Day 3: Basic usage of R and RStudio

2023-10-17

Announcements

  • Be sure to submit your homework on Moodle (paste the URL of your git repo)

Announcements

  • Schedule change
    • Old: Day 4 Quarto, Day 5 Tidyverse (media day), Day 6 Visualization
    • New: Day 4 Tidyverse, Day 5 Visualization (media day), Day 6 Quarto

Why use a programming language?

  • Programming makes your analysis reproducible, because you have an exact, written record of what you have done

What is reproducibility?

  • The ability for other people (including your future self!) to reproduce your analysis results
    • Gives you confidence in your results
    • Makes it easier to switch between projects

Why learn R?

  • R is free
  • R is extensible
  • R produces high-quality graphics
  • R has a large and welcoming community

R vs. RStudio

  • R is the programming language

  • RStudio is software to use R and write R code

  • We will always use R through RStudio! (don’t click on the icon for R)

Introduction to RStudio

  • We will start by navigating RStudio

  • There are four main panels

    • Source (where you write your code)
    • Environment (lists the objects in your R session)
    • R Console (where you talk to R)
    • Files and Plots (shows the files in your project, and any plots you make)

Projects

  • An R Project is a folder that contains all the files you need for a particular data analysis project
    • Basically the same thing as a repo (if you are using git)
  • Projects help organize code and files so we don’t get lost

Create a new project

  • So far, we have only cloned projects from GitHub

  • Today you will create a new project on your computer

Create a new project

  • In RStudio, click File ➡︎ New Project ➡︎ New Directory ➡︎ New Project (again)

    • Enter the name of the project (folder) and where to put it
    • I recommend putting it somewhere easy to find

  • Let’s put call this project day03-practice and put it in the data-analysis-course folder on the Desktop

  • Click “Create a git repository”, since we are using git

Create a new project

  • RStudio will restart, and you will see in the File pane that we are located in the new project

  • RStudio has created two files automatically, day03-practice.Rproj and .gitignore

    • Commit these files with the commit message “Initial commit” (or whatever you want, but that is often used as a first commit message for a new project)

  • Now we are ready to start using R!

R as a calculator

  • You can execute R code to calculate numbers directly in the console by typing the calculation then pressing “Enter”

  • Try something like this:

2 + 2
[1] 4

R as a calculator

5 * 10
[1] 50
2 / 3
[1] 0.6666667
(2 + 1)^2
[1] 9

Congratulations! You are now an R programmer!

Objects (variables)

  • R can do much more than act like a calculator

  • One very useful thing is the ability to store values in variables, often called “objects” in R

  • We assign values to objects using the arrow symbol: <-

x <- 2 + 2

However, R does not show you the value of x when you assign it

Objects (variables)

To check the value of x, type x in the console and press “Enter”

x
[1] 4

The value of x is also shown in the Environment panel (top-right panel)

Objects (variables)

Now that we have saved a value to x, we can do additional calculations with it:

x * 10
[1] 40

Objects (variables)

We can then use that code to build a new object:

y <- x * 10
y
[1] 40

Objects (variables)

But notice that the objects don’t “react” to each other (in other words, assigning a value to one object does not change the values of other objects):

x <- 5
# What is value of x?
x
[1] 5
# What is the value of y?
# (remember, y <- x * 10)
y
[1] 40

Workspace settings and using the .Rproj file

  • Before continuing, we need to change some of the default settings in R

  • I’ll also demonstrate how to use the .Rproj file to open a project

  • Quit RStudio (we will open it again soon) by clicking File ➡︎ Quit Session

Use the .Rproj file to open the project

  • Open your project by navigating to data-analysis-course/day03-practice on your Desktop and clicking on day03-practice.Rproj.

Change Workspace settings

  • What do you see in RStudio when you open your project?

Change Workspace settings

  • Notice in the “Environment” pane (upper-right) that x and y are still there, even though the code that we typed last time is gone!
    • In other words, we don’t know how we got x and y
    • This is bad for reproducibility!

Change Workspace settings

  • The contents of the R session (the “environment”) should only show what we have done using code during that session
    • We will change the default settings to avoid this behavior

Change Workspace settings

  • Click on Tools ➡︎ Global Options
    • Uncheck “Restore .RData into workspace at startup”
    • Select “Never” for “Save workspace to .RData on exit”
    • Click “OK”
  • You can also delete the .RData file, which is where those data were stored

Saving code

  • So far, we have only typed code directly into R
  • It is much more useful to save your code in a file (called a “script”) so that you can run it again without typing everything all over again

Saving code

  • Click File ➡︎ New File ➡︎ R Script

  • Click the disk icon or File ➡︎ Save As... to give your file a name (let’s say, “practice.R”) and save it.

Running code from a file

  • Type the same code as before in your new file: x <- 2 + 2 and hit the “Enter” key

  • Notice that R does not run the code, since this is the file editing pane, not the R console

  • We need to send the code to the console (that is, send it to R)

Running code from a file

  • One way to do this is to copy-and-paste it. But that is annoying.

  • The better way is to use the keyboard shortcut: control (Window) or command (Mac) + the enter key. Try it!

