Data Frame

Author

Dr. Mohammad Nasir Abdullah

Data Frames

Data sets frequently consist of more than one column of data, where each column represents measurements of a single variable. Each row usually represents a single observation. This format is referred to as case-by-variable format.

Most data sets are stored in R as data frames. These are like matrices, but with the columns having their own names.

A data frame is one of the most commonly used data structures in R, especially for data analysis and statistical modelling. Conceptually, it can be thought of as a table or a spreadsheet, where you have rows representing observations and columns representing variables. A data frame is similar to a matrix, but with the added flexibility that different columns can contain different types of data (eg: numeric, character, factor).

Features:

Mixed Data Types: Unlike matrices, data frames can store different classes of objects in each column.
Column Names: Columns in a data frame can have names, which makes accessing and manipulating data easier and more intuitive.
Row Names: By default, rows have index names (from 1 to the number of rows), but these can also be explicitly set to other values.

Creation:

A data frame can be created using the data.frame() function:

df <- data.frame(Name = c("Ali", "Abu", "Ahmad"), 
                 Age = c(9, 6, 2), 
                 Score = c(82, 93, 92))
df

   Name Age Score
1   Ali   9    82
2   Abu   6    93
3 Ahmad   2    92

Indexing:

Columns: You can access a column in a data frame using $ operator or double square brackets [[…]].

#extract names from df
df$Name

[1] "Ali"   "Abu"   "Ahmad"

#extract score from df
df[["Score"]]

[1] 82 93 92

Rows: Rows can be accessed using single square brackets […].

#Extracting first row
df[1, ]

  Name Age Score
1  Ali   9    82

#Extracting 3rd row
df[3, ]

   Name Age Score
3 Ahmad   2    92

Subsetting: You can subset data frames using conditions

#Extracting data that contain more than 90
df[df$Score > 90, ]

   Name Age Score
2   Abu   6    93
3 Ahmad   2    92

Useful functions

head() and tail(): Display the first or last part of a data frame.
str(): Provides the structure of a data frame, showing the data type of each column and the first few entries.
summary(): Gives a statistical summary of all columns in a data frame.
dim(): Returns the dimensions (number of rows and columns) of a data frame.
rownames() and colnames(): Get or set the row or column names of a data frame.
merge(): Merges two data frames by common columns or row names.

Examples

1) head() and tail()

These functions display the first or last part of a data frame, respectively. By default, they show six rows.

# Create a sample data frame
df <- data.frame(Name = c("Ali", "Abu", "Ahmad", "Aminah", "Rosnah", "Rozanae", "Rohana"),
                 Age = c(25, 32, 29, 24, 27, 31, 23),
                 Score = c(85, 90, 93, 87, 78, 91, 82))

# Display the first few rows
head(df)

     Name Age Score
1     Ali  25    85
2     Abu  32    90
3   Ahmad  29    93
4  Aminah  24    87
5  Rosnah  27    78
6 Rozanae  31    91

# Display the last few rows
tail(df)

     Name Age Score
2     Abu  32    90
3   Ahmad  29    93
4  Aminah  24    87
5  Rosnah  27    78
6 Rozanae  31    91
7  Rohana  23    82

2) str()

This function provides a concise display of the structure of an object, such as a data frame.

# Display the structure of df
str(df)

'data.frame':   7 obs. of  3 variables:
 $ Name : chr  "Ali" "Abu" "Ahmad" "Aminah" ...
 $ Age  : num  25 32 29 24 27 31 23
 $ Score: num  85 90 93 87 78 91 82

3) summary()

Gives a statistical summary of all columns in a data frame.

# Get a summary of df
summary(df)

     Name                Age            Score      
 Length:7           Min.   :23.00   Min.   :78.00  
 Class :character   1st Qu.:24.50   1st Qu.:83.50  
 Mode  :character   Median :27.00   Median :87.00  
                    Mean   :27.29   Mean   :86.57  
                    3rd Qu.:30.00   3rd Qu.:90.50  
                    Max.   :32.00   Max.   :93.00

4) dim()

Returns the dimensions of an object.

# Get the dimensions of df (number of rows and columns)
dim(df)

[1] 7 3

5) rownames() and colnames()

Retrieve or set the row or column names of a data frame.

