R Language Frequently Asked Questions - Statistical Computing Graphics in R

Python Basics MCQs 7

March 16, 2025March 16, 2025 by Muhammad Imdad Ullah

The post is about Python Basics MCQs with Answers. There are 20 multiple-choice questions covering topics related to the basics of Python, data types, strings, tuples, lists, and different operations. Let us start with the Online Python Basics MCQs with Answers.

Online Python Basics MCQs with Answers

What is the data type of the entity 43?
What is the result of the following: int(3.99)?
What is the result of the operation: 11//2
What is the value of x after the following is run: x=4 x=x/2
Which line of code will act as required for implementing the following equation? $y =2x^2 − 3$
What is the output of the following code segment? type(int(12.3))
What is the output of the following code segment? int(False)
In Python, what is the output of the following operation? ‘5’+’6′
What is the output of the following? ‘hello’.upper()
What is the output of the following? str(1+1)
What is the output of the following? “ABC”.replace(“AB”, “ab”)
In Python 3, what data type does variable x hold after the operation: x = 1/1?
What data type does 3.12323 represent?
For the string “Fun Python” stored in a variable $x$, what will be the output of x[0:5]?
What data type is represented by “7.1”?
Consider the following tuple: say_what=(‘say’,’what’,’you’,’will’)What is the result of the following? say_what[-1]
Consider the following tuple A=(1,2,3,4,5). What is the outcome of the following? A[1:4]
Consider the following list B=[1,2,[3,’a’],[4,’b’]]. What is the result of B[3][1]?
What is the outcome of the following operation? [1,2,3] + [1,1,1]
What is the outcome of the following? 1=2

Big Data MCQs Quiz

Mastering summary() Function in R: Easy Data Insights 2025

March 11, 2025 by Muhammad Imdad Ullah

To dive into data analysis, one of the first functions encountered is the summary() function in R Language. This versatile function as a tool is a game-changer for quickly getting and understanding the data insights, identifying patterns, and spotting potential issues. For a beginner or an experienced R user, mastering the summary() function can significantly improve not only your R language learning, R programming, and data analytics skills but may also streamline the users’ workflow. This function helps in getting many of the descriptive statistics and exploratory data analysis. In this post, we will explore what the summary() function in R does, provide real-world examples, and share actionable tips to help you get the most out of it.

What is the `summary()` Function in R?

The summary() function in R is a built-in function that provides a concise overview of an R object (such as a data frame, vector, or statistical model) to get a statistical summary of the data. For numeric data, it calculates key statistics like the mean, median, quartiles, and minimum/maximum values. For categorical data, it displays frequency counts. For regression models (e.g., linear regression), it offers insights into coefficients, residuals, and overall model performance.

Real-World Examples of Using `summary()`

1. Exploring a Dataset

Suppose you are analyzing a dataset of $mtcars$. The summary() function in R can be used to get a quick snapshot of the data:

# Load a sample dataset
data("mtcars")

# Get a summary of the dataset
summary(mtcars)

The output will show key statistics for each column, such as:

MPG (miles per gallon): Min, 1st Quartile, Median, Mean, 3rd Quartile, Max
Cylinders: Frequency counts for each category

The above output helps you quickly identify trends, such as the average MPG or the most common number of cylinders in the dataset.

2. Analyzing a Linear Regression Model

Suppose for a linear regression model to predict mile per gallon (mpg), you can use summary() to evaluate its performance:

# Fit a linear model
model <- lm(mpg ~ wt + hp, data = mtcars)

# Summarize the model
summary(model)

The output will include:

Coefficients: Estimates, standard errors, and p-values
R-squared: How well the model explains the variance in the data
Residuals: Distribution of errors

This information is invaluable for understanding the strength and significance of your predictors.

3. Summarizing Categorical Data

For categorical data, such as survey responses, summary() function in R provides frequency counts:

# Create a factor vector
survey_responses <- factor(c("Yes", "No", "Yes", "Maybe", "No", "Yes"))

# Summarize the responses
summary(survey_responses)

## Output
Maybe    No   Yes 
    1     2     3

The output will show:

Counts for each category (e.g., “Yes”: 3, “No”: 2, “Maybe”: 1)

This is a quick way to understand the distribution of responses.

