Ques:- How do you check if “m” is a matrix data object in R?
Asked In :-
Right Answer:
You can check if "m" is a matrix data object in R using the `is.matrix()` function:
“`R
is.matrix(m)
“`
Ques:- What is JIT in R?
Asked In :-
elsevier,
Right Answer:
JIT stands for Just-In-Time compilation in R, which is a technique that improves the performance of R code by compiling functions into machine code at runtime, rather than interpreting them line by line. This can lead to faster execution of R scripts.
JIT stands for Just-In-Time compilation in R, which is a technique that improves the performance of R code by compiling functions into machine code at runtime, rather than interpreting them line by line. This can lead to faster execution of R scripts.
Ques:- What is regular expression syntax?
Asked In :-
Valued Epistemics, ey i3 - data and analytics,
Right Answer:
Regular expression syntax is a sequence of characters that forms a search pattern. It includes:
- **Literal characters**: Match themselves (e.g., `a`, `1`).
- **Metacharacters**: Special characters with specific meanings (e.g., `.`, `*`, `+`, `?`, `^`, `$`, `[]`, `()`, `{}`, `|`).
- **Character classes**: Define a set of characters (e.g., `[abc]` matches `a`, `b`, or `c`).
- **Quantifiers**: Specify the number of occurrences (e.g., `*` for zero or more, `+` for one or more, `?` for zero or one).
- **Anchors**: Indicate positions in the string (e.g., `^` for start, `$` for end).
- **Groups and capturing**: Use parentheses to group patterns and capture matches.
These elements combine to create patterns for
Regular expression syntax is a sequence of characters that forms a search pattern. It includes:
- **Literal characters**: Match themselves (e.g., `a`, `1`).
- **Metacharacters**: Special characters with specific meanings (e.g., `.`, `*`, `+`, `?`, `^`, `$`, `[]`, `()`, `{}`, `|`).
- **Character classes**: Define a set of characters (e.g., `[abc]` matches `a`, `b`, or `c`).
- **Quantifiers**: Specify the number of occurrences (e.g., `*` for zero or more, `+` for one or more, `?` for zero or one).
- **Anchors**: Indicate positions in the string (e.g., `^` for start, `$` for end).
- **Groups and capturing**: Use parentheses to group patterns and capture matches.
These elements combine to create patterns for
Ques:- Vector v is c(1,2,3,4) and list x is list(5:8), what is the output of v*x[[1]]?
Asked In :-
Rubicon Research, minitab,
Right Answer:
The output of `v * x[[1]]` is `c(5, 10, 15, 20)`.
The output of `v * x[[1]]` is `c(5, 10, 15, 20)`.
Ques:- How many types of C/C++ preliminaries are present in R?
Asked In :-
Right Answer:
There are three types of C/C++ preliminaries in R: `.C()`, `.Call()`, and `.External()`.
There are three types of C/C++ preliminaries in R: `.C()`, `.Call()`, and `.External()`.
Ques:- How can you save your data in R?
Asked In :-
Right Answer:
You can save your data in R using the `save()` function to save R objects in a .RData file, or use `write.csv()` to save data frames as CSV files.
You can save your data in R using the `save()` function to save R objects in a .RData file, or use `write.csv()` to save data frames as CSV files.
Ques:- how to import data into R?
Asked In :-
Turing Analytics,
Right Answer:
You can import data into R using functions like `read.csv()` for CSV files, `read.table()` for general text files, and `readRDS()` for R data files. For example:
```R
data <- read.csv("path/to/yourfile.csv")
```
Make sure to specify the correct file path and options as needed.
You can import data into R using functions like `read.csv()` for CSV files, `read.table()` for general text files, and `readRDS()` for R data files. For example:
```R
data <- read.csv("path/to/yourfile.csv")
```
Make sure to specify the correct file path and options as needed.
Ques:- I have a string “contact@dataflair.com”. Which string function can be used to split the string into two different strings: “contact@dataflair” and “com”?
Asked In :-
Data Patterns, latentview analytics,
Right Answer:
You can use the `strsplit()` function in R. For example:
“`R
result <- strsplit("contact@dataflair.com", "\.")[[1]]
“`
This will give you a list with two elements: "contact@dataflair" and "com".
Ques:- Explain different types of objects present in R?
Asked In :-
Tredence Analytics Solutions, Rubicon Research,
Right Answer:
In R, the main types of objects are:
1. **Vectors**: A sequence of data elements of the same basic type.
