Data Structures –
a. DataFrame –
A data frame is a table or a
two-dimensional array-like structure in which each column contains values of
one variable and each row contains one set of values from each column.
Following are the characteristics of
a data frame.
The column names should be
non-empty.
The row names should be unique.
The data stored in a data frame can
be of numeric, factor or character type. Each column should contain same number
of data items.
Create Data Frame – #
Create the data frame.
emp.data <- data.frame( emp_id = c (1:5), emp_name=c("Rick","Dan","Michelle","Ryan","Gary"), salary = c(623.3,515.2,611.0,729.0,843.25)start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11", "2015-03-27")), stringsAsFactors = FALSE )
# Print the data frame.
print(emp.data)
When we execute the above code, it
produces the following result –
emp_id
emp_name salary start_date 1 1
Rick 623.30 2012-01-01
2
2 Dan 515.20 2013-09-23
3
3
Michelle 611.00 2014-11-15
4
4 Ryan 729.00 2014-05-11
5
5 Gary 843.25 2015-03-27
Get the Structure of the Data Frame –
The structure of the data frame can
be seen by using str() function.
#
Create the data frame.
emp.data <- data.frame( emp_id = c (1:5), emp_name = c("Rick","Dan","Michelle","Ryan","Gary"), salary = c(623.3,515.2,611.0,729.0,843.25),start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11", "2015-03-27")), stringsAsFactors = FALSE)
# Get the structure of the data
frame. str(emp.data)
When we execute the above code, it
produces the following result –
'data.frame': 5
obs. of 4 variables:
$ emp_id : int
1 2 3 4 5
$ emp_name : chr
"Rick" "Dan" "Michelle" "Ryan"
...
$ salary : num
623 515 611 729 843
$ start_date: Date,
format: "2012-01-01" "2013-09-23" "2014-1115"
"2014-05-11" ...
Summary of Data in Data Frame –
The statistical summary and nature
of the data can be obtained by applying summary() function.
#
Create the data frame.
emp.data <- data.frame( emp_id = c (1:5), emp_name = c("Rick","Dan","Michelle","Ryan","Gary"), salary = c(623.3,515.2,611.0,729.0,843.25), start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11", "2015-03-27")), stringsAsFactors = FALSE)
# Print the summary.
print(summary(emp.data))
When we execute the above code, it
produces the following result –
emp_id emp_name salary start_date Min.
:1 Length:5 Min. :515.2
Min. :2012-0101
1st Qu.:2 Class :character 1st Qu.:611.0 1st Qu.:2013-0923
Median :3 Mode
:character Median :623.3 Median :2014-0511
Mean :3 Mean :664.4
Mean :2014-0114
3rd Qu.:4 3rd Qu.:729.0 3rd Qu.:2014-1115
Max. :5 Max. :843.2
Max. :2015-03-
27
Extract Data from Data Frame –
Extract specific column from a data
frame using column name.
# Create the data frame.
emp.data <- data.frame( emp_id = c (1:5), emp_name = c("Rick","Dan","Michelle","Ryan","Gary"), salary = c(623.3,515.2,611.0,729.0,843.25)start_date = as.Date(c("2012-01-01","2013-09-23","2014-11-15","2014-05-11", "2015-03-27")), stringsAsFactors = FALSE)
# Extract Specific columns.
result <- data.frame(emp.data$emp_name,emp.data$salary)
print(result)
When we execute the above code, it
produces the following result –
emp.data.emp_name emp.data.salary 1 Rick 623.30
2
Dan
515.20
3
Michelle
611.00
4
Ryan
729.00
5
Gary
843.25
Extract
the first two rows and then all columns # Create the
data frame.
emp.data <- data.frame( emp_id = c (1:5), emp_name = c("Rick","Dan","Michelle","Ryan","Gary"), salary = c(623.3,515.2,611.0,729.0,843.25),start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11", "2015-03-27")), stringsAsFactors = FALSE)
# Extract first two rows.
result <- emp.data[1:2,] print(result)
When we execute the above code, it
produces the following result –
emp_id emp_name
salary start_date 1 1
Rick 623.3 2012-01-01
2 2 Dan
515.2 2013-09-23
Extract 3rd and 5th row with 2nd and
4th column # Create the data frame.
emp.data <- data.frame( emp_id = c (1:5), emp_name = c("Rick","Dan","Michelle","Ryan","Gary"), salary = c(623.3,515.2,611.0,729.0,843.25), start_date = as.Date(c("2012-01-01", "2013-09-23", "201411-15", "2014-05-11", "2015-03-27")), stringsAsFactors = FALSE)
# Extract 3rd and 5th row with 2nd
and 4th column.
result <- emp.data[c(3,5),c(2,4)] print(result)
When we execute the above code, it
produces the following result –
emp_name
start_date 3 Michelle 2014-11-15
5 Gary
2015-03-27
Expand Data Frame
A data frame can be expanded by
adding columns and rows.
Add Column
Just add the column vector using a
new column name.
