R: A Gentle Introduction. Vega Bharadwaj George Mason University Data Services

Transcription

1 R: A Gentle Introduction Vega Bharadwaj George Mason University Data Services

2 Part I: Why R?

3 What do YOU know about R and why do you want to learn it?

4 Reasons to use R Free and open-source User-created packages available to download allow for an endless number of things you can do in R Highly customizable graphing capabilities Lots of free documentation and tutorials available on the web Beware of bad documentation, too You don t need to be a computer scientist to code in R Packages do a lot of the programming (applying fundamental CS concepts) for you

5 R for historians

6 R for scientists

7 R for social scientists

8 R for text mining

9 Part II: Why code?

10 What is pointing and clicking? Clicking, dragging, and using buttons and specified text boxes in user-friendly applications to do things

11 Drawbacks of pointing and clicking Does not usually involve an automatic way to keep track of all of your steps The things you can do are limited by the number of buttons available

12 Example: Microsoft Word Suppose you had to edit a Microsoft Word document Move paragraph 1 between paragraphs 3 and 4 Relabel all paragraphs in chronological order Move paragraph 2 to the end Remove all paragraph labels Create headlines by boldfacing the first line of each paragraph and separating it from the rest of the paragraph with a single line break Indent each paragraph below headlines Change font to 12 pt. Times New Roman

13 Example: Microsoft Word, 2 Troubles with this situation: Word s capabilities are limited Lots of rearranging to do (human effort) No buttons available through Word that can automatically put paragraphs in the order you want No way to document all these steps while ensuring 100% accuracy Might be some ambiguity with English language What if you had to hand this task over to someone else?

14 What is coding? The process of writing out a list of instructions for a computer to read, interpret, and do

15 Summary: pointing and clicking vs. coding SPECIFICITY REPRODUCIBILITY Pointing and clicking Limited by number of buttons available No innate way to keep track of steps while minimizing error Coding Do certain things that can t be done through pointing and clicking alone By nature of coding, keep track of everything you do and save these steps for future use

16 Part III: R & RStudio

17 R and RStudio R is a programming language for statisticians Uses code to allow you to efficiently reshape datasets, perform statistical tests, and create graphics RStudio is an integrated development environment (IDE) for R Translates some R commands into point-and-click features Provides a user-friendly visual interface in which to code

18 R vs. RStudio

19 Open RStudio

20 Different ways to code Console Quickly enter temporary commands Script file A text document in which you save blocks of code you will want to recreate later

21 Exercise: R as a calculator Type 5-3 into the console and hit the Enter key Things to note: > indicates where you should enter your input never type this in yourself! [1] indicates the output s first line

22 Exercise: R as a calculator, 2 Type 5- into the console and hit the Enter key Observe what happens Type 3 and hit the Enter key + indicates R is expecting more input

23 Exercise: R as a calculator, 3.1 Create a new R script

24 Script files (.R) Script files are how you save the R code you want to recreate later Every line that begins with # is a comment, not interpreted by R as code

25 Exercise: R as a calculator, 3.2 Type 5-3 into your R script, followed by 5+3, separated by a line break exactly as it looks like below

26 Exercise: R as a calculator, 3.3 Highlight the first line only and click the Run button

27 Exercise: R as a calculator, 3.4 Examine the output in the Console Do the same for 5+3

28 Concepts: the things you code Objects (NOUNS) The things you work with in R, i.e. datasets and statistical analysis information Functions (VERBS) The actions you perform in R, usually on objects

29 Functions in Excel =SUM(A1, A2) Functions are indicated by their name, followed IMMEDIATELY by parentheses (see text in red) Arguments are references to objects (in this case, specific cells) or other types of descriptors that provide information to the function

30 Object creation Use <- to create new objects Type the object name into the console to get its value

31 Exercise: objects & functions Add the following to your R script and run each line: Pay attention to spacing!

32 Exercise: objects & functions, 2 Examine the output and the Environment

33 Other types of objects

34 Packages A set of functions and object templates available to download and use directly through RStudio Ordinarily, you can open them up using checkboxes, but we will do so using code

35 Summary: what you do in R Using code 1. Create objects ( nouns ) 2. Use functions ( verbs ) to do things to objects

36 Part IV: Working with Data in R

37 What is a CSV file? CSV stands for comma-separated values Preferred format for working with data in R Can be opened in Excel Why CSV over Excel format?.xlsx files can cause problems in R

38 CSV: Excel vs. text editor

39 Functions and datasets read.table(datafile, header=true, sep=",") Function Positional Argument Named Argument Named Argument When working with datasets, it may be necessary to work with more complicated functions Arguments without an equals sign ( positional ) must always be in the same spot whenever the function is called

40 Other function examples help() library(ggplot2) read.table(datafile, header=true, sep=",") read.table(datafile, header=true, sep=",") ggplot(mydata, aes(age, fare)) + geom_point(aes(color = factor(survived)))

41 Script and dataset management For every project, you must create a unique folder on your computer in which to store all your datasets (.CSV files) and scripts (.R files)

42 Set working directory Direct R to the right file folder

43 Save R script NO NEED TO SPECIFY FILE EXTENSION!

44 Exercise: Load in data

45 Load in data, 2

46 Load in data, 3

47 Load in data, 4 Copy & paste into R script

48 Load in data, 5 Highlight and run each line Examine output in the CONSOLE

49 Load in data, 6 You can close out of dataset and click titanic_r in ENVIRONMENT to open it up again

50 Installing and loading packages To install a package, use the install.packages() function followed by the package name in double quotes To load a package, use the library() function followed by the package name without quotes

51 Using the ggplot2 package Copy and paste the following text into your script (after inserting some line breaks): # install.packages( ggplot2 ) library(ggplot2) REMEMBER: the # indicates a comment that is not interpreted by R as code We left this function as a comment because ggplot2 is already installed

52 Exercise: Exploratory data analysis Copy and paste the following lines of code into your script (after inserting some line breaks): head(titanic_r) str(titanic_r) summary(titanic_r$gender) table(titanic_r$pclass, titanic_r$gender)

53 Exercise: Create graphs Copy and paste the following lines of code into your script (after inserting some line breaks): qplot(pclass, fill=gender, data=mydata) ggplot(titanic_r, aes(age, fare)) + geom_point(aes(color = factor(survived)))

54 Things to remember R is case-sensitive No spaces between function name and opening parenthesis Comment every block of code Leave line breaks after every code block

55 For more coding practice:

56 Workshop resources: