R handout 2019-20 Up and Running With R Studio handout...We will be doing all our work in R Studio....

R Handout 2019-20 Up and Running with R Studio

R handout 2019-20 Up and Running With R.docx of 21

Introduction to R 2019-20

Up and Running with R Studio:

Suggested Practices, Tips, and What to Watch Out For! Introduction

We will be doing all our work in R Studio. This is what the icon looks like: This illustration offers some (far from complete) guidance in working with R Studio: issuing R commands directly in the console pane and how to work with R script files. It also encourages you to develop the habit of archiving your work through the use of R markdown files. Pick and choose from the suggestions as you see fit! Table of Contents Page 1. Suggested Practice: Structure Your R Work ………..…………………………………….. 1.1. How to Set your Working Directory to your Desktop ………………………………….. 1.2 Choose a directory (“file path”, “folder”) for your R Work ………………………………

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2. Tips: Working Directly in the Console Pane …………………………………………………

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3. Suggested Practice: Work with R Scripts (Source Pane) …………………………………..

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4. Suggested Practice: Archive Your Work with R Markdown (Source Pane) ……………..

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5. Your Turn: Illustration ……………………………………………..………………………..

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Before You Begin 1. Be sure you have downloaded and installed R 2. Be sure you have downloaded and installed R Studio 3. Download from the course website the excel file toy.xls. Place on your desktop. 4. (One time if you have not done so already). In the console pane ONLY: install.packages(“DescTools”) 5. (One time if you have not done so already). In the console pane ONLY: install.packages(“stargazer”)



1. Suggested Practice: Structure Your R Work 1.1 How to Set your Working Directory to Your Desktop What is the working directory in R Studio? The working directory in R Studio is the file path (spoiler: a file path is the same as a folder) where R Studio will: a) look to find something (unless you tell it otherwise) and b) save things that you save (unless you tell R Studio to save to a different location.

Example – Windows c:\640\mystuff Example – MAC /Users/cbigelow/Desktop/1. Teaching/

Good to know! Determine the File Path of your Desktop (Note – This is done outside R Studio). Why? Sometimes you might want to do a quick bit of your work from your desktop.

Windows Users __1. Launch windows explorer: <windows> - E __2. Navigate until you see Desktop at left __3. RIGHT CLICK on “Desktop”. A drop down menu will appear __4. From this drop down menu, at bottom, click on PROPERTIES __5. Scroll down to read desktop path at location: __6. Tip – Highlight to select and copy, for pasting into your R session later

MAC Users __1. Launch FINDER __2. At left, scroll down to find Desktop __3. RIGHT CLICK on “Desktop”. __4. Scroll down to read desktop path at where: __5. Tip – Highlight to select and copy, for pasting into your R session later



Tip – When you are working from your desktop, set your working directory to be your desktop This is done in R Studio.

Method I: Using command setwd( ) __1. Outside of R, highlight, select and copy the full path name __2. Paste the full path in the command setwd(“FULLPATHNAME”) __3 Tip – The path MUST be enclosed in quotes

Examples – Windows setwd(“c:/Desktop/bigelow”) setwd(“c:\\Desktop\\bigelow”) What could go wrong - Windows __#1. The leading “c” MUST be lower case __#2. Single backward slashes will NOT work. Example – MAC setwd(“/Users/cbigelow/Desktop”) Method II: Using toolbar __1. From toolbar at top, click TOOLS __2. From tools drop down menu, click GLOBAL OPTIONS __3. Click at right on “Browse” to select __4. Click on Apply. Then OK



1.2 Choose a Directory (“file path”, “folder”) for Your R Work Most of the time, you will not be doing “quick work” from your desktop. So why choose a directory for your R work. Moreover, this may be a different choice for each project. This just makes sense, that’s why! Are you smiling? It’s nice to have all the data management and data analysis work associated with a given project all in one place! Oh my goodness. And while we’re at it, think ahead and choose some sub-directories that make sense to you. It’s nice to have all the data management and data analysis work associated with a given project all in one place! Oh my goodness. Example 1 – MS Thesis/

Ø Source (Idea here: this would contain source data that is “raw” and should not ever be edited) Ø Data Management (Idea here: this would data cleaning, new variable creations, merges, etc) Ø Data Analysis (Idea here: this would contain all your R Studio work to produce descriptives, modeling, etc) Ø Tables Ø Figures Ø Reports Ø Miscellaneous Resources (E.g. – You might keep references that you relied upon for various analyses)

Example 2 – BIOSTATS 640/

Ø Data Manipulation Ø Data description Ø Data Visualization Ø Epidemiological Tables Ø Linear Regression Ø Analysis of Variance Ø Logistic Regression Ø Survival Analysis

…. You get the idea ….



