Data fram in R programming



R - Data Frames

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" "2014-11-15" "2014-05-11" ...

Summary of Data in Data Frame

The statistical summary and nature of the data can be obtained by applying summary() funct

# 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. :2012-01-01
1st Qu.:2 Class :character 1st Qu.:611.0 1st Qu.:2013-09-23
Median :3 Mode :character Median :623.3 Median :2014-05-11
Mean :3 Mean :664.4 Mean :2014-01-14
3rd Qu.:4 3rd Qu.:729.0 3rd Qu.:2014-11-15
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", "2014-11-15", "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","Fianance"),
   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 Fianance


programming language that is widely used as a statistical software and data analysis tool. Data Frames in R Language are generic data objects of R which are used to store the tabular data. Data frames can also be interpreted as matrices where each column of a matrix can be of the different data types. DataFrame is made up of three principal components, the data, rows, and columns. 
R – Data Frames
Create Dataframe in R Programming Language
 create a data frame in R use data.() command and then pass each of the vectors you have created as arguments to the function.Example:R
# R program to create dataframe
 
# creating a data frame
friend.data <- data.frame(
    friend_id = c(1:5),
    friend_name = c("Sachin", "Sourav",
                    "Dravid", "Sehwag",
                    "Dhoni"),
    stringsAsFactors = FALSE
)
# print the data frame
print(friend.data)
Output: 
      friend_id friend_name
1         1      Sachin
2         2      Sourav
3         3      Dravid
4         4      Sehwag
5         5       Dhoni
Get the Structure of the R – Data Frame
One can get the structure of the data frame using str() function in R. It can display even the internal structure of large lists which are nested. It provides one-liner output for the basic R objects letting the user know about the object and its constituents. 
Example: 
R
# R program to get the
# structure of the data frame
 
# creating a data frame
friend.data <- data.frame(
    friend_id = c(1:5),
    friend_name = c("Sachin", "Sourav",
                    "Dravid", "Sehwag",
                    "Dhoni"),
    stringsAsFactors = FALSE
)
# using str()
print(str(friend.data))
Output: 
'data.frame':    5 obs. of  2 variables:
 $ friend_id  : int  1 2 3 4 5
 $ friend_name: chr  "Sachin" "Sourav" "Dravid" "Sehwag" ...
NULL
Summary of data in the data frame
In R data frame, the statistical summary and nature of the data can be obtained by applying summary() function. It is a generic function used to produce result summaries of the results of various model fitting functions. The function invokes particular methods which depend on the class of the first argument. 
Example: 
R
# R program to get the
# summary of the data frame
 
# creating a data frame
friend.data <- data.frame(
    friend_id = c(1:5),
    friend_name = c("Sachin", "Sourav",
                    "Dravid", "Sehwag",
                    "Dhoni"),
    stringsAsFactors = FALSE
)
# using summary()
print(summary(friend.data))
Output: 
    friend_id friend_name       
 Min.   :1   Length:5          
 1st Qu.:2   Class :character  
 Median :3   Mode  :character  
 Mean   :3                     
 3rd Qu.:4                     
 Max.   :5   
Extract Data from Data Frame in R Language
Extract data from a data frame means that to access its rows or columns. One can extract a specific column from a data frame using its column name. 
Example: 
R
# R program to extract
# data from the data frame
 
# creating a data frame
friend.data <- data.frame(
    friend_id = c(1:5),
    friend_name = c("Sachin", "Sourav",
                    "Dravid", "Sehwag",
                    "Dhoni"),
    stringsAsFactors = FALSE
)
 
# Extracting friend_name column
result <- data.frame(friend.data$friend_name)
print(result)
Output: 
    friend.data.friend_name
1                  Sachin
2                  Sourav
3                  Dravid
4                  Sehwag
5                   Dhoni
Expand Data Frame
A data frame in R can be expanded by adding new columns and rows to the already existing data frame. 
Example: 
R
# R program to expand
# the data frame
 
# creating a data frame
friend.data <- data.frame(
    friend_id = c(1:5),
    friend_name = c("Sachin", "Sourav",
                    "Dravid", "Sehwag",
                    "Dhoni"),
    stringsAsFactors = FALSE
)
 
# Expanding data frame
friend.data$location <- c("Kolkata", "Delhi",
                       "Bangalore", "Hyderabad",
                       "Chennai")
resultant <- friend.data
# print the modified data frame
print(resultant)
Output: 
    friend_id friend_name  location
1         1      Sachin   Kolkata
2         2      Sourav     Delhi
3         3      Dravid Bangalore
4         4      Sehwag Hyderabad
5         5       Dhoni   Chennai
In R, one can perform various types of operations on a data frame like accessing rows and columns, selecting the subset of the data frame, editing data frames, delete rows and columns in a data frame, etc. Please refer to DataFrame Operations in R to know about all types of operations that can be performed on a data frame.


Comments

Post a Comment

Popular posts from this blog

📚Gds-Tech 📚 EMS

Image
In this competitive world, 'Vocabulary' plays a vital role in every exam. Several banking and Government exams are round the corner like IBPS PO, Clerk, IBPS RRB, SSC-MTS, CGL, CHSL, State Level exams , Other Exams etc.  Gds-Tech Educational sollution  provide you 10 new words in  Gds-vocab  on the daily basis which will benefit you in upcoming examinations.  The Gds-Vocab  includes the word with the meaning, synonyms, antonyms along with the example given in  " The Gds Info-Skill Times "   pages. As we all Know in English section the vocabulary comes from THE HINDU Newspapers in the most bank and competitive exams and it is very essential to read and memories vocab from The Gds  Vocab daily. You can test your vocabulary and evaluate yourself with regard to your respective exams. You can easily learn The words form Gds  Vocab   regular basis .          Gds ...
Read more

vocab

Image
       Meaning vocabulary is a term that describes how people learn the meaning of words. Children can learn the meanings of words by using context clues, word part instruction and graphic organizers. Meaning vocabulary entails words for which their meanings are understood and it is an important component of comprehension. Direct instruction in meaning vocabulary is essential. If vocabulary is not developed, children will be unprepared to handle vocabulary demands of content-area subjects. Its significance is also displayed in comprehension and interaction with others. Meaning vocabulary helps to build social skills, confidence and self-esteem in children. It also enables children to use context clues to decipher word meaning. It helps children to construct more interesting stories and to communicate effectively with their peers and just about anyone with whom they interact. The more words they know, the more aptly they will be able to communi...
Read more

spreadsheet in excel

Image
What is a Spreadsheet A spreadsheet is a file that exists of cells in rows and columns and can help arrange, calculate and sort data. Data in a spreadsheet can be numeric values, as well as text,  formulas ,  references  and  functions . Rows, columns and cells in a spreadsheet The rows in a spreadsheet have numeric names (1, 2, 3 etc) and the columns have alphabetic names (a, b, c etc), thus the names of cells subsequently exist of a letter and a number that conveniently point out their position in the grid Selection of a row In the image above row 3 is selected. This can be done by clicking on the number 3 in front of the row. Selection of a column In the image above column B is selected. This can be done by clicking on the letter B above the column. Selection of a cell In the image above cell B3 is selected. This can be done by clicking on the cell. Worksheets A spreadsheet can contain one or several worksheets (tabs). Adding worksheets can be done by clicking the...
Read more