DataFrame Operations in R

DataFrames are generic data objects of R which are used to store the tabular data. Data frames are considered to be the most popular data objects in R programming because it is more comfortable to analyze the data in the tabular form. Data frames can also be taught as mattresses where each column of a matrix can be of the different data types. DataFrame are made up of three principal components, the data, rows, and columns. 

Operations that can be performed on a DataFrame are: 
 

• Creating a DataFrame
• Accessing rows and columns
• Selecting the subset of the data frame
• Editing dataframes
• Adding extra rows and columns to the data frame
• Add new variables to dataframe based on existing ones
• Delete rows and columns in a data frame

Creating a DataFrame

In the real world, a DataFrame will be created by loading the datasets from existing storage, storage can be SQL Database, CSV file, and an Excel file. DataFrame can also be created from the vectors in R. Following are some of the various ways that can be used to create a DataFrame:

Creating a data frame using Vectors: To create a data frame we use the data.frame() function in R. To create a data frame use data.frame() command and then pass each of the vectors you have created as arguments to the function. 

Example:  

# R program to illustrate dataframe

# A vector which is a character vector
Name = c("Amiya", "Raj", "Asish")

# A vector which is a character vector
Language = c("R", "Python", "Java")

# A vector which is a numeric vector
Age = c(22, 25, 45)

# To create dataframe use data.frame command and
# then pass each of the vectors 
# we have created as arguments
# to the function data.frame()
df = data.frame(Name, Language, Age)

print(df)

Output: 

  Name  Language  Age
1 Amiya        R  22
2   Raj   Python  25
3 Asish     Java  45

Creating a data frame using data from a file: Dataframes can also be created by importing the data from a file. For this, you have to use the function called 'read.table()'.

Syntax:  

newDF = read.table(path="Path of the file")

To create a dataframe from a CSV file in R: 

Syntax:  

newDF = read.csv("FileName.csv")

Accessing rows and columns

The syntax for accessing rows and columns is given below,  

df[val1, val2]

df = dataframe object
val1 = rows of a data frame
val2 = columns of a data frame

So, this 'val1' and 'val2' can be an array of values such as "1:2" or "2:3" etc. If you specify only df[val2] this refers to the set of columns only, that you need to access from the data frame.

Example: Row selection  

# R program to illustrate operations
# on a data frame

# Creating a dataframe
df = data.frame(
  "Name" = c("Amiya", "Raj", "Asish"),
  "Language" = c("R", "Python", "Java"),
  "Age" = c(22, 25, 45)
)
print(df)

# Accessing first and second row
cat("Accessing first and second row\n")
print(df[1:2, ])

Output: 

   Name Language Age
1 Amiya        R  22
2   Raj   Python  25
3 Asish     Java  45

Accessing first and second row
   Name Language Age
1 Amiya        R  22
2   Raj   Python  25

Example: Column selection 

# R program to illustrate operations
# on a data frame

# Creating a dataframe
df = data.frame(
  "Name" = c("Amiya", "Raj", "Asish"),
  "Language" = c("R", "Python", "Java"),
  "Age" = c(22, 25, 45)
)
print(df)

# Accessing first and second column
cat("Accessing first and second column\n")
print(df[, 1:2])

Output: 

   Name Language Age
1 Amiya        R  22
2   Raj   Python  25
3 Asish     Java  45

Accessing first and second column
   Name Language
1 Amiya        R
2   Raj   Python
3 Asish     Java

Selecting the subset of the DataFrame

A subset of a DataFrame can also be created based on certain conditions with the help of following syntax.  

newDF = subset(df, conditions)
df = Original dataframe 
conditions = Certain conditions 
 

Example:  

# R program to illustrate operations
# on a data frame

# Creating a dataframe
df = data.frame(
  "Name" = c("Amiya", "Raj", "Asish"),
  "Language" = c("R", "Python", "Java"),
  "Age" = c(22, 25, 45)
)
print(df)

# Selecting the subset of the data frame
# where Name is equal to Amiya
# OR age is greater than 30
newDf = subset(df, Name =="Amiya"|Age>30)

cat("After Selecting the subset of the data frame\n")
print(newDf)

Output: 

Name Language Age
1 Amiya        R  22
2   Raj   Python  25
3 Asish     Java  45

After Selecting the subset of the data frame
   Name Language Age
1 Amiya        R  22
3 Asish     Java  45

Editing DataFrames

In R, DataFrames can be edited in two ways:
Editing data frames by direct assignments: Much like the list in R you can edit the data frames by a direct assignment. 

Example:  

# R program to illustrate operation on a data frame

# Creating a dataframe
df = data.frame(
  "Name" = c("Amiya", "Raj", "Asish"),
  "Language" = c("R", "Python", "Java"),
  "Age" = c(22, 25, 45)
)
cat("Before editing the dataframe\n")
print(df)

# Editing dataframes by direct assignments
# [[3]] accessing the top level components 
# Here Age in this case
# [[3]][3] accessing inner level components 
# Here Age of Asish in this case
df[[3]][3] = 30

cat("After edited the dataframe\n")
print(df)

Output: 

Before editing the data frame
   Name Language Age
1 Amiya        R  22
2   Raj   Python  25
3 Asish     Java  45

After edited the data frame
   Name Language Age
1 Amiya        R  22
2   Raj   Python  25
3 Asish     Java  30

Editing dataframes using the edit() command: 
Follow the given steps to edit a DataFrame:

Step 1: So, what you need to do for this is you have to create an instance of data frame, for example, you can see that here an instance of a data frame is created and named as "myTable" by using the command data.frame() and this creates an empty data frame. 

myTable = data.frame() 
 

Step 2: Next we will use the edit function to launch the viewer. Note that "myTable" data frame is passed back to the "myTable" object and this way the changes we make to this module will be saved to the original object. 

myTable = edit(myTable) 
 

So, when the above command is executed it will pop up a window like this, 

Step 3: Now, the table is completed with this small roster.  

