Learn to transform/reshape data in R. This is part of the Working With Data module of the R Programming course by r-squared.

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Slide 1 www.r-squared.in/rprogramming
R Programming
Learn the fundamentals of data analysis with R.

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Course Modules
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✓ Introduction
✓ Elementary Programming
✓ Working With Data
✓ Selection Statements
✓ Loops
✓ Functions
✓ Debugging
✓ Unit Testing

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Working With Data
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✓ Data Types
✓ Data Structures
✓ Data Creation
✓ Data Info
✓ Data Subsetting
✓ Comparing R Objects
✓ Importing Data
✓ Exporting Data
✓ Data Transformation
✓ Numeric Functions
✓ String Functions
✓ Mathematical Functions

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Data Transformation
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In this section, we will explore built-in R function that can be used for
transforming/reshaping data. This section is further divided into 4 sub-sections:
● Reorder Data
● Subset/Filter Data
● Combine Data
● Transform Data

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Reorder Data
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In the course of analyzing data, sometimes it is necessary to reorder the data as we
cannot use the data in its original format. Sorting the data is the best example of such
reordering. In this section, we will learn the following functions:
✓ t (transpose)
✓ order
✓ sort
✓ rank

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t()
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Description:
t() returns the transpose of a matrix or data frame.
Syntax:
t(matrix/data frame)
Returns:
The transpose of the matrix or data frame.
Documentation
help(t)

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t()
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Examples
> # example 1
> m <- matrix(1:6, nrow = 2)
> dim(m)
[1] 2 3
> dim(t(m))
[1] 3 2
> m # 2 x 3 matrix
[,1] [,2] [,3]
[1,] 1 3 5
[2,] 2 4 6
> t(m) # t() returns a 3 x 2 matrix
[,1] [,2]
[1,] 1 2
[2,] 3 4
[3,] 5 6

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t()
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Examples
> # example 2
> data <- mtcars
> head(data)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
> data_transpose <- t(data)
> head(data_transpose)
Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive Hornet Sportabout Valiant
mpg 21.00 21.000 22.80 21.400 18.70 18.10
cyl 6.00 6.000 4.00 6.000 8.00 6.00
disp 160.00 160.000 108.00 258.000 360.00 225.00
hp 110.00 110.000 93.00 110.000 175.00 105.00
drat 3.90 3.900 3.85 3.080 3.15 2.76
wt 2.62 2.875 2.32 3.215 3.44 3.46

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order()
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Description:
order() sorts a given vector and returns the indices of the elements.
Syntax:
order(vector / data frame)
Returns:
The indices of the sorted object (vector / data frame).
Documentation
help(order)

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10
order()
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Examples
> # example 1
> x <- sample(1:10)
> x
[1] 10 9 6 2 4 3 8 1 7 5
# let us sort x using the indices
> x[c(8, 4, 6, 5, 10, 3, 9, 7, 2, 1)]
[1] 1 2 3 4 5 6 7 8 9 10
> order(x)
[1] 8 4 6 5 10 3 9 7 2 1
> x[order(x)]
[1] 1 2 3 4 5 6 7 8 9 10

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order()
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Examples
> # example 2
> data_ascending <- data[order(data$mpg),]
> data_descending <- data[order(-data$mpg),]
> head(data_ascending)
mpg cyl disp hp drat wt qsec vs am gear carb
Cadillac Fleetwood 10.4 8 472 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460 215 3.00 5.424 17.82 0 0 3 4
Camaro Z28 13.3 8 350 245 3.73 3.840 15.41 0 0 3 4
Duster 360 14.3 8 360 245 3.21 3.570 15.84 0 0 3 4
Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4
Maserati Bora 15.0 8 301 335 3.54 3.570 14.60 0 1 5 8
> head(data_descending)
mpg cyl disp hp drat wt qsec vs am gear carb
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2

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sort()
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Description:
sort() will sort the elements of a vector or factor in ascending/descending order.
Syntax:
sort(vector / factor)
Returns:
Sorted vector or factor in ascending/descending order
Documentation
help(sort)

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sort()
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Examples
> # example 1
> x <- sample(1:10)
> x
[1] 10 9 6 2 4 3 8 1 7 5
> sort(x) # ascending order
[1] 1 2 3 4 5 6 7 8 9 10
> # example 2
> x <- sample(1:10)
> x
[1] 10 9 6 2 4 3 8 1 7 5
> sort(x, decreasing = TRUE) # descending order
[1] 10 9 8 7 6 5 4 3 2 1

