Day 3: Basic usage of R and RStudio

Recordings

  • I will record lectures starting from today and post them on Moodle

  • This does not mean you can skip class. The best way to learn is to come to class and ask questions!

What to do if the Git pane doesn’t work

What to do if the Git pane doesn’t work

  • Finish editing the homework assignment

  • In the terminal, do the following commands:

    • git add FILENAME (for example, git add day03_homework.R)
    • git commit -m "Finish homework"
    • git push

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 right.

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 typeof() 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

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.