The document discusses data frames in R. It defines data frames as a two-dimensional structure that can store different data types across rows and columns. It then demonstrates how to create data frames from vectors, access elements, perform operations like subsetting, merging, and applying functions using lapply() and sapply(). Data frames allow storing and manipulating tabular data in R.

1. DATA
FRAMES
R.Racksavi
II M.Sc IT.,

2. Objectives
 What is Data Frames?
 Creating Data Frames
 Other Matrix-Like Operations
 Merging Data Frames
 Applying functions to Data Frames.

3. What is data frame?
 A Data Frames in R Language is a table or a two-dimensional array-
like structure(rows and columns structure).
 Data Frame is made up of three principal components, the data, rows,
and columns.
 Data frames are the heterogeneous analogs of matrices for
two-dimensional data.
 Data Frames can have different types of data like character, Numeric
and Logical.

4. Creating DataFrames
To create a data frame in R use data.frame() command
//creating a data frame
kids <- c("Jack","Jill")
> ages <- c(12,10)
> d <- data.frame(kids, ages, stringsAsFactors=FALSE)
> d # matrix-like viewpoint
kids ages
1 Jack 12
2 Jill 10
Output
Here Two vectors:
kids and ages

5. Accessing Data Frames
“d is a list”
component index value
d[[1]]
Output: [1] "Jack" "Jill”
d$kids
Output: [1] "Jack" "Jill“
view column 1
d[,1]
Output:[1] "Jack" "Jill“
using str():
str(d)
'data.frame': 2 obs. of 2 variables:
Output:
$ kids: chr "Jack" "Jill“
$ ages: num 12 10
R tells that d consists of
two observations—our
two rows—that
store data on two
variables—our two
columns.
kids ages
1 Jack 12
2 Jill 10

6. Extended Example: Regression Analysis
of Exam Grades Continued
 This is the classic two-dimensional file
 Data frame is to encapsulate such data, along with
variable names, into one object.
 Separated the fields by spaces.
To read the file:
examsquiz <- read.table("exams",header=TRUE)
column names appear with periods replacing blanks:
head(examsquiz)
Output: Exam.1 Exam.2 Quiz
1 2.0 3.3 4.0
2 3.3 2.0 3.7
3 4.0 4.0 4.0
4 2.3 0.0 3.3
5 2.3 1.0 3.3
6 3.3 3.7 4.0
"Exam 1" "Exam 2" Quiz
2.0 3.3 4.0
3.3 2.0 3.7
4.0 4.0 4.0
2.3 0.0 3.3
2.3 1.0 3.3
3.3 3.7 4.0

7. Other Matrix-Like Operations
(I) Extracting Subdata Frames
a data frame can be viewed in row-and-column,
extract subdata frames by rows or columns.
examsquiz[2:5,]
Output:
Exam.1 Exam.2 Quiz
2 3.3 2 3.7
3 4.0 4 4.0
4 2.3 0 3.3
5 2.3 1 3.3
examsquiz[2:5,2]
Output: [1] 2 4 0 1
"Exam 1" "Exam 2" Quiz
2.0 3.3 4.0
3.3 2.0 3.7
4.0 4.0 4.0
2.3 0.0 3.3
2.3 1.0 3.3

8. class(examsquiz[2:5,2])
Output: [1] "numeric“
examsquiz[2:5,2,drop=FALSE] Output:
FILTERING:
Here’s how to extract the subframe
of all Students whose first exam
score was at least 3.8:
examsquiz[examsquiz$Exam.1 >= 3.8,]
Output:
Exam.1 Exam.2 Quiz
3 4 4.0 4.0
9 4 3.3 4.0
11 4 4.0 4.0
14 4 0.0 4.0
16 4 3.7 4.0
Exam.2
2 2
3 4
4 0
5 1

9. (II) Treatment of NA Values
In R missing values are represented by the symbol NA .
The rm() function in R is used to delete or remove a
variable from a workspace.
2.0 NA 4.0
x <- c(2,NA,4)
> mean(x)
Output:[1] NA
> mean(x,na.rm=TRUE)
Output:[1] 3
X examsquiz[examsquiz$Exam.1 >= 3.8,]
 > subset(examsquiz,Exam.1 >= 3.8)

10. The complete.cases() function in R, To eliminate
missing values from a vector, matrix, or data frame.
d4
> complete.cases(d4)
[1] TRUE FALSE
TRUE FALSE
> d5 <- d4[complete.cases(d4),]
> d5
kids states
1 Jack CA
3 Jillian MA
kids states
1 Jack CA
2 <NA> MA
3 Jillian MA
4 John <NA>