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rank()
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Description:
rank() returns the sample ranks of values in a vector.
Syntax:
rank(vector)
Returns:
Sample ranks of values in a vector.
Documentation
help(rank)

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rank()
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Examples
> # example 1
> x <- sample(1:10)
> x
[1] 7 9 1 8 6 5 3 2 10 4
> rank(x)
[1] 7 9 1 8 6 5 3 2 10 4
> # example 2
> x2 <- c(3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5)
> order(x2)
[1] 2 4 7 1 10 3 5 9 11 8 6
> sort(x2)
[1] 1 1 2 3 3 4 5 5 5 6 9
> (r2 <- rank(x2)) # ties are averaged
[1] 4.5 1.5 6.0 1.5 8.0 11.0 3.0 10.0 8.0 4.5 8.0

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Subset/Filter Data
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In this section, we will look at functions that can be used for subsetting/filtering data.
✓ subset
✓ which
✓ with
✓ drop
✓ droplevels

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subset()
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Description:
subset() can be used to subset data from vectors and data frames.
Syntax:
subset(vector / data frame)
Returns:
Vector or data frame.
Documentation
help(subset)

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subset()
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Examples
> # example 1
> # subsetting vectors
> x
[1] 7 9 1 8 6 5 3 2 10 4
> subset(x, x > 5)
[1] 7 9 8 6 10
> subset(x, x == 4)
[1] 4
> subset(x, x > 4 & x < 7)
[1] 6 5

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subset()
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Examples
> # example 2
> # subsetting data frames
> subset(mtcars, mpg >= 23 & mpg <= 27)
mpg cyl disp hp drat wt qsec vs am gear carb
Merc 240D 24.4 4 146.7 62 3.69 3.19 20.0 1 0 4 2
Porsche 914-2 26.0 4 120.3 91 4.43 2.14 16.7 0 1 5 2
> subset(mtcars, mpg >= 23 & mpg <= 27, select = c(cyl, hp))
cyl hp
Merc 240D 4 62
Porsche 914-2 4 91
> subset(mtcars, cyl == 4 & hp > 100, select = mpg:wt)
mpg cyl disp hp drat wt
Lotus Europa 30.4 4 95.1 113 3.77 1.513
Volvo 142E 21.4 4 121.0 109 4.11 2.780

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which()
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Description:
which() tests if the values in a object evaluate to TRUE for a given condition and return
the indices of such values.
Syntax:
which(object, condition)
Returns:
Indices of values which evaluate to TRUE for a given condition.
Documentation
help(which)

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which()
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Examples
> # example 1
> x
[1] 7 9 1 8 6 5 3 2 10 4
> which(x == 5) # returns index of value 5.
[1] 6
> which(x > 4) # returns indices of all values greater than 4.
[1] 1 2 4 5 6 9
> # example 2
> # using data frame
> which(data$mpg > 20) # returns indices of values greater than 20.
[1] 1 2 3 4 8 9 18 19 20 21 26 27 28 32
> data$mpg[which(data$mpg > 20)] # returns values greater than 20.
[1] 21.0 21.0 22.8 21.4 24.4 22.8 32.4 30.4 33.9 21.5 27.3 26.0 30.4 21.4

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which()
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Examples
> # example 3
> m
[,1] [,2] [,3]
[1,] 1 3 5
[2,] 2 4 6
> which(m > 5)
[1] 6
> which(m == 5)
[1] 5
> which(letters == "r") # r is the 18th alphabet
[1] 18
> div_by_3 <- m %% 3 == 0
> div_by_3
[,1] [,2] [,3]
[1,] FALSE TRUE FALSE
[2,] FALSE FALSE TRUE
> which(div_by_3) # which values in m are divisible by 3.
[1] 3 6

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droplevels()
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Description:
droplevels() drops all unused levels from a factor.
Syntax:
droplevels(factor)
Returns:
Factors without unused levels.
Documentation
help(droplevels)

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droplevels()
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Examples
> # example 1
> data_cyl <- subset(data, cyl == 4 | cyl == 6)
> levels(data_cyl$cyl)
[1] "4" "6" "8"
> droplevels(data_cyl$cyl)
[1] 6 6 4 6 6 4 4 6 6 4 4 4 4 4 4 4 6 4
Levels: 4 6
> levels(data_cyl$cyl)
[1] "4" "6" "8"
> summary(droplevels(data_cyl$cyl))
4 6
11 7