2. **Lists**: An ordered collection of objects, which can be of different types.
3. **Matrices**: A two-dimensional array where all elements are of the same type.
4. **Data Frames**: A table-like structure where each column can contain different types of data.
5. **Factors**: Used for categorical data, representing discrete values.
6. **Functions**: Objects that contain a set of instructions to perform specific tasks.
7. **Arrays**: Multi-dimensional generalizations of matrices.
These objects allow for various data manipulations and analyses in R.
In R, the main types of objects are:
1. **Vectors**: A sequence of data elements of the same basic type.
2. **Lists**: An ordered collection of objects, which can be of different types.
3. **Matrices**: A two-dimensional array where all elements are of the same type.
4. **Data Frames**: A table-like structure where each column can contain different types of data.
5. **Factors**: Used for categorical data, representing discrete values.
6. **Functions**: Objects that contain a set of instructions to perform specific tasks.
7. **Arrays**: Multi-dimensional generalizations of matrices.
These objects allow for various data manipulations and analyses in R.
Ques:- How to compile and run code in R?
Asked In :-
Right Answer:
To compile and run code in R, you can use the following steps:
1. Open R or RStudio.
2. Write your R code in the script editor or directly in the console.
3. To run the code, you can:
- Highlight the code and press `Ctrl + Enter` (or `Cmd + Enter` on Mac) in RStudio.
- Type the code directly in the console and press `Enter`.
4. If you have a script file (e.g., `script.R`), you can run it by using the command `source("script.R")` in the console.
To compile and run code in R, you can use the following steps:
1. Open R or RStudio.
2. Write your R code in the script editor or directly in the console.
3. To run the code, you can:
- Highlight the code and press `Ctrl + Enter` (or `Cmd + Enter` on Mac) in RStudio.
- Type the code directly in the console and press `Enter`.
4. If you have a script file (e.g., `script.R`), you can run it by using the command `source("script.R")` in the console.
Ques:- What are applications of Predictive analysis in R?
Asked In :-
Right Answer:
Applications of predictive analysis in R include:
1. **Customer Segmentation**: Identifying distinct groups within a customer base for targeted marketing.
2. **Sales Forecasting**: Predicting future sales based on historical data.
3. **Risk Assessment**: Evaluating the likelihood of default in finance or insurance.
4. **Churn Prediction**: Identifying customers likely to leave a service or product.
5. **Healthcare Predictions**: Forecasting patient outcomes or disease outbreaks.
6. **Stock Market Analysis**: Predicting stock prices and trends.
7. **Recommendation Systems**: Suggesting products or content based on user behavior.
8. **Sentiment Analysis**: Analyzing social media or customer feedback to gauge public opinion.
Applications of predictive analysis in R include:
1. **Customer Segmentation**: Identifying distinct groups within a customer base for targeted marketing.
2. **Sales Forecasting**: Predicting future sales based on historical data.
3. **Risk Assessment**: Evaluating the likelihood of default in finance or insurance.
4. **Churn Prediction**: Identifying customers likely to leave a service or product.
5. **Healthcare Predictions**: Forecasting patient outcomes or disease outbreaks.
6. **Stock Market Analysis**: Predicting stock prices and trends.
7. **Recommendation Systems**: Suggesting products or content based on user behavior.
8. **Sentiment Analysis**: Analyzing social media or customer feedback to gauge public opinion.
Ques:- How do you get the standard deviation for a vector x?
Asked In :-
KPIT Technologies, spss,
Right Answer:
You can get the standard deviation for a vector `x` in R using the `sd()` function:
```R
sd(x)
```
You can get the standard deviation for a vector `x` in R using the `sd()` function:
```R
sd(x)
```
Ques:- What is iPlots?
Right Answer:
iPlots is an R package that provides interactive graphics for data visualization, allowing users to create dynamic plots that can be manipulated in real-time using a web browser.
iPlots is an R package that provides interactive graphics for data visualization, allowing users to create dynamic plots that can be manipulated in real-time using a web browser.
Ques:- Explain in brief preliminaries of C/C++?
Asked In :-
ey i3 - data and analytics, transorg analytics,
Right Answer:
The question seems to be about C/C++ rather than R programming. However, if you are looking for a brief explanation of the preliminaries of C/C++, here it is:
C/C++ preliminaries include understanding the basic syntax, data types (like int, float, char), control structures (if, for, while), functions, and the use of libraries. It also involves knowing how to compile and run programs, as well as memory management concepts like pointers and dynamic allocation.
