Module 1: An Introduction and Motivation for R Programming

• Why are we here?
• What are we going to do?
• A quick introduction to R

• Instructor: Jacob Jameson
• Experience:
  • 2nd year MSCAPP student
  • Data Analyst at the Center for Health and the Social Sciences
  • Previous TA for the Booth course Data Analysis in R and Python
• Research Interests: I am interested in studying the quantitative techniques that are used for medical decision-making at the individual and collective level.

You will also have several TAs who will be helping you along the way

Many public policy jobs and the Harris curriculum rely on programming

• to quickly engage with policy data
• to complete statistical analyses

Why R?

• Great data manipulation and visualization suite
• Strong statistical packages (e.g. program evaluation, machine learning)
• Complete programming language with low barriers to entry
• Open source and free

Quantifying the impact of a proposed federal partnership with the company Dollar General to serve as vaccination sites and comparing vaccine access with Dollar General to the current Federal Retail Pharmacy Partnership Program.

Coding Camp:

1. Motivation/Installation of R
2. Installing Packages and Reading Data
3. Basic Data Manipulation
4. Markdown Files, Knitting, and Data Structures
5. More on Data Manipulation and Analysis
6. Data Cleaning and Debugging

This is just the beginning of your programming journey!

• There is a lot of information out there

• We will only cover some of it

• If you think we are covering too much, also let us know

• We learn coding by experimenting with code.
• Coding requires a different modality of thinking
• Coding can be frustrating
• We develop self-sufficiency by learning where to get help and how to ask for help
• Coding lab is for you.

• Video lectures:

  • Have R open. Pause regularly.
  • Focus on main idea first.

• Practice in labs (most important part):

  • You learn coding by coding.
  • Break up into small groups and work on problems with peer and TA support

• Q and A (live session):

  • Please send me questions ahead of class
  • May include additional practice problems.

Objectives

• Discuss what Rstudio is
• Introduce minimal information to get started working with R
• Expose you to the different data types in R
• Learn different types of operators

R Studio is an “integrated development environment” for R.

• It provides a console to access R directly.
• A text editor to write R scripts and work with Rmds
• An environment and history tab that provide useful information about what objects you have in your R session
• A help / plots / files / packages etc. section

If you have not already downloaded RStudio– do so now!

Let's Take a tour in Rstudio

We can think of a variable as a container with a name, such as x, current_temperature, or subject_id that contains one or more values. We can create a new variable and assign a value to it using = or ->.

x = 7
x

[1] 7

In the above bit of code, we created a variable called x and stored the value 7 inside of it.

We can think of a variable as a container with a name, such as x, current_temperature, or subject_id that contains one or more values. We can create a new variable and assign a value to it using = or ->.

my_number <- 5
my_number

[1] 5

In the above bit of code, we created a variable called my_number and stored the value 5 inside of it.

2 * x

[1] 14

my_number + 5

[1] 10

We can add comments to our code using the # character. It is useful to document our code in this way so that others (and us the next time we read it) have an easier time following what the code is doing.

#my_number is the variable I set equal to 5
my_number - 10

[1] -5

• character/string: “a”, “swc”

• numeric: 2, 15.5

• logical/boolean: TRUE (T), FALSE (F)

• special values: missing values (NA), impossible values (NaN), infinity (Inf), etc

• +, -, *, and /. Also, %% (for mod), ^ (Exponentiation).

-7
7 + 5
7 - 5
7 %% 5
2^4

Inf is infinity. You can have either positive or negative infinity.

1/0

[1] Inf

2^1234

[1] Inf

NaN means Not a Number. It's an undefined value.

0/0

[1] NaN

These are also binary operators; they take two objects, and give back a Boolean

7 > 5
7 < 5
7 >= 7

7 <= 5
7 == 5
7 != 5

Warning: == is a comparison operator, = is not!

• TRUE & FALSE -> FALSE
• TRUE & TRUE -> TRUE
• FALSE & FALSE -> FALSE
• TRUE | FALSE -> TRUE
• TRUE | TRUE -> TRUE
• FALSE | FALSE -> FALSE

(5 > 7) & (6 * 7 == 42)

[1] FALSE

(5 > 7) | (6 * 7 == 42)

[1] TRUE

(5 > 7) | (6 * 7 == 42) & (0 != 0)

[1] FALSE

• What are the atomic (basic) data types in R?

• Will (6 * 10 > 55 ) & (9 != 10-1) | (0 == 1) return TRUE?

After going through this video, you should understand how to:

• navigate and use Rstudio’s features
  • particularly, the console, the text editor and help
• assign objects to names with <- or =
• Understand the importance of data analysis in public policy

R Markdown is a file format for making dynamic documents with R.

An R Markdown document is written in markdown (an easy-to-write plain text format) and contains chunks of embedded R code, like the document below.