Welcome to the labs!

• Throughout the year, you’ll be putting your data science skills to work by completing the labs.
• You’ll learn how to program in the R programming language.
  • The programming language used by actual data scientists.
• Your code will be written in RStudio which is an easy to use interface for coding using R.

So let’s get started!

• The data for our first few labs comes from the Centers for Disease Control (CDC)
  • The CDC is a federal institution that studies public health.
• Type these two commands into your console:

data(cdc)

View(cdc)

• Describe the data that appeared after running View(cdc):
  • Who is the information about?
  • What sorts of information about them was collected?
• To find out more information about the cdc data, type the command below into your console.
  • To get back to the slides find and click on the Viewer tab.

?cdc

Data: Variables & Observations

• Data can be broken up into two parts.
  1. Observations
  2. Variables
  • Observations are the who or what we are collecting data from/about.
  • Variables are the measurements or characteristics about our observations.
• If need be, re-type the command you used to View your data. Then answer the following:
  • Based on the data, describe a few characteristics about the first observation.
  • What does the first column tell us about our observations?
• In order to describe the first observation, notice that you had to look at the first row of the spreadsheet. Each row, in this case, describes a person.
• The columns of the spreadsheet represent variables.

Uncovering our Data’s Structure

• Now that we’ve looked at our data, let’s look at how RStudio is organized.
• RStudio’s main window is composed of four panes.
• Find the pane that has a tab titled Environment and click on the tab.
  • This pane contains a list of everything that’s currently available for R to use.
  • Notice that R knows we have our cdc data loaded.
• How many students are in our cdc dataset?
• How many variables were measured for each student?

Some new functions

• Type the following commands into the console:

dim(cdc)

nrow(cdc)

ncol(cdc)

names(cdc)

• Which of these functions tell us the number of observations in our data?
• Which of these functions tell us the number of variables?

First Steps

• Typing commands into the console is your first step into the larger world of programming or coding (terms which are often used interchangeably).
• Coding is all about learning how to send instructions to your computer.
  • The way we speak to the computer, using a coding language, is syntax.
• R is one of many coding languages. Each coding language is slightly different, and these differences are reflected in the syntax.
• Capitalization, spelling and punctuation are REALLY important.

Syntax matters

• Run the following commands. What happens after each command?

Names(cdc)

NAMES(cdc)

names(cdc)

names(CDC)

• Which does R understand?

R’s most important syntax

• Most of the commands you will be using follow the syntax below:

function(y ~ x, data = ___)

• To create graphs or plots you need to provide R with the following:
  • The name of the R function, often the plot’s name, that tells the computer how to create your graph.
  • The variable(s) containing the information we want the function to use.
  • The dataset containing the variables.
• Notice that when we analyze a single variable the value for y is left blank.

bargraph(~grade, data = cdc)

• Later on, we’ll see we can use this syntax to do more than create graphs.

Syntax in action

• Search through the different panes. Find and then click on the Plots tab.

  • To get back to the slides, find and then click on the Viewer tab.

• Which one of these plots would be useful for answering the question: Is it unusual for students in the CDC dataset to be taller than 1.8 meters?

• Run the three commands below then answer the question that follows.

histogram(~height, data = cdc)

bargraph(~drive_text, data = cdc)

xyplot(weight~height, data = cdc)

• Do you think it’s unusual for students in the data to be taller than 1.8 meters? Why or why not?

• Hint: Use the arrow keys on the Plots tab to toggle between the plots.

On your own

• After completing the lab, answer the following questions:
  • What is public health and do we collect data about it?
  • How do you think our data was collected? Does it include every high school aged student in the US?
  • How might the CDC use this data? Who else could benefit from using this data?
  • Write the code to visualize the distribution of weights of the students in the CDC data with a histogram. What is the typical weight?
  • Write the code to create a bargraph to visualize the distribution of how often students ate fruit. About how many students did not eat fruit over the previous 7 days?