Note that, change variable names by clicking on their labels and typing your changes. Variables can also be set as numeric or character. Once the data in the DataFrame looks like the above, close the table. Changes are saved automatically.

Step 4: Check out the resulting data frame by printing it.  

> myTable 
 

   Name Language Age
1 Amiya        R  22
2   Raj   Python  25
3 Asish     Java  45 

Adding rows and columns to the data frame

Adding extra rows: We can add extra row using the command rbind(). The syntax for this is given below,  

newDF = rbind(df, the entries for the new row you have to add )
df = Original data frame 
 

Note that the entries for the new row you have to add you have to be careful when using rbind() because the data types in each column entry should be equal to the data types that are already existing rows. 

Example:  

# R program to illustrate operation on a data frame

# Creating a dataframe
df = data.frame(
  "Name" = c("Amiya", "Raj", "Asish"),
  "Language" = c("R", "Python", "Java"),
  "Age" = c(22, 25, 45)
)
cat("Before adding row\n")
print(df)

# Add a new row using rbind()
newDf = rbind(df, data.frame(Name = "Sandeep",
                            Language = "C",
                            Age = 23
                           ))
cat("After Added a row\n")
print(newDf)

Output: 

Before adding row
   Name Language Age
1 Amiya        R  22
2   Raj   Python  25
3 Asish     Java  45

After Added a row
     Name Language Age
1   Amiya        R  22
2     Raj   Python  25
3   Asish     Java  45
4 Sandeep        C  23

Adding extra columns: We can add extra column using the command cbind(). The syntax for this is given below,  

newDF = cbind(df, the entries for the new column you have to add )
df = Original data frame 
 

Example:  

# R program to illustrate operation on a data frame

# Creating a dataframe
df = data.frame(
  "Name" = c("Amiya", "Raj", "Asish"),
  "Language" = c("R", "Python", "Java"),
  "Age" = c(22, 25, 45)
)
cat("Before adding column\n")
print(df)

# Add a new column using cbind()
newDf = cbind(df, Rank=c(3, 5, 1))

cat("After Added a column\n")
print(newDf)

Output: 

Before adding column
   Name Language Age
1 Amiya        R  22
2   Raj   Python  25
3 Asish     Java  45

After Added a column
   Name Language Age Rank
1 Amiya        R  22    3
2   Raj   Python  25    5
3 Asish     Java  45    1

Adding new variables to DataFrame

In R, we can add new variables to a data frame based on existing ones. To do that we have to first call the dplyr library using the command library() . And then calling mutate() function will add extra variable columns based on existing ones. 

Syntax:  

library(dplyr) 
newDF = mutate(df, new_var=[existing_var])
df = original data frame 
new_var = Name of the new variable 
existing_var = The modify action you are taking(e.g log value, multiply by 10) 
 

Example:  

# R program to illustrate operation on a data frame

# Importing the dplyr library
library(dplyr)

# Creating a dataframe
df = data.frame(
  "Name" = c("Amiya", "Raj", "Asish"),
  "Language" = c("R", "Python", "Java"),
  "Age" = c(22, 25, 45)
)
cat("Original Dataframe\n")
print(df)

# Creating an extra variable column
# "log_Age" which is log of variable column "Age"
# Using mutate() command
newDf = mutate(df, log_Age = log(Age))

cat("After creating extra variable column\n")
print(newDf)

Output: 

Original Dataframe
   Name Language Age
1 Amiya        R  22
2   Raj   Python  25
3 Asish     Java  45

After creating extra variable column
   Name Language Age  log_Age
1 Amiya        R  22 3.091042
2   Raj   Python  25 3.218876
3 Asish     Java  45 3.806662

Deleting rows and columns from a data frame

To delete a row or a column, first of all, you need to access that row or column and then insert a negative sign before that row or column. It indicates that you had to delete that row or column. 

Syntax:  

newDF = df[-rowNo, -colNo]
df = original data frame 
 

Example:  

# R program to illustrate operation on a data frame

# Creating a dataframe
df = data.frame(
  "Name" = c("Amiya", "Raj", "Asish"),
  "Language" = c("R", "Python", "Java"),
  "Age" = c(22, 25, 45)
)
cat("Before deleting the 3rd row and 2nd column\n")
print(df)

# delete the third row and the second column
newDF = df[-3, -2]

cat("After Deleted the 3rd row and 2nd column\n")
print(newDF)

Output: 

Before deleting the 3rd row and 2nd column
   Name Language Age
1 Amiya        R  22
2   Raj   Python  25
3 Asish     Java  45
After Deleted the 3rd row and 2nd column
   Name Age
1 Amiya  22
2   Raj  25