Reference

• R Programming for Data Science: Chapter 4

• Hadley Wickham’s Advanced R: Chapter 3 on Vectors

Additional

• Hands-On Programming with R: Part II

Quick Refresher from Previous Classes

✅ Tidyverse style guide

✅ Frequently used [some] functions

✅ Data Types in

❌ Data Creation in

R Objects

• R has five basic or “atomic” classes of objects:

  • character (“a”, “1”, “TRUE” - with quotation marks)

  • numeric (real numbers, 1.234, 18, 1e25)

  • integer (1, 2, 100)

  • complex (1 - 2i, 3 + 5i): We do not discuss or use this class

  • logical (True/False, T, F, TRUE, FALSE)

• The most basic type of R object is a vector

• A vector can only contain objects of the same class

• Before moving to creating different data types and using/manipulating them, let’s discuss some basic built-in functions we will use frequently

is a Vector Language: Sample Output

• Learning from a sample of 5 observations

• Functions used:

  • c(): Combine Values into a Vector or List
  • sum(): Sum of all the values

x_vec = c(1, 2.2, -3, 4, 0.5) # some values 
x_vec

## [1]  1.0  2.2 -3.0  4.0  0.5

is.vector(x_vec)

## [1] TRUE

x_vec > 0 # finding which elements in x are larger than 0

## [1]  TRUE  TRUE FALSE  TRUE  TRUE

is.vector(x_vec > 0)

## [1] TRUE

sum(1,2) # 1 + 2

## [1] 3

is.vector(sum(1,2))

## [1] TRUE

sum(x_vec > 0) # summing the number of elements (i.e., how many are > 0)

## [1] 4

sum(x_vec < 0) # summing the number of elements (i.e., how many are < 0)

## [1] 1

• Focus on the obtained outputs: they are vectors:
  • x_vec > 0 returns a vector of size 5 (TRUE/FALSE for each element)
    
  • sum(x_vec > 0) returns a vector of size 1

is a Vector Language: Types and Attributes

• Vectors come in two flavors, which differ by their elements’ types:
  • atomic vectors – all elements must have the same type
    
  • lists – elements can be different
• Vector have two important attributes:
  • Dimension turns vectors into matrices and arrays, checked using dim(object_name)
    
  • The class attribute powers the S3 object system, checked using class(object_name)

Source: The content and image are from Hadley Wickham’s Advanced R: Chapter 3 on Vectors

: Atomic Vectors

dim(x_vec) 

## NULL

Atomic vectors have a dim of NULL, which distinguishes it from 1-D arrays 😲!!!

Atomic Vectors: Making Longer Vectors

dbl_var <- c(1, 2.5, 4.5)
int_var <- c(1L, 6L, 10L)
lgl_var <- c(TRUE, FALSE)
chr_var <- c("these are", "some strings")

- You can determine the type of a vector with typeof() and its length with length()

typeof(dbl_var)
#> [1] "double"
typeof(int_var)
#> [1] "integer"
typeof(lgl_var)
#> [1] "logical"
typeof(chr_var)
#> [1] "character"

• When the inputs are atomic vectors, c() always creates another atomic vector

c(c(1,2), c(3,4))

## [1] 1 2 3 4

Missing Values

• Missing, or unknown values, are represented with a value: NA (short for Not Applicable)

• Most computations involving a missing value will return another missing value

NA > 5
#> [1] NA
10 * NA
#> [1] NA
!NA
#> [1] NA

• There are a few exceptions

NA^0

## [1] 1

#> [1] 1
NA | TRUE

## [1] TRUE

#> [1] TRUE
NA & FALSE

## [1] FALSE

#> [1] FALSE

• Missingness leads to a common mistake when determining which values in a vector are missing

x <- c(NA, 5, NA, 10)
x == NA
#> [1] NA NA NA NA

• Use is.na() to test for the presence of missingness

is.na(x)
#> [1]  TRUE FALSE  TRUE FALSE

Names

• You can name a vector in three ways:

# When creating it: 
x <- c(a = 1, b = 2, c = 3)
x
#> a b c 
#> 1 2 3

# By assigning a character vector to names()
x <- 1:3
names(x) <- c("a", "b", "c")
x
#> a b c 
#> 1 2 3

# Inline, with setNames():
x <- setNames(1:3, c("a", "b", "c"))
x
#> a b c 
#> 1 2 3

• You can remove names from a vector by using:
  • x <- unname(x) or
  • names(x) <- NULL

Summary

• Popular atomic vector are

Source: The content and image are from isa401: An Undergrad Course on Business Intelligence & Data Visualization, Introduction to R as of July 22, 2025

Data Types: Formal Definitions

• Logicals: (TRUE or FALSE), or abbreviated (T or F).