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droplevels()
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Examples
> # example 2
> aq <- transform(airquality, Month = factor(Month, labels = month.abb[5:9]))
> aq <- subset(aq, Month != "Jul")
> table(aq$Month)
May Jun Jul Aug Sep
31 30 0 31 30
> table(droplevels(aq)$Month)
May Jun Aug Sep
31 30 31 30
> droplevels(data_cyl)$cyl
[1] 6 6 4 6 6 4 4 6 6 4 4 4 4 4 4 4 6 4
Levels: 4 6
> table(droplevels(data_cyl)$cyl)
4 6
11 7
> table(data_cyl$cyl)
4 6 8
11 7 0

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Combine Data
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In this section, we will look at functions that combine data.
✓ append
✓ merge
✓ cbind
✓ rbind
✓ interaction

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append()
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Description:
append() adds elements to a vector. We can specify the index where the element must be
added.
Syntax:
append(vector, elements)
Returns:
A vector with appended elements.
Documentation
help(append)

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append()
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Examples
> # example 1
> x1 <- sample(1:10)
> x2 <- sample(1:5)
> append(x1, x2)
[1] 8 7 4 9 5 10 6 1 2 3 2 5 3 1 4
> example 2
> x1 <- sample(1:10)
> x2 <- sample(1:5)
> append(x1, x2, after = 2)
[1] 8 7 2 5 3 1 4 4 9 5 10 6 1 2 3

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merge()
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Description:
merge() will merge two data frames by common column or row names.
Syntax:
merge(dataframe1, dataframe2, by)
Returns:
Data frame.
Documentation
help(merge)

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merge()
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Examples
> # example 1
> name <- c("John", "Jane", "Tom", "Jennifer")
> age <- c(20, 25, 30, 28)
> gender <- factor(c("male", "female", "male", "female"))
> data_1 <- data.frame(name, age)
> data_2 <- data.frame(name, gender)
> data_3 <- merge(data_1, data_2, by = "name")
> head(data_3)
name age gender
1 Jane 25 female
2 Jennifer 28 female
3 John 20 male
4 Tom 30 male

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cbind()
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Description:
cbind() combines objects by columns.
Syntax:
cbind(object1, object2)
Returns:
Matrix / Data frame
Documentation
help(cbind)

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cbind()
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Examples
> # example 1
> cbind(1, 1:4)
[,1] [,2]
[1,] 1 1
[2,] 1 2
[3,] 1 3
[4,] 1 4
> # example 2
> m1 <- matrix(1:4, nrow = 2)
> m2 <- matrix(5:8, nrow = 2)
> cbind(m1, m2)
[,1] [,2] [,3] [,4]
[1,] 1 3 5 7
[2,] 2 4 6 8

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cbind()
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Examples
> # example 3
> name <- c("John", "Jane", "Tom", "Jennifer")
> age <- c(20, 25, 30, 28)
> gender <- factor(c("male", "female", "male", "female"))
> data_1 <- data.frame(name, age)
> data_2 <- data.frame(name, gender)
> data_3 <- merge(data_1, data_2, by = "name")
> data_4 <- cbind(data_3, income)
> head(data_4)
name age gender income
1 Jane 25 female 25000
2 Jennifer 28 female 30000
3 John 20 male 35000
4 Tom 30 male 40000

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rbind()
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Description:
rbind() combines objects by rows.
Syntax:
rbind(object1, object2)
Returns:
Matrix / Data frame
Documentation
help(rbind)

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rbind()
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Examples
> # example 1
> rbind(1, 1:4)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 1 1 1 1 1 1 1 1
[2,] 1 2 3 4 5 6 7 8
> # example 2
> m1 <- matrix(1:4, nrow = 2)
> m2 <- matrix(5:8, nrow = 2)
> rbind(m1, m2)
[,1] [,2]
[1,] 1 3
[2,] 2 4
[3,] 5 7
[4,] 6 8

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rbind()
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Examples
> # example 3
> name <- c("John", "Jane", "Tom", "Jennifer")
> age <- c(20, 25, 30, 28)
> gender <- factor(c("male", "female", "male", "female"))
> data_1 <- data.frame(name, age)
> data_2 <- data.frame(name, gender)
> data_3 <- merge(data_1, data_2, by = "name")
> data_4 <- data_3
> data_rbind <- rbind(data_3, data_4)
> head(data_rbind)
name age gender
1 Jane 25 female
2 Jennifer 28 female
3 John 20 male
4 Tom 30 male
5 Jane 25 female
6 Jennifer 28 female

