## Important Data Frame Questions (2024)

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?

MyTable = data.frame()
edit(MyTable)

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

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## Data Frame in R Language

### 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

# 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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## Important R Language Questions

The post is about R Language Questions that are commonly asked in interviews or R Language-related examinations and tests.

### R Language Questions

Question: What is a file in R?
Answer: A script file written in R has a file extension of R. Since, R is a programming language designed to perform statistical computing and graphics on given data, that is why, a file in R contains code that can be executed within the R software environment.

Question: What is the table in R?
Answer: A table in R language is an arbitrary R object, that is inherited from the class “table” for the as.data.frame method. A table in R language refers to a data structure that is used to represent categorical data and frequency counts. A table provides a convenient way to summarize and organize the data into a tabular format, making it easier to analyze and interpret.

### Factor Variables in R

Questions: What is the factor variable in R language?
Answer: Factor variables are categorical variables that hold either string or numeric values. The factor variables are used in various types of graphics, particularly for statistical modeling where the correct number of degrees of freedom is assigned to them.

### Data Structure in R

Questions: What is Data Structure in R?
Answer: A data structure is a specialized format for organizing and storing data. General data structure types include the array, the file, the record, the table, the tree, and so on. R offers several data structures, each with its characteristics and purposes. In R common data structures are: vector, factor, matrix, array, data frame, and lists.

### scan() Function in R

Question: What is a scan() in R?
Answer: The scan() in R is used to Read Data Values: Read data into a vector or list from the console or file. For Example:

Z <- scan()
1: 12 5
3: 2
4:

> z
[1] 12 5 2

Questions: What is readline() in R?
Answer: The deadline() function in R, read text lines from a Connection: Read some or all text lines from a connection. One can use readline() for inputting a line from the keyboard in the form of a string. For Example:

w <- readline()
xyz vw u
> w

[1] "xyz vw u"

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## read.table Function in R (2016)

The post is about how to import data using read.table() function in R. You will also learn what is a file path and how to get and set the working directory in R language. The read.table() function in R is a powerful tool for importing tabular data, typically from text files, into the R environment. The read.table function converts the tabular data from a flat-file format into a more usable data structure called the data frame.

Question: How can I check my Working Directory so that I would be able to import my data in R? Answer: To find the working directory, the command getwd() can be used, that is

getwd()

Question: How can I change the working directory to my path?
Answer: Use function setwd(), that is

setwd("d:/mydata")
setwd("C:/Users/XYZ/Documents")

### Import Data using read.table Function in R

Question: I have a data set stored in text format (ASCII) that contains rectangular data. How can I read this data in tabular form? I have already set my working directory.
Answer: As data is already in a directory set as the working directory, use the following command to import the data using read.table() command.

mydata <- read.table("data.dat")
mydata <- read.table("data.txt")

The mydata is a named object that will have data from the file “data.dat” or “data.txt” in data frame format. Each variable in the data file will be named by default V1, V2,…

Question: How this stored data can be accessed?
Answer: To access the stored data, write the data frame object name (“mydata”) with the $sign and name of the variable. That is, mydata$V1
mydata\$V2
mydata["V1"]
mydata[ , 1]

Question: My data file has variable names in the first row of the data file. In the previous question, the variable names were V1, V2, V3, … How can I get the actual names of the variables stored in the first row of the data.dat file?

read.table("data.dat", header = TRUE)

Question: I want to read a data file that is not stored in the working directory.
Answer: To access the data file that is not stored in the working directory, provide a complete path of the file, such as.

read.table("d:/data.dat" , header = TRUE)
read.table("d:/Rdata/data.txt" , header = TRUE)

Note that read.table() is used to read the data from external files that have normally a special form:

• The first line of the file should have a name for each variable in the data frame. However, if the first row does not contain the name of a variable then the header argument should not be set to FALSE.
• Each additional line of the file has its first item a row label and the values for each variable.

In R it is strongly suggested that variables need to be held in the data frame. For this purpose read.table() function in R can be used. For further details about read.table() function use,

help(read.table)