Clearing the Work space

For most of us, we used R in STA 112. This means that when you open RStudio, the “data environment” (the upper right hand panel of RStudio) where all our data sets are stored may be very full! If your Environment is blank, don’t worry about it! If it’s not blank, there is a way to clear your Environment, i.e., remove data sets that you no longer need.

To clear all of the data in the upper right hand panel, look at the top of the panel. Beside “Import Dataset” there is an image that looks like a small broom. Pushing that will “clean” your space, meaning that it will remove all the contents within the panel. It is good practice to do this before you start each lab. This keeps us from having a lot of unnecessary stuff cluttering up this window, making the data sets you need easier to find.

Loading the Data

Now that our Environment is clear, we need to load a new data set into it. For our lab today, we will be working with a data set on animal adoption that you can find on Canvas. We will access data from a variety of sources in this course, but today we will be loading our data from a csv file.

To load the data, complete the following steps:

• Step 1: Go to Canvas and download the data for Lab 1.
• Step 2: Go back to R and look at the upper right hand panel of your RStudio screen (the Environment tab).
• Step 3: Find “Import Dataset” or “Import” and click on it.
• Step 4: Choose “Text (base)” or “From CSV” (it will depend on your computer).
• Step 5 Find your data (AdoptedData.csv) in the list that comes up. Choose it!
• Step 6: Now, look at the bottom right hand panel of your screen. You should see a line of code with something like AdoptedData <- read.csv(“AdoptedData.csv”) or AdoptedData <- read_csv(“AdoptedData.csv”).
• Step 7 Copy that ENTIRE line of code.
• Step 8: In your Markdown file, insert a code chunk. Look at the top of your Markdown file, and find Insert. Click it. From the drop-down menu, choose R. Click it! A gray box should appear in your Markdown file. This is a code chunk.
• Step 9: Paste the line from Step 6 into this gray code chunk, and press the green arrow (the play button).

Now, look on the upper right hand panel of your RStudio screen. See how you now have a data set called AdoptedData? Great! We are ready to go!

Professionalism: Note 1

One thing to note that is that the name of our current variable of interest is very cumbersome: AdoptedLessThan30. When we are writing about our conclusions to a client or anyone who is interested in our results, we never use the name of the variable that is given in R. The only exception is if that variable name is a proper word (like height or weight).

In addition to how we write about data results, we also need to think about the format of any output that we show. For instance, we have created a table to analyze the data so far, but that table is raw R output and is not formatted very professionally. Luckily, there is a very cool code that helps us convert an R table into a professionally formatted table:

knitr::kable( )

If you put a code that produces a table inside of the ( ), the table will be formatted professionally, ready for a report.

knitr::kable( table(AdoptedData$AdoptedLessThan30) )

You can even add a title:

knitr::kable( table(AdoptedData$AdoptedLessThan30), caption ="The Title You Want")

Go ahead and run this code in R. Hmm…that doesn’t look so pretty. Why are we excited about this?? Knit your document, and take a look at the table you see there.

Ah, there we go. It looks great, except that the column names are strange. Let’s fix those:

knitr::kable( table(AdoptedData$AdoptedLessThan30), caption ="The Title You Want", col.names = c("Column 1 Name", "Column 2 Name"))

We will learn more of these tips as we move throughout the course. The key is to remember to always apply these tips when you are creating output for an analysis that will be seen by anyone other than just you.

Installing ggplot2

To make visualizations in this course, we will be working with a group of functions in the ggplot2 package in R. The package ggplot2 is a powerful collection of R functions for creating flexible, professional graphics.

The first step to using ggplot2 is to install the ggplot2 package. Go to the top of your RStudio window and find “Tools”. From there, click on “Install Packages.” In the blank box, type in ggplot2, and hit install. The computer should automatically begin to load in the packages that you need, but this may take a minute.

Note that this process of installing a package is one you need to do only once. Think of this as teaching R a new set of skills. Once it knows the skills, you don’t have to teach it again.

Now, some of you may see an error about language parsing, or an error involving rlang. If you do, go ahead and install the rlang package. Then, copy and paste the following into a chunk and hit play. Nothing will seem to happen, and that’s okay.

library(rlang)

Creating a Bar Plot

Once you have installed the ggplot2 package, you need to tell R that you would like to begin using the function by loading the library. Remember that we said installing a package is like teaching R a skill? Loading a library is how we tell we R we want it to use those skills. To do this, create a chunk in your RMarkdown and copy and paste the following, and hit play.

suppressMessages(library(ggplot2))

Note that this process of loading a library is one you have to do ONCE each time we start a lab or project.This tells R “Hey, remember those skills we taught you? Use them.”

To create a bar plot for the adoption time, paste the following code in a chunk and hit play.

ggplot(AdoptedData, aes(x=AdoptedLessThan30)) +  
  geom_bar(fill='blue', col = 'white')

The creation of plots in ggplot2 requires building the plot in layers. First, we build the background, the grid on which we will be building our graph. This is the job of the ggplot() part of the code.

ggplot()

Once we have built the background, we need to tell R what data we want to use to create the graph. We need to tell it the name of the data set we want to use (AdoptedData) and then the variable we want to work with (AdoptedLessThan30). The aes part of the code stands for “axis”, we want body mass to be on the x-axis of our graph.

ggplot(AdoptedData, aes(x=AdoptedLessThan30))

The next step is to tell R what type of graph we want to make using this variable. In this case, we want a bar plot. The command we use to build a histogram is + geom_barplot().

ggplot(AdoptedData, aes(x=AdoptedLessThan30))
  + geom_barplot() 

Now, we want to specify some things about our bar plot. We want the bars to be filled ( 'fill') in blue and we want them outlined ( 'col') in white.

ggplot(AdoptedData, aes(x=AdoptedLessThan30)) +  
  geom_bar(fill="blue", col = "white")

Let’s try it.

We can add on another layer to our plot by adding x and y axis labels, as well as a title for the plot. The command labs, which stands for labels, is used for this.

ggplot(AdoptedData, aes(x=AdoptedLessThan30)) +  
  geom_bar(fill='blue', col = 'white') + 
  labs(title="Figure 1:", x = "Number of Days before Adoption", y = "Number of Animals") 

This ggplot syntax actually mimics the ways humans would draw a graph by hand. First, you draw the axes. Then, you add on your data. Finally, you add a label. Thinking through the steps in this manner will help you understand the syntax of this package.

One VERY important thing to remember when we make plots is to make sure the axes are easily interpretable by your reader. You do not want to use default variable names, like “FosterYes”. Instead, we want clear labels like “Was the animal fostered?”.

This is a requirement for all graphs you make in this course - you must label your axes appropriately and title your graphs.