• Doubles: decimal (0.1234), scientific (1.23e4), or hexadecimal (0xcafe) form.

  • Three special values unique to doubles: Inf, -Inf, and NaN (not a number).
    
  • These are special values defined by the floating point standard.

• Integers: similar to doubles but must be followed by L(1234L, 1e4L, or 0xcafeL), and can not contain fractional values.

• Strings: surrounded by " (e.g., "hi") or ' (e.g., 'bye'). Special characters are escaped with \ see ?Quotes for full details.

• Dates/times: more complicated they seem. Number of days differs in leap years. Daylight saving time (DST) may be important, since at this day number of hours may become 23 or 25. Format of the dates changes with geographic regions/countries. MIS 306 Data Analysis: Forecasting course cover this object class in details.

Data Structures: ➡️ 2D: Matrices

• A matrix is a 2D data structure made of one/homogeneous data type.

• Number of rows and columns are two dimension attributes

• The functions that may be used for matrix object:

  • matrix(): creates a matrix from the given set of values
    
  • dim(): retrieve or set the dimension of an object
  • rownames(), colnames(): row or column names of a matrix-like object
    
  • nrow(), ncol(): number of rows or columns
    
  • rbind(), cbind(): Take a matrix and combine by columns or rows
    
  • t(): transpose of a matrix
    
  • is.matrix(): as name suggested

Matrices

x_mat = matrix(1:4, nrow = 2, ncol = 2 ) 
str(x_mat) # its structure?

##  int [1:2, 1:2] 1 2 3 4

x_mat # printing it nicely

##      [,1] [,2]
## [1,]    1    3
## [2,]    2    4

print('-----------------')

## [1] "-----------------"

x_mat[1, 2] # subsetting #<< 

## [1] 3

Matrices

x_char = matrix(letters[1:12], nrow = 3, ncol =4)
x_char

##      [,1] [,2] [,3] [,4]
## [1,] "a"  "d"  "g"  "j" 
## [2,] "b"  "e"  "h"  "k" 
## [3,] "c"  "f"  "i"  "l"

x_char[1:2, 2:3] # subsetting #<< 

##      [,1] [,2]
## [1,] "d"  "g" 
## [2,] "e"  "h"

Matrices: matrix() and dim()

• Matrices are constructed column-wise

m <- matrix(1:6, nrow = 2, ncol = 3) 
m
#>      [,1] [,2] [,3]
#> [1,]    1    3    5
#> [2,]    2    4    6

dim() function may help to create a matrix directly from vectors by adding a dimension attribute

m <- 1:10 
m

##  [1]  1  2  3  4  5  6  7  8  9 10

dim(m) <- c(2, 5)
m

##      [,1] [,2] [,3] [,4] [,5]
## [1,]    1    3    5    7    9
## [2,]    2    4    6    8   10

Matrices: rbind() and cbind()

rbind(), cbind(): functions may help to create a matrix by column-binding or row-binding

x <- 1:3
y <- 10:12
cbind(x, y)

##      x  y
## [1,] 1 10
## [2,] 2 11
## [3,] 3 12

rbind(x, y) 

##   [,1] [,2] [,3]
## x    1    2    3
## y   10   11   12

Matrices: t()

t(): transpose of a matrix

x <- matrix(1:8, nrow = 4)
x

##      [,1] [,2]
## [1,]    1    5
## [2,]    2    6
## [3,]    3    7
## [4,]    4    8

t(x)

##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]    5    6    7    8

t(t(x))

##      [,1] [,2]
## [1,]    1    5
## [2,]    2    6
## [3,]    3    7
## [4,]    4    8

z <- 1:4
t(z)

##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4

t(t(z))

##      [,1]
## [1,]    1
## [2,]    2
## [3,]    3
## [4,]    4

Matrices: rownames(), colnames(): Row names and Column names of a matrix

x

##      [,1] [,2]
## [1,]    1    5
## [2,]    2    6
## [3,]    3    7
## [4,]    4    8

rownames(x)