# Create the data frame.
emp.data <- data.frame( emp_id = c (1:5), emp_name = c("Rick","Dan","Michelle","Ryan","Gary"), salary = c(623.3,515.2,611.0,729.0,843.25), start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11","2015-03-27")), stringsAsFactors = FALSE)
# Add the "dept" coulmn.
emp.data$dept <-
c("IT","Operations","IT","HR","Finance")
v <- emp.data
print(v)
When we execute the above code, it
produces the following result –
emp_id emp_name
salary start_date dept
1
1 Rick 623.30 2012-01-01 IT
2
2 Dan 515.20 2013-09-23 Operations
3
3
Michelle 611.00 2014-11-15 IT
4
4 Ryan 729.00 2014-05-11 HR
5
5 Gary 843.25 2015-03-27 Finance
Add Row
To add more rows permanently to an
existing data frame, we need to bring in the new rows in the same structure as
the existing data frame and use the rbind() function.
In the example below we create a
data frame with new rows and merge it with the existing data frame to create
the final data frame.
# Create the first data frame.
emp.data <- data.frame( emp_id = c (1:5), emp_name = c("Rick","Dan","Michelle","Ryan","Gary"), salary = c(623.3,515.2,611.0,729.0,843.25),start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11", "2015-03-27")), dept =c("IT","Operations","IT","HR","Finance"), stringsAsFactors = FALSE)
# Create the second data frame
emp.newdata <-
data.frame( emp_id = c
(6:8), emp_name =
c("Rasmi","Pranab","Tusar"), salary = c(578.0,722.5,632.8), start_date =
as.Date(c("2013-05-21","2013-07-30","2014-06-
17")), dept = c("IT","Operations","Finance")stringsAsFactors = FALSE)
# Bind the two data frames.
emp.finaldata <- rbind(emp.data,emp.newdata)
print(emp.finaldata)
When we execute the above code, it
produces the following result –
emp_id emp_name
salary start_date dept
1
1 Rick 623.30 2012-01-01 IT
2
2 Dan 515.20 2013-09-23 Operations
3
3
Michelle 611.00 2014-11-15 IT
4
4 Ryan 729.00 2014-05-11 HR
5
5 Gary 843.25 2015-03-27 Finance
6
6 Rasmi 578.00 2013-05-21 IT
7
7 Pranab 722.50
2013-07-30 Operations
8
8 Tusar 632.80 2014-06-17 Finance
b. Factors –
Factors are the data objects which
are used to categorize the data and store it as levels. They can store both
strings and integers. They are useful in the columns which have a limited
number of unique values. Like "Male, "Female" and True, False
etc. They are useful in data analysis for statistical modeling.
Factors are created using the factor
() function by taking a vector as input. Example
# Create a vector as input.
data <-
c("East","West","East","North","North","East","West","West","West
","East","North")
print(data)
print(is.factor(data))
# Apply the factor function.
factor_data <- factor(data)
print(factor_data)
print(is.factor(factor_data))
When we execute the above code, it
produces the following result –
[1] "East"
"West"
"East"
"North" "North" "East" "West"
"West"
"West" "East"
"North"
[1] FALSE
[1] East West East
North North East West West
West East
North
Levels: East North West
[1] TRUE
Factors in Data Frame –
On creating any data frame with a
column of text data, R treats the text column as categorical data and creates
factors on it.
# Create the vectors for data frame.
height <- c(132,151,162,139,166,147,122) weight
<- c(48,49,66,53,67,52,40) gender <-
c("male","male","female","female","male","female","male")
# Create the data frame.
input_data <- data.frame(height,weight,gender)
print(input_data)
# Test if the gender column is a
factor.
print(is.factor(input_data$gender))
# Print the gender column so see the
levels.
print(input_data$gender)
When we execute the above code, it
produces the following result –
height
weight gender 1 132 48
male
2
151 49 male
3
162 66 female
4
139 53 female
5
166 67 male
6
147 52 female
7
122 40 male
[1] TRUE
[1] male male female female male female male
Levels: female male
Changing the Order of Levels –
The order of the levels in a factor
can be changed by applying the factor function again with new order of the
levels.
data <- c("East","West","East","North","North","East","West",
"West","West","East","North")
# Create the factors factor_data <- factor(data)
print(factor_data)
# Apply the factor function with
required order of the level.
new_order_data <- factor(factor_data,levels =
c("East","West","North"))
print(new_order_data)
When we execute the above code, it
produces the following result –
[1] East West East
North North East West West
West East
North
Levels: East North West
[1] East West
East North North East West
West West East
North
Levels: East West North
Generating Factor Levels –
We can generate factor levels by
using the gl() function. It takes two integers as input which indicates how
many levels and how many times each level.
Syntax
gl(n, k, labels)
Following is the description of the
parameters used −
n is a integer giving the number of
levels.
k is a integer giving the number of
replications.
labels is a vector of labels for the
resulting factor levels.
Example
v <- gl(3, 4, labels = c("Tampa",
"Seattle","Boston")) print(v)
When we execute the above code, it
produces the following result –
Tampa Tampa Tampa
Tampa Seattle Seattle Seattle
Seattle
Boston
[10] Boston
Boston Boston
Levels: Tampa Seattle Boston