2. Tips: Working Directly in the Console Pane Introduction Sometimes, you will want to do some “quick work” Commands typed into the console pane are executed immediately. The console pane is one of the R Studio windows. It is typically located as the lower left window:

__1. > is the prompt that tells you R Studio is waiting for you to enter a command (code) __2. At the upper right, you can minimize or maximize. __3. Commands typed into the console are executed immediately. R Studio will return either:

- result (for example the result of the calculation of 2+2); - nothing (you will get no response if you simply create an object); - error message (join the club); or - + (the symbol “+” is telling you that your command was incomplete. R Studio wants the rest)

__4. Comments (R Studio ignores), begin with a #. For example - #Thisisacomment.

Tips - 1 Use the console pane for quick tasks only. Quick tasks are things that you do not need to save. Or they might

be commands that you want to debug. As a general strategy, it’s a better habit to work from a R Script file or an R Markdown. More on this later.

2 Use the console pane as a giant calculator 3 Use the UP and DOWN arrows to cycle backward and forward among the commands. This is especially

handy when you want to correct a command that has an error in it. 4 Control-l (here, this is the letter “el”) to clear the console window.

Don’t worry. Your history of commands is not lost.



3. Suggested Practice: Work with R Scripts (Source Pane) Introduction For the most part, you will be doing work related to a course or a project. Depending, you will want to work with R scripts. Or you will want to work with R Markdown files. Here we focus on R scripts. An R script file is one type of source file that is available to you in the source pane. Heads up - When you launch R Studio for the first time, you may not even see the source pane (window). How to Access the Source Pane At the upper left, click on either The “+” symbol > R Script FILE > NEW FILE > R Script

Clicking on R Script opens a new R script file. R script files have the extension “.R”



Suggestion #1 - Create a permanent “boiler” plate R Script file. Handy! This will then be available to you in every R Studio session. For example, mine is organized into sections: #RScriptFilename:RFragments640.R#LastUpdate:5/25/19#-------------------------------------------------------------------------#0.HOWTO-UpgradeRwithoutlosingpackages#1.Readingindata.SavingtoRdata.Listthedata.#2.Workingwvariables,observations,datacleaning,etc.#3.ProbabilityDistributionCalculations#4.Numericaldescriptives#5.Graphs#6.OneandTwoSampleTests-ContinuousOutcomes#7.ContingencyTableTestsandEpiTables#8.SimpleLinearRegression#9.MultipleLinearRegression&RegressionDiagnostics#10.LogisticRegression#11.SurvivalAnalysis#12.Miscellaneous#------------------------------------------------------------------------- Example, continued - And here is a little snippet from that R Script file #_____________________________________________________________________________#6.OneandTwoSampleTests-ContinuousOutcomes#_____________________________________________________________________________#####Preliminary-Turnofdisplayofscientificnotationoptions(scipen=1000)#####6a)Shapiro-WilktestofnormalityDescTools::ShapiroFranciaTest(DF$VARIABLE)#####6b)OneSamplet-testofNull:mu=950t.test(DF$VARIABLE,mu=950)#####6c)OnesampleCIforMeanUsingt-distribution(3solutions)t.test(DF$VARIABLE,conf.level=0.95)DescTools::MeanCI(DF$VARIABLE,conf.level=0.95)Rmisc::CI(DF$VARIABLE,ci=0.95)



Suggestion #2 – Depending, you might want to do your work in a R script file. Step 1 - Open a new R script file FILE > NEW FILE > R Script This will create a new script file called untitled1 Step 2 – Before typing anything, save to a new name Step 3 – Type your commands into your R script file Now you have a repository of your commands which can then be recycled, edited, and re-used. You’ll find this is very handy. IMPORTANT – Commands typed into an R script file are not executed. You must send your commands to be executed Step 4 – Send your commands to be executed! There are multiple ways to do this.

METHOD I (not recommended) Select > EDIT-COPY > Paste into the console METHOD II (best for single command) Position your cursor anywhere in the command. Then do a <control- enter> METHOD III (use for multiple commands) Highlight to select the entire command (or multiple commands). Then at upper right, click on RUN

Step 5 – All done? Clean and save. - Tidy up your R script file work (get rid of all the commands that produced errors) - Annotate with lots of comments (recall – comments begin with #) - Save.

Tip

Make it a habit to annotate with comments! Comments begin with the hashtag character # R ignores these.



4. Suggested Practice: Archive Your Work with R Markdown (Source Pane) Introduction Again, for the most part, you will be doing work related to a course or a project. R Markdown files are a terrific “practice” for archiving text, code, and output. Like R script files, an R Markdown file is one type of source file that is available to you in the source pane. Briefly, here is how your session will go …..

