R Data Frame: Create, Append, Select, Subset

Details

What is a Data Frame?

A 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-

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)

Arguments:

  • 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')
c <- c(TRUE,FALSE,TRUE,FALSE)
d <- c(2.5, 8, 10, 7)
# Join the variables to create a data frame
df <- data.frame(a,b,c,d)
df

Output: 

##   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')
df

Output: 

##   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
str(df)

Output: 

## '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
df[1,2]

Output: 

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

Output: 

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

Output: 

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

Output: 

##   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')]

Output: 

##   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
df

Output:

##   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
df$ID

Output: 

## [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)
arguments:
- 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)

Output: 

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

 

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