This lab will go through the same workflow as class with R / RStudio and git / GitHub.

Remember, git is a version control system (like “Track Changes” from Microsoft Word but more powerful) and GitHub is the home for your Git-based projects on the internet (like DropBox but much better).

Getting started

Configuring SSH and GitHub

Until recently, you could use a user name and password to log into GitHub. GitHub has deprecated using a password in that way. Instead, we will be authenticating GitHub using public/private based keys. This is a short overview for how to do authenticate in this way.

First, type credentials::ssh_setup_github() into your console.
Second, R will ask “No SSH key found. Generate one now?” You should click 1 for yes.
Third, you will generate a key. It will begin with “ssh-rsa….” R will then ask “Would you like to open a browser now?” You should click 1 for yes.
Fourth, you may be asked to provide your username and password to log into GitHub. This would be the ones associated with your account that you set up. After entering this information, you should paste the key in and give it a name.

Clone the repo & start new RStudio project

Click on this link to accept the assignment: https://classroom.github.com/a/136Nt6V2
Click on the green CODE button, select Use SSH (this might already be selected by default, and if it is, you’ll see the text Clone with SSH). Click on the clipboard icon to copy the repo URL.

How to Clone using SSH in GitHub

Go to https://vm-manage.oit.duke.edu/containers and login with your Duke NetID and Password.
Click RStudio - STA198-199 to log into the Docker container. You should now see the RStudio environment.
Go to File \(\rightarrow\) New Project \(\rightarrow\) Version Control \(\rightarrow\) Git.
Copy and paste the URL of your assignment repo into the dialog box Repository URL. Again, please make sure to have SSH highlighted under Clone when you copy the address.

Version Control and Cloning Repo

Click Create Project, and the files from your GitHub repo will be displayed in the Files pane in RStudio.
Click lab01.Rmd to open the template R Markdown file.

Possible Error Message

If you get an error message that begins with WARNING: UNPROTECTED PRIVATE KEY FILE! then this can be fixed by clicking on “Terminal” (the tab next to the console) and pasting in chmod 400 ~/.ssh/id_rsa and hitting enter. Then, try to create a projet again and it should work.

Configure git

There is one more piece of housekeeping we need to take care of before we get started. We need to configure git so that RStudio can communicate with GitHub. This requires two pieces of information: your name and email address.

To do so, you will use the terminal, which you can find right next to the console.

git config —global user.name 'username'
git config --global user.email ‘useremail’

For example, mine would be

git config —global user.name 'jacobfhsmith'
git config --global user.email ‘jacob.f.smith@duke.edu’

R and R Studio

These are the general components of a R Markdown File

These are the general components of a R Project

Warm up

Before we introduce the data, let’s warm up with some simple exercises. We’re going to go through our first commit and push.

YAML

The top portion of your R Markdown file (between the three dashed lines) is called YAML. It stands for “YAML Ain’t Markup Language”. It is a human friendly data serialization standard for all programming languages. All you need to know is that this area is called the YAML (we will refer to it as such) and that it contains meta information about your document.

Open the R Markdown (Rmd) file in your project, change the author name to your name, and knit the document. Examine the knitted document.

Commiting changes

Now, go to the Git pane in your RStudio instance. This will be in the top right hand corner in a separate tab.

If you have made changes to your Rmd file, you should see it listed here. Click on it 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 including changes. You should see deletions in red and additions in green.

If you’re happy with these changes, we’ll prepare the changes to be pushed to your remote repository. First, stage your changes by checking the appropriate box on the files you want to prepare. Next, write a meaningful commit message (for instance, “updated author name”) in the Commit message box. Finally, click Commit. Note that every commit needs to have a commit message associated with it.

You don’t have to commit after every change, as this would get quite tedious. You should commit 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.

Pushing changes

Now that you have made an update and committed this change, it’s time to push these changes to your repo on GitHub.

In order to push your changes to GitHub, you must have staged your commit to be pushed. click on Push.

Packages

In this lab we will work with two packages: datasauRus which contains the dataset, and tidyverse which is a collection of packages for doing data analysis in a “tidy” way.

If you want, you can Knit your template document and see the results.

The packages we are using should already be installed and only need to be loaded.

library(tidyverse) 
library(datasauRus)

Datasaurus Data

The data frame we will be working with today is called datasaurus_dozen and it’s in the datasauRus package. Actually, this single data frame contains 13 datasets, designed to show us why data visualization is important and how summary statistics alone can be misleading. The different datasets are marked by the dataset variable.

To find out more about the dataset, type the following in your console.

?datasaurus_dozen

Based on the help file, how many rows and how many columns does the datasaurus_dozen file have? What are the variables included in the data frame? Add your responses to your lab report. When you’re done, commit your changes with the commit message “added answer for exercise 1”, and push.