1. Begin your R-Studio session by opening an empty R Markdownfile that will contain your future code and output;

2. Chunk by chunk, navigate between writing code, fixing code, and executing code; 3. All done? Save (archiving) your work (nifty either as a record of your work, or for re-use later!)

Step 1 (One time) – Install the package “knitr” There are (at least) 2 methods.

METHOD I – From the console window

Step 1. Launch R Studio Step 2. In the console window, type the following EXACTLY at the prompt > TIPS – 1) don’t forget the period in install.packages and 2) don’t forget to enclose knitr in quotes: install.packages(“knitr”)

METHOD II – From the Environment, Files, Plots, Packages, etc window

Step 1. Launch R Studio Step 2. In the Files, Files, Plots, Packages window, click on PACKAGES tab. Next, click INSTALL. Step 3. From the drop down menu, the default selections are fine. In the box PACKAGES (separate multiple with space or comma), type knitr. Click on INSTALL at bottom right.



Step 2 - Begin your R-Studio Session by Opening a new (empty) R Markdown file __1) From the top menu bar: FILE > NEW FILE > R Markdown __2) You should see something like the following (note – yours won’t say Carol Bigelow of course):

• At top right, at title: Type in a title of your choosing • Just below, under Default Output Format: choose your output format

o HTML – This is the default selection. It’s fine to choose this. • Click OK



Example –

Key: * R Markdown has provided you with a “shell” with, seemingly, a bunch of stuff! * Each gray shaded area is called a chunk. A chunk is a set of R commands with a “beginning” and “end” CHUNK BEGINNING: Each “chunk” begins with ```{r} or it begins with ```{r SOMETHING YOU CHOOSE HERE} IF you choose ```{r include=FALSE} THEN messages and code will be NOT SHOWN (I do not recommend this) If you choose ```{r echo=FALSE} THEN code will NOT BE SHOWN (I do not recommend this either) Personally, I recommend sticking with beginning each “chunk” using ```{r } CHUNK END: Each “chunk” ends with ``` __3) Clear this “shell” - Place your cursor at line 7 of the “shell” R Markdown. Drag to highlight and select all below - Click delete



You should now see something like the following (with your name, not mine, obviously):

__4. Chunk by chunk, navigate between writing code, fixing code and executing code What we will do is to cycle, as follows:

1st – We open a new blank chunk (to do a specific task that we want to do) 2nd - We type some commands into this chunk and then we run it. 3rd - As, typically is the case, we EDIT the commands in this chunk until we get what we like and then re-run it 4th - Once, we’re happy with the current chunk and the current task, we move on to the next chunk/next task!

1st – How to Open a New Blank Chunk Click on the little green “insert a chunk” icon at top (on the right). From the drop down menu, choose R

You should see the following. - You should see the gray chunk start ```{r} - You should see your cursor placed inside - You should see the chunk end ``` - TIP - Do NOT edit or delete the chunk start or end!



2nd – Type Some Commands in the Chunk and Run it For Example, here I’ve typed some commands

How to Run/Execute the commands in the chunk Use your cursor to select the lines or chunk that you want to run/execute. From the Run drop down at top right, make your selection

You should see your output/results BELOW, in the CONSOLE window. Error messages appear here, too



3rd - As, typically is the case, we EDIT the commands in this chunk until we get what we like and then re-run it

That’s right. Most of the time, you will tinker with a chunk over and over (either to fix a mistake or to obtain a better solution to the task at hand). To do this:

- Position your cursor in the link of the chunk that you want to edit. - Edit, and re-execute

HACK! Is the stuff in the console window getting to be too much? Want to clear it? Position your cursor at the <- prompt in the CONSOLE window Click control-L to clear.

4th - Once, we’re happy with the current chunk and the current task, we move on to the next chunk/next task! Repeat 1st, 2nd’ and 3rd for each chunk/task you want to do.



__3. All done? Save (archiving) your work (nifty either as a record of your work, or for re-use later!)

The action of saving your work is what is meant by knit. How to knit:

- At top click on the drop down menu for the knit icon - From the drop down menu, I recommend that you choose KNIT TO WORD

(Why? Answer – so that you can open this file later in word and perhaps fancy it up a bit) - Tip: Take care to choose a destination folder that you’ll remember (I always choose DESKTOP)

One more thing. Save the R Markdown file itself! This has extension “.Rmd” (Handy if you want to be able to re-use it)

Huzzah. You will now have TWO files: - A R Markdown file (“.Rmd”) - An MS Word file (“.docx”)



Voila! This is what my MS Word file looks like

Untitled

CarolBigelow

1/29/2020

#Commentsbeginwitha##Illustration:Createanobjectthatisnamedweightthatisacolumnvecorwith4entries#KEY#objectname<-c(value,value,value,value)weight<-c(161.3,120.1,223.2,124.0)#Illustration:List(Echo)theobjectjustcreatedbytypingthenameoftheobject#Key#objectnameweight

