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

Explain how to import these types of data in R using base R functions: a) Comma separated values text file b) Excel data file c) SPSS data file

Explain how you can do sub-setting with codes in R software: a) Define the 6x5 matrix and select last two rows b) Select third and fifth row with second and fourth column c) Add 3 new rows in this matrix

Explain differences of these terms with examples using R codes: a) Arrays and matrices b) List and factors c) Data frame and tibble

Explain the following concepts on working efficiently with “big data” in R software: a) Sample and model b) Chunk and pull c) Push compute to data

Explain different types of pipe operators with R codes and examples: a) Compound assignment operator b) Tee operator c) Exposition operator

Do the followings with R script in R Studio: a) Define a column vector X with numbers between 1 and 30 b) Define another column vector Y with cubes of X c) Combine the two column vectors in a new data frame called DF d) Get plot X and Y variables and decide which type of relationship is seen e) Show the code to make this a linear relationship

Create a function and do as follows: a) Define a function: “roll” of a fair “die” twice with random sampling with replacement as true b) Get the first roll and interpret the result c) Get the second roll and interpret the result d) Get the third roll and interpret the result e) Write a summary of the results obtained in the earlier steps with conclusion

Do the following in R Studio with tidy verse package using R Script to knit HTML output: a) Define a tibble having country, year, cases and population variables with 10 random data each b) Transform this tibble to long format and interpret it carefully in terms of tidy data format c) Transform the cases variable as log of cases (LnCase) and population variable as log of population (LnPop) d) Create scatter plots of 1. Cases and population, 2. LnCase and population, 3. Cases and LnPop and 4. LnCase and LnPop e) Show the four graphs in a single graph window

Use the “mtcars” dataset of tidiverse package and do as follows with R script to knit HTML output: a) Plot histogram of mpg variable and interpret it carefully b) Refine the histogram by filling the bars with “blue” color and changing number of bins to 10 c) Add a vertical abline at mean of the mpg variable d) Plot Q-Q plot of mpg variable, add normal Q-Q line of red color on it and interpret it carefully e) Plot density plot of mpg variable without the border, fill it with yellow color and interpret it

OR

Use the “air quality” dataset of R to do following using base R to knit HTML output with R script: a) Create line plot of “Temp” with “Day” as the row index and interpret it carefully b) Create bar plot of “Temp” variable after defining class intervals systematically c) Create histogram of “Temp” variable and compare it with the bar plot of “Temp” variable d) Plot Normal Q-Q plot of “Temp” variable and interpret it carefully e) Create a scatter plot of “Temp” and “Wind” variables and interpret it carefully

Load the “igraph” package in R studio and do the basic SNA as follows with R scripts to knit HTML output: a) Define g as graph object with (1,2,3,4) as its elements b) Plot the g and interpret it carefully c) Define g1 as graph object with (“Sita”, “Ram”, “Rita”, “Gita”, “Gita”, “Sita”, “Sita”, “Gita”, “Anita”, “Rita”, “Ram”, “Sita”) as its elements d) Plot g1 with node color as green, node size as 20, link color as red and link size as 10 and interpret it e) Get degree, closeness and betweenness of g1 and interpret them carefully.

OR

Do as follows in R console and then to R Studio with R script to knit HTML outputs: a) Open R console and then go to Help and Manuals (in PDF) and open “An Introduction to R” file b) Save this file in the working directory and import this pdf file in R studio using “pdftools” package c) Perform pre-processing and create ‘corpus’ using “tm” package d) Find the most frequent terms and create histogram of the most frequent terms e) Create word cloud of the corpus, color it using rainbow or R Color Brewer package f) Perform topic modelling and interpret the result carefully