Data Analysis With R Interview Questions

Checkout Vskills Interview questions with answers in Data Analysis with R to prepare for your next job role. The questions are submitted by professionals to help you to prepare for the Interview.

Q.1 What is R, and why is it used for data analysis?

R is a programming language and environment for statistical computing and graphics. It is widely used for data analysis, visualization, and statistical modeling.

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Q.2 How do you install R and RStudio?

R can be downloaded from the CRAN website, and RStudio, an integrated development environment (IDE) for R, can be downloaded from the RStudio website.

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Q.3 What is the difference between a vector and a list in R?

A vector contains elements of the same data type, while a list can contain elements of different data types.

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Q.4 How do you create a data frame in R?

You can create a data frame using the data.frame() function, combining vectors of data as columns.

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Q.5 Explain the role of the ggplot2 package in R.

ggplot2 is a popular R package for creating data visualizations, providing a flexible and powerful grammar for graphics.

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Q.6 What is the purpose of the dplyr package in R?

The dplyr package is used for data manipulation and transformation tasks, providing functions like filter(), mutate(), and group_by().

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Q.7 How do you read data from a CSV file in R?

You can use the read.csv() function to read data from a CSV file and store it as a data frame.

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Q.8 Explain the concept of "tidy data" in R.

Tidy data is a structured format where each variable has its column, each observation has its row, and each value has its cell. It simplifies data analysis.

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Q.9 What is the purpose of the reshape2 package in R?

The reshape2 package is used for data reshaping and restructuring, making it easier to work with data in different formats.

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Q.10 How do you install and load R packages?

Packages can be installed using the install.packages() function and loaded into the R session using the library() function.

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Q.11 What is an R script, and how do you create one?

An R script is a file containing a series of R commands. You can create one using a text editor or directly in RStudio.

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Q.12 Explain the purpose of the summary() function in R.

The summary() function provides a summary of the key statistics for a data frame, such as mean, median, and quartiles.

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Q.13 How do you handle missing values in R?

Missing values can be handled using functions like is.na(), na.omit(), or by imputing missing values with the mean or median.

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Q.14 What is the apply() function used for in R?

The apply() function is used to apply a function to the rows or columns of a matrix or data frame.

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Q.15 Explain the purpose of the %>% operator in R.

The %>% operator, known as the pipe operator, is used for chaining together multiple data manipulation or transformation operations. It enhances code readability.

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Q.16 How do you create a histogram in R?

You can create a histogram using the hist() function, providing a vector of data as input.

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Q.17 What is the purpose of the cor() function in R?

The cor() function calculates the correlation between two numeric vectors, indicating the strength and direction of their linear relationship.

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Q.18 How do you perform data merging in R?

Data merging can be done using functions like merge() or dplyr verbs such as left_join(), inner_join(), etc., depending on the type of merge needed.

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Q.19 Explain the concept of "vectorization" in R.

Vectorization is a feature of R where operations are applied to entire vectors or matrices, making code more concise and efficient.

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Q.20 What is the purpose of the str() function in R?

The str() function provides the structure of an R object, showing its data type and the first few elements.

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Q.21 How do you create a scatterplot in R?

You can create a scatterplot using the plot() function, specifying two numeric vectors as the x and y variables.

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Q.22 What is a boxplot, and how is it created in R?

A boxplot is a graphical representation of the distribution of a dataset. You can create one using the boxplot() function.

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Q.23 How do you perform data sampling in R?

Data sampling can be done using functions like sample(), which randomly selects a specified number of elements from a dataset.

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Q.24 What is a p-value in statistical analysis?

A p-value measures the strength of evidence against a null hypothesis in statistical hypothesis testing. It helps determine statistical significance.

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Q.25 How do you conduct a t-test in R?

You can perform a t-test using functions like t.test(), comparing means between two groups to determine if they are significantly different.

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Q.26 Explain the concept of "ANOVA" in R.

Analysis of Variance (ANOVA) is used to compare means between multiple groups to determine if there are statistically significant differences.

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Q.27 What is the purpose of the lm() function in R?

The lm() function is used for linear regression analysis, modeling the relationship between a dependent variable and one or more independent variables.

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Q.28 How do you create a bar chart in R?

You can create a bar chart using the barplot() function or by using the ggplot2 package with the geom_bar() function.

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Q.29 What is "data wrangling," and why is it important?

Data wrangling involves cleaning, transforming, and preparing raw data for analysis, ensuring it is in a usable format. It is crucial for accurate analysis.

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Q.30 How do you install and load external packages in R?

External packages can be installed using the install.packages() function and loaded with the library() function.

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Q.31 What is the purpose of the purrr package in R?