##[1]161.3120.1223.2124.0



5. Your Turn Illustration

__1. Download toy.xls and place it on your desktop The dataset toy.xls is a tiny excel dataset that is on the course website. RIGHT click to download. Place it on your desktop. __2. In Excel, open toy.xls. Minimize Excel but do NOT exit Excel We’re going to do an easy hack job of getting the excel data into our R Studio session. It will involve a nifty little EDIT-COPY-PASTE Your excel file should look like this:

__3. Launch R Studio What could go wrong: You launch R instead of launching R Studio. __4. Set your working directory to your desktop. Check that all is well. What could go wrong – All: (1) You forgot to enclose the file path in quotes What could go wrong – WINDOWS Users: (1) You need to fix the leading “c” to be lower case (2) You may need to fix the slashes to be either FORWARD or DOUBLE BACKWARD. Single backward will NOT work. Here is what I have. Your file path will be different than mine (yellow highlight): >#Setworkingdirectorytodesktop>setwd("/Users/cbigelow/Desktop")>#Showworkingdirectorytoverifythatalliswell>getwd()[1]"/Users/cbigelow/Desktop">



__5. TEMPLATE FOR COPY/PASTE: Open a new R Script file. Then create template for importing Excel data by typing the following EXACTLY. Key: This template is reading in a selection of text from an excel file and then producing an R object that is called a dataframe. The save( ) command that follows then saves the R dataframe as an R dataset. It has the extension “.Rdata”. What could go wrong: (1) You inadvertently execute this command and R Studio returns a “+”. The “+” is R Studio telling you that your command is incomplete and it is waiting for you to finish the command. Solution: <escape> Type into your R Script file EXACTLY as shown here datatable=read.table(text="",header=TRUE)toyrdata<-as.data.frame.matrix(datatable)save(toyrdata,file=”toyrdata.Rdata”) __6. COPY: Activate EXCEL. Select data from toy.xls, including the top row with the variable names. Do a <control>-C to copy (Tip – Select by sweeping across, then down)

__7. PASTE: Back in your R Script file, position cursor just before the SECOND QUOTATION (start of 2nd line). Paste your excel data here. You should now see in your R Script file datatable=read.table(text="light temp growth1 1 4.5500001911 1 4.2399997711 2 4.8899998661 2 4.8800001141 3 5.0100002291 3 5.1100001342 1 5.5500001912 1 4.0799999242 2 6.0900001532 2 5.0100002292 3 7.0100002292 3 6.920000076",header=TRUE)toyrdata<-as.data.frame.matrix(datatable)save(toyrdata,file=”toyrdata.Rdata”)



__8. Finished with copy/paste? Now execute. To create your R data set: In R Script, highlight the multiple rows you have just typed. To execute, do EITHER: 1) control-enter; or 2) Click on the run button at top. Check out the console window now! You should now see below, in the console window, that R studio as executed the selected commands in your R Script file and has saved an R dataset onto your desktop:



__9. Produce a relative frequency table for a discrete variable. Package is DescTools. Command is Freq() What could go wrong: (1) You forgot to install the package DescTools. Solution: Go back to page 1 (2) You forget to issue the command library( ). Solution: library(DescTools) (3) You made a mistake in keeping track of upper and lower case. R Studio is case sensitive! In your R script file, type and then execute the following #DiscreteVariable-Frequency/RelativeFrequencyTablelibrary(DescTools)options(scipen=100)Freq(toyrdata$temp) You should now see below, in your console pane the following >#DiscreteVariable-Frequency/RelativeFrequencyTable>library(DescTools)>options(scipen=100)>Freq(toyrdata$temp)levelfreqperccumfreqcumperc1[1,1.5]433.3%433.3%2(1.5,2]433.3%866.7%3(2,2.5]00.0%866.7%4(2.5,3]433.3%12100.0%



__10. Produce descriptive statistics for a continuous variable. Package is stargazer. Command is stargazer() What could go wrong: same as above, namely: (1) You forgot to install the package stargazer. Solution: Go back to page 1 (2) You forget to issue the command library( ). Solution: library(stargazer) (3) You made a mistake in keeping track of upper and lower case. R Studio is case sensitive! In your R script file, type and then execute the following (yellow highlights are mine) #ContinuousVariable–Descriptives#KEY#stargazer(DFNAME[c("varname")],type="text",summary.stat=c("n","mean","sd","min","p25","median","p75","max"))library(stargazer)stargazer(toyrdata[c("growth")],type="text",summary.stat=c("n","mean","sd","min","p25","median","p75","max")) You should now see below, in your console pane the following

R handout 2019-20 Up and Running With R Studio handout...We will be doing all our work in R Studio....

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