R, RStudio, and the console · vectors · types

Tuesday, May 19, 2026

NoteToday: Tuesday May 19

Before class

• First Perusall Assignment: Data Computing §2.1-2.8 (Click here).

Plan for today

• Tour of RStudio
• Using R as a calculator
• Data types and vectors
• Creating objects

Helpful links

• Syllabus · AI policy · Schedule · Data Computing §2

What is Computing?

Computing is about turning human ideas into reproducible instructions that a computer can carry out.

In this course, we will learn how to:

• organize data
• automate calculations
• visualize information
• build reproducible analyses
• communicate results

R is the tool we will use to do that.

Another Introduction to R

• R is a programming language designed for data analysis, statistics, and visualization.
• We use R to tell the computer exactly what computations we want to perform.
• R is widely used in:
  • statistics
  • data science
  • research
  • industry analytics
• R is free, open-source, and highly extensible through thousands of packages that contain additional tools and functions.

RStudio Desktop (RStudio)

• First version released in 2011.
• Integrated development environment (IDE) provides additional tools and window management to R to help coders, statisticians, and data scientists use R more effectively.
• While R is required to run RStudio, you do not need to have RStudio to use R.

R vs. RStudio

• R is the programming language.
• RStudio is the environment/interface we use to work with R more comfortably.

A tour of RStudio

RStudio’s Four Pane Layout

RStudio’s four pane layout.

Source Pane

RStudio’s source pane, annotated.

Console Pane

RStudio’s console, annotated.

Environment Pane

RStudio’s environment pane, annotated.

Output Pane - Files Tab

RStudio’s output pane (files tab), annotated.

Output Pane - Plots Tab

RStudio’s output pane (plots tab), annotated, displaying a 2D density contour plot created with ggplot2.

Darker regions indicate higher concentrations of observations; lighter regions represent lower densities. The plot combines filled density polygons with contour boundaries to visualize the distribution of simulated data.

# Set Seed
set.seed(184)

# Library
library(tidyverse)

# Data
a <- data.frame(x = rnorm(20000, 12,   2),   y = rnorm(20000, 10,   0.5))
b <- data.frame(x = rnorm(20000, 15,   1.5), y = rnorm(20000, 11,   0.5))
c <- data.frame(x = rnorm(20000, 10,   1.1), y = rnorm(20000, 11.5, 0.7))
data <- rbind(a, b, c)

# Area + contour
ggplot(data, aes(x = x, y = y)) +
  stat_density_2d(aes(fill = after_stat(level)),
                  geom = "polygon", colour = "white") +
  theme_light() +
  scale_fill_viridis_c(option = "magma")

Output Pane - Packages Tab

RStudio’s output pane (packages tab).

• The “Packages” tab will list all packages that you have currently installed on your machine. You can load a package by clicking the box to the left of the name.
• You can also use the “Install” and “Update” buttons to help you get new packages and keep them current.

Output Pane - Help Tab

RStudio’s output pane (help tab).

• The “Help” tab displays help documentation that has been included with the packages for functions, data sets, and other objects.
• Help Shortcuts: ?barplot, help(barplot), or ??barplot (broad sweep). You can also place your cursor in the object name and press fn + F1 (or just F1 if your function keys are active).

Data types and structures

Data Types

• Data type refers to the nature of how we choose to record some value.
• Common Data Types in R:
  • Numeric (Double)
  • Integer
  • Complex
  • Character
  • Logical

Data Structures

• Data structures are the most important type of base objects in R as they will form the basis for all manipulations we do.
• R’s data structures may be organized along two aspects:
  1. How many dimensions we need for values.
  2. If all values have to be the same data type.

Table 1: Summary of R’s main base data structures by dimension and type-mixing.

Structure	Dimensions	Mixed types?
Vector	1	No
List	1	Yes
Matrix	2	No
Data frame	2	Yes (across columns)
Array	n (\(\geq\) 2)	No

Data Structures Examples

Table 2: Examples of each data structure showing its printed form in the R console.

Dimensions	All Values Same Data Type	A Mix of Data Types
1 Dimension	Atomic Vectors [1] 1 3 5 [1] "apple" "grape" "peach"	List [[1]] [1] 1 3 5 [[2]] [1] "apple" "grape" "peach"
2 Dimensions	Matrix [,1] [,2] [1,] 1 5 [2,] 3 7	Data Frame id fruit 1 1 apple 2 3 grape 3 5 peach
n Dimensions	Array , , 1 [,1] [,2] [1,] 1 5 [2,] 3 7 , , 2 [,1] [,2] [1,] 2 6 [2,] 4 8	Not applicable

Data Structures Visualization

# Color palette
bg_color    <- "#FFFFFF"
light_blue  <- "#DDEAF5"
mid_blue    <- "#9BB8DC"
edge_color  <- "#001E44"   # Penn State Navy