# Get row names of df
rownames(df)

[1] "1" "2" "3" "4" "5" "6" "7"

# Get column names of df
colnames(df)

[1] "Name"  "Age"   "Score"

# Set new row names for df
rownames(df) <- c("A", "B", "C", "D", "E", "F", "G")

6) merge()

Merge two data frames by common columns or row names.

# Create another sample data frame
df2 <- data.frame(Name = c("Ali", "Abu", "Rosnah", "Rohana"),
                  Grade = c("A", "B", "A", "C"))

# Merge df and df2 by the "Name" column
merged_df <- merge(df, df2, by="Name")
print(merged_df)

    Name Age Score Grade
1    Abu  32    90     B
2    Ali  25    85     A
3 Rohana  23    82     C
4 Rosnah  27    78     A

Let’s use mtcars data set.

This dataset comprises various specifications and details about different car models from the 1970s.

1. Quick Glance at the Dataset

First, let’s take a quick look at the mtcars dataset:

head(mtcars)

                   mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

2. Structure of the Dataset (str())

Examining the structure of mtcars:

str(mtcars)

'data.frame':   32 obs. of  11 variables:
 $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
 $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
 $ disp: num  160 160 108 258 360 ...
 $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
 $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
 $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
 $ qsec: num  16.5 17 18.6 19.4 17 ...
 $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
 $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
 $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
 $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

3. Summary of the Dataset (summary())

Providing a statistical summary:

summary(mtcars)

      mpg             cyl             disp             hp       
 Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
 1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
 Median :19.20   Median :6.000   Median :196.3   Median :123.0  
 Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
 3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
 Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
      drat             wt             qsec             vs        
 Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
 1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
 Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
 Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
 3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
 Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
       am              gear            carb      
 Min.   :0.0000   Min.   :3.000   Min.   :1.000  
 1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
 Median :0.0000   Median :4.000   Median :2.000  
 Mean   :0.4062   Mean   :3.688   Mean   :2.812  
 3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
 Max.   :1.0000   Max.   :5.000   Max.   :8.000

4. Dimensions of the Dataset (dim())

Checking the number of rows and columns:

dim(mtcars)

[1] 32 11

5. Column Names (colnames())

Retrieving the names of the columns:

colnames(mtcars) #same as names(mtcars)

 [1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear"
[11] "carb"

7. Subsetting Example

Extracting data for cars with 6 cylinders and horsepower (hp) greater than 150:

mtcars[mtcars$cyl == 6 & mtcars$hp > 150, ]

              mpg cyl disp  hp drat   wt qsec vs am gear carb
Ferrari Dino 19.7   6  145 175 3.62 2.77 15.5  0  1    5    6

Exercise

Exercise 1:

Create a data frame named students with the following columns: Name, Age, Grade, and Subject. Populate it with at least 5 rows of sample data.
Display the structure of the students data frame using the str() function.
Add a new column to the students data frame named Attendance and populate it with sample data.

Exercise 2:

From the mtcars dataset, extract the mpg (miles per gallon) and hp (horsepower) columns and save them as a new data frame named car_specs.
Retrieve the first 6 rows of the car_specs data frame.
Create a subset of mtcars containing only cars with 6 cylinders (cyl).

Exercise 3:

Calculate the median horsepower (hp) of all cars in the mtcars dataset.
How many cars in the dataset have an automatic transmission (am column: 0 represents automatic, 1 represents manual)?
Which car model in the mtcars dataset has the highest miles per gallon (mpg)?

Exercise 4:

Extract and display all cars from mtcars with 4 cylinders (cyl).
How many cars in the mtcars dataset have more than 100 horsepower (hp) and weigh (column wt) less than 3,000 lbs?
Retrieve all car models from mtcars that have an automatic transmission and can cover more than 20 miles per gallon.

Exercise 5:

How many rows and columns are present in the mtcars dataset?
What are the names of all the columns in the dataset?
Display the last 8 rows of the dataset.

Exercise 6:

Calculate the median horsepower (hp) of all cars in the dataset.
How many cars in the dataset have an automatic transmission (am column: 0 represents automatic, 1 represents manual)?
Which car model has the highest miles per gallon (mpg)?

Exercise 7:

Extract and display all cars with 4 cylinders (cyl).
How many cars have more than 100 horsepower (hp) and weigh (column wt) less than 3,000 lbs?
Retrieve all car models that have an automatic transmission and can cover more than 20 miles per gallon.