## NULL

colnames(x)

## NULL

rownames(x) <- paste0("row_",1:4)
colnames(x) <- paste0("col_",1:2)

rownames(x)

## [1] "row_1" "row_2" "row_3" "row_4"

colnames(x)

## [1] "col_1" "col_2"

t(x)

##       row_1 row_2 row_3 row_4
## col_1     1     2     3     4
## col_2     5     6     7     8

rownames(x) <- colnames(x) <- NULL
x

##      [,1] [,2]
## [1,]    1    5
## [2,]    2    6
## [3,]    3    7
## [4,]    4    8

Lists

lst <- list( # list constructor/creator
  1:3, # atomic double/numeric vector  of length = 3 #<< 
  "a", # atomic character vector of length = 1 (aka scalar) #<< 
  c(TRUE, FALSE, TRUE), # atomic logical vector of length = 3 #<< 
  c(2.3, 5.9) # atomic double/numeric vector of length =3 #<< 
)
lst # printing the list

## [1] "1:3"                  "a"                    "c(TRUE, FALSE, TRUE)"
## [4] "c(2.3, 5.9)"

Lists

• Lists are a special type of vector that can contain elements of different classes

• Lists can be explicitly created using the list() function, which takes an arbitrary number of arguments

x <- list(1, "a", TRUE, 1 + 4i) 
x

## [[1]]
## [1] 1
## 
## [[2]]
## [1] "a"
## 
## [[3]]
## [1] TRUE
## 
## [[4]]
## [1] 1+4i

y <- list(a=1, b="a", c=TRUE, d=1 + 4i) 
y

## $a
## [1] 1
## 
## $b
## [1] "a"
## 
## $c
## [1] TRUE
## 
## $d
## [1] 1+4i

Lists

• We can also create an empty list of a prespecified length with the vector() function

x <- vector("list", length = 5)
x

## [[1]]
## NULL
## 
## [[2]]
## NULL
## 
## [[3]]
## NULL
## 
## [[4]]
## NULL
## 
## [[5]]
## NULL

Lists: A brief Summary

lst <- list( # list constructor/creator
  1:3, # atomic double/numeric vector  of length = 3 #<< 
  "a", # atomic character vector of length = 1 (aka scalar) #<< 
  c(TRUE, FALSE, TRUE), # atomic logical vector of length = 3 #<< 
  c(2.3, 5.9) # atomic double/numeric vector of length =3 #<< 
)
lst # printing the list

## [[1]]
## [1] 1 2 3
## 
## [[2]]
## [1] "a"
## 
## [[3]]
## [1]  TRUE FALSE  TRUE
## 
## [[4]]
## [1] 2.3 5.9

str(lst) # structure 

## List of 4
##  $ : int [1:3] 1 2 3
##  $ : chr "a"
##  $ : logi [1:3] TRUE FALSE TRUE
##  $ : num [1:2] 2.3 5.9

lst[1] # subsetting 

## [[1]]
## [1] 1 2 3

lst[[1]]

## [1] 1 2 3

paste(lst) # use paste() 

## [1] "1:3"                  "a"                    "c(TRUE, FALSE, TRUE)"
## [4] "c(2.3, 5.9)"

Factors

• Factors are used to represent categorical and ordinal data and can be unordered or ordered

• One can think of a factor as an integer vector where each integer has a label

• factor() function is used to create a factor object

x <- factor(c("yes", "yes", "no", "yes", "no")) 
x

## [1] yes yes no  yes no 
## Levels: no yes

as.character(x)

## [1] "yes" "yes" "no"  "yes" "no"

table(x) 

## x
##  no yes 
##   2   3

levels(x) ## Levels are put in alphabetical order

## [1] "no"  "yes"

x <- factor(c("yes", "yes", "no", "yes", "no"),
           levels = c("yes", "no"))
x

## [1] yes yes no  yes no 
## Levels: yes no

as.character(x)

## [1] "yes" "yes" "no"  "yes" "no"

Data Frames

• Data frames are used to store tabular data in R

• They are extremely important type of object in R for our program. Many courses will require to use tabular data

• Data frames are represented as a special type of list where every element of the list has to have the same length

• Each element of the list can be thought of as a column and the length of each element as the number of rows

