Data Frame

Data frames in R are one of the most essential data structures. A data frame in R is a list with the class “data.frame“. The data frame structure is used to store tabular data. Data frames in R Language are essentially lists of vectors of equal length, where each vector represents a column and each element of the vector corresponds to a row.

Data frames in R are the workhorse of data analysis, providing a flexible and efficient way to store, manipulate, and analyze data.

Restrictions on Data Frames in R

The following are restrictions on data frames in R:

1. The components (Columns or features) must be vectors (numeric, character, or logical), numeric matrices, factors, lists, or other data frames.
2. Lists, Matrices, and data frames provide as many variables to the new data frame as they have columns, elements, or variables.
3. Numeric vectors, logical vectors, and factors are included as is, by default, character vectors are coerced to be factors, whose levels are the unique values appearing in the vector.
4. Vecture structures appearing as variables of the data frame must all have the same length, and matrix structures must all have the same row size.

A data frame may for many purposes be regarded as a matrix with columns possibly of differing modes and attributes. It may be displayed in matrix form, and its rows and columns are extracted using matrix indexing conventions.

Key Characteristics of Data Frame

• Column-Based Operations: R language provides powerful functions and operators for performing operations on entire columns or subsets of columns, making data analysis and manipulation efficient.
• Heterogeneous Data: Data frames can store data of different data types within the same structure, making them versatile for handling various kinds of data.
• Named Columns: Each column in a data frame has a unique name, which is used to reference and access specific data within the frame.
• Row-Based Indexing: Data frames are indexed based on their rows, allowing you to easily extract or manipulate data based on row numbers.

Making/ Creating Data Frames in R

Objects satisfying the restrictions placed on the columns (components) of a data frame may be used to form one using the function data.frame(). For example:

BMI <- data.frame(
  age = c(20, 40, 33, 45),
  weight = c(65, 70, 53, 69),
  height = c(62, 65, 55, 58)
)

Note that a list whose components conform to the restrictions of a data frame may coerced into a data frame using the function as.data.frame().

Other Way of Creating a Data Frame

One can also use read.table(), read.csv(), read_excel(), and read_csv() functions to read an entire data frame from an external file.

Accessing and Manipulating Data

• Accessing Data: Use column names or row indices to extract specific values or subsets of data.
• Creating New Columns: Calculate new columns based on existing ones using arithmetic operations, logical expressions, or functions.
• Grouping and Summarizing: Group data by specific columns and calculate summary statistics (e.g., mean, median, sum).
• Sorting Data: Arrange rows in ascending or descending order based on column values.
• Filtering Data: Select rows based on conditions using logical expressions and indexing.

# Create a data frame manually
data <- data.frame(
  Name = c("Ali", "Usman", "Hamza"),
  Age  = c(25, 30, 35),
  City = c("Multan", "Lahore", "Faisalabad")
)

# Accessing data
print(data$Age)      # Displays the "Age" column
print(data[2, ])  # Displays the second row

# Creating a new column
data$Age_Category <- ifelse(data$Age < 30, "Young", "Old")

# Filtering data
young_people <- data[data$Age < 30, ]

# Sort data
sorted_data <- data[order(data$Age), ]

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The post contains Data frame Questions and Answers. A data frame in R is a fundamental data structure used to store and organize tabular data. A Data Frame is like a spreadsheet with rows and columns, but more flexible in data types.

Merging Data Frames inR

Question 1: How two data frames can be merged in R language?

Answer: Data frames in the R language can be merged manually using the column bind function cbind() or by using the merge() function on common rows or columns.

Question 2: What is the difference between a data frame and a matrix in R?

Answer: A Data frame can contain heterogeneous inputs while a matrix cannot. In a matrix only similar data types (say either numeric or symbols) can be stored whereas in a data frame, there can be different data types like characters, integers, or other data frames. In short columns of a matrix have the same data type while different columns of a data frame can have different data types.

Dropping Variables Using Indices

Question 3: How will you drop variables using indices in a data frame?

Answer: Consider the data frame the following data frame

df <- data.frame(v1 = c(1:5),
                 v2 = c(2:6),
                 v3 = c(3:7),
                 v4 = c(4:8))
df

# output
  v1 v2 v3 v4
1  1  2  3  4
2  2  3  4  5
3  3  4  5  6
4  4  5  6  7
5  5  6  7  8

Suppose we want to drop variables $v2$ & $v3$, the variables $v2$ and $v3$ can be dropped using negative indicies as follows:

df1 <- df[-c(2, 3)]
df1

#output
  v1 v4
1  1  4
2  2  5
3  3  6
4  4  7
5  5  8

One can do the same by using the positive indexes.

df2 <- df[c(1, 4)]
df2

#output
  v1 v4
1  1  4
2  2  5
3  3  6
4  4  7
5  5  8

Merging Data Frame in R Language

Question 4: How two Data Frames can be merged in the R programming language?