# Draw a single unit cube at grid position (gx, gy, gz)
# Uses simple axonometric projection: x' = gx + 0.4*gz, y' = gy + 0.4*gz
draw_cube <- function(gx, gy, gz, fill = light_blue) {
  s  <- 1
  dx <- 0.4 * s
  dy <- 0.4 * s

  px <- gx + dx * gz
  py <- gy + dy * gz

  # Front face
  polygon(c(px, px + s, px + s, px),
          c(py, py,     py + s, py + s),
          col = fill, border = edge_color, lwd = 1.2)
  # Top face
  polygon(c(px,        px + s,        px + s + dx, px + dx),
          c(py + s,    py + s,        py + s + dy, py + s + dy),
          col = fill, border = edge_color, lwd = 1.2)
  # Right face (slightly darker for shading)
  shade <- adjustcolor(fill, red.f = 0.92, green.f = 0.92, blue.f = 0.95)
  polygon(c(px + s,    px + s + dx,   px + s + dx, px + s),
          c(py,        py + dy,       py + s + dy, py + s),
          col = shade, border = edge_color, lwd = 1.2)
}

# Alternating fill in 3D (checkerboard)
checker_fill <- function(i, j, k = 0) {
  if ((i + j + k) %% 2 == 0) light_blue else mid_blue
}

par(mar = c(0, 0, 0, 0), bg = bg_color)
plot.new()
plot.window(xlim = c(0, 18), ylim = c(0, 6), asp = 1)

# 1. Vector: 1 x 3 row
ox <- 1; oy <- 2.2
for (i in 0:2) {
  draw_cube(ox + i, oy, 0, fill = checker_fill(i, 0))
}
text(ox + 1.7, oy - 1, "Vector",
     cex = 1.4, col = edge_color, font = 2)

# 2. Matrix: 3 x 3 grid
ox <- 7; oy <- 1
for (j in 0:2) {
  for (i in 0:2) {
    draw_cube(ox + i, oy + j, 0, fill = checker_fill(i, j))
  }
}
text(ox + 1.7, oy - 1, "Matrix",
     cex = 1.4, col = edge_color, font = 2)

# 3. Array: 3 x 3 x 3 cube — draw back-to-front, bottom-up, left-right
ox <- 13; oy <- 1
for (k in 2:0) {
  for (j in 0:2) {
    for (i in 0:2) {
      draw_cube(ox + i, oy + j, k, fill = checker_fill(i, j, k))
    }
  }
}
text(ox + 2.3, oy - 1, "Array",
     cex = 1.4, col = edge_color, font = 2)

Figure 1: R’s data structures as cubes: a vector (1D), matrix (2D), and array (3D).

R as a calculator · creating objects

R as a Calculator

You can use R as a calculator:

184.101 * 9.35

[1] 1721.344

(8 + 4) / 9

[1] 1.333333

sqrt(8)

[1] 2.828427

cos(2 * pi)

[1] 1

Your Turn to Use the Calculator

Your turn. Open the console and try each line below; don’t just read them, type them.

2 + 2

sqrt(16)

c(1, 2, 3) * 2

10 %% 3

x <- log(10^2, base = 10)
x

Vector Operations

c(1, 8, 4) + c(9, 3, 5)

[1] 10 11  9

c(1, 8, 4) * c(9, 3, 5)

[1]  9 24 20

• The c() function concatenates multiple individual values into one single vector.

Built-in Functions and Constants

exp(1)

[1] 2.718282

sqrt(pi)

[1] 1.772454

dnorm(0)

[1] 0.3989423

• sqrt computes the square root.
• exp computes the function \(e^x\).
• pi is the constant \(\pi\).
• dnorm computes the standard normal density function.

Creating objects

You can create new objects with the assignment operator, <-:

x <- 3 * 5

The above is an example of an assignment statement. In general, assignment statements have the form:

object_name <- value

In English, this is “object_name gets value.”

• Even though typing <- for the many assignments you will make can be tedious, don’t use =; it will work, but will cause confusion later.
• You can use the RStudio keyboard shortcut Alt + - (or Option + - on a Mac).
• Object names must start with a letter, and can only contain letters, numbers, _, and .

Setup checklist — due by Wednesday

Install R and RStudio

• R: https://cloud.r-project.org
• RStudio: https://posit.co/download/rstudio-desktop

WarningMac users

Install XQuartz too — some packages we’ll use later in the course need it.

Configure RStudio

• Tools > Global Options... > General
• Restore .RData into workspace at startup: Unchecked
• Save workspace to .RData on exit: Never

Wrap-up & looking ahead

Tomorrow: Functions, packages, the tidyverse, and objects.

Upcoming Deadlines

• Wed 5/20, before class — DC §3.1–3.5
• Wed 5/20, 11:59 p.m. — HW 0: Setup
• Fri 5/22, in class — Quiz 1
• Fri 5/22, 11:59 p.m. — HW 1: Basic R & Plots

Reach out

Stuck? Office hours, or email me (cgc5478@psu.edu) or Xinyue (xpw5228@psu.edu).

Reference

Syllabus · AI policy · Schedule · Data Computing