11. apply() takes Data frame or matrix as an input and gives output in
vector, list or array.
Syntax: apply( x, margin, function )
x: determines the input array including matrix.
margin: If the margin is 1 function is applied across row, if the
margin is 2 it is applied across the column.
function: determines the function that is to be applied on input data.
apply(examsquiz,1,max)
[1] 4.0 3.7 4.0 3.3 3.3 4.0 3.7 3.3 4.0 4.0 4.0 3.3 4.0 4.0 3.7 4.0 3.3 3.7 4.0
[20] 3.7 4.0 4.0 3.3 3.3 4.0 4.0 3.3 3.3 4.0 3.7 3.3 3.3 3.7 2.7 3.3 4.0 3.7 3.7
[39] 3.7

12. Using the rbind() and cbind() Functions
rbind() function is used to combine specified Vector, Matrix or
Data Frame by rows.
Syntax: rbind(x1, x2, …, deparse.level = 1)
Parameters: x1, x2: vector, matrix, data frames
deparse.level: This value determines how the column names
generated. The default value of deparse.level is 1.
cbind() function is used to combine specified Vector, Matrix or
Data Frame by columns.
Syntax: cbind(x1, x2, …, deparse.level = 1)
Parameters:x1, x2: vector, matrix, data frames
deparse.level: This value determines how the column names
generated. The default value of deparse.level is 1.

13. To add a row
>d
kids ages
1 Jack 12
2 Jill 10
> rbind(d,list("Laura",19))
kids ages
1 Jack 12
2 Jill 10
3 Laura 19
To create new columns
from old ones, to add a
variable that is the
difference between exams 1
and 2:
eq<-
cbind(examsquiz,examsqui
z$Exam.2-
examsquiz$Exam.1)
> class(eq)
[1] "data.frame"

14. The head() function is used to display the first n rows
present in the input data frame.
> head(eq)
Exam.1 Exam.2 Quiz examsquiz$Exam.2 - examsquiz$Exam.
1 2.0 3.3 4.0 1.3
2 3.3 2.0 3.7 -1.3
3 4.0 4.0 4.0 0.0
4 2.3 0.0 3.3 -2.3
5 2.3 1.0 3.3 -1.3
6 3.3 3.7 4.0 0.4
To add a column that is of a different length than those in the data frame:
>d
kids ages
1 Jack 12
2 Jill 10

15. >d
kids ages
1 Jack 12
2 Jill 10
> d$one <- 1
>d
kids ages one
1 Jack 12 1
2 Jill 10 1

16. Merging Data Frames
merge() function is used to merge two data frames by common columns.
Syntax: merge(arg1, arg2, by.x, by.y)
Parameters: arg1 and arg2: Data frames to be merged
by.x: Common argument of first data frame
by.y: Common argument of second data frame
> d <- merge(d1,d2)
Output:
>d
kids states ages
1 Jack CA 12
2 Jill MA 10
> d1
kids states
1 Jack CA
2 Jill MA
3 Jillian MA
4 John HI
> d2
ages kids
1 10 Jill
2 7 Lillian
3 12 Jack

17. > merge(d1,d3,by.x="kids",by.y="pals")
kids states ages
1 Jack CA 12
2 Jill MA 10
> d2a <- rbind(d2,list(15,"Jill"))
> d2a
ages kids
1 12 Jack
2 10 Jill
3 7 Lillian
4 15 Jill
> d3
ages pals
1 12 Jack
2 10 Jill
3 7 Lillian
> d1
kids states
1 Jack CA
2 Jill MA
3 Jillian MA
4 John HI

18. Applying Functions to Data Frames
Using lapply() and sapply() on DataFrames
lapply() function:
 The lapply() function helps us in applying
functions on list objects and returns a list object of
the same length.

19. lapply() function
 The lapply() function in the R Language takes a list, vector, or data
frame as input and gives output in the form of a list object.
 Since the lapply() function applies a certain operation to all the
elements of the list it doesn’t need a MARGIN.
Syntax: lapply( x, fun )
Parameters:
x: determines the input vector or an object.
fun: determines the function that is to be applied to input data.

20. Example:
The use of the lapply() function to a vector.
names <- c("priyank", "abhiraj", "pawananjani", " sudhanshu", "devraj")
print( "original data:")
names
# apply lapply() function
print("data after lapply():")
lapply(names, toupper)
O/P

21. sapply() function
 The sapply() function helps us in applying functions on a list, vector, or
data frame and returns an array or matrix object of the same length.
 The sapply() function in the R Language takes a list, vector, or data frame
as input and gives output in the form of an array or matrix object.
 Since the sapply() function applies a certain operation to all the elements
of the object it doesn’t need a MARGIN.
 It is the same as lapply() with the only difference being the type of return
object.

22. Syntax: sapply( x, fun )
Parameters:
x: determines the input vector or an object.
fun: determines the function that is to be applied to input data.
Example:
# create sample data
sample_data<- data.frame( x=c(1,2,3,4,5,6) ,
y=c(3,2,4,2,34,5))
print( "original data:")
sample_data
# apply sapply() function
print("data after sapply():")
sapply(sample_data, max)
O/P

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