Statistics with 'R’ — DU Skill Enhancement Courses Previous Year Question Papers

Statistics with ‘R’ CREDIT DISTRIBUTION, ELIGIBILITY AND PRE-REQUISITES OF THE COURSE Course title Credits Credit distribution of the course Eligibility Pre-requisite of Lecture Tutorial Practical/ criteria the course Statistics with R 2 0 0 2 Class XII Basic course in Learning Objectives The Learning Objectives of this course are as follows:  To enable students to handle data in the R software thereby helping them to understand meaningful statistical analysis performed on the data.  To enable students to extract data, and perform basic statistical operations entailing data analysis such as – data cleaning, data visualisation, data summarisation, and regression amongst others. Learning outcomes The Learning Outcomes of this course are as follows:  After studying this course, students will be able to extract and Read data into R, manipulate, and analyse it  After studying this course, students will be able Tto debug, organize, and comment R code  After studying this course, students will be able to understand the R environment for downloading, in stalling, and using packages  After studying this course, students will be able to do basic programming to write own functions  After studying this course, students will be able to use loops  After studying this course, students will be able to create standard and customized graphics  After studying this course, students will be able to perform basic statistical operations and regression. Unit 1: Data Extraction and Spread Sheet Exploration (12 hours) Extraction of economics and financial data from Prowessiq, RBI, IMF, World bank or an equivalent financial/economic database. The students should be able to save and export the data to ‘R-environment’ for further analysis. Unit 2: Basics of R-language (28 hours) Overview of the R language: Installing R and R Studio : Using R studio, Scripts, Text editors for R, Graphical User Interfaces (GUIs) for R, Creating and storing R workspaces, in stalling packages and libraries, Mathematical operations. Data Types in R – Numeric, Integer, Character, Logical, Complex and missing data. Data Structures in R  Vectors – Creation, Arithmetic operations of Vectors, Vector Sub setting, Sorting and Sequencing functions.  Matrix and Arrays – Creation, Arithmetic Operations of matrix, Sub setting, Use of Drop  Factors – Converting a vector into factor, assigning levels and labels, ordered Factor.  List – Creating a list, accessing elements from a list, adding a new element and eliminating an existing element form the list, converting list to vectors.  Data Frames – Creation of Data Frame, adding new columns, rows and removing columns, accessing column using the $ sign, importing a data set (important file for mats such as csv, txt and spreadsheet), aggregate function and subsetting of dataframes, tapply function, manipulation using dplyr package (select, filter, arrange, mutate and group by function, pipe operator).