R Language Frequently Asked Questions - Statistical Computing Graphics in R

R Language Basics and R FAQs: Learning Made Easy

August 11, 2024March 10, 2024 by Muhammad Imdad Ullah

The post is about R Language Basics and R Language Frequently Asked Questions. The contents are in the form of questions and answers. Let us start with R Language Basics Questions and Answers.

R Basics

Question: How to start (Run) R Language in the Windows Operating System?
Answer: In Microsoft Windows, during installation, the R installer will have created a Start menu item and an icon for R on your system’s desktop. Double-click the R icon from the desktop or the start menu list to Run the R program.

For Windows 7, 8, or 10, you can use a search term like “R x64 3.2.1” (64-bit version) or “R i386 3.2.1” (32-bit version). R GUI will launch.

Using R as a Calculator

Question: How R can be used as a calculator?
Answer: One can easily use R as a calculator. Starting R will open the console where the user can type commands. To use R as a calculator one has to enter the arithmetical expression after > prompt. For example

5 + 4
sqrt(37)
2*4^2+17*4-3

R Workspace

Question: What is a workspace in R?
Answer: The workspace in R is an image that contains a record of the computations one has done and it may contain some saved results.

Question: How to record works[ace in R?
Answer: Rather than saving the workspace, one can record all the commands that one has entered in the R console. Recording work in R, the R workspace can be reproduced. The easiest way is to enter the commands in R’s script editor available in the File menu of R GUI.

R Script Editor

Question: What is R Script Editor?
Answer: The r script editor is a place where one can enter commands. Commands can be executed by highlighting them and hitting CTRL+R (mean RUN). At the end of an R session, one can save the final script for a permanent record of one’s work.

A text editor such as Notepad can also be used for this purpose.
Note that in the R console, only one command can be entered at a time because after pressing the Enter key the R command executed immediately.

Quitting R Session

Question: How to quit the R session?
Answer: In the R console on the R command prompt just type

q( )

Question: What is q()?
Answer: The q() is a function that is used to tell R to quit. When q() is entered in the R console and press the Enter key, you will be asked whether to save an image of the current workspace or not or to cancel. Note that only typing q tells R to show the content of this function. The action of this function is to quit R.

MCQs Statistics with Answers

Computer MCQs Online Test

Important R Programming Quiz Questions

October 31, 2024March 10, 2024 by Muhammad Imdad Ullah

The post contains a list of R Programming Quiz Questions with Answers. Click on the link to start with R Programming Quiz.

R Programming Quiz Questions

R Programming Quiz 18	MCQs Graph Plotting in R 17
MCQs dplyr in R 16	MCQs on Basic of R 15
Statistical Inference in R Quiz 14	MCQS R Package Development 13
R Markdown Quiz 12	R Programming Basics Quiz 11
Sampling, tidyverse, & dplyr R MCQs 10	R Quiz Data Frame & ggplot 09
MCQs R Tidy Data and ggplot2 Quiz 08	R Language MCQs Basic & ggplot2 07
R Programming Debug Quiz 06	Quiz R Vectors & Data Structure 05
MCQs R History & Basics 04	MCQs R Basics 03
R Language Test 02	MCQs R Programming Language 01

Computer MCQs Online Test

MCQs in Statistics

Simulation in R for Sampling (2024)

June 24, 2024February 24, 2024 by Muhammad Imdad Ullah

Introduction to Simulation in R Language

The post is about simulation for sampling in R Programming Language. It contains useful examples for generating samples and then computing basic calculations in generated data.

Simulations are a powerful tool in R for exploring “what-if” scenarios without the need for real-world data. One can use R Language to simulate data from various probability distributions or even design customized functions for more complex simulations.

Question 1: Simulate a coin toss 20 times.

sample(c("H", "T"), 20, replace=T)

Question 2: Write R commands to find out the 95% confidence interval for the mean (unknown variance) from the following population

yp <- c(111, 150, 121, 198, 112, 136, 114, 129, 117, 115, 186, 110, 121, 115, 114)
N  <- length(yp)
ys <- sample(yp, 5)
n  <- length(ys)
mys <- mean(ys)
vys <- vary(ys)
vybar <- var(yp)/n
sdr <- sqrt(vybar)
error <- qnorm(0.975)*sdr
ll <- mys - error
ul <- mys + error

Sampling without Replacement and Histogram

Question 3: If we have a population ِye <- c(112, 114, 119, 125, 158, 117, 135, 141, 185, 128) then simulate this population with $k=100$ and $n=3$ for Simple Random Sampling without Replacement (SRSWOR). Also, find out the sample mean. Draw the histogram of the sample means generated.

k = 100; n = 3
m1 <- c()
ye <- c(112, 114, 119, 125, 158, 117, 135, 141, 185, 128)

for(i in 1:100){
  s <- sample(ye, 3)
  m1[i] <- mean(s)
}

m1
hist(m1)

Question 4: Perform a simulation in R by writing the R code considering generating a population of size 500 values from a normal distribution with a mean = 20 and a standard deviation = 30. Select 5000 samples, each of size 50 using the systematic sampling technique, and estimate the mean of each sample. Find the mean and variance of 5000 means.

N = 500; n = 50;
k = N/n; m = c();
pop <- rnorm (N, mean=20, sd=30)

for(i in 1:5000){
  start <- sample(1: k, 1)
  s <- seq(start, N, k)
  sys.sample <- pop[s]
  m[i] = mean(sys.sample)
}

mean(m); var(m)

Question 5: Why do we use simulation for sampling?
Answer: The simulation study is useful to evaluate a sampling strategy. We can generate the populations considering specific situations. Generating the population, the sample of size $n$ is obtained $k$ times. From each sample, the estimator is obtained. The variance of $k$ estimators is calculated for examining the efficiency.

Coin Toss Experiment in R

Question 6: Write an R code to Simulate a coin-tossing experiment.

# Define the Number of tosses of a coin
n_tosses <- 100

# Simulate coin tosses (1 for heads, 0 for tails)
coin_tosses <- sample(c(0, 1), n_tosses, replace = TRUE)

# Calculate the proportion of heads
prop_heads <- mean(coin_tosses)

# Display results
cat("Number of Heads:", sum(coin_tosses), "\n")
cat("Proportion of Heads:", prop_heads, "\n")
# Plot the results
barplot(c(sum(coin_tosses), n_tosses - sum(coin_tosses)),
        names.arg = c("Heads", "Tails"),
        col = c("skyblue", "salmon"),
        main = "Coin Toss Simulation"
       )

One can adapt these examples for more complex statistical simulations or specific scenarios by modifying the simulation process and analyzing the results accordingly. Simulations are commonly used in various fields, such as statistics, finance, and operations research, to model and analyze uncertain or random processes.

Simulation Data in R using For Loops

Learn Basic Statistics and Data Analysis