Programming in R and Python Lab

Instructions:

1. 1. The course objectives and course outcomes are identical to that of (Programming in R and Python) as this is the practical component of the corresponding theory paper.
2. 2. The practical list shall be notified by the teacher in the first week of the class commencement under intimation to the office of the Head of Department / Institution in which the paper is being offered from the list of practicals below. Atleast 10 experiments must be performed by the students, they may be asked to do more. 

List of Programs

1. Demonstrate the following functions/methods which operates on lists in Python with suitable examples: i) list( ) ii) len( ) iii) count( ) iv) index ( ) v) append( ) vi) insert( ) vii) extend() viii) remove( ) ix) pop( ) x) reverse( ) xi) sort( ) xii) copy( ) xiii) clear( ).
2. Demonstrate the following kinds of Parameters used while writing functions in Python. i) Positional Parameters ii) Default Parameters iii) Keyword Parameters iv) Variable length Parameters.
3. Demonstrate lambda functions in Python with suitable example programs.
4. Python program to perform read and write operations on a file.
5. Create a CSV file by entering user-id and password, read and search the password for given user id.
6. Write an R program that takes input from the user and display the values. Also print the version of R installation.
7. Write a R program to list containing a vector, a matrix and a list and give names to the elements in the list.
8. Write a R program to create an empty data frame.
9. Write a R script, to create R objects for calculator application and save in a specified location in disk
10. Write a R program to find basic descriptive statistics using summary and find subset of dataset by using subset().
11. Reading different types of data sets (.txt, .csv) from web and disk and writing in file in specific disk location and reading Excel data sheet and XML dataset in R.
12. Find the data distributions using box and scatter plot.Find the outliers using plot and plot the histogram, bar chart and pie chart on sample data.
13. Find the correlation matrix and analyse variance (ANOVA), if data have categorical variables on iris data. Plot the correlation plot on dataset and visualize giving an overview of relationships among data on iris data.