The purrr package is used for working with and manipulating lists and vectors in a functional programming style.

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Q.32 Explain the concept of "ggplot2" grammar of graphics.

The "ggplot2" grammar of graphics is a structured approach to creating data visualizations in R, allowing customization and layering of elements.

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Q.33 How do you create a line chart in R?

You can create a line chart using the plot() function with the argument type = "l" or by using the ggplot2 package with the geom_line() function.

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Q.34 What is the purpose of the lapply() function in R?

The lapply() function is used to apply a given function to each element of a list and returns a list of the results.

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Q.35 How do you create a time series plot in R?

Time series plots can be created using the ts() function for time series objects and then plotted with plot().

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Q.36 How do you create a density plot in R?

You can create a density plot using the density() function to estimate and plot the probability density function of a continuous variable.

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Q.37 What is "regression analysis," and how is it done in R?

Regression analysis is used to model the relationship between a dependent variable and one or more independent variables. It can be performed using functions like lm().

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Q.38 How do you conduct a chi-squared test in R?

You can perform a chi-squared test using functions like chisq.test(), which tests for the independence of categorical variables.

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Q.39 Explain the purpose of the readRDS() function in R.

The readRDS() function is used to read serialized R objects from a file, allowing you to load saved data or models.

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Q.40 What is "data imputation," and how is it done in R?

Data imputation is the process of replacing missing values in a dataset. R provides various methods and packages for imputation, such as mice.

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Q.41 How do you create a scatterplot matrix in R?

You can create a scatterplot matrix using the pairs() function, which displays scatterplots for pairs of variables in a dataset.

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Q.42 What is the purpose of the aggregate() function in R?

The aggregate() function is used for data aggregation, allowing you to calculate summary statistics by group.

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Q.43 How do you create a word cloud in R?

You can create a word cloud using the tm and wordcloud packages, which are specialized for text data visualization.

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Q.44 Explain the concept of "resampling" in R.

Resampling involves techniques like bootstrapping and cross-validation to assess the stability and accuracy of statistical models.

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Q.45 How do you perform logistic regression in R?

Logistic regression is conducted using the glm() function with the appropriate family and link functions for binary or multinomial regression.

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Q.46 What is the purpose of the strsplit() function in R?

The strsplit() function is used to split strings into substrings based on a specified delimiter or pattern.

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Q.47 How do you create a heat map in R?

You can create a heat map using the heatmap() function, which visualizes data as a grid of colors, typically used for displaying correlations or patterns.

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Q.48 What is the purpose of the k-means clustering algorithm in R?

The k-means algorithm is used for unsupervised clustering to partition data into clusters based on similarity. It is implemented using the kmeans() function.

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Q.49 How do you handle outliers in R?

Outliers can be identified using statistical methods and then either removed, transformed, or treated separately based on the analysis.

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Q.50 What is the purpose of the caret package in R?

The caret package provides tools for training and evaluating machine learning models, making it easier to work with various algorithms and data.

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Q.51 How do you create a bar chart in R using ggplot2?

You can create a bar chart using ggplot2 with the geom_bar() function, specifying variables for the x-axis and y-axis.

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Q.52 What is the purpose of the purrr::map() function in R?

purrr::map() is used to apply a function to each element of a list or vector and return a list of results, providing a cleaner alternative to lapply().

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Q.53 How do you perform data aggregation using dplyr in R?

Data aggregation can be done with dplyr using the group_by() function followed by aggregation functions like summarize().

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Q.54 What is a "tibble" in R, and how is it different from a data frame?

A tibble is a modern data frame in R, providing improved printing, subsetting, and compatibility with tidyverse packages.

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Q.55 How do you create a time series object in R?

Time series objects can be created using the ts() function, specifying the data and time intervals.

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Q.56 What is the purpose of the str_replace() function in R?

The str_replace() function, from the stringr package, is used to replace specific substrings in character vectors.

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Q.57 How do you handle multicollinearity in regression analysis in R?

Multicollinearity can be addressed by removing highly correlated variables or using regularization techniques like ridge regression.

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Q.58 What is the "ggplot2" facet system in R?

The ggplot2 facet system allows you to create multiple plots, each showing a subset of the data based on one or more variables, using facet_wrap() or facet_grid().

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Q.59 How do you create a line chart with error bars in R?

You can create a line chart with error bars using ggplot2 and the geom_line() function along with geom_errorbar().

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Q.60 What is the purpose of the "K-nearest neighbors" (KNN) algorithm in R?

KNN is a supervised learning algorithm used for classification and regression tasks, relying on the similarity of data points. It is implemented using the knn() function.

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Q.61 How do you normalize or standardize data in R?

