Lecture 6

R Basics

Byeong-Hak Choe
bchoe@geneseo.edu

SUNY Geneseo

February 8, 2024

Announcement

  • DANL Tutoring Schedule, Spring 2024
  • Liza Mitchell
    • Wednesday 9:00 AM – 10:00 AM
    • Friday 8:00 AM – 9:00 AM
  • Nada Trabelsi
    • Tuesday 12:30 PM – 1:30 PM
    • Thursday 12:30 PM – 1:30 PM
  • Daniel Noone
    • Tuesday 5:00 PM - 6:00 PM
    • Thursday 5:00 PM - 6:00 PM
  • Dominic Rodriguez-Donohue
    • Tuesday 11:00 AM – 12:15 PM
    • Thursday 11:00 AM – 12:15 PM

Tasks & Objectives

  • Setting up the DANL tools
    • R/RStudio or Posit Cloud
    • tidyverse Package
    • Personal Website
  • Learning
    • Website Basics
    • R Basics

R Basics

R Basics

RStudio Environment

  • Script Pane is where you write R commands in a script file that you can save.
    • An R script is simply a text file containing R commands.
    • RStudio will color-code different elements of your code to make it easier to read.

R Basics

RStudio Environment

  • Console Pane allows you to interact directly with the R interpreter and type commands where R will immediately execute them.

R Basics

RStudio Environment

  • Environment Pane is where you can see the values of variables, data frames, and other objects that are currently stored in memory.

R Basics

RStudio Environment

  • Plots Pane contains any graphics that you generate from your R code.

R Basics

Workflow: Code and comment style

  • The two main principles for coding and managing data are:
    • Make things easier for your future self.
    • Don’t trust your future self.
  • The # mark is R’s comment character.
    • In R scripts (*.R files) and in a code chunk in Quarto (*.qmd), # indicates that the rest of the line is to be ignored.
    • Write comments before the line that you want the comment to apply to.
    • Ctrl/command + Shift + C is the shortcut for # (commenting).
  • When using Quarto, use Markdown to explain data analysis and code chunks.

R Basics

Workflow: Shortcuts in RStudio

  • Home/End moves the blinking cursor bar to the beginning/End of the line.

    • Ctrl (command for Mac Users) + ⬅️ and ➡️ works too.

  • Ctrl (command for Mac Users) + Z undoes the previous action.

  • Ctrl (command for Mac Users) + Shift + Z redoes when undo is executed.

  • Ctrl (command for Mac Users) + F is useful when finding a phrase (and replace the phrase) in the RScript.

R Basics

Workflow: Auto-completion

libr

  • Auto-completion of command is useful.
    • Type libr in the RScript in RStudio and wait for a second.

R Basics

Workflow: STOP icon

  • When the code is running, RStudio shows the STOP icon ( 🛑 ) at the top right corner in the Console Pane.
    • Do not click it unless if you want to stop running the code.

R Basics

Values, Variables, and Types

  • A value is datum (literal) such as a number or text.

  • There are different types of values:

    • 352.3 is known as a float or double;
    • 22 is an integer;
    • “Hello World!” is a string.

R Basics

Values, Variables, and Types

  • A variable is a name that refers to a value.
    • We can think of a variable as a box that has a value, or multiple values, packed inside it.
  • A variable is just a name!

R Basics

Assignment

x <- 2
x < - 3

  • What is going on here?

  • The shortcut for the assignment <- is:

    • Windows: Alt + -
    • Mac: option + -

R Basics

Assignment

x <- 2
y <- x + 12
  • In programming code, everything on the right side needs to have a value.
    • The right side can be a literal value, or a variable that has already been assigned a value, or a combination.
  • When R reads y <- x + 12, it does the following:
    1. Sees the <- in the middle.
    2. Knows that this is an assignment.
    3. Calculates the right side (gets the value of the object referred to by x and adds it to 12).
    4. Assigns the result to the left-side variable, y.

R Basics

Values, Variables, and Types

  • Sometimes you will hear variables referred to as objects.