• Unlike matrices, data frames can store different classes of objects in each column

• Functions that may be used for data frame object:

  • data.frame(): creates data frames
    
  • rownames(), colnames()
    
  • dim()
  • nrow(), ncol()
    
  • str()
    
  • …

Data Frames

x <- data.frame(x = 1:4, z = c(T, T, F, F))
x

##   x     z
## 1 1  TRUE
## 2 2  TRUE
## 3 3 FALSE
## 4 4 FALSE

str(x)

## 'data.frame':    4 obs. of  2 variables:
##  $ x: int  1 2 3 4
##  $ z: logi  TRUE TRUE FALSE FALSE

nrow(x)

## [1] 4

ncol(x)

## [1] 2

Data Frames: Subsetting

x

##   x     z
## 1 1  TRUE
## 2 2  TRUE
## 3 3 FALSE
## 4 4 FALSE

x$x

## [1] 1 2 3 4

x[,1]

## [1] 1 2 3 4

x$z 

## [1]  TRUE  TRUE FALSE FALSE

x[,2]

## [1]  TRUE  TRUE FALSE FALSE

x[1,]

##   x    z
## 1 1 TRUE

x[1:2,1]

## [1] 1 2

Data Frames: Subsetting

Example: Edgar Anderson’s Iris Data

• Built in data set iris gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris

• Image Source: https://codesignal.com/learn/courses/intro-to-unsupervised-machine-learning/lessons/exploring-and-visualizing-the-iris-dataset

Data Frames: Subsetting

Example: Edgar Anderson’s Iris Data: first 6 rows

head(iris) # first 6 rows 

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

Data Frames: Subsetting

Example: Edgar Anderson’s Iris Data: last 6 rows

tail(iris) # last 6 rows 

##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 145          6.7         3.3          5.7         2.5 virginica
## 146          6.7         3.0          5.2         2.3 virginica
## 147          6.3         2.5          5.0         1.9 virginica
## 148          6.5         3.0          5.2         2.0 virginica
## 149          6.2         3.4          5.4         2.3 virginica
## 150          5.9         3.0          5.1         1.8 virginica

Data Frames: Subsetting

Example: rows 111 through 116 (all columns)

# [row numbers,]  
iris[111:116,] # rows 111 through 116 (all columns) 

##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 111          6.5         3.2          5.1         2.0 virginica
## 112          6.4         2.7          5.3         1.9 virginica
## 113          6.8         3.0          5.5         2.1 virginica
## 114          5.7         2.5          5.0         2.0 virginica
## 115          5.8         2.8          5.1         2.4 virginica
## 116          6.4         3.2          5.3         2.3 virginica

Data Frames: Subsetting

Example: accessing columns

• Using three methods:

  • Single brackets: ([]) using index or name

  • Double brackets ([[]]) using index or name

  • The dollar sign ($) using name

# [,] : by indexing
head(iris[,1]) # first column   

## [1] 5.1 4.9 4.7 4.6 5.0 5.4

# [,"column name"] by column name   
colnames(iris)

## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"

head(iris[,"Sepal.Length"]) # Sepal.Length column   

## [1] 5.1 4.9 4.7 4.6 5.0 5.4

all.equal(head(iris[,1]), head(iris[,"Sepal.Length"]))

## [1] TRUE

head(iris[["Sepal.Length"]]) # Sepal.Length column   

## [1] 5.1 4.9 4.7 4.6 5.0 5.4

all.equal(head(iris[,1]), head(iris[["Sepal.Length"]]))

## [1] TRUE

head(iris["Sepal.Length"]) # Sepal.Length column returning data frame 

##   Sepal.Length
## 1          5.1
## 2          4.9
## 3          4.7
## 4          4.6
## 5          5.0
## 6          5.4

Data Frames: Subsetting by Filtering

Example: column values > some value

head(iris$Sepal.Length > 7.5) # comparison returns TRUE/FALSE

## [1] FALSE FALSE FALSE FALSE FALSE FALSE

sum(iris$Sepal.Length > 7.5) # number of TRUE 

## [1] 6

iris[iris$Sepal.Length > 7.5,] # filter based on Sepal.Length

##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 106          7.6         3.0          6.6         2.1 virginica
## 118          7.7         3.8          6.7         2.2 virginica
## 119          7.7         2.6          6.9         2.3 virginica
## 123          7.7         2.8          6.7         2.0 virginica
## 132          7.9         3.8          6.4         2.0 virginica
## 136          7.7         3.0          6.1         2.3 virginica

iris[iris$Sepal.Length > 7.5,c(2,5)] # filter and select column(s)