Data normalization or standardization can be done by scaling data to have a mean of 0 and a standard deviation of 1 using functions like scale().

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Q.62 What is the purpose of the "randomForest" package in R?

The "randomForest" package provides an implementation of the random forest algorithm for classification and regression tasks.

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Q.63 How do you create a bar chart with stacked bars in R?

You can create a stacked bar chart using ggplot2 and the geom_bar() function with the fill aesthetic to represent different categories within a bar.

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Q.64 What is "cross-validation," and why is it used in machine learning?

Cross-validation is a technique used to assess a model's performance by splitting data into training and testing sets multiple times, helping to estimate how well a model generalizes to new data.

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Q.65 How do you handle imbalanced data in classification problems in R?

Imbalanced data can be addressed using techniques like oversampling, undersampling, or using algorithms that handle imbalanced datasets, such as the SMOTE method.

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Q.66 What is "feature engineering," and why is it important in machine learning?

Feature engineering involves creating new features or modifying existing ones to improve a model's predictive performance. It plays a crucial role in model building.

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Q.67 How do you create a scatterplot with colored points in R?

You can create a scatterplot with colored points in ggplot2 by mapping a categorical variable to the color aesthetic within the geom_point() function.

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Q.68 What is "grid search" in machine learning, and how is it done in R?

Grid search is a hyperparameter tuning technique where various combinations of hyperparameters are tested to find the best model. It can be done using functions like tuneGrid() in R.

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Q.69 How do you perform feature selection in R?

Feature selection can be done using techniques like recursive feature elimination (RFE) or by evaluating feature importance from models like random forests.

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Q.70 What is "bagging," and how does it work in the context of ensemble learning in R?

Bagging is an ensemble learning technique that involves training multiple models on different subsets of the data and aggregating their predictions to reduce variance. The randomForest package in R implements bagging.

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Q.71 How do you handle skewed data in R?

Skewed data can be addressed by applying transformations like log or square root to make the distribution more symmetric.

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Q.72 Is R good for data analysis?

R is an open source programming language that's optimized for statistical analysis and data visualization. Developed in 1992, R has a rich ecosystem with complex data models and elegant tools for data reporting.

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Q.73 What is the purpose of the "xgboost" package in R?

The "xgboost" package provides an implementation of the gradient boosting algorithm, which is widely used for classification and regression tasks.

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Q.74 How R is used in data analysis?

As a programming language, R provides objects, operators and functions that allow users to explore, model and visualize data. R is used for data analysis. R in data science is used to handle, store and analyze data. It can be used for data analysis and statistical modeling.

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Q.75 How do you perform k-fold cross-validation in R?

K-fold cross-validation can be done using functions like createFolds() to create data partitions and then training and testing models for each fold.

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Q.76 What mean by data analysis?

Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. Indeed, researchers generally analyze for patterns in observations through the entire data collection phase.

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Q.77 What is the purpose of the "kernlab" package in R?

The "kernlab" package provides support vector machine (SVM) implementations for classification and regression tasks.

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Q.78 What are the methods of data analysis?

Some common methods of data analysis are: Cluster analysis, Cohort analysis, Regression analysis, Factor analysis, Neural Networks, Data Mining and Text analysis.

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Q.79 How do you create a line chart with shaded areas in R?

You can create a line chart with shaded areas in ggplot2 by using geom_ribbon() to fill the area between two lines.

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Q.80 How R can be used for predictive analysis?

Predictive analysis in R Language is a branch of analysis which uses statistics operations to analyze historical facts to make predict future events. Methods like time series analysis, non-linear least square, etc. are used in predictive analysis.

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Q.81 What is "dimensionality reduction," and how is it done in R?

Dimensionality reduction techniques like PCA (Principal Component Analysis) can be applied to reduce the number of features while preserving important information. It is implemented using the prcomp() function in R.

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Q.82 What do you understand by Data Cleansing?

Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.

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Q.83 How do you handle class imbalance in a classification problem in R?

Class imbalance can be addressed using techniques like oversampling the minority class, using different evaluation metrics, or applying specialized algorithms like the SMOTE method.

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Q.84 Differentiate between data profiling and data mining?

Data mining mines actionable information while making use of sophisticated mathematical algorithms, whereas data profiling derives information about data quality to discover anomalies in the dataset.

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Q.85 What is "regularization," and why is it used in machine learning?

Regularization techniques like L1 (Lasso) and L2 (Ridge) are used to prevent overfitting by adding penalty terms to the loss function based on model complexity.

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Q.86 What is KNN imputation method?