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interaction()
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Description:
interaction() creates interaction variables.
Syntax
interaction(factor1, factor2)
Returns:
Interaction variable.
Documentation
help(interaction)

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interaction()
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Examples
> # example 1
> mtcars$gear <- as.factor(mtcars$gear)
> mtcars$cyl <- as.factor(mtcars$cyl)
> interaction(mtcars$cyl, mtcars$gear)
[1] 6.4 6.4 4.4 6.3 8.3 6.3 8.3 4.4 4.4 6.4 6.4 8.3 8.3 8.3 8.3 8.3 8.3 4.4 4.4 4.4
[21] 4.3 8.3 8.3 8.3 8.3 4.4 4.5 4.5 8.5 6.5 8.5 4.4
Levels: 4.3 6.3 8.3 4.4 6.4 8.4 4.5 6.5 8.5
> # example 2
> mtcars$am <- as.factor(mtcars$am)
> mtcars$cyl <- as.factor(mtcars$cyl)
> interaction(mtcars$cyl, mtcars$am)
[1] 6.1 6.1 4.1 6.0 8.0 6.0 8.0 4.0 4.0 6.0 6.0 8.0 8.0 8.0 8.0 8.0 8.0 4.1 4.1 4.1
[21] 4.0 8.0 8.0 8.0 8.0 4.1 4.1 4.1 8.1 6.1 8.1 4.1
Levels: 4.0 6.0 8.0 4.1 6.1 8.1

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Reshape Data
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In this section, we will look at functions that transform/reshape data.
✓ transform
✓ cut
✓ diff
✓ replace
✓ scale
✓ split
✓ with
✓ within
✓ by

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transform()
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Description:
transform() is used to transform variables in a data frame.
Syntax
transform(data frame, expression)
Returns:
Data frame with transformed variables.
Documentation
help(transform)

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transform()
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Examples
> # example 1
> data <- mtcars
> head(data)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
> head(transform(data, mpg = -mpg, disp = disp / wt))
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 -21.0 6 61.06870 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag -21.0 6 55.65217 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 -22.8 4 46.55172 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive -21.4 6 80.24883 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout -18.7 8 104.65116 175 3.15 3.440 17.02 0 0 3 2
Valiant -18.1 6 65.02890 105 2.76 3.460 20.22 1 0 3 1

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transform()
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Examples
> # example 2
> data <- mtcars
> head(transform(data, wtdrat = wt * drat))
mpg cyl disp hp drat wt qsec vs am gear carb wtdrat
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 10.2180
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 11.2125
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 8.9320
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 9.9022
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 10.8360
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 9.5496

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cut()
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Description:
cut() divides the range of an object into intervals.
Syntax
cut(object, breaks)
Returns:
Intervals
Documentation
help(cut)

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transform()
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Examples
> # example 1
> x <- jitter(sample(1:100))
> x
[1] 69.8630359 44.8489212 61.1385505 50.8402454 26.0350033 97.0510463 42.8749472
[8] 2.0313452 69.0713593 9.1714536 64.8470522 62.8787114 98.9115336 58.8020429
[15] 81.9908416 87.1495953 16.9303676 11.9593307 38.0015233 20.1833953 14.0838761
………………………………………………………………………………………………………………………………………
……………………………………………………………………………………………
[85] 3.0042776 33.9052141 97.8309652 47.1207229 77.1890815 41.8063134 39.9223398
[92] 27.8306122 80.0271128 18.1951342 85.1410689 23.1750646 6.1861739 27.0493739
[99] 36.9679664 18.9148518
> c <- cut(x, breaks = 10)
> table(c)
c
(0.827,10.8] (10.8,20.8] (20.8,30.7] (30.7,40.6] (40.6,50.5] (50.5,60.4] (60.4,70.3]
10 10 10 10 10 10 10
(70.3,80.3] (80.3,90.2] (90.2,100]
10 10 10

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diff()
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Description:
diff() creates sequences with lags and iterated differences.
Syntax
diff(object, lag)
Returns:
Lagged sequence
Documentation
help(diff)

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diff()
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Examples
> # example 1
> diff(1:10, 2)
[1] 2 2 2 2 2 2 2 2
> diff(1:10, 2, 2)
[1] 0 0 0 0 0 0
> x <- cumsum(cumsum(1:10))
> x
[1] 1 4 10 20 35 56 84 120 165 220
> diff(x, lag = 2)
[1] 9 16 25 36 49 64 81 100