##     Sepal.Width   Species
## 106         3.0 virginica
## 118         3.8 virginica
## 119         2.6 virginica
## 123         2.8 virginica
## 132         3.8 virginica
## 136         3.0 virginica

# more complex filtering (using AND)
iris[iris$Sepal.Length > 7.5 & iris$Sepal.Width < 3.5, ] 

##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 106          7.6         3.0          6.6         2.1 virginica
## 119          7.7         2.6          6.9         2.3 virginica
## 123          7.7         2.8          6.7         2.0 virginica
## 136          7.7         3.0          6.1         2.3 virginica

tail(iris$Petal.Length)

## [1] 5.7 5.2 5.0 5.2 5.4 5.1

Data Frames: Subsetting by Filtering

What really happens here?

x <- c(1, 3, NA, 6, -5, -0.5) 
x 

## [1]  1.0  3.0   NA  6.0 -5.0 -0.5

x[c(TRUE, TRUE, TRUE, FALSE, FALSE, TRUE)] # subset using Logical values

## [1]  1.0  3.0   NA -0.5

x < 4 # chect NA 

## [1]  TRUE  TRUE    NA FALSE  TRUE  TRUE

x[x < 4]

## [1]  1.0  3.0   NA -5.0 -0.5

Data Frames: Column selecting by comparison

Example: column select

x <- colnames(iris) # iris column names 
x  

## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"

head(iris[,c(TRUE, FALSE, FALSE, FALSE, FALSE)]) # first column

## [1] 5.1 4.9 4.7 4.6 5.0 5.4

head(iris[,colnames(iris)==x[1]]) # first column 

## [1] 5.1 4.9 4.7 4.6 5.0 5.4

Filtering and Selecting: Better way (Teaser)

• In tidyverse collection, dplyr package has several utilities for data manipulation (we will discuss later)

• Functions for filtering and selecting available in dplyr

  • filter()
    
  • select()

library(dplyr)
x <- filter(iris, iris$Sepal.Length > 7.5)
x

##   Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 1          7.6         3.0          6.6         2.1 virginica
## 2          7.7         3.8          6.7         2.2 virginica
## 3          7.7         2.6          6.9         2.3 virginica
## 4          7.7         2.8          6.7         2.0 virginica
## 5          7.9         3.8          6.4         2.0 virginica
## 6          7.7         3.0          6.1         2.3 virginica

select(x, Sepal.Width, Species)

##   Sepal.Width   Species
## 1         3.0 virginica
## 2         3.8 virginica
## 3         2.6 virginica
## 4         2.8 virginica
## 5         3.8 virginica
## 6         3.0 virginica

Ordering Data Frame

• order() function

# order data frame by column 

iris[1:6,] # first 6 obs.  

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

# Ordered - ascending by Sepal.Length
iris[order(iris$Sepal.Length),][1:6,] 

##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 14          4.3         3.0          1.1         0.1  setosa
## 9           4.4         2.9          1.4         0.2  setosa
## 39          4.4         3.0          1.3         0.2  setosa
## 43          4.4         3.2          1.3         0.2  setosa
## 42          4.5         2.3          1.3         0.3  setosa
## 4           4.6         3.1          1.5         0.2  setosa

# descending by Sepal.Length
iris[order(-iris$Sepal.Length),][1:6,]  

##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 132          7.9         3.8          6.4         2.0 virginica
## 118          7.7         3.8          6.7         2.2 virginica
## 119          7.7         2.6          6.9         2.3 virginica
## 123          7.7         2.8          6.7         2.0 virginica
## 136          7.7         3.0          6.1         2.3 virginica
## 106          7.6         3.0          6.6         2.1 virginica

# by Sepal.Length (descending) and Petal.Length (ascending)
iris[order(-iris$Sepal.Length,iris$Petal.Length),][1:6,] 

##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 132          7.9         3.8          6.4         2.0 virginica
## 136          7.7         3.0          6.1         2.3 virginica
## 118          7.7         3.8          6.7         2.2 virginica
## 123          7.7         2.8          6.7         2.0 virginica
## 119          7.7         2.6          6.9         2.3 virginica
## 106          7.6         3.0          6.6         2.1 virginica