  • Everything that is not a literal value, such as 10, is an object.

R Basics

Values, Variables, and Types

  • Logical: TRUE or FALSE.
  • Numeric: Numbers with decimals
  • Integer: Integers
  • Character: Text strings
  • Factor: Categorical values.
    • Each possible value of a factor is known as a level.

  • vector: 1D collection of variables of the same type
  • data.frame: 2D collection of variables of multiple types
    • A data.frame is a collection of vectors.

R Basics

R Variable and Data Types

  • class() function returns the data type of an object.
myname <- "my_name"
class(myname)
  • Strings are known as character in R.
  • Use the double quotes " or single quotes ' to wrap around the string
favorite.integer <- as.integer(2)
class(favorite.integer)

favorite.numeric <- as.numeric(8.8)
class(favorite.numeric)
  • Numbers have different classes.
    • The most common two are integer and numeric. Integers are whole numbers.
class(TRUE)
class(FALSE)
favorite.numeric == 8.8
favorite.numeric == 9.9
class(favorite.numeric == 8.8)
  • We use the == to test for equality in R
a <- 1:10  # colon operator
b <- c("3", 4, 5)
beers <- c("BUD LIGHT", "BUSCH LIGHT", "COORS LIGHT", 
           "MILLER LITE", "NATURAL LIGHT")
class(a)
class(b)
class(beers)

  • We can create one-dimensional data structures called “vectors”.

  • c(...): Returns a vector that is constructed from one or more arguments, with the order of the vector elements corresponding to the order of the arguments.

beers <- as.factor(beers)
class(beers)

levels(beers)
nlevels(beers)

  • Factors store categorical data.

  • Under the hood, factors are actually integers that have a string label attached to each unique integer.

    • For example, if we have a long list of Male/Female labels for each of our patients, this will be stored a “column” of zeros and ones by R.

R Basics

Workflow: Quotation marks, parentheses, and +

x <- "hello
  • Quotation marks and parentheses must always come in a pair.
    • If not, Console Pane will show you the continuation character +:
  • The + tells you that R is waiting for more input; it doesn’t think you’re done yet.

R Basics

R Variable and Data Types

R Basics

Functions

  • A function can take any number and type of input parameters and return any number and type of output results.

  • R ships a vast number of built-in functions.

  • R also allows a user to define a new function.

  • We will mostly use built-in functions.

R Basics

Functions, Arguments, and Parameters

library(tidyverse)

# The function `str_c()`, provided by `tidyverse`, concatenates characters.
str_c("Data", "Analytics")
str_c("Data", "Analytics", sep = "!")

  • We invoke a function by entering its name and a pair of opening and closing parentheses.

  • Much as a cooking recipe can accept ingredients, a function invocation can accept inputs called arguments.

  • We pass arguments sequentially inside the parentheses (, separated by commas).

  • A parameter is a name given to an expected function argument.

  • A default argument is a fallback value that R passes to a parameter if the function invocation does not explicitly provide one.

R Basics

Arithmetic Operations and Mathematical Functions

5 + 3
5 - 3
5 * 3
5 / 3
5^3
( 3 + 4 )^2
3 + 4^2
3 + 2 * 4^2
3 + 2 * 4 + 2
(3 + 2) * (4 + 2)

  • All of the basic operators with parentheses we see in mathematics are available to use.

  • R can be used for a wide range of mathematical calculations.

  • R has many built-in mathematical functions that facilitate calculations and data analysis.
5 * abs(-3)
sqrt(17) / 2
exp(3)
log(3)
log(exp(3))
exp(log(3))
  • abs(x): the absolute value \(|x|\)
  • sqrt(x): the square root \(\sqrt{x}\)
  • exp(x): the exponential value \(e^x\), where \(e = 2.718...\)
  • log(x): the natural logarithm \(\log_{e}(x)\), or simply \(\log(x)\)

R Basics

Vectorized Operations

a <- c(1, 2, 3, 4, 5)
b <- c(5, 4, 3, 2, 1)

a + b
a - b
a * b
a / b
sqrt(a)
  • Vectorized operations mean applying a function to every element of a vector without explicitly writing a loop.
    • This is possible because most functions in R are vectorized, meaning they are designed to operate on vectors element-wise.
    • Vectorized operations are a powerful feature of R, enabling efficient and concise code for data analysis and manipulation.

