Master in Data Science (SMS, TU) Statistical Computing with R Question Paper 2080 (Set First Assessment 2080) 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 following data types in R with examples and R codes:

a) Numeric and integer b) Categorical and factor c) Data and Date as well as time

Explain these terms with examples for R:

a) Arrays and matrices b) List and unlist c) Data frame and data table

Explain the followings with examples for R:

a) Base R code vs pipe operator code b) For loop code vs pipe operator code c) "tee" pipe operator vs "exposition" pipe operator

Explain the following in R with example:

a) Package b) Installation of package c) Development of package

Open the R studio and do the followings with R script and knit HTML output:

a) Define integers from 1 to 15 using three different coding approaches as I b) Define these five numbers: 1.1, 2.2, 3.3, 4.4 and 5.5 and save it as N c) Add, subtract, multiply and divide I from N and interpret the results carefully d) Perform matrix multiplication of I and N and interpret the result carefully e) Transpose I and N, perform matrix multiplication and interpret the result carefully

Do the following in R studio and with R script to knit HTML output:

a) Load the in-built "air quality" data available in base R as AQ object b) Check the structure of AQ and explain class of each variable c) Replace missing values of Ozone variable with median of this variable d) Replace missing values of Solar.R variable with mean of this variable e) Create "Date" variable on AQ object using "Month" and "Day" variables for year 2020

Use the cleaned "AQ" object to do following in R Studio with R script to knit HTML output:

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

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 in a single graph window and interpret it carefully

OR

Use the cleaned "AQ" file in R studio and do as follows with R Scripts and HTML outputs:

a) Get reference range of "Temp" variable using mean and standard deviation b) Plot histogram of "Temp" variable and show the outliers of "Temp" with reference range limits c) Get reference range of "Temp" variable using median and inter-quartile range d) Plot box plot of "Temp" variable and show the outliers of "Temp" with reference range limits e) Which measure of central tendency and dispersion should be used for this variable? Why?

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

a) Define g as graph object with (1,2) as its elements b) Plot the g and interpret it carefully c) Define g1 as graph object with ("R", "S", "S", "T", "T", "R", "R", "T", "U", "S") as its elements d) Plot g1 with node color as green, node size as 30, link color as red and link size as 5 and interpret it e) Get degree, closeness and betweenness of g1 and interpret them carefully.

OR

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

a) Open R and then go to Help and Manuals if 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 and create histogram of the most frequent e) Create word cloud of the corpus, color it using rainbow or RColorBrewer package f) Perform topic modelling and interpret the result carefully