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replace()
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Description:
replace() replaces the elements in object given by indices in list with values.
Syntax
replace(object, list, values)
Returns:
New object with replaced values.
Documentation
help(replace)

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replace()
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Examples
> # example 1
> x <- sample(1:10)
> x
[1] 6 2 7 9 1 5 4 8 10 3
> replace(x, 5, 10)
[1] 6 2 7 9 10 5 4 8 10 3
# replace the value in the index position 5 in the vector x with the value 10.
> # example 1
> x <- sample(1:10)
> x
[1] 6 2 7 9 1 5 4 8 10 3
> replace(x, 3:5, c(2, 4, 6))
[1] 6 2 2 4 6 5 4 8 10 3

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scale()
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Description:
scale() scales the columns of a numeric matrix.
Syntax
scale(numeric matrix)
Returns:
Matrix with scaled columns
Documentation
help(scale)

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scale()
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Examples
> # example 1
> m <- matrix(1:9, nrow = 3)
> m
[,1] [,2] [,3]
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9
> scale(m)
[,1] [,2] [,3]
[1,] -1 -1 -1
[2,] 0 0 0
[3,] 1 1 1
attr(,"scaled:center")
[1] 2 5 8
attr(,"scaled:scale")
[1] 1 1 1

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split()
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Description:
split() divides the data in the vector x into the groups defined by f.
Syntax
split(x, f)
Returns:
x split into groups defined by f.
Documentation
help(split)

52. r-squared
Slide 52
split()
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Examples
> # example 1
> x <- split(data$mpg, data$cyl)
> x
$`4`
[1] 22.8 24.4 22.8 32.4 30.4 33.9 21.5 27.3 26.0 30.4 21.4
$`6`
[1] 21.0 21.0 21.4 18.1 19.2 17.8 19.7
$`8`
[1] 18.7 14.3 16.4 17.3 15.2 10.4 10.4 14.7 15.5 15.2 13.3 19.2 15.8 15.0
> sapply(x, mean)
4 6 8
26.66364 19.74286 15.10000
> unsplit(x, data$cyl)
[1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4 10.4 14.7 32.4 [19]
30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7 15.0 21.4

53. r-squared
Slide 53
with()
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Description:
with() applies an expression to an object.
Syntax
with(object, expression)
Returns:
Result of the expression
Documentation
help(with)

54. r-squared
Slide 54
with()
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Examples
> # example 1
> with(mtcars, table(cyl))
cyl
4 6 8
11 7 14
> with(mtcars, summary(mpg))
Min. 1st Qu. Median Mean 3rd Qu. Max.
10.40 15.42 19.20 20.09 22.80 33.90
> with(mtcars, lm(mpg ~ hp))
Call:
lm(formula = mpg ~ hp)
Coefficients:
(Intercept) hp
30.09886 -0.06823

55. r-squared
Slide 55
within()
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Description:
within() applies an expression to an object and returns a copy of the modified object.
Syntax
within(object, expression)
Returns:
Copy of the modified object
Documentation
help(within)

56. r-squared
Slide 56
within()
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Examples
> # example 1
> data <- mtcars
> data <- within(data, mpg_cyl <- mpg * cyl)
> head(data)
mpg cyl disp hp drat wt qsec vs am gear carb mpg_cyl
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 126.0
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 126.0
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 91.2
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 128.4
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 149.6
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 108.6

57. r-squared
Slide 57
by()
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Description:
by() applies an expression to each level of a factor in an object
Syntax
by(object, factor, expression)
Returns:
Result of the expression applied to different levels of the factor
Documentation
help(by)

58. r-squared
Slide 58
by()
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Examples
> # example 1
> by(mtcars$mpg, mtcars$cyl, summary)
mtcars$cyl: 4
Min. 1st Qu. Median Mean 3rd Qu. Max.
21.40 22.80 26.00 26.66 30.40 33.90
----------------------------------------------------------------
mtcars$cyl: 6
Min. 1st Qu. Median Mean 3rd Qu. Max.
17.80 18.65 19.70 19.74 21.00 21.40
----------------------------------------------------------------
mtcars$cyl: 8
Min. 1st Qu. Median Mean 3rd Qu. Max.
10.40 14.40 15.20 15.10 16.25 19.20

59. r-squared
In the next unit, we will explore the following numeric functions:
Slide 59
Next Steps...
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● signif()
● jitter()
● format()
● formatC()
● abs()
● round()
● ceiling()
● floor()

60. r-squared
Slide
60
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