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.).

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
a b c d
10 book TRUE 2.5
20 pen FALSE 8.0
30 textbook TRUE 10.0
40 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
ID items store price
10 book TRUE 2.5
20 pen FALSE 8.0
30 textbook TRUE 10.0
40 pencil_case FALSE 7.0 
# Print the structure
str(df)
## 'data.frame':    4 obs. of  4 variables:
##  $ ID   : num  10 20 30 40
##  $ items: chr  "book" "pen" "textbook" "pencil_case"
##  $ 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.

## Select row 1 in column 2
df[1, 2]
## [1] "book"
## Select Rows 1 to 2
df[1:2, ]
ID items store price
10 book TRUE 2.5
20 pen FALSE 8.0 
## Select Columns 1
df[, 1]
## [1] 10 20 30 40

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
10 TRUE
20 FALSE
30 TRUE
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
ID items store price quantity
10 book TRUE 2.5 10
20 pen FALSE 8.0 35
30 textbook TRUE 10.0 40
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$quantity <- quantity
## Error in `$<-.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
## [1] 10 20 30 40

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)
ID items store price quantity
2 20 pen FALSE 8 35
3 30 textbook TRUE 10 40
4 40 pencil_case FALSE 7 5
 

A work by Gianluca Sottile

gianluca.sottile@unipa.it