Answer: The merge() function in R is used to combine two data frames and it identifies common rows or columns between the 2 data frames. The merge() function finds the intersection between two different sets of data. The merge() function in R language takes a long list of arguments as follows

The syntax for using the merge() function in R language:

 merge (x, y, by.x, by.y, all.x  or all.y or all )

• $X$ represents the first data frame.
• $Y$ represents the second data frame.
• $by.X$ Variable name in dataframe $X$ that is common in $Y$.
• $by.Y$ Variable name in dataframe $Y$ that is common in $X$.
• $all.x$ It is a logical value that specifies the type of merge. The $all.X$ should be set to TRUE if we want all the observations from data frame $X$. This results in Left Join.
• $all.y$ It is a logical value that specifies the type of merge. The $all.y$ should be set to TRUE if we want all the observations from data frame $Y$. This results in Right Join.
• $all$ The default value for this is set to FALSE which means that only matching rows are returned resulting in an Inner join. This should be set to true if you want all the observations from data frame $X$ and $Y$ resulting in Outer join.

Question 5: What is the process to create a table in R language without using external files?

Answer:

MyTable = data.frame()
edit(MyTable)

The above code will open an Excel Spreadsheet for entering data into MyTable.

Read more about “R FAQ about Data Frame“.

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Introduction to Data Frame in R Language

In R Programming language a data frame is a two-dimensional data structure. The data frame objects contain rows and columns. The number of rows for each column should have equal length. The cross-section of the row and column can be considered as a cell. Each cell of the data frame is associated with a combination of row number and column number.

A data frame in R Programming Langauge has:

• Rows: Represent individual observations or data points.
• Columns: Represent variables or features being measured. Each column holds values for a single variable across all observations.
• Data Types: Columns can hold data of different types, including numeric, character, logical (TRUE/FALSE), and factors (categorical variables).

One can modify, extract, and re-arrange the data contents of a data frame; the process is called the manipulation of the data frame. To create a data frame a general syntax can be followed

Data Frame Syntax in R

The general syntax of a data frame in R Language is

df <- data.frame(first column = c(data values separated with commas,
                           second column = c(data values separate with commans,
                           ......
          )

An exemplary data frame in the R Programming language is

df = data.frame(age = c(23, 24, 25, 26, 23, 25, 29, 20),
                marks = c(99, 80, 67, 56, 98, 65, 45, 77),
                grade = c("A", "A", "C", "D", "A", "B", "F", "B")
                )
print(df)

One can name or rename the columns and rows of the data frame

# Naming / renaming columns 
colnames(df) <- c("Age", "Score", "Grad")

# Naming / renaming rows
row.names(df) <- c("1st", "2nd", "3rd", "4th", "5th", "6th", "7th", "8th")

Subsetting a Data Frame

The subset() method can be used to create a new data set by removing specified column(s). This splits the data frame into two sets, one with excluded columns and the other with included columns. To understand subsetting a data frame, let us create a data frame first.

# creating a data frame
df = data.frame(row1 = 0:3, row2 = 3:6, row3 = 6:9)

# creating a subset
df <- subset(df, select = c(row1, row2))

Question: Data Frame in R Language

Suppose we have a frequency distribution of sales from a sample of 100 sales receipts.

Calculate the mean, median, variance, standard deviation, and coefficient of variation by using the R code.

Solution

# Crate a data frame 

df <- data.frame(lower_class = seq(0, 100, by = 20), upper_class=seq(20, 120, by=20), freq = c(16, 18, 14, 24, 20, 8))

# mid points
m <- (df["lower_class"] + df["upper_class"])/2

mf <- df["freq"] * m
mfsquare <- df["freq"] * m^2


data <- cbind(df, m, mf, mfsquare)
colnames(data) <- c("LL","UL", "freq" , "M", "mf", "mf2")

# Computation
avg = sum(data$mf)/sum(data$freq)
var = (sum(data$mf2) - sum(data$mf)^2 / sum(data$freq))/(sum(data$freq)-1)
sd = sqrt(var)
CV = sd/avg * 100

## Outputs
paste("Mean = ", round(avg, 3))
paste("Variance = ", round(var, 3))
paste("Standard Deviation = ", round(sd, 3))
paste("Coefficient of Variation = ", round(CV, 3))

Using Logical Conditions for Selecting Rows and Columns

For selecting rows and columns using logical conditions, we consider the iris data set. Here, suppose we are interested in Selecting rows whose values are higher than the median for Sepal Length and whose Petal.Width >= 1.7. In the code below, each value is Sepal.Length variable (column) is compared with the median value of Sepal.Length. Similarly, each value of Petal.Width is compared with 1.7 to extract the required values from these two columns.

attach(iris) 

iris[(Sepal.Length > median(Sepal.Length) & Petal.Width >= 1.7), ]

One can select only the numeric columns from the data frame by following the code below

# Selecting Numeric Columns only
iris[ , sapply(iris, is.numeric)]

# Selecting factor columns only
iris[, sapply(iris, is.factor)]

# Selecting only certain Species
 iris[Species == "virginica", ]

Omitting Missing Observations in a Data Frame

# Omit rows with missing data
na.omit(iris)

# check for missing data across rows
apply(iris, 2, is.na)
iris[complete.cases(iris), ]

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