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title published cover title description programming_language learning objectives facilitators estimated time dependencies authors editors ethical considerations projects resources goals
Introduction to R
false
R
R is an increasingly popular language for data analysis, especially in academia.
R
Familiarize you with R language
Understand R syntax, logic, and
name description
Leanne Fan
Here is a short bio.
3 - 4 hours
workshop prerequisites
command-line data-ethics
description required
Introduction to the Command Line (Required) This workshop makes reference to concepts from the Command Line workshop, and having basic knowledge about how to use the command line will be central for anyone who wants to learn about programming with Python.
true
description recommended
Data Ethics (Recommended) This workshop will give you a basis for thinking through the ethical considerations of your programming projects.
true
Yuxiao Luo
Sam O'Hana
Di Yoong
Leanne Fan
Di Yoong
Lisa Rhody
Stephen Zweibel
understanding where your data comes from
carefully drawing conclusions
relationship between theory and data anlaysis
description The Fossil Fuel Non-Proliferation Tracker
Projects that use the skills you'll learn in this workshop:
description link
The data presented here is based on the Fossil-Fuel Non-Proliferation Database which semi-automatically searches the internet to identify existing climate change supply-side policies. We also rely on data from the Global Fossil Fuel Divestment Commitments Database (managed by Stand.Earth), CAIT Climate Data Explorer, ourworldindata.org, Carbon Brief, Go Fossil Free, The Fossil Fuel Registry, BP Statistical Review of World Energy, Shift Data Project, and reference other available data sources such as the “Fossil Fuel Supply Cuts Database“ beside others. The data used in this Tracker is updated from various sources on a rolling basis. test test test
Why R is Hard to Learn
description link
This article helps beginner learners articulate the "un-intuitive" parts of the R language.
description
In this workshop, you will learn to:
Become familiar with core R programming concepts, including data vs. functions, tabular data, and operators.

Intended Audience

Are you a student in academic fields like humanities, social sciences, or various science programs where R isn't typically taught? This workshop isn't about diving deep into data wrangling or how to use R to run statistial models; instead, it lays the foundation for you to appreciate R's functionality, syntax and logic. While you don't need to install R or R Studio (more on these later) to get started, by the end of the workshop, you will be able to get started with those.

We'll start with interactive exercises and practical challenges to provide you with rapid feedback and tangible outcomes.

Why Learn R?

R is a versatile, open source programming/scripting language that’s useful both for statistics but also data science. It has become a standard language for statistical computing and data analysis, especially in academia. Its open source nature has contributed to its widespread adoption and increasing role as a standard for writing reproducible code.

  • Open source software under GPL.
  • R has over 7,000 user contributed packages at this time. It’s widely used both in academia and industry.
  • Available on all platforms.
  • Not just for statistics, but also general purpose programming.
  • For people who have experience in programmming: R is both an object-oriented and a so-called functional language
  • Large and growing community of peers.
  • You can use R as it is but combining it with the RStudio interface will help with organization and also provide us with extra options.

Interacting With R

This workshop is meant to be interactive--it intends to immediately engage you with the concepts you are learning. To that end, all of the R programming you will learn about can be done here, directly in your browser. Throughout the workshop, you will encounter several designated code sections in which you can write and run your R code. These emulators are meant to allow you quick and easy access to coding in R. However, there are many ways to interact with R - the last section of the workshop will be learning how to install R onto your computer.

With that said, let's get started!

A Little Math

Let's try a little math in the R prompt. When you hit "Run Code," a code editor (really just a mini R engine) will pop up and you can start playing around! The top part is the code and the bottom part is the output.

1+1

First Puzzle

You see the following series of numbers and the end result of 8. Play with the code editor to come up with the right arithamtic operations to end up with 8!

10 __ 5 __ 6 __ 4 = 8

Replace the _ with some combination of the following operators in the code editor:

  • "*"
  • "/"
  • "+"
  • "-"

10 [] 5 [] 6 [] 4

10 / 5 * 6 - 4

Try adding paraentheses - does it change the answer?

Getting used Boolean operations

== is a comparison operator. Equal to operator. This returns true only if the two values are equal.

What do you predict will happen when we hit run code?

3 == 3 4 == 10 4 == "hello"

Why might something like this be useful...these statements are obviously true or false!

Data types

Data type are classifications associated with specific data that let the computer know how to interpret the value and how a programmer intends to use a piece of data. We've already encoutered a data type, integers, in the previous section when we tried the expression 3 + 3. Data types are common in many programming language, and each data type has its own unique properties. In R, certain functions can only take specific data types, and attempts at using a different data type can result in errors.

Here is a list of some of the data types you might use:

  • int stands for integers, or whole numbers.

  • dbl stands for doubles, or numbers with decimals.

  • chr stands for character vectors, or strings.

There are other data types but these are the basic ones to get us started.

typeof()

typeof() is a handy function that returns the data type of your data. If you’re ever in doubt, check your data with typeof()!

What do you think 5.44445 is?

How about "5.44445"?

typeof(5.44445) typeof("5.44445")

So typeof() is our first function! Function is simply a way of doing something, a way of saving some code for reuse, and a way of taking an input, transforming that input, and returning an output.

Creating objects

This is where we leave simple calculator land and enter...computer language!

Let's look at the difference between:

2 / 3

and...

head_size <- 2 / 3

We are going from values to objects. Storing values in objects allows you to do interesting things with them later. To create an object, you pick a succinct, easy to remember name and then use the assignment operator <- to give the name a value.

The format is name <- value.

The value on the right is assigned to the object on the left.

This statement can be interpreted as “2/3 goes into head_size”. For historical reasons, you can also use = for assignments, but not in every context. Because = can throw unexpected results in some situations, it is good practice to always use <- for assignments.

Objects can be given any name such as x, body_size, or signal_length. You want your object names to be explicit and not too long. They cannot start with a number (2x is not valid, but x2 is). R is case sensitive (e.g., weight_kg is different from Weight_kg).

Which of the following object names is invalid?

  • site_num

  • R2

  • 1st_try

  • fish.scale.size

Try assigning numbers of each of these variable names. What happens?

You might have noticed that assigning a value to an object doesn’t print anything. To print the value, you can either use parentheses around the assignment or type the object name:

head_size <- 40 # doesn't print anything (head_size <- 40) # prints output

head_size # prints output if you've already assigned a value to the object

Important aside: The comments written to the right of the hash mark don’t get run as code. Using informative comments is good coding practice. Clear comments are essential for when you want someone else to run your code and be able to interpret what you’re typing.

Let's do something with head_size - how do we multiply by 2?

What is a vector?