Stat 350 purdue

R Tutorial for Lab 1 Author: Leonore Findsen, Min Ren

1 STAT 350: Introduction to Statistics Department of Statistics, Purdue University, West Lafayette, IN 47907

0. Downloading Data You will have to download all of the data used from the internet before R can access the data. If the file is accessed via a link, then right click on the file name and save it to a directory on your hard drive. It can be difficult to find the files, so if you are unfamiliar with PCs, I suggest that you download it directly to the W drive (main campus drive). R cannot read a zipped file. Therefore, if any file is zipped, remember to extract it before you try to access it in R. Please remember where your files are stored so that you can access them for the labs.

1. General Information for R a) R uses functions to perform operations. To run a function with two inputs called

funcname, we type funcname(input1, input2). b) There is online help that is available. All you need to do is type “? command” at the ‘>’

command prompt and R studio will display the help file in a window. For example, “?setwd” at the ‘>’ prompt will open the available help documentation on utilizing the “setwd()” function. I also find that google is very effective if you are confused about a command.

c) The ‘#’ symbol/sign indicates a comment in R, i.e. everything to the right of the ‘#’ sign

will NOT be run as code. d) Data set names need to be one word (no spaces) and start with a letter. A common way to indicate a space is to use an underscore '_' or period '.'. Capitalization is important. Not all special characters can be used. Please change your table and variable names to match each situation. Points will be deducted if this is not done. e) Do not print out the whole data set inside of the program! We will take off points if there is code for this in your submission.

R Tutorial for Lab 1 Author: Leonore Findsen, Min Ren

2 STAT 350: Introduction to Statistics Department of Statistics, Purdue University, West Lafayette, IN 47907

e) RStudio: Once you open RStudio, you will see a window similar to what is provided below:

Fig. 1

Set working Directory I strongly suggest you set your default directory to where your data files are located. To accomplish this, navigate in the "Files" window (the lower right hand panel indicated by the blue arrow in Fig. 1) to the directory that you want. Then, Files → More → Set as Working Directory.

Create R Script file For your convenience, I recommend that you create a R script for each lab. To accomplish go to the red circled File (Fig. 1) → New File → R Script, then an “Untitled1” file will be created.

R Tutorial for Lab 1 Author: Leonore Findsen, Min Ren

3 STAT 350: Introduction to Statistics Department of Statistics, Purdue University, West Lafayette, IN 47907

You can use the small “save” button to save and change the name of “Untitled1” File.

Execution: Move your cursor to the beginning of the first line of code that you wish to run. Then click the Run button circled below. The cursor will automatically move down one line. If you want to run multiple lines, you may repeat this process. Alternatively, you may select all of the lines that you wish to run and then click on the Run button.

Import data Depending on the version of Rstudio, the procedure to import data may be considerably different. As we are unsure what version will be on the ITAP computers (or on your PC), we are including instructions for both. The older version is listed first and then the newer version. Each version will be separated by horizontal lines.

Older Version: In the right hand side of RStudio (red arrow in Fig. 1), you will find the Environment tab. Click import Dataset → From Local File … and this wizard will help you intuitively import the dataset and automatically display the data.

R Tutorial for Lab 1 Author: Leonore Findsen, Min Ren

4 STAT 350: Introduction to Statistics Department of Statistics, Purdue University, West Lafayette, IN 47907

When loading files from this class, be sure that you have the Heading setting at Yes. See the figure below for details.

Newer Version In the right hand side of RStudio (red arrow in Fig. 1), you will find the Environment tab. Click Import Dataset → From CSV …

Click Browse and navigate to where the file is saved. Make sure to check First Row as Names and Select Delimiter: Tab. Check the data preview to make sure everything looks good. You can also use the Name: Import option (lower left) to change the name R gives to the data set. I have chosen helicon. Then click Import.

R Tutorial for Lab 1 Author: Leonore Findsen, Min Ren

5 STAT 350: Introduction to Statistics Department of Statistics, Purdue University, West Lafayette, IN 47907

You can use “View” command to check your data manually. Note this starts with a capital 'V'. For example, “View(data)” This can also be accomplished by clicking on the 'View icon' which is circled in red in the diagram below. Please note all the variables will be displayed in the “Environment” window when you click the icon circled in green below.

Autocomplete In the RStudio, you can enjoy the “autocomplete” feature by hitting “Tab”. The prompt window will be like below:

R Tutorial for Lab 1 Author: Leonore Findsen, Min Ren

6 STAT 350: Introduction to Statistics Department of Statistics, Purdue University, West Lafayette, IN 47907

2. Importing Data sets, cleaning, manipulating, and printing them.

Hummingbirds and flowers. (Data Set: ex01-88helicon_m.txt) Different varieties of the tropical flower Heliconia are fertilized by different species of hummingbirds. Over time, the lengths of the flowers and the form of the hummingbirds’ beaks have evolved to match each other. Here are data on the lengths in millimeters of three varieties of these flowers on the island of Dominica:

When you import your data set, the name may be the name of the file (if you didn’t use the Name: option while importing). I strongly recommend that you change that name to something easier to type.

# this copies the dataset into another one with a shorter name

helicon <-ex01.88helicon_m

Please name your table in context. In this case, we are interested in Heliconia flowers, so I named the table helicon. Since capitalization is important, I often only use small letters. If you want to put a space in the name, use an underscore, _. This line will print out the entire data set. helicon

NEVER USE THIS COMMAND WHEN USING THE LAB DATASET; it is too big. Since it is important to look at your data after you import it to ensure that there are no problems, you can either use the View command or “head(tablename”) and" “tail(tablename)” which will print out the top and bottom rows of the data set. Unless explicitly stated, never print your data in your lab reports.

