Master in Data Science (SMS, TU) Statistical Computing with R Question Paper 2082 (Set pages 19 + top of 20; 2082 board/final exam (IOST,TU)) Nepal

Explain the following concepts with examples: a) Categorical variable and its type in R b) Date variable and its type in R

Describe the following concept with example: a) Reviewing the data frame for missing values in R b) Getting summary statistics without missing values in R

Explain the following concept with examples: a) Grammar of graphics – Wilkinson's approach b) Five number summary

Explain the following concepts with examples: a) Decision Tree b) Support Vector Machine

Explain the following concepts with examples: a) Biplot from principal component analysis b) Biplot from classical multidimensional scaling

Load the "igraph" package in R studio and do the basic SNA with R script to knit PDF 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 of g1 and interpret them carefully d) Get closeness of g1 and interpret them carefully

OR

Do the following in R Studio using "airquality" dataset with R script to knit PDF output: a) Replace missing values of "Ozone" variable with its median and save it 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 "corrected Ozone" variable using based R plot and interpret it carefully d) Get the appropriate summary measures of "corrected Ozone" variable with justification

Do as follows in R Studio and do as follows with R script to knit PDF output: a) Open R and then go to Help and "Manuals in PDF" and open "An Introduction to R" file b) Import this pdf file in R studio using "pdftools" package c) Perform pre-processing and create 'corpus' afterwards using "tm" package d) Find the most frequent terms and create histogram of the most frequent terms

OR

Do the following in R Studio using "mtcars" dataset with R script to knit PDF output: a) Get the bar plot of the "mpg" variable using ggplot2 package and interpret it carefully b) Get the boxplot of "mpg" variable using ggplot2 package and interpret it carefully c) Get scatterplot of "mpg" and "wt" variable using ggplot2 package and interpret it carefully d) Get appropriate correlation coefficient for "mpg" and "wt" and interpret it carefully

Do the following in R Studio using "airquality" dataset with R markdown to knit PDF output: a) Perform Shapiro-Wilk test on "Wind" variable and check normality of this variable b) Perform Bartlett test on "Wind" variable by "Month" variable and check equality of variance 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 followings in R Studio using "mtcars" dataset with R markdown to knit PDF output: a) Divide the data into train and test datasets with 70:30 random splits and your roll number as random seed b) Fit a supervised linear regression model and KNN regression model on train data with "mpg" as dependent variable and all other variables as independent variable c) Predict the miles per gallon variable in the test data using these models and get values for "wt=6000 lbs" d) Compare the fit indices (R-square, MSE, RMSE) of the predicted models and choose the best model

Use the "USArrests" data and do as follows in the R Studio with R markdown to knit PDF output: a) Fit a hierarchical clustering model using single linkage and get the dendogram for this model b) Fit a hierarchical clustering model using complete linkage and get the dendogram for this model c) Fit a hierarchical clustering model using average linkage and get the dendogram for this model d) Show the number of clusters (k) to retain for the data using ablines in the dendogram of the best model