Master in Data Science (SMS, TU) Statistical Computing with R Question Paper 2081 Nepal

Explain the following concepts with R codes:

a) Numeric variable and its type with example b) Categorical variable and its type with example

Explain the following concept with focus on R software:

a) Manipulating row and column of data frame in dplyr package with an example b) Extract, Transform and Load in dplyr package with an example

Explain the following concepts with focus on R software:

a) Boxplot with five number summaries with example b) Boxplot with outliers with example

Explain the following concept with focus on R software:

a) Leverage in linear regression with example b) Multicollinearity in logistic regression with example

Explain the following concepts with focus on R software:

a) Biplot from principal component analysis b) Biplot from classical multidimensional scaling

Load the "igraph" package in R studio and do the basic SNA as follows with R script and HTML output:

a) Define "g1" as graph object with ("R", "S", "S", "T", "T", "R", "R", "T", "U", "S") as its elements b) Plot "g1" with node color as green, node size as 30, link color as red and link size as 5 and interpret it c) Get degree, closeness and betweenness of g1 and interpret them carefully d) Get hub and communities of this data and interpret them carefully

OR

Do the following in R Studio using "airquality" dataset with R script:

a) Replace missing values of "Ozone" variable with median of this variable as corrected Ozone b) Get the histogram of the corrected Ozone variable using base R plot and interpret it carefully c) Get the boxplot of Wind variable using based R plot and interpret it carefully d) Get the Wind variable outliers using median and interquartile range and compare them with boxplot outlier values with justification

Do as follows in R Studio and do as follows with R script and HTML outputs:

a) Open R and go to Help and Manuals in PDF and open "An Introduction to R" file b) Import this pdf file in R using "pdftools" package c) Perform pre-processing and create 'corpus' afterwards d) Find the most frequent terms, create its bar diagram and interpret carefully

OR

Do the following in R Studio using "airquality" dataset with R script:

a) Get the boxplot of Temp variable using ggplot2 package and interpret it carefully b) Create class intervals of Temp variable using dplyr package and show it as frequency distribution c) Get pie chart of Temp variable class intervals using ggplot2 package and interpret it carefully d) Get scatter plot of corrected Temp and Wind variables using ggplot2 package and interpret it carefully

Do the following in R Studio using "airquality" dataset with R script:

a) Perform Shapiro-Wilk test on "Wind" variable to check if it follows normal distribution or not b) Perform Bartlett test on "Wind" variable by "Month" variable to check if the variances of Wind are equal or not on Month variable categories c) Fit 1-way ANOVA to compare "Wind" variable by "Month" variable and interpret the result carefully d) Fit the TukeyHSD post-hoc test with 95% confidence interval and interpret the result carefully

Do the following in R Studio using "USArrests" dataset with R script:

a) Divide the mtcars data into train and test datasets with 70:30 random splits b) Fit a supervised linear regression model and KNN regression model on train data with "Urban population – UrbanPop" as dependent variable and all other variables as independent variable c) Predict the UrbanPop variable in the test datasets using these two models and interpret results carefully d) Compare the fit indices (R-square, MSE, RMSE) of the two predicted models and choose the best model

Use the first four variables "iris" data and do as follows in the R Studio with R Script:

a) Fit a hierarchical clustering model using average linkage and get the dendogram for this model b) Get the best value of number of clusters to form (k) using the fitted model above c) Fit the k-means clustering with the best value of k identified above and interpret it carefully d) Compare k-means result with the last variable of this data usig confusion matrix and interpret the result carefully