The idea in kNN methods is to identify 'k' samples in the dataset that are similar or close in the space. Then we use these 'k' samples to estimate the value of the missing data points. Each sample's missing values are imputed using the mean value of the 'k'-neighbors found in the dataset.

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Q.87 How do you create a boxplot with outliers displayed in R?

You can create a boxplot with outliers displayed using ggplot2 and the geom_boxplot() function with the outlier.shape or outlier.colour aesthetics.

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Q.88 What to do with missing or suspected data?

The most common approach to the missing data is to simply omit those cases with the missing data and analyze the remaining data. This approach is known as the complete case (or available case) analysis or listwise deletion.

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Q.89 What do you understand by Outlier?

An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. An outlier is a data point that differs significantly from other observations.

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Q.90 What is “Clustering?”

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Clustering divides the population into a number of groups with similar traits and assign them into clusters.

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Q.91 What is K-mean Algorithm?

K-Means Clustering is an Unsupervised Learning algorithm, which groups the unlabeled dataset into different clusters. In K-means clustering algorithm, the data points are assigned to a cluster in such a manner that the sum of the squared distance between the data points and centroid would be minimum.

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Q.92 What do you understand by Collaborative Filtering?

Collaborative filtering (CF) is a technique used by recommender systems and is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating).

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Q.93 What is a hash table collision?

A collision occurs when two keys are hashed to the same index in a hash table. Collisions are a problem because every slot in a hash table is supposed to store a single element. All key-value pairs mapping to the same index will be stored in the linked list of that index.

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Q.94 What do you understand by Time Series Analysis?

Time series analysis is a specific way of analyzing a sequence of data points collected over an interval of time. In time series analysis, analysts record data points at consistent intervals over a set period of time rather than just recording the data points intermittently or randomly.

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Q.95 What are the characteristics of a good data model?

The criteria of a good data model are: Data can be easily consumed, Large data changes are scalable, provides predictable performance and adapts to changes in requirements.

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Q.96 Differentiate between variance and covariance.

Variance refers to the spread of a data set around its mean value, while a covariance refers to the measure of the directional relationship between two random variables.

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Q.97 What do you understand by Normal Distribution?

Normal distribution or the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve.

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Q.98 What do you understand by univariate, bivariate, and multivariate analysis?

Univariate analysis looks at one variable, Bivariate analysis looks at two variables and their relationship. Multivariate analysis looks at more than two variables and their relationship.

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Q.99 Differentiate between R-Squared and Adjusted R-Squared.

The difference between R Squared and Adjusted R Squared is that R Squared is the type of measurement that represent the dependent variable variations in statistics, where Adjusted R Squared is a new version of the R Squared that adjust the variable predictors in regression models.

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Q.100 What are the different data types in R?

R's basic data types are character, numeric, integer, complex, and logical.

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Q.101 What does class () do in R?

The function class prints the vector of names of classes an object inherits from. Correspondingly, class<- sets the classes an object inherits from. Assigning NULL removes the class attribute. unclass returns (a copy of) its argument with its class attribute removed.

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Q.102 What is the list in R?

A list is an object in R Language which consists of heterogeneous elements. A list can even contain matrices, data frames, or functions as its elements. The list can be created using list() function in R. Named list is also created with the same function by specifying the names of the elements to access them.

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Q.103 What are data frames in R?

Data Frames in R Language are generic data objects of R which are used to store the tabular data. Data frames can also be interpreted as matrices where each column of a matrix can be of the different data types. DataFrame is made up of three principal components, the data, rows, and columns.

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Q.104 What is the difference between list and vector in R?

A list holds different data such as Numeric, Character, logical, etc. Vector stores elements of the same type or converts implicitly. Lists are recursive, whereas vector is not. The vector is one-dimensional, whereas the list is a multidimensional object.

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Q.105 What is the difference between factor and character in R?

Factors are used to represent categorical data. Factors are stored as integers, and have labels associated with these unique integers. While factors look (and often behave) like character vectors, they are actually integers under the hood, and you need to be careful when treating them like strings.

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Q.106 What is Dimnames R?

The dimnames() is a built-in R function that can set or get the row and column names of R Objects. The dimnames() function accepts an R object like matrix, array, or data frame. The dimnames() function operates on both rows and columns at once.

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Q.107 What is head and tail in R?

The head() and tail() function in R are often used to read the first and last n rows of a dataset.

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Q.108 What does N () do in R?

The function n() returns the number of observations in a current group. A closed function to n() is n_distinct(), which count the number of unique values.

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Q.109 Why do you want the Data Analysis with R professional job?

I want the Data Analysis with R professional job as I am passionate about data analysis and R programming language and applying both to make companies more efficient by using them and leverage the present technology portfolio to maximize their utility.

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