R Basics

More Math Functions

x <- c(1, 2, 3, 4, 5)

sum(x)
mean(x)
sd(x)
  • sum() calculates the sum of all numbers in a vector.
  • mean() calculates the arithmetic mean of the values in a vector. \[ \overline{x} = \frac{x_{1} + x_{2} + \cdots + x_{N}}{N} \]
  • sd() calculates the standard deviation (SD) of the values in a vector.
    • SD measures the amount of variation or dispersion of a set of values. \[ \overline{s} = \sqrt{ \left( \frac{(x_{1}-\overline{x})^{2} + (x_{2}-\overline{x})^{2} + \cdots + (x_{N}-\overline{x})^{2}}{N-1}\;\, \right) } \]

R Basics

NULL and NA values

c(c(), 1, NULL)
c("a", NA, "c")
  • NULL is just an alias for c(), the empty vector.
  • NA indicates missing or unavailable data.

R Basics

NULL and NA values

  • is.na() is to check whether expression evaluates to NA.
  • Q. Why does "A" == NA evaluate to NA?

R Basics

Casting Variables

orig_number <- 4.39898498
class(orig_number)
mod_number <- as.integer(orig_number)
class(mod_number)
# TRUE converts to 1; FALSE does to 0.
as.numeric(TRUE)
as.numeric(FALSE)

  • Sometimes we need to explicitly cast a value from one type to another.

    • We can do this using built-in functions like as.character(), as.integer(), as.numeric(), and as.factor().
    • If we try these, R will do its best to interpret the input and convert it to the output type we’d like and, if they can’t, the code will throw NA.

R Basics

R Variable and Data Types

R Basics

Absolute vs. Relative Pathnames

  • Complete path from the root directory to the target file or directory.

  • Independent of the current working directory.

  • Example (Mac): /Users/user/documents/car_data.csv

  • Example (Windows): C:\\Users\\user\\Documents\\car_data.csv

  • Path relative to the current working directory.
  • Changes based on the current directory.
  • Example: If the current directory is /Users/user, the relative path to car_data.csv would be documents/car_data.csv
  • For the website R project, we can use a relative path.

R Basics

Working with Data from Files

  • We use the read_csv() function to read a comma-separated values (CSV) file.

  1. Download the CSV file, car_data.csv from the Class Files module in our Brightspace.

  2. Find the path name for the file, car_data.csv from the File Explorer / Finder.

  3. Provide the path name for the file, car_data.csv, to the read_csv() function.

uciCar <- read_csv('HERE WE PROVIDE A PATHNAME FOR car_data.csv')
View(uciCar)
  • View()/view() displays the data in a simple spreadsheet-like grid viewer.
    • We can click the data.frame object, displayed in the Environment Pane.

R Basics

Examining data.frames

dim(uciCar)
nrow(uciCar)
ncol(uciCar)
class(uciCar)
library(skimr)
skim(uciCar)
  • dim() shows how many rows and columns are in the data for data.frame.
  • nrow() and ncol() shows the number of rows and columns for data.frame respectively.
  • skimr::skim() provides a more detailed summary.
    • skimr is the R package that provides the function skim().

R Basics

Reading data.frames from an URL

tvshows <- read_csv(
        'https://bcdanl.github.io/data/tvshows.csv')
  • We can import the CSV file from the web.

R Basics

Tidy data.frame: Variables, Observations, and Values

  • There are three rules which make a data.frame tidy:

    1. Each variable must have its own column.
    2. Each observation must have its own row.
    3. Each value must have its own cell.

R Basics

R Variable and Data Types