# for vectors see sort() function 

Data Frame: Renaming Columns and Rows

• colnames() and rownames() functions

colnames(iris)

## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"

rownames(iris)[1:5]

## [1] "1" "2" "3" "4" "5"

colnames(iris) <- c("L.Sepal","W.Sepal","L.Petal","W.Petal","Species")
colnames(iris)

## [1] "L.Sepal" "W.Sepal" "L.Petal" "W.Petal" "Species"

# if one column name is not specified 
colnames(iris) <- c("L.Sepal","W.Sepal","L.Petal","W.Petal")
colnames(iris)

## [1] "L.Sepal" "W.Sepal" "L.Petal" "W.Petal" NA

# back to original data 
data(iris) 
colnames(iris)

## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"

# only first and second column names 
colnames(iris)[1:2] <- c("L.Sepal","W.Sepal")
colnames(iris)

## [1] "L.Sepal"      "W.Sepal"      "Petal.Length" "Petal.Width"  "Species"

# rownames(iris) <- iris$Species
# Error in `.rowNamesDF<-`(x, value = value) :
#  duplicate 'row.names' are not allowed

rownames(iris) <- paste0(iris$Species,"_",rownames(iris))
head(rownames(iris))

## [1] "setosa_1" "setosa_2" "setosa_3" "setosa_4" "setosa_5" "setosa_6"

tail(rownames(iris))

## [1] "virginica_145" "virginica_146" "virginica_147" "virginica_148"
## [5] "virginica_149" "virginica_150"

Data Frame: Adding Column(s)

data(iris)

# adding a column, new.column with values Sepal.Length * Sepal.Width
iris$new.column <- iris$Sepal.Length * iris$Sepal.Width
head(iris[,c("Sepal.Length","Sepal.Width","new.column")])

##   Sepal.Length Sepal.Width new.column
## 1          5.1         3.5      17.85
## 2          4.9         3.0      14.70
## 3          4.7         3.2      15.04
## 4          4.6         3.1      14.26
## 5          5.0         3.6      18.00
## 6          5.4         3.9      21.06

# assume sepal size defined as large if new.column > median  
# ifelse function - teaser 
cat("Median of Sepal.Size: ",median(iris$new.column))

## Median of Sepal.Size:  17.66

iris$Sepal.Size <- ifelse(iris$new.column > median(iris$new.column), c("Large"), c("Small"))
head(iris[,c("Sepal.Length","Sepal.Width","new.column","Sepal.Size")])

##   Sepal.Length Sepal.Width new.column Sepal.Size
## 1          5.1         3.5      17.85      Large
## 2          4.9         3.0      14.70      Small
## 3          4.7         3.2      15.04      Small
## 4          4.6         3.1      14.26      Small
## 5          5.0         3.6      18.00      Large
## 6          5.4         3.9      21.06      Large

# manually 
iris$Sepal.Size2 <- "Small"  # initialization
iris$Sepal.Size2[iris$new.column > median(iris$new.column)] <- "Large"  # initialization
head(iris[,c("new.column", "Sepal.Size", "Sepal.Size2")])

##   new.column Sepal.Size Sepal.Size2
## 1      17.85      Large       Large
## 2      14.70      Small       Small
## 3      15.04      Small       Small
## 4      14.26      Small       Small
## 5      18.00      Large       Large
## 6      21.06      Large       Large

Data Frame: Adding Row(s)

# original
data(iris)
tail(iris, 2)

##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 149          6.2         3.4          5.4         2.3 virginica
## 150          5.9         3.0          5.1         1.8 virginica

# adding a row (here species is important, it is a factor)
iris <- rbind(iris,c(5,4,7,6,"virginica"))
tail(iris, 3)

##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 149          6.2         3.4          5.4         2.3 virginica
## 150          5.9           3          5.1         1.8 virginica
## 151            5           4            7           6 virginica

Data Frame: Removing Column(s)

# original
data(iris)
tail(iris,2)

##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 149          6.2         3.4          5.4         2.3 virginica
## 150          5.9         3.0          5.1         1.8 virginica