Let’s take a look at what these datasets are. To do so we can make a frequency table of the dataset variable. Run the code chunk below.

datasaurus_dozen %>%
  count(dataset) %>%
  print(13)

## # A tibble:
## #   13 × 2
##    dataset   
##    <chr>     
##  1 away      
##  2 bullseye  
##  3 circle    
##  4 dino      
##  5 dots      
##  6 h_lines   
##  7 high_lines
##  8 slant_down
##  9 slant_up  
## 10 star      
## 11 v_lines   
## 12 wide_lines
## 13 x_shape   
## # … with 1
## #   more
## #   variable:
## #   n <int>

The original Datasaurus (dino) was created by Alberto Cairo in this great blog post. The other Dozen were generated using simulated annealing and the process is described in the paper Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing by Justin Matejka and George Fitzmaurice. In the paper, the authors simulate a variety of datasets that the same summary statistics to the Datasaurus but have very different distributions.

Data visualization and summary

Plot y vs. x for the dino dataset. Then, calculate the correlation coefficient between x and y for this dataset.

Below is the code you will need to complete this exercise. Basically, the answer is already given, but you need to include relevant bits in your Rmd document and successfully knit it and view the results.

Start with the datasaurus_dozen and pipe it into the filter function to filter for observations where dataset == "dino". Store the resulting filtered data frame as a new data frame called dino_data.

dino_data <- datasaurus_dozen %>%
  filter(dataset == "dino")

There is a lot going on here, so let’s slow down and unpack it a bit.

First, the pipe operator: %>%, takes what comes before it and sends it as the first argument to what comes after it. So here, we’re saying filter the datasaurus_dozen data frame for observations where dataset == "dino".

Second, the assignment operator: <-, assigns the name dino_data to the filtered data frame.

Next, we need to visualize these data. We will use the ggplot function for this. Its first argument is the data you’re visualizing. Next we define the aesthetic mappings. In other words, the columns of the data that get mapped to certain aesthetic features of the plot, e.g. the x axis will represent the variable called x and the y axis will represent the variable called y. Then, we add another layer to this plot where we define which geometric shapes we want to use to represent each observation in the data. In this case we want these to be points, hence geom_point.

ggplot(data = dino_data, mapping = aes(x = x, y = y)) +
  geom_point()

For the second part of this exercise, we need to calculate a summary statistic: the correlation coefficient. The correlation coefficient (r) measures the strength and direction of the linear association between two variables. You will see that some of the pairs of variables we plot do not have a linear relationship between them. This is exactly why we want to visualize first: visualize to assess the form of the relationship, and calculate \(r\) only if relevant. In this case, calculating a correlation coefficient really doesn’t make sense since the relationship between x and y is definitely not linear.

For illustrative purposes only, let’s calculate the correlation coefficient between x and y.

dino_data %>%
  summarize(r = cor(x, y))

## # A tibble: 1 × 1
##         r
##     <dbl>
## 1 -0.0645

Now pause, knit and commit changes with the commit message “added answer for exercise 2” Push these changes when you’re done.

Plot y vs. x for the star dataset. You can (and should) reuse code we introduced above, just replace the dataset name with the desired dataset. Then, calculate the correlation coefficient between x and y for this dataset. How does this value compare to the r of dino?

Now pause, knit, commit changes with the commit message “Added answer for Ex 3”, and push.

⊕Facet by the dataset variable, placing the plots in a 3 column grid, and don’t add a legend.

Finally, let’s plot all datasets at once. In order to do this we will make use of faceting, given by the code below:

ggplot(datasaurus_dozen, aes(x = x, y = y, color = dataset))+
  geom_point()+
  facet_wrap(~ dataset, ncol = 3) +
  theme(legend.position = "none")

And we can use the group_by function to generate all the summary correlation coefficients. We’ll go through these functions next week when we learn about data wrangling.

datasaurus_dozen %>%
  group_by(dataset) %>%
  summarize(r = cor(x, y))

Include the faceted plot and the summary of the correlation coefficients in your lab write-up by including relevant code in R chunks (give them appropriate names). In the narrative below the code chunks, briefly comment on what you notice about the plots and the correlations between x and y values within each of them (one or two sentences is fine!).

You’re done with the data analysis exercises, but we’d like to do one more thing to customize the look of the report.

Resize your figures

We can customize the output from a particular R chunk by including options in the header that will override any global settings.

In the R chunks you wrote for Exercises 2-4, customize the settings by modifying the options in the R chunks used to create those figures.

For Exercises 2 and 3, we want square figures. We can use fig.height and fig.width in the options to adjust the height and width of figures. Modify the chunks in Exercises 2 and 3 to be as follows:

```{r ex2-chunk-name, fig.height = 4, fig.width = 4}

Your code that created the figure

```

For Exercise 4, please modify your figure to have fig.height of 6 and fig.width of 8.

Now, save and knit. Once you’ve created this .pdf file, you’re done!

Commit all remaining changes, use the commit message “done with lab 1!” and push.

Lab #01: Hello R!

due Monday January 10 at 11:59 PM