  • You can also either press the “Run” button in RStudio to run one line at a time, or the “Source” button to run all of the contents of your script

Comments

  • In addition to the actual code, it is very useful to include notes in your script so you can remember why you did things

  • These notes are called “comments”

  • You write a comment by starting with #. Anything after that will be ignored by R

Comments

# This is a comment.
# Let's use R like a calculator:
22 / 7
[1] 3.142857

Functions and arguments

  • The next step in our R journey is to learn about functions

  • A function takes input, does something to it, and returns output

  • For example, let’s try using the round function, which rounds numbers

Functions and arguments

  • The input to the function is indicated by using parentheses, like this:
function_name(input)
  • Try it with round:
round(3.142857)
[1] 3

Functions and arguments

  • In addition to the input, functions also have various settings, which are called “arguments”
  • For example, say we want to round to greater precision. We can use the digits argument to round to 3 digits.
round(3.142857, digits = 3)
[1] 3.143

Functions and arguments

  • R will recognize the input and arguments by their order, so you don’t actually have to specify the argument name (this can save you some typing):
round(3.142857, 3)
[1] 3.143
  • But you need to remember the order yourself! If you aren’t sure, it’s always better to be explicit and include the argument name

Getting help

  • This is all fine if you already know everything about what the function does, but nobody knows everything about R!

  • To see the settings (arguments) of a function, type a question mark followed by the name of the function, like this: ?round

  • A description will appear in the help panel on the lower left.

Getting help

  • Googling or asking ChatGPT* are also OK

  • *but always be sure to check what ChatGPT tells you!

Vectors

  • So far, we have been doing calculations on one value at a time. But we want to be able to calculate many things at once.

  • We can do that with vectors, which are a series of values

  • You make a vector with the c() function

    • (from now on I will always refer to functions by their name plus the parentheses, since you always need them to actually use the function)

Vectors

# Create a vector of numbers
# (a "numeric vector")
numbers <- c(1, 2, 3, 4, 5)
numbers
[1] 1 2 3 4 5

Vectors

We can now do use the vector as input. Say we want to double each of the numbers:

numbers * 2
[1]  2  4  6  8 10

…or obtain their mean value:

mean(numbers)
[1] 3
  • Almost everything you do in R relies on objects, functions, and vectors

Data types

  • Vectors have a rule: each item (called an “element”) of the vector must be of the same data type

  • The basic data types in R include:

    • Numeric (numbers, also confusingly called "double")
    • Character (words)
    • Logical (TRUE or FALSE)

Data types

# A numeric vector:
c(1, 2, 3)
[1] 1 2 3
# A character vector:
c("banana", "orange", "apple")
[1] "banana" "orange" "apple" 
# A logical vector:
c(TRUE, FALSE, TRUE)
[1]  TRUE FALSE  TRUE

Data types

We can check the type of the vector using the typof() function.

nums <- c(1, 2, 3)
typeof(nums)
[1] "double"
fruit <- c("banana", "orange", "apple")
typeof(fruit)
[1] "character"
tf <- c(TRUE, FALSE, TRUE)
typeof(tf)
[1] "logical"

Data types

  • What do you think happens if you try to combine data of different types?

  • Try it!

mixed <- c(1, 2, "banana", "orange")

Data types

mixed <- c(1, 2, "banana", "orange")
mixed
[1] "1"      "2"      "banana" "orange"
typeof(mixed)
[1] "character"
  • The numeric data and the character data were all forced to be character (even though "1" may look like a number, the quotation marks show you that it is stored as a character)

Comparisons

  • We will finish by demonstrating a very useful thing in programming: comparing values

  • The comparison symbols are:

    • > greater than
    • < less than
    • == equals (be careful! use two equals signs, not one)
    • != not equal

Comparisons

  • The comparisons will return a logical vector:
# Here are some ages of people
ages <- c(21, 8, 40)

# Which of these people are adults?
ages > 20
[1]  TRUE FALSE  TRUE

Subsetting

  • The reason that comparisons are so useful is that you can use them for subsetting, that is, to narrow down the data

  • You perform subsetting with square brackets, []

Subsetting

  • Here we obtain the second value of the vector:
ages[2]
[1] 8

Subsetting

  • Or we could obtain the first and second values:
ages[c(1, 2)]
[1] 21  8

Subsetting

  • Or we can use a logical vector to indicate which values to keep:
ages[c(TRUE, TRUE, FALSE)]
[1] 21  8

Subsetting

  • However, you typically don’t type such logical vectors by hand

  • It is more useful to subset by using the output of a comparison

  • For example, let’s subset to only ages of adults. Recall how we set up that comparison:

ages > 20
[1]  TRUE FALSE  TRUE

Subsetting

  • Now, use that code to subset the values:
ages[ages > 20]
[1] 21 40
  • This kind of subsetting is very helpful for working with data, which will do starting next week!

Homework

  • Go to Moodle, click on Day 3 Homework and click on the link to accept the assignment

  • Clone the repo to your data-analysis-course on your Desktop, like we did last time

Homework

  • Edit the day03_homework.R file to answer the questions.

  • Make sure to run the code. Your R code should not have any errors!

  • Commit your changes as you work on your homework, and push them to the remote

  • Submit the URL for the remote as your answer on Moodle

Homework and ChatGPT

  • I provide homework to give you a chance to think and learn

  • For basic R homework, ChatGPT can answer all of the questions instantly, and I can’t tell if you used it or not

  • But if you only use ChatGPT, you will not learn anything

  • Please think about why you are taking this class (and why you are paying money to attend Chiba U): do you just want a grade, or do you want to learn? It is up to you.