R Tutorial
R Data Interfaces
R Charts & Graphs
R Statistics Examples
R Useful Resources
Selected Reading
- R - Data Reshaping
- R - Packages
- R - Data Frames
- R - Factors
- R - Arrays
- R - Matrices
- R - Lists
- R - Vectors
- R - Strings
- R - Functions
- R - Loops
- R - Decision Making
- R - Operators
- R - Variables
- R - Data Types
- R - Basic Syntax
- R - Environment Setup
- R - Overview
- R - Home
R Data Interfaces
- R - Database
- R - Web Data
- R - JSON Files
- R - XML Files
- R - Binary Files
- R - Excel Files
- R - CSV Files
R Charts & Graphs
R Statistics Examples
- R - Chi Square Tests
- R - Survival Analysis
- R - Random Forest
- R - Decision Tree
- R - Nonlinear Least Square
- R - Time Series Analysis
- R - Analysis of Covariance
- R - Poisson Regression
- R - Binomial Distribution
- R - Normal Distribution
- R - Logistic Regression
- R - Multiple Regression
- R - Linear Regression
- R - Mean, Median & Mode
R Useful Resources
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- Who is Who
- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes
R - Data Frames
R - Data Frames
A data frame is a table or a two-dimensional array-pke 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() 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. :2012-01-01
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 FiananceAdvertisements