 1: Rstudio and its four panes 3

4 2.1 Explore R studio Set a working directory Whenever you work on the computer, it is likely that at some stage you will need to write out data into a file or to read in data from a file. To avoid confusion, organize your files using a sensible directory structure and useful names for your files (and directories). For example, as suggested in the diagram below, you could create a SCIE4401 directory in your home folder to store all materials related to the unit. Then create a subdirectory called Day0 for the first day materials. If you are not creating a project with its own directory as suggested below, make this Day0 directory your working directory. You choose your working subdirectory by going through the Session menu in R studio. At the end, check the displayed working directory in the Files tab of the Plots/Files pane to be sure that it is what you expected. If not, set your working directory properly Create a project You do not need to create a project. But it can be useful. A project is a good way to organize your work and to quickly switch between different tasks in Rstudio. For the first days exercises, for example, you could create a project called R and Rstudio. To do this, click on the File > New Project > New Directory menu sequence, choose the Day1 directory for the parent folder and then create the project R and Rstudio under it (this project would have its own subdirectory with the same name). Finally, open a file that will hold your R commands or source code. Click on File in Rstudio top menu and create an R script file (you can call this test.r). The new file and the file path to it should now be visible in the Files tab listing (If they are not, refresh the file listing in the Plots/Files pane). 4

5 Figure 2: Directory structure for Day 1 5

6 2.1.3 Learning more about R studio Useful documentation is available at the Rstudio website: org/docs/. Explore the material on the following topics: 1. Working in the Console 2. Editing and Executing Code 3. Command History Testing the R/Rstudio environment Run some R commands to get a feel for how things work. You can do this by typing the commands in your new test.r script (source code) file and then executing the lines. You could also directly type the commads on the R Console and execute them. The first option is recommended as you would have copies of all the commands you tried out in a file that you can reuse later. Let s get started! 1. What is the value of the constant pi? > pi [1] What is the square of 9? > 9^2 [1] Calculate the square root of 2 and store it in a variable called t1. And print t1. > t1 = sqrt(2) > t1 [1] > #or > print(t1) [1] Take a random draw from the standard normal distribution and store it in the variable xi. However, to avoid having to type xi to examine the result, put the whole command in parentheses so that the variable is generated, assigned to xi and then displayed for you to see. > ( xi = rnorm(1) ) [1]

7 > print(xi) [1] Take a random draw of 100 values from the standard normal distribution and store it in the vector x. Calculate the mean of these random draws and store it in a variable (scalar) called xbar. (Let the values be displayed as they are generated.) > ( x = rnorm(100) ) [1] [7] [13] [19] [25] [31] [37] [43] [49] [55] [61] [67] [73] [79] [85] [91] [97] > ( xbar = mean(x)) [1] Generate summary statistics for the vector x. > summary(x) Min. 1st Qu. Median Mean 3rd Qu. Max Generate a histogram of x, with golden colours. > hist(x,col="gold") 7

8 Histogram of x Frequency x 8. Generate a boxplot of x. > boxplot(x, col="gray")

9 2.1.5 Reusing commands Note that Rstudio keeps track of the commands that you have executed. The list can be viewed by clicking on the History tab in the Workspace/History pane (top/right section of screen). Commands from the history listing can be inserted into the source code or sent down to the R console for execution. The history feature is especially useful if you are trying out different commands by typing directly into the R console. Once you have found a sequence of commands that works for you, you can go to the History pane, highlight those commands and then store them in your source code (script). If this does not make much sense on the first day, ignore this feature. 3 Learn R On the first day, you should attempt to familiarise yourself with R and the resources available to support your learning. Some are listed below. (A) Online R Tutorial 1: Start today and, over the coming week, work your way through the top third (chapters 1 to 6) of Kelly Black s tutorial at covering the following topics: 1. Input methods (How do you create variables inside R?): Variables can be created inside R either through direct assignment (e.g. a = 3 or a <- 3) or by reading values from a file, e.g. a comma separated spreadsheet file (CSV file), where the file might be on your local drive or on the web (e.g. heisenberg <- read.csv(" simple.csv"). Do section 1 in the tutorial to learn about both methods. 2. Data types (What are the different classes of variables?): The key types are Numbers (numeric data types), strings, factors, logical (TRUE/FALSE) values. The data set you work on commonly comes in the form of a table that has different variables (columns). In R, these are usually read into a data frame. The section in the tutorial also discusses data tables, which can have multiple dimensions. Data frames are commonly used and useful to know about. For example, when you read in a CVS file using R s read.csv command, the values are automatically stored in a data frame. Use the class command to find out the type of a data object (e.g. class(heisenberg)). 3. Basic operations (How do you carry out arithematic and other operations on variables, vectors and data frames?). Find out how you add, multiply, exponentiate, take logs, etc. and how you generate the minimum, maximum, mean and other summary statistics for a vector or for all the columns of a data frame simultaneously using the summary command. 4. Probability distributions (How do you generate random values, calculate the probability of a value, etc.?) The tutorial shows you how to work with some of the most commonly used distributions, the standard normal and t-distributions. For example, try to find out what 9

10 percentage of women in the US would be taller than 69.5 inches if the height of US women is normally distributed with a mean and a standard deviation of 64.1 and 2.75 inches, respectively. (Use the pnorm function to find the answer to this.) 5. Plot generation (How do you visualise data using plots?) Learn to generate strip charts, histograms, box plots, scatter plots, etc. Also learn how to generate chart & axes titles and how to enhance the impact of your plots by using colours. (B) Online R Tutorial 2: Use the interactive tutorial Try R at tryr.codeschool.com to accomplish the following: 1. Learn how to create and access variables, how to call R functions and how to access R s help functionality (chapter 1). 2. Learn how to create, access (using indices or names) and modify vectors; how to plot vector values; how to operate on one or more vectors; how to plot vectors; how to deal with Not Available or NA values when operating on vectors, e.g. removing NA values from a summation operation using the na.rm=true option in a call to the sum function (chapter 2). 3. Go through chapter 3 of the interactive tutorial to learn: how to create matrices through direct assignment or by re-shaping a vector; how to access values within a matrix one-by-one or in sections; and also how to visualise data in a matrix (or table) using contours, perspective plots and heat maps. 4. Create summary statistics for your data set (mean, median, standard deviation) and visualise the raw data along with some additional values (chapter 4). 5. In chapter 5, learn how to work with factor data types to group your observations into categories and how to use factors to plot your data. 6. Learn about what data frames are and the different ways of accessing values or columns inside data frames; how to read in (or load) data frames using read.csv and the more general read.table commands; and how to merge two columns (chapter 6). (C) Explore Tom Short s R reference card at doc/contrib/short-refcard.pdf (D) Reading/Practice: Skim through the first two (electronic) chapters of Applied Econometrics with R by Christian Kleiber & Achim Zeileis, You can access this book online through the UWA library. The AER package that accompanies the book is installed in the labs; you can load it up using the library(aer) command in the R Console. (E) Rseek: Check out the Rseek search engine at This is an engine that would throw up a lot of possibly useful links. It also shows you how extensively documented R is. The key is to learn how to get to the answer quickly. That requires patience and some experience. 10