Over the past few years, R programming language has gained significant traction in the Data Science and Machine Learning communities. This is mainly because it is a multi-purpose language that can be used for statistical analysis, data visualization, data manipulation, predictive modeling, forecast analysis, and much more.
As job opportunities surrounding R are increasing rapidly & data science courses are thriving, today, we’re going to focus on the first part of landing a job the domain – the R interview. Here is a list of the most commonly asked questions in R interviews!
- What is R?
R is a programming language and environment specifically designed for statistical computing and graphics. It comes with an extensive catalog of statistical and graphical methods including linear regression, classification, clustering, time-series analysis, statistical inference, and ML algorithms, to name a few.
- Name the different data structures in R.
R has four primary data structures:
- Vector – It is a sequence of data elements belonging to the same type. Members within a Vector are known as components.
- List – It is an R object that can contain elements of different types, including numbers, strings, vectors, or another list.
- Matrix – It is a two-dimensional data structure that can bind vectors of the same length. The elements within a Matrix must be of the same type – numeric, or character, or logical, or complex.
- Dataframe – It is a more generic version of a matrix, that is it can contain elements of different data types. A Dataframe combines the characteristics of Matrices and Lists like a rectangular list, and its columns usually have different data types.
- Name the various components of the grammar of graphics?
The different components of the grammar of graphics are:
- Data layer
- Facet layer
- Themes layer
- Aesthetics layer
- Geometry layer
- Co-ordinate layer
- How to install a package in R?
To install a package in R, you have to write this command:
install.packages(“<package_name>”)
- How is data imported in R?
To import data in R, you have to use the R commander GUI by typing the command “Rcmdr” into the R console. There are three ways to import data in R:
You can either enter the name of the data set or choose the data set in the dialog box as you deem fit.
- You can enter the data directly using the editor of R Commander: Data->New Data Set. This works best for small to medium-sized datasets.
- You can import data from the clipboard, or a URL, or a plain text file (ASCII), or any statistical package.
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- What is Rmarkdown?
RMarkdown is R’s reporting tool. It allows you to create high-quality reports of R code.
There are three types of output format of Rmarkdown:
- HTML
- WORD
- What is “t-tests()” in R?
In R, the t-test() is used to determine whether or not the means of two groups are equal to each other.
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- What are the R packages used for data imputation?
The R packages most commonly used for data imputation are:
- Mi
- MICE
- Hmisc
- Amelia
- imputeR
- missForest
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- What is a “confusion matrix” in R?
In R, a confusion matrix is used to assess the accuracy of a developed model. It offers a cross-tabulation calculation of observed and predicted classes by using the “confusionmatrix()” function contained within the “caTools” package.
10. What is a Random Forest? How can you build and evaluate a Random Forest in R?
Random Forest is an ensemble classifier built from a combination of many decision tree models. Since it combines the results of numerous decision tree models, the result is much more accurate than those of individual models.
To build a Random Forest model in R, you must have a training dataset. Then proceed by doing the following:
First, segregate the dataset into the training set and test set->
- Now, build the Random Forest model on the train set->
- Finally, predict the Random Forest model on the test set->
- What is ShinyR?
ShinyR is an R package that allows for easy and secure development of interactive web apps directly using R.
With ShinyR, you can host standalone apps on a webpage, or you can also embed them in Rmarkdown documents. Also, you can extend your shiny apps to work with CSS themes, JavaScript actions, and HTML widgets.
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- Name the packages used for data mining in R.
The R packages used for data mining are:
- Rpart and caret
- Data.table
- Forecast
- GGplot
- Arules
- tm
- What are the purposes of Logistic Regression and Poisson Regression?
While Logistic Regression helps to predict the binary outcome from the given set of continuous predictor variables, Poisson Regression is used to predict the outcome variable representing “counts” from the given set of continuous predictor variables.
- How are missing values represented in R?
In R, the missing values are represented by NA (Not Available) function. However, for impossible values, NaN (not a number) is used.
- Which function is used for adding datasets in R?
In R, the “rbind” function is used to join two dataframes or datasets. However, the two dataframes/datasets must contain variables of the same type.
- How do you save data in R?
While there are many ways to save data in R, the most efficient way to do it is:
Data > Active Data Set > Export Active Data Set
After this, you will see a dialogue box appear before you. When you click on that dialogue box, you can save your data like you normally would.
- What are the sorting algorithms in R?
R has five types of sorting algorithms:
- Selection Sort
- Bucket Sort
- Bubble Sort
- Merge Sort
- Quick Sort
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- What is a White Noise model?
A White Noise (WN) model is a time series model. It is the simplest way of depicting a stationary process.
A WN model comprises of:
- A fixed constant mean
- A fixed constant variance
- No correlation over time
- Name the import functions in R.
The different import functions in R include:
- read.csv()->
- read_sas()->
- read_excel()->
- read_sav()->
- Name the functions used for debugging in R.
The functions used for debugging in R are:
- traceback()
- debug()
- browser()
- trace()
- recover()
So, there you go! These are some of the most commonly asked R interview questions. Hope this will help you break the ice and steadily dig into the language as you go.
Happy learning!
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