Actionable Tips for Using `summary()` Effectively

Combine with str() for a Comprehensive Overview
Use str() alongside summary() to get both the structure and summary statistics of your data. This helps you understand the data types and distributions simultaneously.

str(mtcars)
## Output
'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 ...

summary(mtcars)

Use summary() for Data Cleaning
Look for missing values (NA) in the summary output. This can help you identify columns that require imputation or removal.

Customize Output for Specific Columns
If you’re only interested in specific columns, subset your data before applying summary()

summary(mtcars$mpg)

## Output
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  10.40   15.43   19.20   20.09   22.80   33.90

Leverage summary() for Model Diagnostics
When working with statistical models, use summary() function in R to check for significant predictors and assess model fit.

Visualize Summary Statistics
Pair summary() with visualization tools like ggplot2 or boxplot() to better understand the distribution of your data.

Conclusion: Start Using `summary()` Today!

The summary() function in R Language is a simple yet powerful tool that every R user should have in their toolkit. Whether one is exploring data, cleaning datasets, or evaluating models, summary() provides the insights one needs to make informed decisions. Incorporating summary() function into workflow, will save time and gain a deeper understanding of your data.

Summary Statistics using the measure of central tendency

R Language Shiny App Quiz 25

March 8, 2025March 8, 2025 by Muhammad Imdad Ullah

The post contains a list of R Language Shiny App Quiz Questions with Answers. There are 20 multiple-choice question about shiny app and dashboard. Let us start with the R Language Shiny App Quiz now.

Please go to R Language Shiny App Quiz 25 to view the test

R Language Shiny App Quiz with Answers

What is the maximum number of bins for sliderInput()?
What is the purpose of the shinyApp() function?
What software is most typically used to write and run a Shiny App?
Which objects must be created in a Shiny app to create a Shiny application with input and output?
Which of these most closely reflects the basic purpose of Shiny?
What is the role of the fluidPage() function in a Shiny application?
What is the first argument in any input function in Shiny, e.g., sliderInput(), selectInput(), numericInput()?
Say you use a function like sliderInput to have the user input some information into a Shiny application. You assign the inputId to “my_input”. How do you call that input in the server function?
What is the purpose of plotOutput()?
What is the purpose of the renderPlot() function?
Which of these are appropriate ways to run a Shiny app?
Which of the following are necessary for creating a functioning shiny app?
What are the main differences between creating a Shiny Gadget and creating a regular Shiny App?
If you want to share flexdashboard with someone, which of these are available options?
Suppose, you are writing a Shiny app and want users to be able to move a slider to select a single value between 1 and 5. The slider will start at 3. You want the value selected on the slider to be assigned to the input slot “slider_value.” Which of these is the correct code?
Suppose, you are writing a Shiny app and want to create a dropdown menu that allows users to select from a set of options, which you are going to use to filter results in a Shiny app. You want users to be able to select from the values, “Company A”, “Company B”, and “Company C”. You want to assign this to the “company” input slot. Which is the correct code?
Suppose, you are writing a Shiny app and want a user to be able to input text into a textbox, assigning the input to an input slot named “user_name”. You want the textbox to be automatically populated with the character string “Your name here”, which the user can overwrite. Which is the correct code?
Suppose, you are writing a Shiny app and want to display a table for a data frame or tibble in the user interface of your Shiny app. The only lines you have written in your code so far are:
my_dat<-read_csv(“my_dat.csv”)
ui<-fluidPage(tableOutput(“table1”))
server<-function(input,output){ }
Which of the following lines of code could you put inside the brackets of the server function to create a table of the data frame?
Which of the following statements will install shiny?
All of the styled elements are handled through the server.R.

SPSS and Data Analysis

Online Python Basics MCQs with Answers

Table of Contents

What is the summary() Function in R?

Real-World Examples of Using summary()