| Join Telegram |  |
| Join Whatsapp Groups |  |
R Programming MCQs and Answers With Explanation – R Programming is an open-source programming language that has gained popularity in recent years for its effectiveness in data analysis and statistical computing. It is widely used in the fields of finance, healthcare, and marketing, among others. R’s extensive library of packages and functions makes it a preferred choice among data analysts and scientists. To test your knowledge of R Programming, you can try answering the following R Programming Multiple Choice Questions that cover a wide range of topics related to the programming language.
R Programming MCQ Questions & Answers
These top 55 MCQs on the R programming language will help you assess your understanding of the key concepts and functionalities of R. If you are unsure about the correct answer to a particular question, you can check out the R programming questions and answers along with a detailed explanation for each. These MCQs on R programming can help beginners to gain a complete understanding of R programming easily.
R Programming Multiple Choice Questions
| Name | R Programming |
| Exam Type | MCQ (Multiple Choice Questions) |
| Category | Technical Quiz |
| Mode of Quiz | Online |
Top 55 MCQs on R Programming Language
1. What is R?
a. A statistical programming language
b. A spreadsheet program
c. A web development language
d. An operating system
Answer: a. A statistical programming language
Explanation: R is a free and open-source programming language used for statistical computing and graphics. It is widely used among data analysts, statisticians, and data scientists for data analysis, visualization, and modeling.
2. Which of the following is the correct syntax for assigning a value to a variable in R?
a. var = 10
b. 10 = var
c. var == 10
d. var := 10
Answer: a. var = 10
Explanation: In R, variables are assigned using the assignment operator, which is the equal sign (=). The value on the right-hand side of the operator is assigned to the variable on the left-hand side of the operator.
3. Which of the following is a valid variable name in R?
a. 2var
b. var2
c. var_2
d. var#2
Answer: b. var2
Explanation: In R, variable names can contain letters, numbers, and underscores, but cannot begin with a number or contain special characters such as #, $, %, etc.
4. What is the output of the following code in R?
x <- 1:5 y <- x^2 plot(x, y)
a. A scatterplot of x versus y
b. A line plot of x versus y
c. A histogram of x
d. An error message
Answer: a. A scatterplot of x versus y
Explanation: The code creates two vectors, x and y, by assigning them the values 1 to 5 and their squares, respectively. The plot() function is then used to create a scatterplot of x versus y.
5. What is the output of the following code in R?
x <- c(1, 2, 3) y <- c(4, 5, 6) z <- x + y
a. An error message
b. The vector [5, 7, 9]
c. The vector [1, 2, 3, 4, 5, 6]
d. The vector [1, 4, 9]
Answer: b. The vector [5, 7, 9]
Explanation: The code creates two vectors, x and y, each with three elements, and then adds them element-wise to create a new vector, z. The resulting vector contains the sum of each pair of corresponding elements from x and y.
6. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- x[x > 2] print(y)
a. The vector [2, 3, 4, 5]
b. The vector [1, 2, 3]
c. The vector [3, 4, 5]
d. An error message
Answer: c. The vector [3, 4, 5]
Explanation: The code creates a vector x with the values 1 to 5, and then creates a new vector y by subsetting x with the condition x > 2, which returns only the elements of x that are greater than 2. The resulting vector y contains the values 3, 4, and 5.
7. What is the output of the following code in R?
`x <- c(1, 2, 3)
y <- c(4, 5, 6)
z <- cbind(x, y)
a. A matrix with two rows and three columns
b. A matrix with three rows and two columns
c. A list with two elements
d. An error message
Answer: a. A matrix with two rows and three columns
Explanation: The code combines the vectors x and y column-wise using the cbind() function to create a matrix. Since x and y have three elements each, the resulting matrix has two rows and three columns.
8. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) mean(x)
a. 3
b. 3.5
c. 4
d. 5
Answer: b. 3.5
Explanation: The code creates a vector x with the values 1 to 5, and then calculates the mean of the vector using the mean() function, which returns the average value of the elements in the vector.
9. What is the output of the following code in R?
x <- c(1, 2, 3) y <- c(2, 4, 6) cor(x, y)
a. -1
b. 0
c. 1
d. 2
Answer: c. 1
Explanation: The code creates two vectors, x and y, and then calculates the correlation between them using the cor() function. Since the elements of y are twice the corresponding elements of x, the vectors are perfectly correlated, and the correlation coefficient is 1.
10. What is the output of the following code in R?
x <- c(1, 2, 3) y <- c(4, 5, 6) lm(y ~ x)
a. An error message
b. A linear regression model object
c. A scatterplot of x versus y
d. A summary of the regression model
Answer: b. A linear regression model object
Explanation: The code creates two vectors, x and y, and then fits a linear regression model to predict y from x using the lm() function. The resulting object is a linear regression model that can be used to predict new values of y based on new values of x.
11. Which of the following is a valid way to read in a CSV file in R?
a. read.csv(“data.csv”)
b. read.table(“data.csv”)
c. read.excel(“data.csv”)
d. load(“data.csv”)
Answer: a. read.csv(“data.csv”)
Explanation: The read.csv() function is used to read in a CSV (Comma Separated Values) file in R. This function creates a data frame from the CSV file, where each column corresponds to a variable, and each row corresponds to an observation.
12. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) lm(y ~ x)$coefficients
a. A vector containing the intercept and slope of the linear regression model
b. A scatterplot of x versus y
c. A correlation matrix between x and y
d. An error message
Answer: a. A vector containing the intercept and slope of the linear regression model
Explanation: The code creates two vectors, x and y, and then fits a linear regression model to predict y from x using the lm() function. The $coefficients element of the resulting object contains the intercept and slope of the regression model.
13. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) summary(lm(y ~ x))$r.squared
a. The R-squared value of the linear regression model
b. A scatterplot of x versus y
c. A summary of the regression model
d. An error message
Answer: a. The R-squared value of the linear regression model
Explanation: The code creates two vectors, x and y, and then fits a linear regression model to predict y from x using the lm() function. The summary() function is used to display a summary of the regression model, and the $r.squared element of the resulting object contains the R-squared value, which measures the proportion of variance in the dependent variable (y) that is explained by the independent variable (x).
14. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) plot(x, y)
a. A scatterplot of x versus y
b. A line plot of x versus y
c. A bar plot of x versus y
d. An error message
Answer: a. A scatterplot of x versus y
Explanation: The code creates two vectors, x and y, and then creates a scatterplot of y versus x using the plot() function. This function plots the points (x[i], y[i]) for each i in a two-dimensional coordinate system, where x is the horizontal axis and y is the vertical axis.
15. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) cor(x, y)^2
a. The R-squared value of the linear regression model
b. A scatterplot of x versus y
c. The correlation coefficient between x and y
d. An error message
Answer: a. The R-squared value of the linear regression model
Explanation: The code creates two vectors, x and y, and then calculates the correlation coefficient between them using the cor() function. Since the correlation coefficient squared is equal to the R-squared value of a linear regression model between x and y, the output is the R-squared value.
16. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) lm(y ~ x)
a. A scatterplot of x versus y
b. A summary of the regression model
c. The regression coefficients of the model
d. An error message
Answer: b. A summary of the regression model
Explanation: The code creates two vectors, x and y, and then fits a linear regression model to predict y from x using the lm() function. The output of this function is a summary of the regression model, which includes information about the coefficients of the model, the standard errors of the coefficients, the t-statistics and p-values for testing the null hypothesis that each coefficient is equal to zero, and the R-squared value.
17. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) summary(lm(y ~ x))$coefficients
a. A scatterplot of x versus y
b. A summary of the regression model
c. The regression coefficients of the model
d. An error message
Answer: c. The regression coefficients of the model
Explanation: The code creates two vectors, x and y, and then fits a linear regression model to predict y from x using the lm() function. The summary() function is used to display a summary of the regression model, and the $coefficients element of the resulting object contains the estimates of the coefficients of the model, including the intercept and the slope of the predictor variable x.
18. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) predict(lm(y ~ x))
a. A scatterplot of x versus y
b. The predicted values of y based on the regression model
c. A summary of the regression model
d. An error message
Answer: b. The predicted values of y based on the regression model
Explanation: The code creates two vectors, x and y, and then fits a linear regression model to predict y from x using the lm() function. The predict() function is used to obtain the predicted values of y based on the regression model. In this case, it returns the predicted values of y for the same values of x used to fit the model.
19. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) resid <- residuals(lm(y ~ x)) sum(resid)
a. The R-squared value of the linear regression model
b. A scatterplot of x versus y
c. The sum of squared residuals of the regression model
d. An error message
Answer: d. An error message
Explanation: The code creates two vectors, x and y, and then fits a linear regression model to predict y from x using the lm() function. The residuals() function is used to obtain the residuals, which are the differences between the observed values of y and the predicted values of y based on the regression model. However, the sum() function is not applicable to a vector of residuals, so the code produces an error message.
20. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) resid <- residuals(lm(y ~ x)) sum(resid^2)
a. The R-squared value of the linear regression model
b. A scatterplot of x versus y
c. The sum of squared residuals of the regression model
d. An error message
Answer: c. The sum of squared residuals of the regression model
Explanation: The code creates two vectors, x and y, and then fits a linear regression model to predict y from x using the lm() function. The residuals() function is used to obtain the residuals, which are the differences between the observed values of y and the predicted values of y based on the regression model. The sum() function is applied to the squared residuals using the “^2” operator, which calculates the sum of squared residuals, a measure of the overall fit of the model.
21. Which of the following statements is true about missing values in R?
a. R automatically replaces missing values with the mean of the non-missing values
b. Missing values are denoted by the character “NA”
c. R automatically removes observations with missing values from analyses
d. All of the above
Answer: b. Missing values are denoted by the character “NA”
Explanation: In R, missing values are denoted by the character “NA” and are handled differently depending on the function being used. R does not automatically replace missing values with the mean of the non-missing values, and some functions may remove observations with missing values from analyses while others may impute missing values or retain them in the analysis.
22. Which of the following statements is true about factors in R?
a. Factors are used to represent continuous variables
b. Factors are used to represent categorical variables
c. Factors are always stored as integers
d. Factors can be used in mathematical calculations
Answer: b. Factors are used to represent categorical variables
Explanation: In R, factors are used to represent categorical variables, such as variables with levels or categories like “Yes” or “No”, “Red”, “Green”, “Blue”, or “Low”, “Medium”, “High”. Factors are always stored as integers and can be used in some mathematical calculations, but they are generally treated as nominal or ordinal variables.
23. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) z <- c(“A”, “B”, “C”, “D”, “E”) df <- data.frame(x, y, z) head(df)
a. A scatterplot of x versus y
b. A summary of the data frame
c. The first six rows of the data frame
d. An error message
Answer: c. The first six rows of the data frame
Explanation: The code creates three vectors, x, y, and z, and then combines them into a data frame using the data.frame() function. The head() function is then used to display the first six rows of the resulting data frame, which includes the values of x, y, and z for each observation.
24. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) z <- c(“A”, “B”, “C”, “D”, “E”) df <- data.frame(x, y, z) subset(df, y > 6)
a. A scatterplot of x versus y with only the points where y is greater than 6
b. A summary of the data frame with only the rows where y is greater than 6
c. The first six rows of the data frame
d. An error message
Answer: b. A summary of the data frame with only the rows where y is greater than 6
Explanation: The code creates three vectors, x, y, and z, and then combines them into a data frame using the data.frame() function. The subset() function is then used to select only the rows of the data frame where y is greater than 6. This will result in a summary of the data frame with only the rows that meet this criterion.
25. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) z <- c(“A”, “B”, “C”, “D”, “E”) df <- data.frame(x, y, z) levels(df$z)
a. The number of unique values in the z variable
b. The names of the levels in the z factor
c. The values of x where z is equal to “A”
d. An error message
Answer: b. The names of the levels in the z factor
Explanation: The code creates three vectors, x, y, and z, and then combines them into a data frame using the data.frame() function. The levels() function is then used to display the names of the levels in the z factor, which are “A”, “B”, “C”, “D”, and “E”. Factors are created in R using the factor() function and can be used to represent categorical variables with ordered or unordered levels.
26. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) z <- c(“A”, “B”, “C”, “D”, “E”) df <- data.frame(x, y, z) cor(df$x, df$y)
a. The correlation between x and y
b. A scatterplot of x versus y
c. The coefficients of a linear regression model predicting y from x
d. An error message
Answer: a. The correlation between x and y
Explanation: The code creates three vectors, x, y, and z, and then combines them into a data frame using the data.frame() function. The cor() function is then used to calculate the correlation between x and y, which measures the strength and direction of the linear relationship between the two variables.
27. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) z <- c(“A”, “B”, “C”, “D”, “E”) df <- data.frame(x, y, z) summary(lm(y ~ x, data = df))
a. A scatterplot of x versus y with a linear regression line
b. A summary of the data frame with the coefficients of a linear regression model predicting y from x
c. The p-value of a t-test for the slope of the linear regression model predicting y from x
d. An error message
Answer: b. A summary of the data frame with the coefficients of a linear regression model predicting y from x
Explanation: The code creates three vectors, x, y, and z, and then combines them into a data frame using the data.frame() function. The lm() function is then used to fit a linear regression model predicting y from x, and the summary() function is used to display a summary of the model, including the coefficients of the predictor variables. This will give us information on how much change in y we can expect for a unit change in x.
28. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) z <- c(“A”, “B”, “C”, “D”, “E”) df <- data.frame(x, y, z) t.test(df$x, df$y)
a. A scatterplot of x versus y with a t-test for the difference in means
b. A summary of the data frame with the p-value of a t-test for the difference in means of x and y
c. The coefficients of a linear regression model predicting y from x with a t-test for the slope
d. An error message
Answer: b. A summary of the data frame with the p-value of a t-test for the difference in means of x and y
Explanation: The code creates three vectors, x, y, and z, and then combines them into a data frame using the data.frame() function. The t.test() function is then used to perform a t-test for the difference in means between x and y. The output of the function will include a summary of the results, including the p-value of the test, which will tell us the likelihood of observing the difference in means between the two variables under the assumption that they have the same population mean.
29. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) z <- c(“A”, “B”, “C”, “D”, “E”) df <- data.frame(x, y, z) library(ggplot2) ggplot(df, aes(x = x, y = y)) + geom_point()
a. A scatterplot of x versus y
b. A summary of the data frame
c. The first six rows of the data frame
d. An error message
Answer: a. A scatterplot of x versus y
Explanation: The code creates three vectors, x, y, and z, and then combines them into a data frame using the data.frame() function. The ggplot() function from the ggplot2 package is then used to create a scatterplot of x versus y, with the x variable mapped to the x-axis and the y variable mapped to the y-axis. The geom_point() function is used to add points to the plot, which represent the values of x and y. This will help us visualize the relationship between the two variables.
30. Which of the following functions in R can be used to create a histogram?
a. scatterplot()
b. boxplot()
c. density()
d. hist()
Answer: d. hist()
Explanation: The hist() function in R can be used to create a histogram, which is a graphical representation of the distribution of a numerical variable. The function takes a numeric vector as its argument and plots the frequency of the observations in each bin or interval.
31. Which of the following statements about factors in R is true?
a. Factors are used to represent numeric data in R.
b. Factors are used to represent categorical data in R.
c. Factors are used to represent missing values in R.
d. Factors are used to represent character data in R.
Answer: b. Factors are used to represent categorical data in R.
Explanation: Factors are a data type in R that are used to represent categorical variables. Categorical variables have a limited number of possible values, such as colors, categories, or labels. Factors are used to store these values as levels, which are treated as separate categories by R. Factors are an important data type in R because they enable statistical analysis of categorical variables, such as the calculation of frequencies and proportions.
32. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) z <- c(“A”, “B”, “C”, “D”, “E”) df <- data.frame(x, y, z) subset(df, x > 2)
a. A subset of the data frame where x is greater than 2
b. A subset of the data frame where y is greater than 2
c. A subset of the data frame where z is greater than 2
d. An error message
Answer: a. A subset of the data frame where x is greater than 2
Explanation: The code creates three vectors, x, y, and z, and then combines them into a data frame using the data.frame() function. The subset() function is then used to extract a subset of the data frame where the values of x are greater than 2. The resulting subset will contain all rows of the original data frame where the value of x is greater than 2.
33. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) z <- c(“A”, “B”, “C”, “D”, “E”) df <- data.frame(x, y, z) aggregate(df$y, by = list(df$x), mean)
a. A summary of the data frame with the mean value of y for each value of x
b. A summary of the data frame with the median value of y for each value of x
c. A summary of the data frame with the maximum value of y for each value of x
d. An error message
Answer: a. A summary of the data frame with the mean value of y for each value of x
Explanation: The code creates three vectors, x, y, and z, and then combines them into a data frame using the data.frame() function. The aggregate() function is then used to compute a summary of the y variable for each unique value of x. The mean() function is used as the aggregation function to calculate the mean value of y for each group defined by the unique values of x. The output of the code is a summary of the data frame with the mean value of y for each value of x.
34. Which of the following functions in R can be used to generate a sequence of numbers?
a. rep()
b. seq()
c. sort()
d. length()
Answer: b. seq()
Explanation: The seq() function in R can be used to generate a sequence of numbers. The function takes three arguments: from, to, and by. The from argument specifies the starting value of the sequence, the to argument specifies the end value of the sequence, and the by argument specifies the increment between values in the sequence. For example, seq(1, 10, by = 2) would generate the sequence 1, 3, 5, 7, 9.
35. Which of the following functions in R can be used to calculate the standard deviation of a vector of numbers?
a. mean()
b. median()
c. var()
d. sd()
Answer: d. sd()
Explanation: The sd() function in R can be used to calculate the standard deviation of a vector of numbers. The function takes a numeric vector as its argument and returns the standard deviation of the values in the vector.
36. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) z <- c(“A”, “B”, “C”, “D”, “E”) df <- data.frame(x, y, z) colnames(df) <- c(“X”, “Y”, “Z”) head(df)
a. A data frame with three columns named X, Y, and Z
b. A data frame with three columns named x, y, and z
c. An error message
d. A vector with the names of the columns in the data frame
Answer: a. A data frame with three columns named X, Y, and Z
Explanation: The code creates three vectors, x, y, and z, and then combines them into a data frame using the data.frame() function. The colnames() function is then used to assign new names to the columns of the data frame. The head() function is used to display the first few rows of the data frame, which will have the new column names X, Y, and Z.
37. Which of the following functions in R can be used to generate a random sample from a vector?
a. mean()
b. median()
c. var()
d. sample()
Answer: d. sample()
Explanation: The sample() function in R can be used to generate a random sample from a vector. The function takes two arguments: the vector to sample from, and the size of the sample to generate. For example, sample(1:10, 5) would generate a random sample of size 5 from the vector 1, 2, 3, 4, 5, 6, 7, 8, 9, 10.
38. Which of the following is a valid way to select the first row of a data frame named df in R?
a. df[0,]
b. df[1,]
c. df[,1]
d. df[,0]
Answer: b. df[1,]
Explanation: In R, data frames are indexed using the row and column number. To select the first row of a data frame named df, we use the index df[1,]. The comma indicates that we want to select all columns, and the 1 indicates that we want to select the first row. Option a, df[0,], would not select any rows because indexing in R starts from 1. Option c, df[,1], would select the first column of the data frame. Option d, df[,0], is not a valid way to index a data frame because indexing in R starts from 1.
39. Which of the following functions in R can be used to remove missing values from a vector?
a. na.rm()
b. na.omit()
c. na.fill()
d. na.exclude()
Answer: b. na.omit()
Explanation: The na.omit() function in R can be used to remove missing values from a vector or a data frame. The function takes a vector or a data frame as its argument and returns the object with any rows containing missing values removed. The na.rm() function can also be used to remove missing values, but it is typically used as an argument in functions that perform calculations (e.g., sum(), mean()) rather than as a standalone function. The na.fill() function can be used to replace missing values with specified values, and the na.exclude() function can be used to exclude missing values from calculations.
40. What is the output of the following code in R?
x <- 1:10 y <- 11:20 z <- cbind(x, y) z[,2]
a. A vector containing the values 1 through 10
b. A vector containing the values 11 through 20
c. A matrix containing the values 1 through 10 in the first column and the values 11 through 20 in the second column
d. An error message
Answer: b. A vector containing the values 11 through 20
Explanation: The cbind() function in R is used to combine vectors into a matrix, with each vector forming a column of the matrix. In this case, the code creates two vectors, x and y, and combines them into a matrix z using cbind(). The expression z[,2] then extracts the second column of the matrix, which corresponds to the y vector. The output will be a vector containing the values 11 through 20.
41. Which of the following functions can be used to convert a factor variable in R to a character variable?
a. as.factor()
b. as.character()
c. as.numeric()
d. as.logical()
Answer: b. as.character()
Explanation: The as.character() function in R can be used to convert an object of any class to a character object. This includes converting a factor variable to a character variable. The as.factor() function, on the other hand, converts an object to a factor variable. The as.numeric() function converts an object to a numeric variable, and the as.logical() function converts an object to a logical variable.
42. Which of the following functions in R can be used to calculate the standard deviation of a vector?
a. var()
b. sd()
c. mean()
d. min()
Answer: b. sd()
Explanation: The sd() function in R can be used to calculate the standard deviation of a vector or a data frame. The function takes a vector or a data frame as its argument and returns the standard deviation of the values. The var() function, on the other hand, returns the variance of the values. The mean() function returns the mean of the values, and the min() function returns the minimum value of the vector.
43. Which of the following functions in R can be used to generate random numbers from a normal distribution?
a. rnorm()
b. runif()
c. rpois()
d. rbeta()
Answer: a. rnorm()
Explanation: The rnorm() function in R can be used to generate random numbers from a normal distribution with a specified mean and standard deviation. The function takes three arguments: the number of values to generate, the mean of the distribution, and the standard deviation of the distribution. The runif() function generates random numbers from a uniform distribution, the rpois() function generates random numbers from a Poisson distribution, and the rbeta() function generates random numbers from a beta distribution.
44. What is the output of the following code in R?
x <- list(1:3, 4:6, 7:9) lapply(x, sum)
a. A list containing the sums of the vectors in x
b. A list containing the mean of the vectors in x
c. A list containing the median of the vectors in x
d. An error message
Answer: a. A list containing the sums of the vectors in x
Explanation: The lapply() function in R is used to apply a function to each element of a list and return the results as a list. In this case, the code creates a list x containing three vectors, and uses lapply() to apply the sum() function to each vector in x. The output will be a list containing the sums of the vectors in x.
45. Which of the following functions in R can be used to calculate the correlation between two vectors?
a. cor()
b. cov()
c. sd()
d. var()
Answer: a. cor()
Explanation: The cor() function in R can be used to calculate the correlation between two vectors or between the columns of a data frame. The function takes two vectors or a data frame as its argument and returns the correlation coefficient between the vectors or columns. The cov() function returns the covariance between the vectors or columns, and the sd() and var() functions calculate the standard deviation and variance of the vectors or columns.
46. Which of the following functions in R can be used to create a scatterplot matrix?
a. plot()
b. pairs()
c. hist()
d. boxplot()
Answer: b. pairs()
Explanation: The pairs() function in R can be used to create a scatterplot matrix, which shows pairwise scatterplots of all variables in a data frame. The function takes a data frame as its argument and plots scatterplots of all combinations of variables in the data frame. The plot() function is used to create a scatterplot of two variables, the hist() function is used to create a histogram of a single variable, and the boxplot() function is used to create a boxplot of a single variable or a group of variables.
47. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) cor(x, y)
a. The correlation coefficient between x and y
b. A scatterplot of x vs. y with a regression line
c. The R-squared value for the regression of y on x
d. An error message
Answer: a. The correlation coefficient between x and y
Explanation: The cor() function in R is used to calculate the correlation coefficient between two vectors. In this case, the code creates two vectors x and y, and uses cor() to calculate the correlation coefficient between them. The output will be the correlation coefficient between x and y.
48. Which of the following functions in R can be used to perform principal component analysis (PCA)?
a. prcomp()
b. cor()
c. lm()
d. t.test()
Answer: a. prcomp()
Explanation: The prcomp() function in R can be used to perform principal component analysis (PCA) on a data matrix. PCA is a technique used to reduce the dimensionality of a data set by transforming the original variables into a smaller number of linearly uncorrelated variables called principal components. The function takes a data matrix as its argument and returns an object containing the principal components and their loadings. The cor() function is used to calculate the correlation matrix of a data matrix, the lm() function is used to fit a linear regression model, and the t.test() function is used to perform a t-test on two sample means.
49. Which of the following functions in R can be used to read in data from a CSV file?
a. read.csv()
b. read.table()
c. read.delim()
d. All of the above
Answer: d. All of the above
Explanation: All three functions read.csv(), read.table(), and read.delim() can be used to read in data from a CSV file in R. The read.csv() function is used to read in comma-separated value (CSV) files, the read.table() function is used to read in tab-delimited files, and the read.delim() function is used to read in files delimited by any other character. All three functions take a file name as their argument and return a data frame containing the data from the file.
50. Which of the following functions in R can be used to create a boxplot?
a. boxplot()
b. hist()
c. plot()
d. lines()
Answer: a. boxplot()
Explanation: The boxplot() function in R is used to create a boxplot, which is a graphical summary of a distribution of a variable or a group of variables. The function takes one or more vectors or a data frame as its argument and creates a boxplot of the variables. The hist() function is used to create a histogram of a single variable, the plot() function is used to create a scatterplot of two variables, and the lines() function is used to add lines to a plot.
51. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) summary(lm(y ~ x))
a. A summary of the linear regression of y on x
b. A summary of the correlation between x and y
c. A summary of the principal components of x and y
d. An error message
Answer: a. A summary of the linear regression of y on x
Explanation: The lm() function in R is used to fit a linear regression model. In this case, the code creates two vectors x and y, and uses lm() to fit a linear model of y on x. The summary() function is used to print a summary of the linear regression model. The output will be a summary of the linear regression of y on x, which includes the coefficients, standard error, t-value, and p-value of the model.
52. Which of the following functions in R can be used to calculate the median of a vector?
a. mean()
b. median()
c. mode()
d. var()
Answer: b. median()
Explanation: The median() function in R is used to calculate the median of a vector or a data frame. The mean() function is used to calculate the arithmetic mean of a vector or a data frame, the mode() function is not built-in to R but can be calculated using other functions, and the var() function is used to calculate the variance of a vector or a data frame.
53. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) z <- x + y mean(z)
a. The arithmetic mean of the vector z
b. The arithmetic mean of the vector x
c. The arithmetic mean of the vector y
d. An error message
Answer: a. The arithmetic mean of the vector z
Explanation: The mean() function in R is used to calculate the arithmetic mean of a vector or a data frame. In this case, the code creates two vectors x and y, and adds them together to create a new vector z. The mean() function is then used to calculate the arithmetic mean of the vector z. The output will be the arithmetic mean of the vector z.
54. Which of the following functions in R can be used to create a cumulative distribution function (CDF) plot?
a. hist()
b. ecdf()
c. density()
d. qqnorm()
Answer: b. ecdf()
Explanation: The ecdf() function in R is used to create an empirical cumulative distribution function (ECDF) plot, which is a plot of the cumulative distribution function of a variable based on its observed values. The ecdf() function takes a vector as input and returns a function that can be used to create the ECDF plot. The hist() function is used to create a histogram of a single variable, the density() function is used to create a kernel density plot of a single variable, and the qqnorm() function is used to create a normal probability plot of a single variable.
55. What is the output of the following code in R?
x <- c(1, 2, 3, 4, 5) y <- c(4, 5, 6, 7, 8) cor(x, y, method = “spearman”)
a. The Spearman correlation coefficient between x and y
b. The Pearson correlation coefficient between x and y
c. The Kendall correlation coefficient between x and y
d. An error message
Answer: a. The Spearman correlation coefficient between x and y
Explanation: The cor() function in R can be used to calculate the correlation coefficient between two vectors or two columns of a data frame using different methods, including Pearson, Spearman, and Kendall. In this case, the code creates two vectors x and y, and uses cor() to calculate the Spearman correlation coefficient between them. The output will be the Spearman correlation coefficient between x and y.
The R Programming MCQ Questions & Answers cover a broad range of topics and concepts related to the programming language. Attempting these R Programming MCQ Quiz Questions will enable you to enhance your knowledge of R and assess your proficiency in the language. For more latest quizzes on various software-related concepts kindly keep following our Freshersnow website.