Introduction

This is a demonstration file to go in lecture 01. I will run through it in front of the class.

Housekeeping

⊕Your email address is the address tied to your GitHub account and your name should be first and last name.

Before we can get started we need to take care of some required housekeeping. These should only need to be done once, or maybe once a month for the PAT.

Specifically, we need to configure your git so that RStudio can communicate with GitHub. This requires two pieces of information: your email address and your name.

• Install GIT, R, Rstudio (see the Resources on the homepage for links).

• Open RStudio and go to the Console for these one-off bits. Run these lines in interactive mode (you can copy-and-paste them but you’ll have to make them specific to yourself).

  library(usethis, gitcreds)

  #if that doesn't work, install the packages and try the previous    line again.
  install.packages("usethis")  #These should only be needed once
  install.packages("gitcreds") 
  

• Let git know about your github username and email by editing this as appropriate and entering it into the Console:

I put

usethis::use_git_config(
scope = "user",
user.name = "Tanzy Love",
user.email = "Tanzy_Love@urmc.rochester.edu")

• Next, we need to set up a personal access token so that you can write from rstudio cloud to github. To do this, enter use the following command into the Console:

  usethis::create_github_token()

  Follow the instructions. Be sure to save the PAT in a password manager, you will have to re-enter it, depending on your set-up.

  gitcreds::gitcreds_set()

  paste in the token from the last step. If you picked this token to have a 6 month expiration, you shouldn’t have to do this again.

Getting started

Each of your assignments will begin with the following steps. You can always refer back to this lab (or other labs) for a detailed list of the steps involved for getting started with an assignment.

This part you will do every time you have an assignment or lab.

• Click on the assignment link that you should have received in your email to create your GitHub repository (which we’ll refer to as “repo” going forward) for the assignment. This repo contains a template you can build on to complete your assignment.

• Note that it’s not in your GitHub folder, it’s in the coursespace (so I can see it but you can’t see each other’s) and it is a copy of the template that I made to be here.

• On GitHub, click on the green Clone or download button, select Use HTTPS (this might already be selected by default, and if it is, you’ll see the text Clone with HTTPS as in the image below). Click on the clipboard icon to copy the repo URL. It will start with the class “bst-urmc” and end with your username “tanzylove” (for me) and the middle is based on what the assignment is named.

• Go to RStudio clone the assignment repository. This is the one copy and paste into RStudio part that you will do each time you start to work.

###  #'s comment out lines in R code

#you need to change this to the right repository and location for you and your local machine
usethis::create_from_github("https://github.com/bst-urmc/setup-check-assignment-0-tanzylove",
   destdir="~/Documents/work/teaching/BST430/")

• Copy and paste the URL of your assignment repo into the function
• If you need to login to github, a login box should pop up.
• It opens up a new copy of RStudio with all the files from the repository in question.

Data

Now, for the statistics!!!

Read in some data.

hotdogs=read.table("lab00-demonstration/files/Hdcalsod.TXT", header=T)

colnames(hotdogs)

## [1] "Type"     "Calories" "Sodium"

hotdogs$Calories

##  [1] 186 181 176 149 184 190 158 139 175 148 152 111 141 153 190 157 131 149 135
## [20] 132 173 191 182 190 172 147 146 139 175 136 179 153 107 195 135 140 138 129
## [39] 132 102 106  94 102  87  99 107 113 135 142  86 143 152 146 144

attach(hotdogs)

plot(Calories, Sodium, xlab="Calories", ylab="Sodium", pch=16)

cor(Calories, Sodium)

## [1] 0.4671387

plot(Calories, Sodium, xlab="Calories", ylab="Sodium", 
    col=factor(Type), pch=16)
legend("bottomright", levels(factor(Type)), col=1:3, pch=1)

Let’s try a different data set. This one about 50 states.

hsgrads=read.table("lab00-demonstration/files/raingrad.TXT", header=T, sep="\t")
attach(hsgrads)

plot(Rain, Education, 
      xlab="Rainfall", ylab="Average Grade Level", pch=16)

cor(Rain, Education)

## [1] -0.4758416

args(cor)

## function (x, y = NULL, use = "everything", method = c("pearson", 
##     "kendall", "spearman")) 
## NULL

And one final set of flying times.

delta=read.table("lab00-demonstration/files/Deltafly.TXT", header=T)
attach(delta)

hist(Miles, xlab="Distance (Miles)")

hist(DepHr, main=NULL, xlab="Departure Hour (24hour)", xlim=c(0,24))

hist(TravTime, main=NULL, xlab="Travel Time (Hours)")

plot(Miles, TravTime, xlab="Distance (Miles)", ylab="Travel Time (Hours)")
cor(Miles, TravTime)

## [1] 0.9850281

reg.line=lm(TravTime~Miles) #calcuate the regression line for the Delta data
summary(reg.line)

## 
## Call:
## lm(formula = TravTime ~ Miles)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.81510 -0.13912 -0.03479  0.14409  0.79419 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.463e-01  4.340e-02   14.89   <2e-16
## Miles       1.894e-03  3.349e-05   56.56   <2e-16
## 
## Residual standard error: 0.2662 on 98 degrees of freedom
## Multiple R-squared:  0.9703, Adjusted R-squared:   0.97 
## F-statistic:  3199 on 1 and 98 DF,  p-value: < 2.2e-16

abline(reg.line, col=2, lwd=2)

par(mfrow=c(1,2)) #make both plots appear together
plot(reg.line, which=1:2)  # plot residual assumption plots

par(mfrow=c(1,1)) #make only one plot appear at a time

Commiting changes:

• Then Go to the Git pane (next to Environment and History) in your RStudio.

• If you created a new file, you shoud see it’s name here.

• If you have made changes to your Rmd file, you should see it listed here. Click on the change to select it in this list and then click on Diff. This shows you the difference between the last committed state of the document and its current state that includes your changes.

• If you’re happy with these changes, write “Update author name” in the Commit message box and hit Commit.

You don’t have to commit after every change, this would get quite cumbersome. You should consider committing states that are meaningful to you for inspection, comparison, or restoration. In the first few assignments we will tell you exactly when to commit and in some cases, what commit message to use. As the semester progresses we will let you make these decisions.