R tutorials for Business Analyst – R Data Frame: Create, Append, Select, Subset

What is a Data Frame?

data frame is a list of vectors which are of equal length. A matrix contains only one type of data, while a data frame accepts different data types (numeric, character, factor, etc.).

In this tutorial, you will learn-

  • What is a Data Frame?
  • How to Create a Data Frame
  • Append a Column to Data Frame
  • Select a Column of a Data Frame
  • Subset a Data Frame

How to Create a Data Frame

We can create a data frame by passing the variable a,b,c,d into the data.frame() function. We can name the columns with name() and simply specify the name of the variables.

data.frame(df, stringsAsFactors = TRUE)


  • df: It can be a matrix to convert as a data frame or a collection of variables to join
  • stringsAsFactors: Convert string to factor by default

We can create our first data set by combining four variables of same length.

# Create a, b, c, d variables
a <- c(10,20,30,40)
b <- c('book', 'pen', 'textbook', 'pencil_case')
d <- c(2.5, 8, 10, 7)
# Join the variables to create a data frame
df <- data.frame(a,b,c,d)


##   a       b c d
## 1  1        book  TRUE   2.5
## 2  2         pen  TRUE   8.0
## 3  3    textbook  TRUE  10.0
## 4  4 pencil_case FALSE   7.0

We can see the column headers have the same name as the variables. We can change the column name with the function names(). Check the example below:

# Name the data frame
names(df) <- c('ID', 'items', 'store', 'price')


##   ID       items store price
## 1 10        book  TRUE   2.5
## 2 20         pen FALSE   8.0
## 3 30    textbook  TRUE  10.0
## 4 40 pencil_case FALSE   7.0
# Print the structure


## 'data.frame':    4 obs. of  4 variables:
##  $ ID   : num  10 20 30 40
##  $ items: Factor w/ 4 levels "book","pen","pencil_case",..: 1 2 4 3
##  $ store: logi  TRUE FALSE TRUE FALSE
##  $ price: num  2.5 8 10 7

By default, data frame returns string variables as a factor.


Slice Data Frame

It is possible to SLICE values of a Data Frame. We select the rows and columns to return into bracket precede by the name of the data frame.

A data frame is composed of rows and columns, df[A, B]. A represents the rows and B the columns. We can slice either by specifying the rows and/or columns.

From picture 1, the left part represents the rows, and the right part is the columns. Note that the symbol : means to. For instance, 1:3 intends to select values from 1 to 3.

In below diagram we display how to access different selection of the data frame:

  • The yellow arrow selects the row 1 in column 2
  • The green arrow selects the rows 1 to 2
  • The red arrow selects the column 1
  • The blue arrow selects the rows 1 to 3 and columns 3 to 4

Note that, if we let the left part blank, R will select all the rows. By analogy, if we let the right part blank, R will select all the columns.

We can run the code in the console:

## Select row 1 in column 2


## [1] book
## Levels: book pen pencil_case textbook
## Select Rows 1 to 2


##   ID items store price
## 1 10  book  TRUE   2.5
## 2 20   pen FALSE   8.0
## Select Columns 1


## [1] 10 20 30 40
## Select Rows 1 to 3 and columns 3 to 4
df[1:3, 3:4]


##   store price
## 1  TRUE   2.5
## 2 FALSE   8.0
## 3  TRUE  10.0

It is also possible to select the columns with their names. For instance, the code below extracts two columns: ID and store.

# Slice with columns name
df[, c('ID', 'store')]


##   ID store
## 1 10  TRUE
## 2 20 FALSE
## 3 30  TRUE
## 4 40 FALSE

Append a Column to Data Frame

You can also append a column to a Data Frame. You need to use the symbol $ to append a new variable.

# Create a new vector
quantity <- c(10, 35, 40, 5)

# Add `quantity` to the `df` data frame
df$quantity <- quantity


##   ID       items store price quantity
## 1 10        book  TRUE   2.5       10
## 2 20         pen FALSE   8.0       35
## 3 30    textbook  TRUE  10.0       40
## 4 40 pencil_case FALSE   7.0        5

Note: The number of elements in the vector has to be equal to the no of elements in data frame. Executing the following statement

quantity <- c(10, 35, 40)
# Add `quantity` to the `df` data frame
df$quantity <- quantity

Gives error:

Error in `$<-.data.frame`(`*tmp*`, quantity, value = c(10, 35, 40)) 
 replacement has 3 rows, data has 4

Select a Column of a Data Frame

Sometimes, we need to store a column of a data frame for future use or perform operation on a column. We can use the $ sign to select the column from a data frame.

# Select the column ID


## [1] 1 2 3 4

Subset a Data Frame

In the previous section, we selected an entire column without condition. It is possible to subset based on whether or not a certain condition was true.

We use the subset() function.

subset(x, condition)
- x: data frame used to perform the subset
- condition: define the conditional statement

We want to return only the items with price above 10, we can do:

# Select price above 5
subset(df, subset = price > 5)


ID       items store price
2 20         pen FALSE     8
3 30    textbook  TRUE    10
4 40 pencil_case FALSE     7

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!