Use the c() function, e.g., c(1, 2, 3) for numeric vectors.

Use the array() function, e.g., array(1, 2, 3) for numeric vectors.

Create a list using list(1, 2, 3) instead of a vector.

Use the vector() function, e.g., vector(1, 2, 3) for numeric vectors.

import.csv()

read.csv()

read.file()

load.csv()

A list is a collection of elements of different types, while a data frame is a table-like structure with columns of potentially different types but equal length.

A list is a fixed-size structure, while a data frame can change size dynamically.

A list and a data frame are the same, both are used for storing tabular data.

A list is always a numeric vector, while a data frame can only contain character data.

To manage memory allocation in R.

To create new data structures in R.

To visualize data in R.

To apply a function to elements of data structures in R.

Use na.omit(), na.fill(), or imputation methods to handle missing values in R.

Ignore missing values and proceed with analysis without any adjustments.

Replace missing values with zeros only.

Use na.remove() to delete all rows with missing values.

The 'ggplot2' package is used for statistical analysis in R.

The 'ggplot2' package is used for data visualization in R.

The 'ggplot2' package is used for machine learning in R.

The 'ggplot2' package is used for data cleaning in R.

geom_scatter(data, x_variable, y_variable)

ggplot(data) + add_points(x_variable, y_variable)

Use plot(data, x_variable, y_variable)

Use ggplot(data, aes(x = x_variable, y = y_variable)) + geom_point()

for (i in 1:10) { print(i) }

for (i = 1; i <= 10; i++) { print(i) }

for i in 1 to 10 { print(i) }

foreach (i in 1:10) { print(i) }

A function in R is defined using the syntax: function_name <- function(parameters) { # function body }.

In R, a function is defined as: define function_name(parameters) { # code }

A function in R is defined using the syntax: function(parameters) { # function body }.

Functions in R are created with the command: create_function(function_name, parameters)

The scope of a variable in R is always global.

Variables in R can only be defined within functions.

The scope of a variable in R is determined by its data type.

The scope of a variable in R can be local or global, depending on where it is defined.

Factors in R are only used for numerical data.

Factors in R are a type of function that performs calculations.

Factors in R are exclusively for time series data.

Factors in R are categorical variables that store data as integers with corresponding character levels.

Mann-Whitney U test

t-test

Chi-square test

ANOVA

Utilize the cor() function to find correlations.

Use lm() function to fit a linear model and summary() to analyze results.

Apply the t.test() function for regression analysis.

Use the plot() function to visualize data only.

To perform linear regression analysis.

To create visualizations of data.

To provide a summary of statistical properties of an object.

To calculate the mean of a dataset.

You can customize aesthetics using functions like scale_color_manual(), theme(), and labs() in ggplot2.

Aesthetics cannot be customized in ggplot2 plots.

You can only change colors using scale_fill_brewer() in ggplot2.

You must use base R plotting functions to customize ggplot2 aesthetics.

A character vector is used for logical operations; a numeric vector is used for string manipulations.

A character vector contains text strings; a numeric vector contains numbers.

A character vector contains numbers; a numeric vector contains text strings.

Both character and numeric vectors can only contain integers.

Use install.packages('package_name')

Use add.package('package_name')

Install package_name using RStudio

Run setup.packages('package_name')

It is used to create data frames in R.

It ensures reproducibility of random number generation in R.

It generates random numbers without any control.

It automatically installs packages in R.

Create a bar chart with barplot(data$categorical_variable) function.

Use ggplot(data, aes(y = categorical_variable)) + geom_line() to create a bar chart.

Use plot(data, type = 'h') to create a bar chart.

Use ggplot(data, aes(x = categorical_variable)) + geom_bar() to create a bar chart.