# removing a column  
iris$Species <- NULL
tail(iris, 2)

##     Sepal.Length Sepal.Width Petal.Length Petal.Width
## 149          6.2         3.4          5.4         2.3
## 150          5.9         3.0          5.1         1.8

# removing a multiple column 
colnames(iris)

## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"

iris$Sepal.Length <- iris$Petal.Length <- NULL
tail(iris, 2)

##     Sepal.Width Petal.Width
## 149         3.4         2.3
## 150         3.0         1.8

Data Frame: Removing Row(s)

# original
data(iris)
tail(iris, 2)

##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 149          6.2         3.4          5.4         2.3 virginica
## 150          5.9         3.0          5.1         1.8 virginica

# removing a row 
iris <- iris[-150,] # remove 150^th row 
tail(iris, 3) 

##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 147          6.3         2.5          5.0         1.9 virginica
## 148          6.5         3.0          5.2         2.0 virginica
## 149          6.2         3.4          5.4         2.3 virginica

# remove multiple rows 
head(iris,3)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa

iris <- iris[-c(1,3,4),]
head(iris,3)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 2          4.9         3.0          1.4         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

# remove rows based on a filter  
iris[iris$Sepal.Length > 7.6,]

##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 118          7.7         3.8          6.7         2.2 virginica
## 119          7.7         2.6          6.9         2.3 virginica
## 123          7.7         2.8          6.7         2.0 virginica
## 132          7.9         3.8          6.4         2.0 virginica
## 136          7.7         3.0          6.1         2.3 virginica

iris <- iris[!iris$Sepal.Length > 7.6,] # remove obs 
iris[iris$Sepal.Length > 7.5,]

##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 106          7.6           3          6.6         2.1 virginica

Pipes

• Information on Pipe operators largely taken from Base vs Magrittr Pipe. Information below is considered to be true for R version 4.2 and later

• Pipe operators, |> and %>% are used to pipe an object forward to a function or call expression

• Some complex data manipulations can be made using pipe operator as a sequence of operations

• |> is native pipe operator and %>% is a pipe operator coming with magrittr package

pipe passes the object on its left-hand side to the first argument of the function on the right-hand side.

• Both |> and %>% perform the same operations for some simple cases, there are differences as well,

x %>% f(1)   = f(x, 1)  
x %>% f(1, .)= f(1, x)  

df %>% split(.$var) = split(df, df$var)  

df %>% {split(.$x, .$y)} = split(df$x, df$y)  

df %>% .$var = df$var   

# no paranthesis requred 
x %>% mean  
df %>% 
  .$var %>% 
  mean 

x |> f(1) = f(x, 1)  
x |> f(1, y = _) = f(1, y=x)  

# parantheses always necessary 
x |> mean()


# Good 
flights |>  
  filter(!is.na(arr_delay), !is.na(tailnum)) |> 
  count(dest)

# Avoid
flights|>filter(!is.na(arr_delay), !is.na(tailnum))|>count(dest)

Data Frame ➡️ Tibble

library(tibble)
# see ?tibble::tibble

dept <- c('MIS', 'ECON', 'SENG', 'CENG', 'MAN')
some_numbers <- c(18L, 19L, 14L, 25L, 22L)

fsb_tbl <- tibble(
  department = dept, 
  count = some_numbers, 
  percentage = count / sum(count))

fsb_tbl 

## # A tibble: 5 × 3
##   department count percentage
##   <chr>      <int>      <dbl>
## 1 MIS           18      0.184
## 2 ECON          19      0.194
## 3 SENG          14      0.143
## 4 CENG          25      0.255
## 5 MAN           22      0.224

Data Frame ➡️ Tibble

as.data.frame(fsb_tbl) 

##   department count percentage
## 1        MIS    18  0.1836735
## 2       ECON    19  0.1938776
## 3       SENG    14  0.1428571
## 4       CENG    25  0.2551020
## 5        MAN    22  0.2244898

Data Structures: So What is a Tibble Anyway?

Tibble is a modern reimagining of the data frame. Tibbles are designed to be (as much as possible) drop-in replacements for data frames that fix those frustrations. A concise, and fun, way to summarise the main differences is that tibbles are lazy and surly: they do less and complain more. – Hadley Wickham

To learn more about the basics of tibble, please consult the reference below:

• Data frames and tibbles (Click and read from 3.6 up to and including 3.6.5)