The question seems to be about C/C++ rather than R programming. However, if you are looking for a brief explanation of the preliminaries of C/C++, here it is:
C/C++ preliminaries include understanding the basic syntax, data types (like int, float, char), control structures (if, for, while), functions, and the use of libraries. It also involves knowing how to compile and run programs, as well as memory management concepts like pointers and dynamic allocation.
Ques:- Give the syntax for creating scatterplot matrices?
Asked In :-
Right Answer:
The syntax for creating scatterplot matrices in R is:
```R
pairs(data)
```
Where `data` is a data frame or matrix containing the variables to be plotted.
The syntax for creating scatterplot matrices in R is:
```R
pairs(data)
```
Where `data` is a data frame or matrix containing the variables to be plotted.
Ques:- How to use R from python?
Asked In :-
Right Answer:
You can use the `rpy2` library to run R from Python. First, install it using `pip install rpy2`. Then, you can import R functions and run R code in your Python script like this:
```python
import rpy2.robjects as robjects
# Example: Running R code
robjects.r('x <- rnorm(100)')
x = robjects.r('x')
print(x)
```
You can use the `rpy2` library to run R from Python. First, install it using `pip install rpy2`. Then, you can import R functions and run R code in your Python script like this:
```python
import rpy2.robjects as robjects
# Example: Running R code
robjects.r('x <- rnorm(100)')
x = robjects.r('x')
print(x)
```
Ques:- Why is R Good for business?
Asked In :-
genomics england,
Right Answer:
R is good for business because it offers powerful statistical analysis, data visualization capabilities, and a wide range of packages for various data tasks, making it ideal for data-driven decision-making. Additionally, its open-source nature allows for cost-effective solutions and a strong community for support and collaboration.
R is good for business because it offers powerful statistical analysis, data visualization capabilities, and a wide range of packages for various data tasks, making it ideal for data-driven decision-making. Additionally, its open-source nature allows for cost-effective solutions and a strong community for support and collaboration.
Ques:- What is the use of sample and subset functions in R programming language?
Asked In :-
Yagna iQ,
Right Answer:
The `sample` function in R is used to randomly select elements from a vector, allowing for sampling with or without replacement. The `subset` function is used to create a new data frame or vector that contains only the rows or elements that meet specified conditions.
The `sample` function in R is used to randomly select elements from a vector, allowing for sampling with or without replacement. The `subset` function is used to create a new data frame or vector that contains only the rows or elements that meet specified conditions.
Ques:- Who and When R discovered?
Asked In :-
Turing Analytics, h2o.ai,
Right Answer:
R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and it was first released in 1995.
R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and it was first released in 1995.
Ques:- What is TCP/IP in R?
Asked In :-
Right Answer:
TCP/IP in R refers to the Transmission Control Protocol/Internet Protocol, which is a set of communication protocols used for interconnecting network devices on the internet. In R, it is often utilized for network programming and data transfer between R and other systems or services, typically through packages like `socket` or `httr` for web requests.
TCP/IP in R refers to the Transmission Control Protocol/Internet Protocol, which is a set of communication protocols used for interconnecting network devices on the internet. In R, it is often utilized for network programming and data transfer between R and other systems or services, typically through packages like `socket` or `httr` for web requests.
The R-Programming category on takluu.com is designed for data analysts, statisticians, and programmers preparing for interviews and jobs requiring proficiency in R language. Known for its powerful statistical capabilities and rich graphical packages, R is widely used in data science, research, and analytics roles.
This section covers key topics such as basic syntax, data structures (vectors, lists, data frames), control statements, functions, and packages essential for effective programming in R. You’ll also find practical questions on data manipulation, visualization with ggplot2, statistical modeling, and working with large datasets.
Our content explains concepts in a simple, easy-to-understand manner with real-world examples and coding snippets to help you grasp R programming effectively. We also focus on common interview questions, coding challenges, and problem-solving approaches that test your practical skills.
Whether you are a fresher or an experienced professional, this category helps you build confidence for technical rounds, data analysis tasks, and research projects involving R.
At Takluu, we update the R programming content regularly to include the latest packages, functions, and industry trends, ensuring you stay relevant and ready to crack your next interview.