R Tutorial for Lab 1 Author: Leonore Findsen, Min Ren

7 STAT 350: Introduction to Statistics Department of Statistics, Purdue University, West Lafayette, IN 47907

I have highlighted the first five data points from the results from the View command. If you want to copy tables (or parts of tables) or graphs from the R output, I suggest that you use the Snipping Tool. You can also use that tool to highlight your answer. This is the procedure that I used in the above table. If you are just copying information from the console the Snipping Tool is not required. Lastly, in large data sets, it can be important to only print specific rows and variables. The following prints rows 2, 20, and 50 of the variables Length and Variety in that order. Be sure to have at least 2 variable names or the output is very confusing. > helicon[c(2,20,50),c("Length","Variety")]

Length Variety

2 46.75 bihai

20 43.09 red

50 36.66 yellow

Be sure that you include the "c" for each list and enclose all names in double quotes. (OPTIONAL) Assuming that helicon is in your current R session, run the following commands below and observe their outputs. Do not worry if you do not understand the output from each line as we are only currently attempting to build familiarity to R at this point in time.

head(helicon) tail(helicon) helicon[20:30, ] names(helicon) dim(helicon) class(helicon)

class(helicon[1, ]) class(helicon[ , 2]) class(helicon[ , 1])

summary(helicon[ , 2])

sum(helicon[ , 2])

R Tutorial for Lab 1 Author: Leonore Findsen, Min Ren

8 STAT 350: Introduction to Statistics Department of Statistics, Purdue University, West Lafayette, IN 47907

Cleaning and saving datasets Since many datasets have missing values, it is important to process them before beginning the analysis. For this class, we will simply remove those rows. The following command will remove rows that are not complete and then view the cleaned file. When you are viewing, be sure that you include the correct dataset name. helicon_cleaned <-helicon[complete.cases(helicon),]

View(helicon_cleaned)

Remember, the 'View' command has a capital V. Once you have cleaned the data set, you will want to save it so that you don't have to clean the data each time you use the data set. The procedure to save the new data set is as follows: write.table(helicon_cleaned,file="helicon_cleaned.txt",

row.names=FALSE, sep="\t")

This will write the file to your default directory. It is strongly recommended that you set your default directory (see above) to someplace that you can find easily. Manipulating data For readability, you might want to change the shortened name or abbreviation to the full version. This is done by the following commands: #Initialize a new variable by copying old values (which we’ll change)

helicon_cleaned$NewVariety <- as.character(helicon_cleaned$Variety)

# Change names

helicon_cleaned$NewVariety[helicon_cleaned$NewVariety =="red"]

<-"Caribaea_Red"

helicon_cleaned$NewVariety[helicon_cleaned$NewVariety =="yellow"]

<-"Caribaea_yellow"

View(helicon_cleaned)

#You can indicate a range of rows by using a colon (:)

#You have to include the comma (,) after the numbers if you want to

# include all of the variables in the original

orderhelicon_cleaned[c(36:43),]

The output of the last command is shown below: > helicon_cleaned[c(36:43),]

Variety Length NewVariety

36 red 38.23 Caribaea_Red

37 red 38.87 Caribaea_Red

38 red 37.78 Caribaea_Red

39 red 38.01 Caribaea_Red

40 yellow 36.78 Caribaea_yellow

41 yellow 37.02 Caribaea_yellow

42 yellow 36.52 Caribaea_yellow

43 yellow 36.11 Caribaea_yellow

R Tutorial for Lab 1 Author: Leonore Findsen, Min Ren

9 STAT 350: Introduction to Statistics Department of Statistics, Purdue University, West Lafayette, IN 47907

In addition, you might want to create a new variable based on mathematical operations from old variable(s). You can use the sample code below to convert the lengths of the beaks from millimeters to inches. The conversion factor is 1/25.4. helicon_cleaned$length_inches = helicon_cleaned$Length/25.4

head(helicon_cleaned)

> head(helicon_cleaned)

Variety Length NewVariety length_inches

1 bihai 47.12 bihai 1.855118

2 bihai 46.75 bihai 1.840551

3 bihai 46.81 bihai 1.842913

4 bihai 47.12 bihai 1.855118

5 bihai 46.67 bihai 1.837402

6 bihai 47.43 bihai 1.867323

You will see a new column in the data set called length_inches: Note that it is not necessary to create a new dataset variable. However, I strongly recommend that you do change it since (1) in case there’s a mistake, you won’t overwrite the original data, and (2) you can compare the new with the original. In addition, you should never re-use variable names. That is, your modified variable should always have a distinct name from any other variable in your data set. One final note: In Lab 2, we will be discussing different ways of referencing the data and the variables.

Applied Sciences

Architecture and Design

Biology

Business & Finance

Chemistry

Computer Science

Geography

Geology

Education

Engineering

English

Environmental science

Spanish

Government

History

Human Resource Management

Information Systems

Law

Literature

Mathematics

Nursing

Physics

Political Science

Psychology

Reading

Science

Social Science

Home

Blog

Archive

Contact

google+twitterfacebook

Copyright © 2019 HomeworkMarket.com